Novel FTIR and visible infrared imaging assessment of binary biofuel stability and abated NO x for clean environment assisting energy approach

Abstract

To compensate the oil demand and pollution, scientists explore biodiesel as a pollution free alternate energy. But depending on one particular species of feedstock will lead to its extinction like diesel. For this intent, this research proposes a novelty on blending of binary non-edible high oil yielding species. As biodiesel is a natural constituent with elevated oxygen content, a stability analysis has to be performed to diminish its rapid decay. For stabilizing fuel properties synthetic antioxidants have been involved as inhibitors. Previous studies have been performed on the stability analysis individually as oxidation, thermal and storage stability without analyzing them mutually. This research fills the key gap by deeper mutual stability analysis, as the output parameters of these three stabilities are interrelated. Few samples have shown best stability output parameters which challenges in narrowing the best blend. To face this task, a multi objective optimization study has been done. NO_x emission has been reduced with the aid of antioxidants as a twin reward. Two novel assessment tools for validating are, i) FTIR, by which the impact of molecular arrangements on stability variation has been evaluated and ii) Using Infrared Imaging Technique, by which the NO_X has been analyzed visually correlating the emission level and engine combustion temperature.

Keywords

Dual biodiesel FTIR EDAS TOPSIS IIT emission

Introduction

Nowadays researchers are dedicated in bringing out the best alternate fuel instead of fossil fuel for the transportation sector, as fossil fuel were in the stage of extinction. Not only in transportation sector, but also in industries, power generators, agriculture, domestic and other commercial sectors. They use fossil fuel in superior quantity for power generation. The current hike in the price range of petrol and diesel could be a proof for the above statement. It has been increasing because of the deficiency in fossil fuel but it cannot be avoided due to its high demand in energy. It also strikes the price of all other commercial products to bump up. Eventhough electric powered vehicle is found to be a best alternate source for transportation, producing electricity is possible only through burning out coal which is one of the fossil fuels. The fact behind the vast usage of diesel fuel from tiny to monster vehicles is for its better efficiency. But it also generates other harmful emissions like NO_x and particulate matter, which leads to global warming.¹ Regarding the current environmental stats, various agencies like Natural gas and Biofuels, and National Agency of Petroleum were urging the world to execute the resolution N^O. 45/ 2014 ANP to reduce the usage of fossil fuels.² A forecasting study in Russia suggested that, if the diesel fuel is still being used without an alternate fuel, by 2050, the diesel fuel will decrease to 12% which is 5.4 times when compared to 2015.³ The only preference for the researchers to obtain an alternate fuel for diesel is the fuel from naturally available feedstock known as biodiesel which has been used in this study.

Many pieces of research on alternate fuels were being performed on waste cooking oil.⁴ The waste cooking oil while disposing contains 20% more fatty acid (wt%) than pre-usage which is the most important factor for biodiesel yield. The amount of fatty acid determines the production cost of biodiesel which could be a drawback when the concentration is lower or higher. Higher the biodiesel diesel yields lower the cost of biodiesel production. Around 1%, 13%, 84% of biodiesel have been extracted from palm, sunflower, rapeseed oil respectively and only 2% from non edible feedstock like Poison nut and various non edible feedstock.⁵ If the biodiesel extraction from edible products gets a government grant the opposition among corporate sectors and existing demand on food crops will hike to a greater extent further. To avoid all these troubles, there is another preference for extracting biodiesel which is from non-edible products. Previous research proved that the extraction of biodiesel from non-edible seeds like Styrax officinalis L is 89.23% and 20% of bio oil from Citrullus colocynthis which are larger in amount when compared to edible products.^6,7 Not only because of the future argument between ‘Fuel versus edible feedstock’, but also the biodiesel extracted from non-edible feedstock also shows similar characteristics in oil yield as well as in engine behaviours. Non-edible feedstock like Pongamia pinnata possesses longer series saturated structure of fatty acid which is helpful to obtain higher biodiesel yield reducing the production cost.⁸ Although there are wide varieties of modern non edible biodiesel feedstock, the biodiesel has not been commercialized successfully.

The key issue for preventing biodiesel commercialization as an alternate fuel is the presence of tremendous oxygen molecules in Poison nut and Karanja biodiesel, making a boulevard for the rapid oxidation reaction leading to the degradation of biodiesel properties which is recently being paid much attention by previous researchers.^9,10 Though there are many studies on stabilizing the oxidation reaction of various biodiesel feedstock, there are only very few studies on mutual analysis of oxidation, storage and thermal stability properties as they are interrelated to each other. Also an attempt has been made to analyze the mutual stability characteristics of high oil yielding biodiesel feedstock like Alexandrian laurel and Poison nut by blending at equal proportions.

This study involves multiple biodiesel stability attributes like oxidation, storage and thermal stability, among which various output criteria have to be analyzed. But while investigating the stability attributes experimentally, each samples show best result in different attributes without presenting a single best sample leading to confused conclusion. For consolidating obtained experimental results, the multi objective statistical optimization techniques like Evaluation based on Distance from Average Solution (EDAS) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) have been performed mathematically in this study.

The primary novelty of this research is the analysis of biodiesel stability parameters like oxidation, storage, and thermal stability for Alexandrian laurel and Poison nut dual biodiesel blend making this study distinctive form all other current biodiesel studies. Using standard equipment like Rancimat, TGA and FTIR, stability analysis for samples blended with TBHQ and PY antioxidants have been performed. With the help of CRITIC weightage evaluation technique and optimization techniques like EDAS and TOPSIS, the best biodiesel blend has been proposed from the standard equipment experimental results. From the optimized biodiesel blend with antioxidant, 20% of the biodiesel has been blended with TBHQ antioxidant and the emission characteristics have been analyzed for diesel, B20, and B20T3 in which all the test samples can be utilized for the commercial diesel engine without any changes in engine design or parameters.

Through this intensive study, it (i) rectifies the less availability of biodiesel feedstock by introducing a novel blending of two high oil yielding non edible species (ii) rectifies the drawback of biodiesel poor stability character by analyzing the three important individual stability characters as oxidation, storage and thermal stability along with addition of synthetic antioxidants (iii) rectifies the downside of higher NO_x emission from biodiesel with antioxidant dosage analyzed by novel IIT revealing the connection between combustion chamber temperature (CCT) and NO_x emission making this study special from previous studies.

Materials and methodology

Feedstock involved

Among all other non-edible biodiesel feedstock, traditional feedstock like Alexandrian laurel and Poison nut shows higher bio-oil yield, Indian origin and resistance to drought conditions.^11,12 Alexandrian laurel is known for its elevated characteristics in combustion and proved their ability to represent superior stability in oxidation reaction from previous works.¹³ Alexandrian laurel biodiesel (25%) when blended with diesel (75%) showed lower NO_x emission and CO, HC and smoke emissions were observed comparable to diesel whereas BTE also showed closer behaviour to diesel.¹⁴ Performance, emission and combustion study have been performed using ternary mixtures like diesel, n-hexanol and Alexandrian laurel in which hexanol at 40% concentration, Alexandrian laurel at 10% concentration and diesel at 50% concentration blend suppresses emission characteristics like smoke, HC and CO with improved combustion characteristics like Heat Release Rate (HRR) and incylinder pressure and performance characteristics like higher BSFC and lower BTE showing Alexandrian laurel flexibility with multiple mixture blend.¹⁵ Another study uses synthetic BHT and ethanol antioxidant in Alexdandrian laurel for analyzing its engine performance and emission characteristics and observed considerable lessening in Brake Thermal Efficiency (BTE), Brake Specific Fuel Consumption (BSFC) and NO_x emission at 500 ppm and 1000 ppm.¹⁶ This is named due to its better emission reduction of harmful greenhouse gas to environment reducing the global warming which is a greater threat nowadays.

By using Poison nut bio-diesel in Malaysia there occur multiple benefits like reduction in crude oil importing by substituting Poison nut biodiesel, improved livelihood of locals, and similar engine performance to diesel fuel.¹⁷ A low and high viscosity biodiesel like turpentine and Jatropha biodiesel extracted through transesterification process has been mixed at equal proportion (50–50) and engine study has been performed without exhaust gas recirculation mode. At full load condition, dual biodiesel blend shows performance drop of 29.16% BTE and emission drop of 42.5%, 4.56%, 4.72% and 2.9% in smoke, carbon monoxide, hydrocarbon and NOx respectively but 10.7% hike in carbon dioxide.¹⁸ Another study has been performed using Castor and Poison nut as dual biodiesel for analyzing the emission, performance and combustion characteristics. Various ratios of water, surfactants and hydrophilic-lipophylic balance have been used in 10% Poison nut biodiesel + 10% Castor biodiesel + 80% diesel in which water concentration – 5%, surfactants – 2% and hydrophilic-lipophylic – 5.3% shows better engine characteristics.¹⁹ Also, Poison nut biodiesel shows reduction of NO_x and CO to 5 to 10% and 10 to 40% respectively when tested in diesel engine at higher load.²⁰ Considering all the characteristics of Alexandrian laurel and Poison nut biodiesel, this research uses the blending of above said feedstock at equal proportions which has not been focused by any other studies.

Transesterification process for biodiesel extraction

Table 1 represents the physical and chemical properties of diesel, pure oil, and B100 biodiesel and Table 2 represents the fatty acid compositions in weight % (wt%) for Alexandrian laurel and Poison nut oil obtained from gas chromatography which are near to that of previous studies data. When compared to other biodiesel non edible feedstock, Poison nut possess 41% of free fatty acid. By using 2 steps transesterification process the free fatty acid can be converted to fatty acid methyl ester which can be used as an alternate fuel in diesel engine.²¹ After collecting both feedstock seeds, they are kept undisturbed underneath sunlight and permitted to dry for 2 weeks. The dried seeds are then kept in a separate polythene bag and thrashed until it reaches the stage of small particles. This process is performed to obtain higher yield of bio-oil. The small particles are then crushed through mechanical oil extractor and the oil has been extracted individually from each feedstock. By blending two highly acidic bio-oil, the acid value has been raised to massive level which can be reduced using two step transesterification process to obtain biodiesel as done by earlier studies.^22,23 The steps involved in two step transesterification process taken place separately for both feedstock is shown below:

Table 1.

Physical and chemical properties of diesel, pure oil and B100 biodiesel.

Properties	Diesel	B20	B20T3	B100
Viscosity at 40 ^oC (cSt)	3.9	3.99	3.99	4.92
Density at 15°C(kg/ m³)	833	847	848	878
Acid value (mgKOH/ g)	0.034	0.352	0.352	0.47
Calorific value (MJ/ kg)	43.35	41.94	41.77	37.75

Table 2.

Fatty acid compositions in wt% for alexandrian laurel and poison nut oil.

Fatty acid	Alexandrian laurel ²⁴ (%)	Poison nut ²⁵ (%)
Palmitic	14.7	14.2
Palmitoleic	0.3	0.7
Oleic	46.1	44.7
Stearic	13.2	7.0
Linoleic	24.7	32.8
Linolenic	0.2	0.2
Arachidic	0.8	0.2
Mystitic	0.09	0.1

Step 1: For removing the water content in pure bio-oil, under 90^OC the oil has been heated and tends to undergo rotary motion using rotary evaporator for one hour.

Step 2: Acid treatment

Sulphuric acid (H₂SO₄) at 1% volume per volume oil and methanol (CH₃OH) at 55% volume per volume oil have been added to the moisture free pure bio-oil and stirred at 60^OC for 2.5 h. By adding two catalysts and stirring process, there occur double layers which are esterified oil (lower layer) and H₂SO₄ + CH₃OH (Upper layer). The bio-oil has been heated additionally at 90^OC for one hour, removing excess CH₃OH and H₂O contents. The process has been continued until the pure bio-oil acid value has to be reduced under 0.50 mg KOH/g according to ASTM and EN standards.^22,23

Step 3: Base treatment

Sodium hydroxide (NaOH) at 1% weight by weight oil and Methanol (CH₃OH) at 25% volume per volume oil has been added to esterified oil and heated at 70^OC for two hours. NaOH and CH₃OH convert the triglycerides in esterified oil to methyl ester (biodiesel) and glycerol (by product). The catalyst added oil is left uninterrupted for one day to let the separation of glycerol and methyl ester. After visualizing the pure oil from the uninterrupted setup, the oil has been drained out and sprayed distilled water at 55% volume per volume oil on upper surface along with slow stirring at 65^OC. Finally, 84.2% and 74.6% of pure biodiesel yield has been obtained respectively from Alexandrian laurel and Poison nut transesterification process. Each biodiesel extracted from the transesterification process has been mixed at equal proportion and named as B100 which is 50% Alexandrian laurel biodiesel and 50% Poison nut biodiesel.

Mutual stability analysis of Alexandrian laurel and poison nut biodiesel mixture

As the biodiesel is a naturally obtained product, prolonged storage will increase the viscosity which is another barrier for healthy diesel engine indicators. Due to high viscosity, there will be slow travel of biodiesel inside the fuel route and filters which causes deprived fuel atomization and little volatility paving the way for sever deposition of gums and sediments on piston and choking of fuel injectors with partial combustion.²⁶ In association with the oxidation and storage stability fences of Alexandrian laurel biodiesel, there is one more obstacle known as thermal stability. Thermal stability is defined as the ability of the sample to act against the oxidation reaction at high temperature.²⁷ When the high temperature fuel recirculates from fuel tube to the fuel tank, the physiochemical properties of biodiesel tend to degrade.²⁴ By observing various literatures it is visible that, oxidation reaction plays a very important role in stabilizing the biodiesel properties and other parameters like storage and thermal stability. Hence, this study concentrates chiefly on mutual stability analysis for the novel biodiesel mixture makes the research unique from other studies.

Synthetic antioxidants for stabilizing oxidation reaction of biodiesel

The synthetic antioxidants are the chemicals that react with peroxide radicals and break the chain reaction of the oxidation process. In addition to that when compared to biodiesel, antioxidants consist of a huge number of preoccupied hydrogen molecular arrangements. From the reaction of peroxide radicals and antioxidants, free radicals are formed which are responsible for the production of stabilized products instead of engaging in an oxidation reaction. Synthetic antioxidants like tert-butylhydroquinone (TBHQ), butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT) and 2-tert butyl- 4 methoxy phenols are the additives which stabilize the biodiesel properties and increases biodiesel production as the synthetic antioxidants are derived from petroleum products.²⁸ Among various synthetic antioxidants, TBHQ and Pyrogallol (PY) shows elevated stability characteristics. TBHQ dosage upto 1000 ppm for Alexandrian laurel shows 94.65% oxidation, 14.46% elevated storage, and 37.26% thermal stability above which the breakage of structural arrangements of antioxidants takes place.²⁹ PY antioxidant possess triple energetic electron giving hydroxyl clusters in its quinone state of oxidation which discharges large proton numbers effectually to Poison nut biodiesel, avoiding self-oxidation and elevating oxidation stability.³⁰ One more major problem with biodiesel is metal corrosion which has been analyzed using various synthetic antioxidants and found that the efficiency level of improving sustainability of material has been observed in the following order of PG < BHT < PY < TBA < BTA by stabilizing the properties of fuel and the composition of palm biodiesel.³¹

Considering all the above study, this research utilizes two synthetic antioxidants namely Tert-butyl hydroxyquinone (TBHQ) and Pyrogallol (PY) at five equal concentrations analyzing the triple stability criteria mutually for Alexandrian laurel and Poison nut biodiesel blend which has not been performed in earlier studies. This research involves two kinds to antioxidants called Pyrogallol (P) and Tert-butyl hydroxyquinone (T) at concentrations like 500, 750, 1000, 1250, and 1500 ppm measured using microbalance. Table 3 represents the structural arrangements and chemical properties of Tert-butyl hydroxyquinone (T) and Pyrogallol (P). Table 4 represents the biodiesel samples dosed with synthetic antioxidants. Table 5 represents the properties of B100 and synthetic antioxidant dosed samples.

Table 3.

Structural arrangements and chemical properties of tert-butyl hydroxyquinone (t) and pyrogallol (p).

Properties	Tert-butyl hydroxyquinone (T)	Pyrogallol (P)
Molecular structure
Chemical formula	C₁₀H₁₄O₂	C₆H₆O₃
Melting point	127 to 129°C	131 to 134°C
Molar mass	166.22 g/ mol	126.11 g/ mol
Boiling point	273°C	309°C
Density	1.050 g/ mL	1.45 g/ cm³

Table 4.

Biodiesel samples dosed with synthetic antioxidants.

S. No	Samples	Antioxidants	Concentration (ppm)
1	B100	Without T and P	-
2	B100T1	With Tert-butyl hydroxyquinone (T)	500
3	B100T2		750
4	B100T3		1000
5	B100T4		1250
6	B100T5		1500
7	B100P1	With Pyrogallol (P)	500
8	B100P2		750
9	B100P3		1000
10	B100P4		1250
11	B100P5		1500

Table 5.

Properties of B100 and synthetic antioxidant dosed samples.

Samples	Density @ 15°C	Calorific value	Viscosity @ 40°C	Flash Point	Cetane index	Cloud point	Pour Point
Units	^oC	MJ/ kg	^oC	^oC	CCI	^oC	^oC
Diesel	833	43.35	3.9	63	54	6.5	−3
B100	878	37.75	4.92	143	58.12	14.2	5.1
B100T1	878	37.63	4.81	143	57.12	13.4	4.6
B100T2	878	37.6	4.81	144	57.34	13.4	4.6
B100T3	879	37.58	4.82	145	57.75	13.7	4.6
B100T4	881	37.57	4.82	145	57.72	13.7	4.8
B100T5	881	37.54	4.84	147	57.85	13.7	4.8
B100P1	878	37.63	4.81	144	57.11	13.4	4.6
B100P2	879	37.62	4.82	144	57.17	13.4	4.6
B100P3	881	37.6	4.82	145	57.56	13.5	4.8
B100P4	882	37.59	4.86	146	57.58	13.7	4.8
B100P5	882	37.59	4.87	147	58.03	13.8	4.8

Stability analysis using standard equipment

The phrase “fuel stability” represents the conflict against the vigorous oxidation reaction by biodiesel which leads to deprivation in biodiesel properties. The biodiesel can be degraded by stability parameters like oxidation, storage and thermal stability in which vigorous oxidation reaction due to elevated presence of dual bond free fatty acid, unstable physio chemical properties on long term storage due to sunlight exposure, humidity in fuel tanks or lines etc., unbalanced physio-chemical biodiesel properties on exposure to high engine temperature results in asphaltenes deposition which blocks the fuel filters.³² Considering the above parameters, three stability criteria like oxidation, storage, and thermal stability have been analyzed mutually.

According to EN14214 standard, biodiesel should satisfy the standard induction period of more than 6 h at 110^OC for analyzing the oxidation stability which has been followed in stabilizing the oxidation reaction of rapeseed biodiesel with tetra ethylene pentamine antioxidant using rancimat equipment.³³ Using ASTM D675 standard, the Alexandrian laurel biodiesel has satisfied the standard induction period of more than three hours at 110^OC.²⁴ For evaluating the conductivity of B100 samples in the case of oxidation stability in this research, Metrohm 873 Rancimat apparatus has been used, as it gives the best and exact results for previous studies. The KV and AV should be lower for higher performance of the diesel engine when using Poison nut and Madhuca indica biodiesel.³⁴ Storage stability involves both the kinematic viscosity (KV) and acid value (AV) evaluation for obtaining the elevated storage stability results for B100 samples in this research. TGA has been proved to be the best technique for analyzing the weight changes in physio-chemical properties of biodiesel extracted from waste cooking oil when exposed to higher temperature.³⁵ Recycling of waste cooking oil, spent coffee grounds and analyzing the quality of binary as well as ternary molecules for thermal stability has been performed by using TGA equipment. The TGA shows that, higher the onset temperature higher the thermal stability.³⁶ For analyzing the Onset temperature (T_ON) in the case of thermal stability, the Thermogravimetric analyzer TGA Q500 has been implemented in this study.

To obtain more crystal-clear details about the stability experimental outcomes, FTIR instrument can be used as a validating tool which monitors the oxidation deprivation reaction due to carbonyl ester cluster.³⁷ A study of stabilizing the waste cooking oil using FTIR and DSC techniques found that TBHQ enhances stability when compared to butylated hydroxytoluene (TBHQ) and Pyrogallol (PY) antioxidants at varying concentrations like 375, 750, 1000, 1125, 1500 ppm.³⁸ FTIR has given an attractive understanding of cleaner production of Spirulina platensis biodiesel process parameters in which an elevated yield of 92.1% has been obtained.³⁹ Another study involves usage of FTIR and attenuated total reflectance (ATR) as a determining tool for evaluating the presence of various fatty acid alkyl esters in sunflower biodiesel blends in the range of 4000 cm⁻¹ to 650 cm⁻¹.⁴⁰ Heterogeneous catalyst like ZnO-SiO₂ and ZnO are blended with waste cooking oil in which the FAME conversion has been characterized using FTIR equipment and obtained stronger chemical bonded particles.⁴¹ The characterization of waste ostrich bones as a catalyst for the production of biodiesel from waste cooking oil has been analyzed by FTIR which shows O-P-O bond vibrations, discharge of CO₃⁻² ions and the decaying of CaCO₃ particles.⁴²Similarly, the process of analyzing the molecular arrangements of B100 biodiesel samples and correlating the stability results, playing a major role in stabilizing the B100 samples with antioxidants has been performed with Fourier Transform Infrared Spectroscopy (FTIR).

Working of standard equipment

The working principle of standard equipment used in this research for analyzing the oxidation, storage and thermal stability have been discussed below.

Oxidation stability analysis

Oxidation stability test has been performed to analyze how long the synthetic antioxidant helps dual biodiesel to resist against vigorous oxidation reaction. Initially for reference, pure biodiesel and diesel samples have been tested under EN14214 standard in 873 Rancimat biodiesel equipment. The working of the equipment involves exposing the samples to rich oxygen at high temperature and testing the time it takes to lose its chemical properties. The setup involves two glass tubes, one is the sample holder and the other is filled with distilled water. Both the glass tubes are connected with passage for the vapor particles of the samples to travel to the other side of the glass tube with water while heating. The reference samples like B100 and diesel fuel have been taken initially and samples dosed with P and T antioxidants were taken later. When the sample is kept inside the sample holder glass tube, the sample vapor travels to the distilled water. Automated graph will be generated based on how much amount of vaporfrom sample has been settled in distilled water. The graph will start to show in ascending range until all the components in the sample get transferred to water. Finally, a dive in the graph occurs representing that the sample has degraded and reached its maximum induction period. Following the process, all the samples have been tested for obtaining the induction period readings and a sample graph is shown in Figure 1.

Figure 1.

I.P of B100T3 at 110°C.

Storage stability analysis

Storage stability test has been performed to analyze how longthe synthetic antioxidant supports the dual biodiesel storage for a longer period without increasing in the ranges of KV and AV. According to ASTM D4625 standard, the samples have been placed inside incubator at 30^OC in aluminium containers for hundred days and tested at equal interval of ten days. For reference, the diesel and B100 sample have been stored for which the readings have been taken through KV and AV tests. It has been observed that, with an increase in storage days, the samples have been exposed to more oxygen inside an incubator, leading to the formation of more oxidation products from peroxide and hydro peroxide oxidation reaction in B100 sample when compared to diesel sample. As the result, the AV and KV of B100 sample hikes to larger amount.

Thermal stability analysis

Thermal stability tests help to understand the confrontation of dual biodiesel against rapid oxidation reaction. Using Q500 TGA equipment, the thermal stability analysis has been performed initially for diesel and B100 sample for reference. The working of the equipment involves exposing the sample placed in a mini sample holder hanging inside to large amount of oxygen. Along with oxygen flow, the sample is heated for the removal of secondary products responsible for oxidation reaction. As the products tend to vapor out, the sample loses its weight and the difference in the sample holder is showed in the form of graph. Similar procedure has been followed for all the antioxidant dosed samples.

Analyzing uncertainty and accuracy

Uncertainty is defined as the refinement of suspicious results that is gained from any standard equipment. By skipping this refinement process, the appropriate results cannot be obtained from measurement. In order to obtain appropriate result, the variables calculated should be accurate for both the uncertainty analysis and equipment. Uncertainty analysis has been followed in previous studies at 95% confidence level by calculating differences among the obtained mean experimental score.⁴³ To ensure the reliability of the obtained result, triplicates procedure have been followed for all experiments and the outcomes are arranged. Table 6 represents the uncertainty and accuracy of equipment implemented in this study.

Table 6.

Uncertainty and accuracy of experimental equipments.

Experimental equipment	Calculated parameters	Uncertainty	Accuracy
Bomb Calorimeter	Calorific Value (MJ/kg)	± 1.23	± 0.05%
Pensky Martens	Flash Point (^OC)	± 1.68	± 2^OC (< 200^OC)
Rancimat	Induction Period (I.P)	± 0.05	± 0.1 h
Viscometer	Kinematic Viscosity (KV)	± 1.25	Value measured (<3%), calibrated
TGA	Onset temperature (T_ON)	± 0.15	± 0.1%
FTIR	Wavelength	± 0.006	± 0.1 cm⁻¹
Infrared Imager	Temperature of Cylinder Head	± 0.2	± 2% (> 30^OC)
AVL gas analyzer	CO, CO₂, HC, Smoke, NO_x	± 2	± 10 ppm

Weightage and optimization techniques

Statistical optimization is defined as the process of reanalyzing the obtained multiple best results mathematically and gaining the optimum result. Statistical optimization in multiple objective researches brought out the best outcome in previous studies.⁴⁴ Among steam sterilization, incineration, landfill disposal, and microwave waste disposal methods, steam sterilization obtained the highest appraisal score and proposed as the best health care waste disposal method by EDAS technique.⁴⁵ For selecting the high-performance smartphones among seven alternatives, Asus Zenfone selfie pro has been proposed as the best alternatives by the EDAS technique in which the threat of expert influence over the best alternative has been eliminated.⁴⁶ With the help of the TOPSIS method, comprehension evaluation of electric supply, consumption, level of electrification and emission of carbon have been analyzed among 11 countries and the ranking of typical countries has been established.⁴⁷

Due to the user-friendly nature and tougher skill to resolve one dimensional issue of TOPSIS optimization and CRITIC weightage technique, it has been used to rank the best oil yielding non-edible feedstock, including 12 physio-chemical properties as input parameters for ranking.⁴⁸ During the year 2012 to 2016, the rise and fall in City Innovation Capability of twelve cities in China have been ranked using TOPSIS.⁴⁹ Since very small amount of study on biodiesel fuel stability using optimization techniques have been listened carefully, CRITIC weightage evaluation technique, EDAS and TOPSIS optimization techniques have been proposed as a novel approach for ranking the best stable biodiesel blend.

Criteria importance through intercriteria correlation (CRITIC)

CRITIC is another Multi-Criterion Decision Making (MCDM) technique that has been used for evaluating the weightage of the alternative criteria. In the CRITIC technique, the weightage has been evaluated directly from the experimental values of biodiesel blend parameters like I.P, KV, AV, T_ON, O-H, and C-H which has been given as input without fixing or assuming any critters. By using direct data from the biodiesel parameters, the results can be obtained in a precise manner. This makes the CRITIC technique unique from other weightage evaluation techniques. CRITIC technique is considered as another novel weightage evaluating technique as the usage of CRITIC for evaluating the weightage of biodiesel research is very few in previous studies. The steps involved in CRITIC for evaluating the weightage of biodiesel parameters are as follows.⁵⁰

Step 1: To normalize the decision matrix ( ${\bar{X}}_{i j})$

The first and foremost step is to normalize the decision matrix which is the experimental results of biodiesel parameters. The normalization has been performed by using the below-mentioned formula (1).

{\bar{X}}_{ij} = \frac{X_{ij} - X_{j}^{worst}}{X_{j}^{best} - X_{j}^{worst}}

(1)

Were,

$X_{ij}$ - Performance Value

$X_{j}^{best}$ - Best value

$X_{j}^{worst}$ - Worst valu e

From formula (1) it has been observed that, the worst ( $X_{j}^{worst}$ ) and best value ( $X_{j}^{best}$ ) for each criterion should be calculated. The selection of worst and best value for each criterion varies according to the beneficial and non-beneficial criteria. In this research, the three criteria like I.P, T_ON, and O-H at 3400 cm⁻¹ have been considered as beneficial criteria as their respective values should be higher for stabilizing the biodiesel properties. Whereas other criteria like KV, AV, C-H @ 2860.29 cm⁻¹, and C-H @ 2825.37 cm⁻¹ have been considered as non-beneficial criteria as their respective values should be lower for stabilizing the biodiesel properties. For beneficial criteria, the maximum value is the best value and the minimum value is the worst value, whereas for non-beneficial criteria it is vice versa. According to the best and worst values of beneficial and non-beneficial criteria, the decision matrix has been normalized by formula (1) and the normalized matrix is known as a normalized decision matrix.

Step 2: To evaluate the standard deviation ( $σ_{j}$ )

After solving the normalized decision matrix, the standard deviation ( $σ_{j}$ ) for each criterion should be evaluated from the normalized decision matrix by using the formula (2).

σ_{j} = \sqrt{\frac{\sum {(x_{i} - μ)}^{2}}{N}}

(2)

Were,

$x_{i}$ – Normalized performance valu e

$μ$ - Mean

$N$ – Amount of data in each criterion colum n

Standard deviation is nothing but the variation that exists in an average result. Low standard deviation represents that the data points tend to be very close to the mean whereas high standard deviation represents that the data points are spread out over a very large range of values. The standard deviation is calculated by working out the simple average of each criterion column (Mean) and subtracts the values of each number in the column by the respective mean value. After subtracting, the result is squared and another mean has been taken for squared differences. Finally, by taking the square root of squared differences to mean, the standard deviation has been obtained for each criterion.

Step 3: Determining the symmetric matrix (n × n matrix)

The symmetric matrix is nothing but the n × n matrix between two criteria in which the upper diagonal matrix will be the same as the lower diagonal matrix calculated by using the linear correlation method. In this research, there are seven criteria in which the symmetric matrix has been performed for 7 × 7 matrices. After preparing the 7 × 7 matrices, the linear correlation has been evaluated by comparing one criterion in the column with other criteria in rows and the same procedure for all criteria in the column to rows. By comparing one criterion with the same criteria, the linear correlation obtained as one and so all the diagonal values have been obtained as one. Finally, the symmetric matrix has been evaluated by using the linear correlation formula (3).

r_{j k} = \frac{\sum (x - \bar{x}) (y - \bar{y})}{\sqrt{\sum {(x - \bar{x})}^{2} \sum {(y - \bar{y})}^{2}}}

(3)

Were,

$r_{jk}$ - Linear correlation facto r

$\bar{x}, \bar{y}$ – Mean

Step 4: Resolving the measure of conflict formed by criterion J to decision situation defined by other criteria

To minimize the bulk amount of data breeding to the confusion of data, the measure of conflict formed by criterion J concerning decision situation defined by other criteria has been resolved by using the below mentioned formula (4). Each value in the symmetric matrix is represented as $r_{jk}$ . According to formula (4), each value gets subtracted from one and the result from each row gets added to solve the measure of conflict.

\sum_{k = 1}^{m} (1 - r_{jk})

(4)

Step 5: Evaluating the amount of information about each criterion

(C_{j})

The amount of information with each criterion has been evaluated by using the formula (5). By multiplying the standard deviation values to the measure of conflict values, the quantity of information has been obtained.

C_{j} = σ_{j} * \sum_{k = 1}^{m} (1 - r_{jk})

(5)

Were,

$σ_{j}$ - Standard Deviation

$\sum_{k = 1}^{m} (1 - r_{jk})$ - Measure of conflict

Step 6: Determining objective weights ( $W_{j}$ )

With the help of objective weights formula (6), the weightage for each criterion has been evaluated by dividing the sum of $C_{j}$ to each $C_{j}$ value.

W_{j} = \frac{C_{j}}{\sum_{k = 1}^{m} C_{j}}

(6)

EDAS - evaluation based on distance from the average solution

EDAS or Evaluation Based on Distance from the Average Solution is a multi-criterion decision-making method that calculates the distance of each alternative from the average solution and uses this information to select the best alternative. The development of renewable energy sources in developing countries has been blocked by existential barriers in which critical analysis of 23 barriers have been analyzed under 6 headings and ranked using EDAS methodology in a precise manner.⁵¹ As the experimental outcomes in this study have not been obtained in a stable state to select the best biodiesel blend, this research includes EDAS as a novel optimization approach for selecting the best biodiesel blend with antioxidants by ranking various biodiesel stability attributes. The steps involved in EDAS are shown below.

Step 1: Instead of normalizing the decision matrix as in other MCDM methods, the first step is to take the average of all the biodiesel stability attributes with the help of equation (7).

A V_{j} = \frac{\sum_{i = 1}^{n} X_{i j}}{n}

(7)

Were,

$X_{ij}$ - Each data in respective attributes

$n$ - Total amount of data in attributes

Step 2: This step involves calculating the positive distance from the average solution of step 1 by two separate equations (8 and 9). Equation (8) is for calculating beneficial stability attributes like Induction Period (IP), Onset Temperature (T_ON), and FTIR result of O-H @ 3400 cm⁻¹. On the other hand, equation (9) is for the calculation of non-beneficial stability attributes like Acid value (AV),Kinematic Viscosity (KV) and FTIR results namely C-H @ 2825.37 cm⁻¹ and C-H @ 2860.29 cm⁻¹. In equation (8), the maximum between zero and the difference between each data $(X_{i j})$ and the average data $(A V_{j})$ has been taken. Finally, on dividing with average data $(A V_{j})$ , the positive distance from the average solution has been obtained for beneficial stability attributes. In non-beneficial stability attributes equation (9), each data $(X_{i j})$ gets differenced from average data $(A V_{j})$ . The calculations for non-beneficial stability attributes equation (9) are similar to beneficial stability attributes equation (8) which has been calculated manually.

P D A_{i j} = \frac{m a x (0, (X_{i j} - A V_{j}))}{A V_{j}}, if the j_{t h} criterion is beneficial

(8)

P D A_{i j} = \frac{m a x (0, (A V_{j} - X_{i j}))}{A V_{j}}, if the j_{t h} criterion is non - beneficial

(9)

Were,

$X_{ij}$ -Each data in the decision matri x

$A V_{j}$ - Average value from step 1 for each attribute

Step 3: Calculating the negative distance from the average solution is similar to that of calculating the positive distance from the average solution as of equation (10 and 11). The only variation in this step is the attributes of beneficial stability takes the equation of non-beneficial stability attributes in step 2 and for calculating the non-beneficial stability attributes in this step takes the equation of beneficial stability attributes in step 2.

N D A_{i j} = \frac{m a x (0, (A V_{j} - X_{i j}))}{A V_{j}}, if the j_{t h} criterion is beneficial

(10)

N D A_{i j} = \frac{m a x (0, (X_{i j} - A V_{j}))}{A V_{j}}, if the j_{t h} criterion is non - beneficial

(11)

Were,

$X_{ij}$ -Each data in the decision matri x

$A V_{j}$ - Average value from step 1 for each attribute

Step 4: Each data in the PDA matrix gets multiplied individually as of equation (12) by weightage $(w_{j})$ obtained from the CRITIC method.

S P_{i} = \sum_{j = 1}^{m} w_{j} P D A_{i j}

(12)

Were,

$w_{j}$ - Weightage (from CRITIC) value in each attribute

$PD A_{ij}$ - Each data from the PDA matri x

Step 5: Each data in the NDA matrix gets multiplied individually as of equation (13) by weightage $(w_{j})$ obtained from the CRITIC method.

S N_{i} = \sum_{j = 1}^{m} w_{j} N D A_{i j}

(13)

Were,

$w_{j}$ - Weightage (from CRITIC) value in each attribute

$ND A_{ij}$ - Each data from the NDA matri x

Step 6: To normalize the values obtained from $S P_{i}$ and $S N_{i}$ , each data from $S P_{i}$ and $S N_{i}$ gets divided by the average of $S P_{i}$ and $S N_{i}$ as of equation (14) and (15). The only dissimilarity for the NSN_i equation (15) is the difference in the normalized value by one.

N S P_{i} = \frac{S P_{i}}{m a x_{i} (S P_{i})}

(14)

N S N_{i} = 1 - \frac{S N_{i}}{m a x_{i} (S N_{i})}

(15)

Were,

$S P_{i}$ , $S N_{i}$ - Each data in $S P_{i}$ and $S N_{i}$ decision matri x

$ma x_{i} (S P_{i})$ , $ma x_{i} (S N_{i})$ - Average of $S P_{i}$ and $S N_{i}$ decision matrix

Step 7: Appraisal score has been calculated by taking the average of normalized values of NSP_i and NSN_i as shown in equation (16). Based on the decrease in appraisal score, the alternative has been ranked from best to the worst alternative.

A S_{i} = \frac{1}{2} (N S P_{i} + N S N_{i})

(16)

Technique for order preference by similarity to ideal solution (TOPSIS)

A situation of selecting the best biodiesel blend out of eleven samples dosed with various antioxidants based on biodiesel stability parameters like I.P, KV and AV, T_ON, O-H, and C-H can be identified by a well-known optimization method called TOPSIS. Among six hundred test results, twelve criteria and fifty alternatives, the biodiesel samples have been ranked like B100 < B0 < B50 < B20 < B5 using TOPSIS based on emission and performance characteristics.⁵² For selecting the best biodiesel blend from fish oil, hybrid MCDM techniques called Multi-criteria Optimization and Compromise Solution – VIKOR (pronunciation) and Technique for Order Performance by Similarity to Ideal Solution – TOPSIS has been used with Analytical Network Process - ANP weightage technique. Among 20%, 40%, 60%, 80% and 100% biodiesel samples, 20% biodiesel sample is selected as the best blend showing elevated engine characteristics by comparing both MCDM technique.⁵³ With the help of TOPSIS, the shortest distance (i.e. Euclidean distance) of the best alternative from the ideal solution has been identified and the best blends have been given performance score. From previous studies, it is observed that very few experimental researches have been performed in stabilizing the fuel properties and optimizing the obtained best biodiesel blend with the help of TOPSIS. So, TOPSIS has been considered as a novel optimization technique for evaluating the best stability biodiesel blend from obtained experimental results. The steps involved in the TOPSIS method are shown below.⁵⁴

Step 1: Finding vector normalization from the decision matrix ( ${\bar{X}}_{i j}$ )

Initially, the vector normalization has been performed for the decision matrix with the help of formula (19). For calculating the denominator $(\sqrt{\sum_{j = 1}^{n} X_{ij}^{2}})$ of formula (17), each data (i.e performance value ( $X_{ij}$ )) for specific criteria is squared individually and square root has been taken for the whole sum of squared data. The same procedure has been carried out for each criterion for calculating the denominator. After calculating the denominator for each criterion, the performance value in each criterion gets divided by the denominator in which each obtained value is defined as normalized performance value, and the decision matrix is known as a normalized decision matrix.

{\bar{X}}_{i j} = \frac{X_{ij}}{\sqrt{\sum_{j = 1}^{n} X_{ij}^{2}}}

(17)

Were,

${\bar{X}}_{i j}$ - Vector Normalizatio n

$X_{i j}$ - Performance Value

Step 2: Calculating the weighed normalized decision matrix

After evaluating the normalized decision matrix, with the help of formula (18) each performance value in each criterion gets multiplied by the respective weightage value for each criterion obtained from the CRITIC method. After multiplying the weightage values to the normalized decision matrix, the obtained results are known as a weighed normalized decision matrix.

W_{j} X_{i j}

(18)

Were,

$W_{j}$ – Weightage from CRITI C

Step 3: Finding Euclidean distance from Ideal best and ideal worst value

The evaluation of Euclidean distance from Ideal best ( $S_{i}^{+}$ ) and ideal worst value ( $S_{i}^{-}$ ) in the weighed normalized decision matrix using the formula (19 and 20) is the next step.

S_{i}^{+} = \sqrt{\sum_{j = 1}^{m} {(V_{ij} - V_{j}^{+})}^{2}}

(19)

S_{i}^{-} = \sqrt{\sum_{j = 1}^{m} {(V_{ij} - V_{j}^{-})}^{2}}

(20)

Were,

$S_{i}^{+}$ - Euclidean distance from Ideal bes t

$S_{i}^{-}$ - Euclidean distance from Ideal worst

$V_{ij}$ - Each value of weighted normalized decision matri x

$V_{j}^{+}$ - Ideal best value

$V_{j}^{-}$ - Ideal worst value

Before finding the value of Euclidean distance from Ideal best and ideal worst, it is necessary to find the ideal best ( $V_{j}^{+}$ ) and ideal worst ( $V_{j}^{-}$ ) in formula (19 and 20). For beneficial criteria like I.P, T_ON, and O-H, the maximum value from the weighed normalized decision matrix is desired as ideal best ( $V_{j}^{+}$ ) and the minimum value is desired as ideal worst ( $V_{j}^{-}$ ). Whereas for non-beneficial criteria like KV, AV, and C-H, the minimum value is desired as ideal best ( $V_{j}^{+}$ ) and the maximum value is desired as ideal worst ( $V_{j}^{-}$ ). After finding out the ideal best ( $V_{j}^{+}$ ) and ideal worst ( $V_{j}^{-}$ ) with the help of formula (19 and 20) the Euclidean distance from Ideal best ( $S_{i}^{+}$ ) and ideal worst value ( $S_{i}^{-}$ ) has been calculated.

For calculating the Euclidean distance from ideal best value ( $S_{i}^{+}$ ), the alternative one undergoes difference and squaring of the difference value. Finally summing all squared value and taking the square root gives the result of the ideal best value ( $S_{i}^{+}$ ) for the first alternative. Similarly, each alternative undergoes manual calculation for evaluating the ideal best value ( $S_{i}^{+}$ ). For calculating the Euclidean distance from ideal worst value ( $S_{i}^{-}$ ), the only difference is the change in formula and the procedure is parallel to the calculation of the ideal best value ( $S_{i}^{+}$ ).

Step 4: Finding the performance score ( $P_{i}$ )

Finally, after evaluating the Euclidean distance from Ideal best ( $S_{i}^{+}$ ) and ideal worst value ( $S_{i}^{-}$ ), the performance score has been calculated by using the formula (21).

P_{i} = \frac{S_{i}^{-}}{S_{i}^{+} + S_{i}^{-}}

(21)

Were,

$P_{i}$ - performance scor e

$S_{i}^{+} + S_{i}^{-}$ - Sum of Euclidean distance from Ideal best ( $S_{i}^{+}$ ) and ideal worst value ( $S_{i}^{-}$ )

Dividing each ideal worst value ( $S_{i}^{-}$ ) of biodiesel criteria by the sum of Euclidean distance from Ideal best value ( $S_{i}^{+}$ ) and ideal worst value ( $S_{i}^{-}$ ), the performance score has been calculated and arranged in rank order.

Infrared imaging technique (IIT) for combustion analysis

Figure 2 represents the Infrared Imager and Table 7 represents the specifications of the equipment involved in this research. The fuels used in IC engine possess nitrogen compound and atmospheric oxygen plays an important role for burning the fuel.

Figure 2.

Fluke Ti400 infrared imager.

Table 7.

Infrared imager specifications.

Components and its ranges	Specifications
Heat sensing technique	Central Plane grouping
Camera	5.0 Mega Pixel (Industrial Performance)
Temperature range	1200^OC to −20^OC
Temperature accuracy	± 2% error at 30^OC
Temperature sensitivity	± 0.05% at 30^OC

The atmospheric oxygen and nitrogen mixes together in combustion chamber produces NO_x which can be understood from zeldovich mechanism.⁵⁵

Oxygen (O)	Nitrogen (N₂)Oxygen (O)	Nitrogen (N₂)
Nitrogen (N)	Nitric Oxide (NO)Nitrogen (N)	Nitric Oxide (NO)
Oxygen (O₂)	Nitrogen (N)Oxygen (O₂)	Nitrogen (N)

Preceding research shows that blending synthetic antioxidants like butylated hydroxyl toluene (BHT), Butylated hydroxyl anisole (BHA), tert butyl hydroquinone (TBHQ) and 2-ethyl hexylnitrate (EHN) with canola oil results in reduced NO_x as well as increased oxidation stability with no changes in diesel engine parameters and design.⁵⁶ In another study, pig fat and waste cooking oil were mixed together and through transesterification process, bio mix methyl ester has been extracted. It has been observed that, if the temperature within the combustion chamber raises so does the amount of NO_x emission.⁵⁷ To visualize the antioxidant influence on reducing the NO_x emission, Infrared Imaging Technique (IIT) is a novel approach for locating the CCT and exhaust gas temperature (EGT), as the NO_x emission and CCT characteristics are simultaneous to one another. As stated before, temperature of diesel engine cylinder head is the main factor for hike or drop in NOx emission level.⁵⁸ For determining the cylinder head temperature; conventional vehicle engines are equipped with sophisticated sensing devices. Since the sensing devices are feasible and works with high accuracy output, much significance is provided for sensing devices rather than IIT. The main disadvantage of sensing devices is its inability to monitor the combustion chamber hotspot. Only the total temperature of combustion chamber will be measured. This can be avoided by using IIT with two benefits in which, it precisely identifies the hotspot as well as reducing NO_x emissions using antioxidants. Depending on the infrared radiations from combustion chamber, the equipment depicts the images. The Imager depicts the Infrared radiations and computes the predictable value of temperature on particular area. By focusing the laser pointer from the IIT equipment, the CCT has been measured for all the test samples. By doing so, the readings were stored in the IIT equipment memory as image for future analysis. The current study involves IIT to analyze the combustion chamber hotspot, EGT and their connection with NO_x emission for biodiesel blends with various synthetic antioxidant and concentrations.

Antioxidants effect on diesel engine characteristics

With the aid of an AVL gas analyzer and varying compression ratio diesel engine as shown in the Figure 3, the emission characteristics have been analyzed. From previous studies, it has been observed that the emission level of a diesel engine using non edible and edible biodiesel feedstock can be reduced with and without any operating parameters changes in the diesel engine.⁵⁹ In Moringa oleifera biodiesel, the cetane number and NO_x emission has been decreased by blending Pyrogallol antioxidant while testing in diesel engine at standard operating conditions.⁶⁰ As the antioxidant alters the physical properties of biodiesel, the cold flow property which is the major problem for a diesel engine can be reduced.

Figure 3.

AVL gas analyzer and varying compression ratio diesel engine.

Objectives of this study

Current research objectives	Observation from literature review
To blend two non edible biodiesel feedstock	Due to the depletion of petro diesel fuel in which the fuel price hikes and also the dependence on single biofuel species is not viable. Blending of two non-edible biodiesel feedstock is currently obtaining larger scope as there has been huge number of research conducted on single biodiesel feedstock and to avoid the dependence on single biodiesel feedstock like petro diesel.^61,62
To obtain high biodiesel yield percentage with two step transesterification process	More than 70% of cost has been associated for biodiesel production which is huge. To obtain high biodiesel yield with low production cost, two step transesterification process has been proposed as a promising technique for biodiesel production^63,64
To stabilize biodiesel properties with scientific antioxidants and correlating all the stability parameters for selecting best stable biodiesel blend	Even though biodiesel has been proposed as an alternate fuel for diesel fuel, rapid oxidation reaction in biodiesel is the major drawback which decays the biodiesel properties leading to poor storage capacity. To stabilize oxidation reaction, researchers have been focusing on analyzing the oxidation stability⁶⁵ of various biodiesel feedstock associated with storage²⁶ and thermal stability⁶⁶ using scientific antioxidants. But all the stability properties are interrelated to each other in which correlated stability analysis have to be performed which has not been focused by earlier studies.
To justify the triple stability results through FTIR equipment	By analyzing the molecular functional arrangement of biodiesel due to oxidation reaction, the fact behind the stability results can be justified. The composition and functional grouping of waste cooking oil has been examined using Fourier Transform Infrared spectroscopy (FTIR).⁶⁷
To optimize the best stability blend among various blends	In biodiesel research, various optimization techniques have been used to optimize the biodiesel production process⁶⁸ and to optimize the engine parameters.⁶⁹ Very few optimization studies have been performed on biodiesel stability for optimizing the best stability blend which is a novel approach.
To analyze the dual biodiesel emission characteristics	Using scientific antioxidants like butylated hydroxytoluene - BHT, PPhenylene Di Amine - PPPD, Butylated hydroxyanisole – BHA, 2-ethylhexyl nitrate - EHN and tert-butylhydroquinone – TBHQ and Di-Ethyl Amine – DEA in biodiesel from microalgae, the emission has been reduced and increased when compared to diesel fuel.⁷⁰ Through the current research, the emission behaviour of dual biodiesel blend using scientific antioxidants has been projected.
To correlate the relationship between NO_x and CCT using novel IIT	The NO_x formation from biodiesel is higher in amount due to the presence of large number of oxygen molecules which can be understood from the Zeldovich mechanism.⁵⁵ As the NO_x formation increases due to the presence of high oxygen molecule, the temperature of engine cylinder head also increases.⁵⁸ Correlating the temperature of cylinder head associated with CCT and formation of NO_x emission can give a clear idea about higher NO_x formation in biodiesel.

Results and discussion

Fuel stability – oxidation stability analysis

Figure 1 represents the induction period of B100T3 sample obtained from the computerized rancimat software. Table 8 represents the rancimat equipment specifications used for analyzing the European standard (EN14214) of meeting biodiesel induction period at 110^OC for more than 6 h. As the biodiesel blend in this research is a novel mixture, the stability tests have been performed initially for pure biodiesel without diesel mixing.

Table 8.

Rancimat equipment specifications.

Components	Specifications
Maker	873 Metrohm Biodiesel Rancimat
Sample holder	Eight (each block four sample)
Warmer	Double aluminium individual heating blocks
Flow of gas	7 to 25 L/h
Range of temperature	50 to 220^OC (Can be assigned 1^OC additionally)
Temperature correctness	+9.9^OC to −9.9^OC(Can be assigned 0.1^OC additionally)
Conductivity	0 to 500 µS/cm

The assurance has been obtained for all antioxidant dosed B100 samples by satisfying the EN14214 standard induction period of 6 h at 110°C. At 110°C among all antioxidant dosed samples, B100T3 represents an elevated induction period of 48.26 h. The measured I.Ps at 110°C in terms of hour is in the order of B100 (8.15 h) < B100P1 (10.8 h) < B100P2 (19.13 h) < B100T1 (19.46 h) < B100P5 (21.4 h) < B100P4 (27.44 h) < B100T2 (28.04 h) < B100T5 (34.07 h) < B100P3 (35.85 h) < B100T4 (36.5 h) < B100T3 (48.26 h).

Figures 4 and 5 represents the extrapolated I.P in hours for B100 samples dosed with T and P antioxidants at various concentrations. By testing at four equal temperatures as 140°C to 155°C, the variations in I.P of Alexandrian laurel and Poison nut (B100) biodiesel and the effect of T and P dosage with B100 have been investigated. T and P dosed B100 samples show an average of 69.64% and 62.72% of higher stability respectively than the B100 sample without antioxidants. Figure 6 represents the extrapolated I.P in hours at 30^OC for B100 and antioxidant dosed samples. It is observed that up to B100P3 and B100T3 samples (i.e at 1000 ppm) I.P shows ascend in their range and beyond B100P3 and B100T3 samples, the I.P descend in their range. The increase in oxidation stability by adding antioxidant is due to the supply of hydrogen atoms from its phenolic hydroxyl group to peroxyl radical, interrupting the formation of oxygen molecules chain reaction avoiding the production of additional free radical which propagates further oxidation reaction.⁷¹ For both antioxidant dosed samples there occurs declination in oxidation stability beyond 1000 ppm due to the breakage of hydrophilic and hydrophobic chemical structure, exposing the sample to more oxidation reaction.

Figure 4.

Rancimat extrapolated testing temperatures of t dosed samples.

Figure 5.

Rancimat extrapolated testing temperatures of p dosed samples.

Figure 6.

Extrapolated induction periods for test samples.

While comparing T and P dosed B100 samples, Tert-butyl hydroxyquinone shows an average of about 9.93% elevated oxidation stability than Pyrogallol dosed B100 samples. The higher action of TBHQ than PY antioxidant is due to the liberation of TBHQ products while reacting to biodiesel, which is more acidic and same as that of TBHQ antioxidant activity preventing the oxidation reaction.⁷² This characterization can be seen evidently for Tert-butyl hydroxyquinone from the FTIR spectrum by the transmittance percentage (%T) of O-H and C-H bonds (Figure 10).

Fuel stability - storage stability analysis

The biodiesel sample has been stored for hundred days in aluminium containers and storage stability tests have been conducted for 10 days’ time interval. The comparison graphs for KV and AV on the hundredth day for B100 and B100 samples with T and P have been shown in Figure 7 and 8.

Figure 7.

Kv of antioxidant dosed samples.

Figure 8.

Av of antioxidant dosed samples.

The pure biodiesel (B100) and B100 samples dosed by T and P with respective concentrations shows constant peak in the range of kinematic viscosity (KV) and acid value (AV). Though, the redwood antioxidants have been tested for an identical period of 10 days, it has been observed that on the hundredth day, KV ranges of T dosed samples shows 49.79%, 50.20%, 51.21%, 50.30%, and 49.79% for B100T1, B100T2, B100T3, B100T4, and B100T5 whereas an average of 50.25% lower KV have been observed while AV ranges of T dosed samples shows 42.30%, 45.38%, 56.92%, 48.46% and 42.30% for B100T1, B100T2, B100T3, B100T4 and B100T5 also an average of 47.07% lower AV range has been observed. The lengthier the biodiesel storage period, the KV increases due to development of additional polar, oxygen comprising particles and polymeric oxidized molecules which leads to production of deposits and latexes obstructing the fuel injectors.⁷³ The AV also increases due to the chemical reaction which produces fatty acid from fatty acid methyl ester.⁷⁴

On the hundredth day, KV ranges of P dosed samples shows 50.20%, 50.60%, 50.60%, 50.40%, and 49.59% for B100P1, B100P2, B100P3, B100P4, and B100P5 while an average of 50.27% lower KV range have been observed and AV ranges of P dosed samples shows 43.33%, 50.01%, 54.35%, 48.46% and 40.76% for B100P1, B100P2, B100P3, B100P4 and B100P5 and an average of 47.38% lower AV range has also been observed. Among the ten antioxidants dosed samples, B100P3shows 50.60% and 54.35% lower KV and AV, whereas B100T3shows 51.21% and 56.92% lower KV and AV. Taking as a whole, B100T3 shows 1.19% and 4.51% lower KV and AV than B100P3 which proved their superior storage stability. Similar to oxidation stability outcomes, the storage stability test also shows decrement beyond 1000 ppm due to superior chemical structure of synthetic antioxidants. The higher storage stability characteristics of TBHQ antioxidant are the development of shielding layer around copper molecules from fuel tank due to corrosion which is the main source for increase in AV and KV of biodiesel.⁷⁵

Fuel stability - Thermal stability analysis

Figure 9 represents the graphical illustration of diesel and biodiesel samples onset temperature from TGA Q500 Thermo-gravimetric analyzer. Table 9 represents TGA specifications in which the variation in thermal properties of B100 samples has been analyzed. The major composition of non-edible oil is the oleic and linoleic fatty acid which can be observed from Table 2. The weight loss on biodiesel is due to the combustion/evaporation of oleic and linoleic fatty acids which are higher in number in both Alexandrian laurel and Poison nut biodiesel.^25,76 The elevated thermal stability can be justified only if the testing sample shows a higher T_ON.⁷⁷ At higher temperature, the polymer molecules are produced due to Diels Alder reaction which increases the viscosity of biodiesel.⁷⁸ Another study proves that with increase in oxidation temperature the oil stability index decreases.⁷⁹ As the degradation of biodiesel takes place due to thermal reaction, AV value also increases which increases viscosity. All these characteristics take place due to oxidation reaction in biodiesel.

Figure 9.

TON for t and p dosed samples.

Figure 10.

FTIR spectrums of test samples.

Table 9.

TGA specifications.

Components	Specifications
Maker	Thermogravimetric Analyzer (TGA Q500)
Sensitivity	0.2 µg
Rate of Purge gas	Balance 10 mL per min; Furnace 90 mL per min
Rate of Heating	0.1 – 50^OC per min in 0.01^OC per min additionally
Range of temperature	Ambient to 1000^OC
Calibration of temperature	1–5 points
Max Weighing capacity	1.0 gram
Accuracy of Balancing	±0.1%

An extremely small quantity of samples and no pretreatment is necessary for testing in TGA equipment. There are three divisions involved in automated TGA graphs which are onset temperature, max degradation and offset temperature. Onset temperature division represents the time period in which the sample faces around 1% weight drop at the range of 100^OC. Max degradation division represents the rapid weight drop at the range of 200^OC after 30 min. Offset temperature division represents the temperature at which the sample loses its entire properties at the range of 280^OC, beyond where there is no sample conversion occurs. The onset temperature (T_ON) of B100 shows 117.47°C which is 60.60% lower than commercial diesel fuel. B100 samples dosed with T shows 12.68%, 21.40%, 38.36%, 38.55%, and 37.73% for B100T1, B100T2, B100T3, B100T4 and B100T5 and an average of 29.74% elevated (T_ON) thermal stability than pure biodiesel. B100 samples dosed with P shows 7.24%, 17.70%, 33.06%, 32.91%, and 20.09% T_ON for B100P1, B100P2, B100P3, B100P4, and B100P5, and an average of 22.2% higher (T_ON) thermal stability than pure biodiesel (B100). While comparing higher T_ON of T and P dosed B100 samples, Tert-butyl hydroxyquinone sample shows 25.35% higher thermal stability than Pyrogallol dosed B100 samples which is because of the presence of more attachments of OH groups on aromatic ring, providing supplementary positions of complex between antioxidant and free radical for ester chain stabilization.⁸⁰

FTIR – Correlating tool of stability outcome

Table 10 represents FTIR equipment specifications, a novel correlation tool which shows the molecular arrangements and transmittance percentage (%T) for molecules like O-H and C-H in biodiesel samples responsible for longer fuel stability, and from the FTIR results, the stability results can be correlated. The wavelength frequency of 2400 to 4000 cm⁻¹ has been implemented in this study. Depending on the fatty acid concentration of biodiesel in earlier studies, the presence of O-H and C-H molecules which is responsible for biodiesel stability have been obtained in the wavelength between 4000 cm⁻¹ to 300 cm⁻¹.^81,82

Table 10.

FTIR specifications.

Components	Specifications
Maker	Perkin Elmer (Spectrum Two)
Actual desiccant life span	Five years on 90% relative humidity at 25^OC
Optimized wavelength	8300 to 350 cm⁻¹
Range of Temperature	5 to 45^OC
Standard Spectrum resolution	0.5 cm⁻¹
Precision (wavelength)	Superior than 0.01 cm⁻¹ at 3000 cm⁻¹
Precision (accuracy)	0.01 cm⁻¹ at 3000 cm⁻¹

The occurrence of O-H molecule representing the moisture content which augments the oxidation reaction and C-H represents the occurrence of carbon molecule. Figure 10 shows the incidence of variations in C-H and O-H transmittance percentage for diesel, B100T3 and B100 samples. Initially, for commercial diesel, the FTIR spectrum has been analyzed and shows %T of 99.34% for O-H at 3400 cm⁻¹ wavelengths, 5.29% for C-H at 2860.29 cm⁻¹ wavelength, and 8.57% for C-H at 2825.37 cm⁻¹ wavelength. As the presence of carbon in diesel fuel is a natural entity, the carbon molecules consume the oxygen molecules and converts into carbon dioxide paving the way for longer storage days without degradation which can be visualized through FTIR. Secondly, for B100, the FTIR spectrum has been analyzed and shows %T of 82.24% for O-H at 3400 cm⁻¹ wavelength, 60.15% for C-H at 2860.29 cm⁻¹ wavelength, and 68.37% for C-H at 2825.37 cm⁻¹ wavelength. When compared to carbon, oxygen molecule is more electronegative which draws additional oxygen molecules leading to production of hydro-peroxide and peroxide is responsible for further oxidation reaction.⁸³ The diesel spectrum indirectly represents that; %T of diesel contains 0.66% O-H at 3400 cm⁻¹, 94.71% C-H at 2860.29 cm⁻¹ and 91.43% C-H at 2825.37 cm⁻¹. The B100 spectrum indirectly represents that; %T of B100 contains 16.76% O-H at 3400 cm⁻¹, 39.85% for C-H at 2860.29 cm⁻¹and 31.63% for C-H at 2825.37 cm⁻¹. The crowning which occurs between the wavelength of 3000 cm⁻¹ to 2800 cm⁻¹ suggests the presence of methylene or methyl groups in B100 ester chain.⁸⁴ By evaluating diesel and B100 spectrum it is observed that B100 contains an average of 96.04% higher O-H at 3400 cm⁻¹, 57.92% lower C-H at 2860.29 cm⁻¹, and 65.40% lesser C-H at 2825.37 cm⁻¹. To reduce the O-H molecules and to increase the C-H molecules in pure biodiesel, B100 samples with T and P, at respective concentrations have been analyzed. By blending T and P, at respective concentrations it has been observed that Tert-butyl hydroxyquinone sample shows %T of 95.28% O-H at 3400 cm⁻¹, 35.06% C-H at 2860.29 cm^−1, and 43.97% C-H at 2825.37 cm⁻¹ when compared to P dosed B100 samples.

As T antioxidant possess elongated carbon chain structure, there happens a sturdy C-H bonding between antioxidant and biodiesel. This C-H bonding encircles the O-H bonding, preventing the free radical strike. Since the O-H molecules are encircled by C-H molecules, it takes supplementary period for free radicals to assault the O-H molecules leading to longer storage period of biodiesel. When the TBHQ is added in a huge amount (i.e above 1000 ppm), the biodiesel stability has been disturbed due to weakening of formed hydro-peroxide and peroxide by upsetting the alignment of band which exposes the O-H molecules to free radical reaction.⁸³

Core motive for implementing optimization techniques

As of the experimental results, it is observed that better stability results have been obtained in the order as follows. For oxidation and storage stability, B100T3 has been obtained as preeminent whereas, for thermal stability, B100T4 has been obtained as a preeminent result. Similarly, in the FTIR spectrum, for O-H at 3400 cm⁻¹ and C-H at 2825.37 cm⁻¹ sample called B100T4 and for C-H at 2860.29 cm⁻¹ sample called B100T3 has been obtained as a preeminent result. Best performance should be given only by one sample among (n) number of samples. To obtain the best result, the optimization techniques like EDAS and TOPSIS assisted with the weightage calculation method called CRITIC have been implemented in this research as a novel approach.

Weightage evaluation techniques

CRITIC technique

Following the CRITIC technique, the weightage of biodiesel stability parameters has been obtained by manual calculations as follows. For normalizing the decision matrix which is the experimental results using formula (1), the initial step of fixing the beneficial and non-beneficial criteria has been fixed from biodiesel blend according to step 1. Table 11 represents the best and worst value for beneficial and non-beneficial criteria. After setting up the best and worst value, the decision matrix has been normalized using the formula (1) and the normalized decision matrix results for antioxidant added samples were evaluated. From the normalized decision matrix results, the standard deviation for antioxidant dosed samples has been evaluated using formula (2) and it is shown in Table 12. After evaluating the standard deviation results, the symmetric matrix for normalized decision matrix has been evaluated using linear correlation formula ( $r_{j k}$ ) (3) and the results are shown in Table 13. Each value in the symmetric matrix is represented as $r_{jk} .$

Table 11.

Best and worst value for beneficial and non-beneficial criteria.

Criteria	Oxidation stability	Storage stability		Thermal stability	FTIR readings
Criteria	Induction Period (I.P) (h)	KV @ 40°C at 100th day (cSt)	AV @ 100th day (mg KOH/g)	Onset Temperature (T_ON) (^oC)	O-H @ 3400 cm⁻¹ (%T)	C-H @ 2860.29 cm⁻¹ (%T)	C-H @ 2825.37 cm⁻¹ (%T)
Beneficial (B)/ Non- Beneficial (NB)	B	NB	NB	B	B	NB	NB
Best value	1752	4.84	1.68	199.94	95.28	37.06	43.42
Worst value	350.4	9.9	3.9	117.47	83.24	60.15	68.37

Table 12.

Standard deviation ( $σ_{j}$ ) for antioxidant dosed samples.

	Oxidation stability	Storage stability		Thermal stability	FTIR readings
	Induction Period (I.P)	KV @ 40°C at 100th day	AV @ 100th day	Onset Temperature (TON)	O-H @ 3400 cm⁻¹	C-H @ 2860.29 cm⁻¹	C-H @ 2825.37 cm⁻¹
Standard Deviation ( $σ_{j}$ )	0.32	0.30	0.27	0.29	0.28	0.28	0.28

Table 13.

Symmetric matrix for the normalized decision matrix.

	Induction Period (I.P)	KV @ 40°C at 100th day	AV @ 100th day	Onset Temperature (TON)	O-H @ 3400 cm⁻¹	C-H @ 2860.29 cm⁻¹	C-H @ 2825.37 cm⁻¹
Induction Period	1	0.551	0.715	0.970	0.813	0.711	0.760
KV@40^oCAt 100th Day	0.551	1	0.949	0.512	0.877	0.951	0.945
AV @ 100th Day	0.715	0.949	1	0.692	0.897	0.929	0.945
Onset Temperature	0.970	0.512	0.692	1	0.785	0.687	0.730
O-H @ 3400 cm⁻¹	0.813	0.877	0.897	0.785	1	0.945	0.968
C-H @ 2860.29 cm⁻¹	0.711	0.951	0.929	0.687	0.945	1	0.993
C-H @ 2825.37 cm⁻¹	0.760	0.945	0.945	0.730	0.968	0.993	1

By using the formula (4), the bulk amount of data from the symmetric matrix has been reduced by subtracting the values of $r_{j k}$ from one and the results from each row have been added to solve the measure of conflict. For evaluating the quantity of information ( $C_{j})$ , the standard value results get multiplied with a measure of conflict sum of each criterion as shown in formula (5). To end with finding the weights from formula (6), each $C_{j}$ value gets divided by the sum of $C_{j}$ and the weights are shown in Table 14. Figure 11 represents the final weightage score of CRITIC technique.

Figure 11.

Final weightage score of CRITIC technique.

Table 14.

Calculated weights of each criterion.

	Oxidation stability	Storage stability		Thermal stability	FTIR readings
Weightage ( $w_{n}$ )	Induction Period (I.P) ( $w_{1}$ )	KV @ 40°C at 100th day $(w_{2})$	AV @ 100th day ( $w_{3}$ )	Onset Temperature (TON) ( $w_{4}$ )	O-H @ 3400 cm⁻¹ ( $w_{5}$ )	C-H @ 2860.29 cm⁻¹ ( $w_{6}$ )	C-H @ 2825.37 cm⁻¹ ( $w_{7}$ )
	0.3	0.175	0.175	0.135	0.075	0.08	0.06

Optimization evaluation techniques

EDAS technique

As of EDAS optimization procedure, the average solution $(A V_{j})$ of biodiesel stability attributes like Induction Period (I.P), Acid value (AV) and Kinematic Viscosity (KV), Onset Temperature (T_ON), and FTIR results namely O-H @ 3400 cm⁻¹, C-H @ 2860.29 cm⁻1, and C-H @ 2825.37 cm⁻¹ have been calculated manually and shown in Table 15. Table 16 shows the weighed sum $(S P_{i}$ and $S N_{i})$ of PDA and NDA obtained by multiplying the weightage value from CRITIC to data of PDA_ij and NDA_ij decision matrix. Following the weighted sum $(S P_{i}$ and $S N_{i})$ of PDA and NDA calculation, the normalized values (NSP_iand NSN_i) of $S P_{i}$ and $S N_{i}$ along with the appraisal score for various biodiesel blend is shown in Table 17.

Table 15.

Average solution of biodiesel stability attributes.

	Oxidation stability	Storage stability		Thermal stability	FTIR readings
	Induction Period (I.P)	KV @ 40°C at 100th day	AV @ 100th day	Onset Temperature (T_ON)	O-H @ 3400 cm⁻¹	C-H at 2860.29 cm⁻¹	C-H @ 2825.37 cm⁻¹
Average solution (AV_j)	1089.635	5.375	2.225	158.083	92.071	41.25	48.632

Table 16.

Weighted sum $(S P_{i}$ and $S N_{i})$ of PDA and NDA.

Test Samples	SP_i	SN_i
B100	0.000	0.209
B100T1	0.004	0.030
B100T2	0.014	0.010
B100T3	0.086	0.000
B100T4	0.061	0.000
B100T5	0.039	0.000
B100P1	0.005	0.045
B100P2	0.013	0.017
B100P3	0.064	0.000
B100P4	0.032	0.000
B100P5	0.010	0.0183

Table 17.

Normalized values (NSP_i and NSN_i) of $S P_{i}$ and $S N_{i}$ with appraisal score (as_i).

Test Samples	NSP_i	NSN_i	AS_i
B100	0.000	0.000	0.000
B100T1	0.052	0.853	0.452
B100T2	0.165	0.951	0.558
B100T3	1.000	1.000	1.000
B100T4	0.719	1.000	0.859
B100T5	0.456	0.996	0.726
B100P1	0.062	0.781	0.422
B100P2	0.155	0.914	0.534
B100P3	0.744	1.000	0.872
B100P4	0.383	1.000	0.691
B100P5	0.126	0.912	0.519

By observing the appraisal score of the EDAS method, B100T3 at 1000 ppm obtains the highest appraisal score when compared to other biodiesel blending with various quantities of T and P dosage which shows 7.05% eminent fuel stability when compared to B100P3. The higher score in the EDAS method for PA100T3 is due to the suppressing action of T over the hydrophobic and hydrophilic arrangement, leading to a vigorous oxidation reaction that takes place in pure biodiesel (B100) when compared to P antioxidant dosed samples.

TOPSIS technique

From the methodology of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the preference scores for biodiesel blend dosed without and with antioxidants like Tert-butyl hydroxyquinone (T) and Pyrogallol (P) at five equal concentrations like 500, 750, 1000, 1250, and 1500 in ppm has been obtained. Initially from the decision matrix (i.e. experimental results of stability parameters), the normalized decision matrix has been obtained with the help of formula (17) and shown in Table 18. From the normalized decision matrix, the weighed normalized decision matrix has been evaluated manually with the help of formula (18) and shown in Table 19. After evaluating the weighted normalized decision matrix, the Euclidean distance from ideal best ( $S_{i}^{+}$ ) and ideal worst value ( $S_{i}^{-}$ ) has been calculated from formula (19 and 20). To this point, the ideal best ( $V_{j}^{+}$ ) and ideal worst ( $V_{j}^{-}$ ) values have been specified from Table 19 which is shown in Table 20.

Table 18.

Normalized decision matrix.

Sample	Oxidation stability	Storage stability		Thermal stability	FTIR readings
Sample	Induction Period (I.P)	KV @ 40°C at 100th day	AV @ 100th day	Onset Temperature (T_ON)	O-H @ 3400 cm⁻¹	C-H @ 2860.29 cm⁻¹	C-H @ 2825.37 cm⁻¹
B100	0.090	0.536	0.512	0.221	0.272	0.434	0.420
B100T1	0.193	0.269	0.295	0.253	0.298	0.304	0.302
B100T2	0.247	0.267	0.279	0.281	0.306	0.285	0.283
B100T3	0.450	0.261	0.220	0.358	0.310	0.253	0.270
B100T4	0.408	0.266	0.264	0.359	0.311	0.271	0.270
B100T5	0.337	0.269	0.295	0.355	0.310	0.275	0.276
B100P1	0.157	0.267	0.290	0.238	0.297	0.315	0.314
B100P2	0.225	0.265	0.256	0.268	0.299	0.296	0.297
B100P3	0.427	0.265	0.233	0.330	0.304	0.285	0.283
B100P4	0.315	0.266	0.264	0.329	0.304	0.281	0.278
B100P5	0.227	0.270	0.303	0.276	0.298	0.275	0.288

Table 19.

Weighted normalized decision matrix.

Sample	Oxidation stability	Storage stability		Thermal stability	FTIR readings
Sample	Induction Period (I.P)	KV @ 40°C at 100th day	AV @ 100th day	Onset Temperature (T_ON)	O-H @ 3400 cm⁻¹	C-H @ 2860.29 cm⁻¹	C-H @ 2825.37 cm⁻¹
B100	0.027	0.093	0.089	0.029	0.020	0.034	0.025
B100T1	0.058	0.047	0.051	0.034	0.022	0.024	0.018
B100T2	0.074	0.046	0.048	0.037	0.023	0.022	0.017
B100T3	0.135	0.045	0.038	0.048	0.023	0.020	0.016
B100T4	0.122	0.046	0.046	0.048	0.023	0.021	0.016
B100T5	0.101	0.047	0.051	0.047	0.023	0.022	0.016
B100P1	0.047	0.046	0.050	0.032	0.022	0.025	0.018
B100P2	0.067	0.046	0.044	0.036	0.022	0.023	0.017
B100P3	0.128	0.046	0.040	0.044	0.022	0.022	0.017
B100P4	0.094	0.046	0.046	0.044	0.022	0.022	0.016
B100P5	0.068	0.047	0.053	0.037	0.022	0.022	0.017

Table 20.

Ideal best ( $V_{j}^{+}$ ) and ideal worst ( $V_{j}^{-}$ ) value from weighted normalized decision matrix.

Criteria	Oxidation stability	Storage stability		Thermal stability	FTIR readings
Criteria	Induction Period (I.P)	KV @ 40°C at 100th day	AV @ 100th day	Onset Temperature (T_ON)	O-H @ 3400 cm⁻¹	C-H @ 2860.29 cm⁻¹	C-H @ 2825.37 cm⁻¹
Beneficial (B)/ Non- Beneficial (NB)	B	NB	NB	B	B	NB	NB
Ideal best ( $V_{j}^{+}$ )	0.135	0.045	0.038	0.048	0.023	0.020	0.016
Ideal worst ( $V_{j}^{-}$ )	0.027	0.093	0.089	0.029	0.020	0.034	0.025

The Euclidean distance from ideal best ( $S_{i}^{+}$ ) and ideal worst values ( $S_{i}^{-}$ ) have been calculated manually from Table 20 by using the formula (19 and 20) and shown in Table 21. After obtaining all the essential results, with the help of formula (21) the performance score for the blended biodiesel, out of eleven samples based on parameters like I.P, KV, AV, T_ON, O-H, and C-H have been calculated and shown in Table 21. From Table 21 it is observed that B100T3 shows 6.41% higher fuel stability due to a lower presence of oxidation reaction when compared to B100P3. Poor result has been obtained for B100 which is pure biodiesel due to the presence of higher content of oxygen leading to tremendous oxidation reaction.

Table 21.

Euclidean distance from ideal best ( $S_{i}^{+}$ ), ideal worst ( $S_{i}^{-}$ ) results, performance scores for biodiesel blends.

Sample	Ideal best results ( $S_{i}^{+}$ )	Ideal worst results ( $S_{i}^{-}$ )	Performance scores ( $P_{i}$ )
B100	0.131	0.000	0.000
B100T1	0.079	0.069	0.464
B100T2	0.062	0.079	0.560
B100T3	0.000	0.131	0.998
B100T4	0.014	0.117	0.887
B100T5	0.036	0.098	0.730
B100P1	0.090	0.065	0.419
B100P2	0.069	0.0783	0.531
B100P3	0.008	0.123	0.934
B100P4	0.041	0.095	0.697
B100P5	0.069	0.074	0.516

Focalization of EDAS and TOPSIS results

Table 22 represents the appraisal scores (AS_i) of EDAS technique and performance scores ( $P_{i}$ ) of TOPSIS technique for test samples with respective ranks. From Table 22 it is observed that the results have been obtained by dosing the antioxidants like Tert-butyl hydroxyquinone (T) and Pyrogallol (P) at equal concentrations of 500, 750, 1000, 1250, and 1500 ppm to Alexandrian laurel and Poison nut biodiesel. Among the five concentrations of TBHQ (T) antioxidants, from 500 ppm to 1000 ppm, the preference score has been obtained in increasing order in which B100T3 (i.e.B100 at 1000 ppm) shows a better appraisal score of 1.000 in EDAS optimization technique among the other T concentrations. After 1000 ppm, the preference score for 1250 and 1500 ppm descends slowly due to the overturn of hydrophobic and hydrophilic arrangements by an increase of antioxidant concentration making the antioxidant inactive. This leads to blowing out the cover of antioxidant dosed samples from oxygen contact. The same characteristic has been observed for PY (P) dosed biodiesel samples. B100P3 shows an elevated appraisal score of 0.872 in the EDAS technique when compared to other concentrations of P antioxidants.

Table 22.

Appraisal scores (as_i) of EDAS technique and performance scores ( $P_{i}$ ) of TOPSIS technique for test samples with respective ranks.

Sample	EDAS		TOPSIS
Sample	Appraisal scores (AS_i)	Rank	Performance scores ( $P_{i}$ )	Rank
B100	0.000	11	0.000	11
B100T1	0.452	9	0.464	9
B100T2	0.558	6	0.560	6
B100T3	1.000	1	0.998	1
B100T4	0.859	3	0.887	3
B100T5	0.726	4	0.730	4
B100P1	0.422	10	0.419	10
B100P2	0.534	7	0.531	7
B100P3	0.872	2	0.934	2
B100P4	0.691	5	0.697	5
B100P5	0.519	8	0.516	8

While comparing B100T3 and B100P3 samples in the EDAS technique, the T dosed biodiesel sample (i.e.B100T3) shows a superior preference score as the T antioxidant release the chemicals during oxidation reaction which are more effective than TBHQ for stabilizing the biodiesel properties.^85,86 On the whole, B100T3 has been the first rank and B100P3 as the second rank whereas B100 is the pure biodiesel without antioxidant dosage gets the poor rank of eleven. The remaining ranks have been given to biodiesel samples according to the appraisal score of the EDAS technique.

The input data given for the EDAS optimization technique has been given similarly for the TOPSIS optimization technique and the preference score( $P_{i}$ ) has been obtained for antioxidant dosed samples at respective concentrations. PY (P) dosed samples shows a better criterion ( $P_{i}$ ) for B100P3 of 0.934 among the other Pyrogallol concentrations. The identical rise and fall of $P_{i}$ has been obtained up to 1000 ppm and beyond 1000 ppm. T dosed samples shows elevated criterion ( $P_{i}$ ) for B100T3 of about 0.998 among the other four T concentrations. The ranking order has been obtained concerning the EDAS optimization technique like the first rank for B100T3, second rank for B100P3, and poor rank for B100, which is shown in Table 22. By observing all the optimized results from EDAS and TOPSIS techniques, the biodiesel blend added with 1000 ppm Tert-butyl hydroxyquinone antioxidant (B100T3) has been proposed perfectly as the best biodiesel blend with eminent fuel stability.

No_x emission and IIT assessment

The NOx emission reduction from biodiesel is a greater mechanical challenge met by the automobile industries, since there has been strict regulation on emission control from commercial diesel engine.⁸⁷ The NO_x emission affects the ecosystem which disturbs animals and plants that depend on water and land. It also affects the livelihood of human whodepends on those plants and animals. Even for diesel fuel, the hike in injection timing raises the temperature of combustion process which increases the NO_x emission. Whereas, hike in injection pressure raises the density of fuel and reduces the duration of injection, automatically advances the spray of fuel and combustion process. These changes also lead to increase in NO_x emission.

A study done with Roselle and Karanja biodiesel tested on four stroke diesel engines at standard operating procedure of compression ratio 17.5 and injection timing of 23.5^ObTDC also showed better engine characteristics.⁸⁸ Another study of analyzing the performance characteristics like BTE, BSFC and EGT and emission characteristics like NOx, HC, smoke and CO of pure lemon grass biodiesel using synthetic antioxidants in direct injection diesel engine operated at standard operating condition shows better result.⁸⁹ By going through various other literatures, this research implements standard engine operating condition for analyzing the emission level of Alexandrian laurel and Poison nut biodiesel using synthetic antioxidants. This research also bridges a gap of rapid and active analysis of how combustion process plays a chief role in higher or lower NO_x emission of biodiesel dosed with synthetic antioxidants using IIT.

Antioxidant effect on neat biodiesel No_x emission

Figure 12 shows the NOx emission of B100 and antioxidant dosed samples tested using AVL Ditest Gas Analyzer. 80% engine load has been assigned for analyzing the emission of NOx as 80% load condition showed better engine characteristics.⁹⁰ It has been observed from Figure 16 that at 80% engine load, B100 shows 68.82% higher NOx emission when compared to diesel. Higher NOx emission in B100 is due to the existence of oxygen molecules in elevated amount results in complete combustion which increases the in-cylinder temperature generating more oxygen rich area.⁹¹ Another factor for hike in in-cylinder temperature is the transmission of low soot heat energy as the usage of biodiesel reduces the particle matter also promoting NOx emission.⁹²The NOx emission has been lowered using B100 which has been performed using synthetic antioxidants.

Figure 12.

Nox emission of B100, P and T dosed B100 samples.

The synthetic antioxidants have been blended with B100 sample at varying concentrations. From Figure 16 it has been observed that, by adding P antioxidant, it shows 2.55%, 2.76%, 3.03%, 3.24% and 3.56% with lower NOx emission for B100P1, B100P2, B100P3, B100P4 and B100P5. With increase in antioxidant concentration, the NOx emission level has been decreased averagely to 3.02% when compared to pure biodiesel (B100). From Figure 16 it has been observed that, by blending T antioxidant, it shows 2.87%, 3.19%, 3.45%, 3.72% and 4.09% with lower NOx emission for B100T1, B100T2, B100T3, B100T4 and B100T5. With increase in antioxidant concentration, the NOx emission level has been decreased averagely to 3.46% when compared to pure biodiesel (B100). It has been observed that, by adding synthetic antioxidant to biodiesel blend, the NOx emission level can be reduced. While comparing both the antioxidant effect on reducing the NOx emission, T dosed samples shows more action with an average hike of 14.56% when compared to P dosed samples. Hydrocarbon radicals like C₂, C, CH₂, CH and C₂H gets reacted with nitrogen particles in fuel is the fact behind elevated NOx emission. Due to elevated NOx emission, the free radicals like HCN, N and NO also increases. By adding synthetic antioxidants, the formation of free radical is slowed down leading to lower NOx emission when compared to pure biodiesel.⁹³ A similar characteristic has been observed in various studies as mentioned below.

The key factor for NOx formation in C division and SUV diesel engine light duty vehicle is due to the chemical reaction between nitrogen and free radicals of hydrocarbons.⁹⁴ The synthetic antioxidants play as an interrupter in palm oil biodiesel which interrupts the grouping of free radicals and so, the NOx emission level decreases.⁹⁵ Due to the molecular structural arrangement of tert butyl hydroquinone, the by-products produced from the TBHQ increases the oxidation stability of Poison nut when compared to Neem and Karanja biodiesel which acts as a free radical formation interrupter.⁹⁶ This is the fact behind the improved performance of T when compared to P dosed samples.

Infrared imaging technique (IIT)

Figure 13 represents the CCT and EGT IIT images for diesel and B100 samples. Figure 14 represents the CCT and EGT IIT images for P antioxidant dosed samples and Figure 15 represents the CCT and EGT IIT images for T antioxidant dosed samples. High oxygen molecule in biodiesel enhances the chemical reaction between nitrogen and oxygen molecules inside the combustion chamber which obliquely indicates that inside the cylinder the temperature plays a key role in NO_x formation. For this purpose, this research involves the IIT as a novel non-contact testing method to evaluate how the CCT plays an important role in NOx emission.

Figure 13.

IIT images for diesel and B100 samples.

Figure 14.

IIT images for p antioxidant dosed samples.

Figure 15.

IIT images of t antioxidant dosed samples.

Figure 16.

CCT for p and t dosed samples.

From Figure 13 it has been observed that B100 shows 43.37% elevated CCT when compared to diesel fuel due to the presence of rich amount of oxygen. All the outputs from IIT image have been arranged and shown schematically in Figure 16 for easy readability. From Figure 16 it is visible that, when CCT increases NOx also increases for all samples. While analyzing CCT for P antioxidant dosed B100 samples, it shows 6.55%, 12.10%, 14.95%, 18.15%, and 26.38% lower CCT for B100P1, B100P2, B100P3, B100P4 and B100P5 when compared to neat B100 sample. On an average, by blending P to pure B100 sample, it shows 15.62% lower CCT. While analyzing CCT for T dosed B100 samples, it shows 11.09%, 16.47%, 23.19%, 24.03% and 29.07% lower CCT for B100T1, B100T2, B100T3, B100T4 and B100T5 when compared to pure B100 sample.

Averagely, T dosed B100 sample shows 20.77% lower CCT. The trend happened in CCT and NOx emissions are similar, in which by adding synthetic antioxidant reduces the CCT. Comparing P and T dosed samples; T dosed samples shows 32.97% elevated performance in reducing the CCT. The theoretical fact of CCT which plays an important role in NOx emission has been visualized using IIT and proved the fact practically in a novel way. The results obtained from IIT images (Figure 16) is same as that of NOx emission results (Figure 12) which indicates that with increase in synthetic antioxidant concentration, the CCT can be reduced as the NOx emission is reduced.

Figure 17 represents the EGT for P and T dosed samples. Similar to the temperature variations in CCT, all the test blend shows parallel output in EGT too. When compared to B100 without P antioxidant, the samples B100P1, B100P2, B100P3, B100P4 and B100P5 shows 1.01%, 12.64%, 16.47%, 25.07% and 38.07% lower EGT. Whereas for T dosed samples, it shows 4.91%, 24.27%, 25.72%, 30.27% and 43.56% lower EGT for B100T1, B100T2, B100T3, B100T4 and B100T5 while comparing pure B100. Comparing P and T dosed samples, T dosed samples shows 37.55% more impact than P dosed samples. With increase in synthetic antioxidant concentration, the EGT decreases which also connects the link between CCT and NOx.

Figure 17.

EGT for p and t dosed samples.

The CCT, EGT and NOx emission decreases with increase in synthetic antioxidant concentrations. The fuel stability also shows positive result upto 1000 ppm only. Samples blended beyond 1000 ppm shows degradation in fuel properties, beyond which it starts to degrade. The results obtained from the EGT (Figure 17) are same as that of NOx emission (Figure 12) and CCT (Figure 16) results due to the increase in antioxidant concentration. Surveying various literatures for presenting enhanced understanding about the combustion process of biodiesel, this research proposes IIT tool as a simple, effective and novel visual correlation tool for analyzing the correlation between CCT and NOx emission. Therefore, this research proposes B100T3 as the best blend with higher fuel stability and lower NOx emission.

Emission characteristics - impact of diesel blending to biodiesel with and without antioxidants

The pure biodiesel sample, without any addition of diesel denoted as B100 have been analyzed for fuel stability with addition of antioxidants for the reason of storing the same as a biofuel producer concern for prolonged days and it has been depicted that B100T3 has better stability. But this sample could not be used in diesel engine directly due to its high viscosity. A study includes analyzing the comparison of various engine characteristics for five types of vegetable oil like Sunflower, Canola, Corn, Soybean and Hazelnut at 20%, 50% and 100% fuel ratio. Among which Hazelnut at20% ratio shows better engine characteristics.⁹⁷ Using Alexandrian laurel biodiesel in the ratio of 10%, 20% and 30%, the smoke opacity has been reduced upto 20% when compared to diesel fuel at low engine load.⁹⁸ The goal of this research is to condense the usage of diesel fuel to prevent its extinction. So, 80% of diesel has been blended with 20% of B100 biodiesel with and without antioxidants which are denoted as B20 and B20T3 which has been prepared as a suitable blending ratio and fueled in diesel engine to study its emission individualities as follows. Using programmed Kirloskar IC engine united with AVL gas analyzer (specifications shown in Tables 23 and 24), the emission level for biodiesel blends have been assessed.

Table 23.

Kirloskar ic engine specifications.

Components	Specifications
Type of engine	Diesel engine
Cooling method	Air cooling
Engine speed	2000rpm (Constant)
Number of cylinders	1
Number of strokes	4
Engine load	Eddy current dynamometer
Injection timing	23 CAD BtDC
Compression ratio	16:5:1
Rate of power	3.5 kW
Bore diameter	80 mm
Length of stroke	110 mm

Table 24.

MDS 250 AVL gas analyzer specifications.

Emission parameters	Range of measurement (Volume)	Resolution (Volume)	Accuracy (Volume)
Carbon monoxide (CO)	0 to 10%	0.01%	<0.6%: ± 0.03%
Carbon dioxide (CO2)	0 to 20%	0.1%	<10%: ± 0.5% ≥10%: ± 5%
Hydrocarbon (HC)	0 to 20,000 ppm	>2000:10 ppm, ≤2000:1 ppm	<200 ppm: ± 10 ppm ≥ 200 ppm: 5%
Smoke opacity	0 to 100%	0.1%	± 1% of full scale
Nitrogen oxide (NOx)	0 to 5000 ppm	1 ppm	< 500 ppm: ± 50 ppm ≥ 500 ppm: ± 10

Carbon monoxide (CO) emission analysis

Figure 18 represents the variations of CO emission for diesel, B20 and B20T3with increase in engine load. CO is an odorless and colorless poisonous gas. From Figure 18, it has been observed that, with an increase in engine load, the CO emission level also increases for all test samples. CO emission from the diesel engine is generally low due to the lower content of oxygen and the wealthy air-fuel mixture united with partial combustion.⁹⁹ Comparing the emitted CO level, B20 and B20T3, an average hike of 26.47% and 23.52% have been observed when compared to diesel.

Figure 18.

Co emission levels for diesel, B20 and B20T3.

This effect is due to the higher oxygen content in biodiesel chemical structure which sustains complete combustion.¹⁰⁰ But while comparing the CO level of B20T3 to B20, B20T3 shows an average decrement of 4%. This behaviour has been observed by the addition of TBHQ antioxidant in which OH radicals in antioxidant reduces the conversion process of carbon monoxide to carbon dioxide.⁵⁶

Carbon dioxide (CO₂) emission analysis

Figure 19 represents the CO₂ emission level for diesel, B20 and B20T3 with increase in engine load. Always, the CO2 emission is in reverse to CO. This is due to the oxidation of CO by oxygen molecule in the fuel which converts into CO₂.

Figure 19.

Co2 emission levels for diesel, B20 and B20T3.

From the Figure 19, it has been observed that the CO₂ emission level increases for all test samples with an increase in engine load. When compared to the diesel, biodiesel emits more CO₂ emission levels due to the presence of lower carbon molecule and higher oxygen which is responsible for complete combustion.¹⁰¹ Average hikes of 39.39% and 31.77% have been observed for B20 and B20T3 when compared to diesel. But while comparing B20T3 CO₂ level to B20 level, B20T3 shows an average drop of 5.63%. As the oxygen providing catalyst in TBHQ is the prime factor for the combustion process, the CO₂ emission level decreases when compared to the B20 level.¹⁰²Eventhough the addition of T hikes the emission of CO₂ for biodiesel blend; it’s not a major threat to the environment. As the function of plant is to absorb CO₂ and emit oxygen, the higher the CO₂ level the more worthy for human to inhale healthier oxygen.

Hydrocarbon (Hc) emission analysis

Variations in the HC emission level are shown in the Figure 20 for diesel, B20 and B20T3 with increase in engine load. HC is emitted due to the unburned fuel. Previous studies proved that using biodiesel instead of diesel fuel reduces the HC emission level.^103,104 Complete combustion takes place due to elevated oxygen content in biodiesel and reduced delay in burning biodiesel due to higher cetane number which leads to lower unburned hydrocarbon.⁹⁴ The HC emission level increases with an increase in engine load for all test samples that have been observed from the Figure 20. B20 shows an average drop of 20.70% and B20T3 shows an average drop of 16.84% when compared to diesel. Due to the 20% blending of biodiesel to diesel fuel, the ignition delay has been reduced due to higher oxygen molecules which result in a premature start in the injection of fuel inside the combustion chamber and enhancing the combustion process. An increase of 4.86% in the HC level for B20T3 has been observed when compared to B20. By blending TBHQ antioxidant, the action of converting hydrocarbon into the water and carbon dioxide and carbon monoxide to carbon dioxide has been distressed due to the reduction of hydrogen peroxides and peroxides in biodiesel which hikes the quantity of hydrocarbons.¹⁰⁵

Figure 20.

Hc emission levels for diesel, B20 and B20T3.

Smoke opacity emission analysis

Figure 21 represents the smoke opacity emission level for diesel, B20 and B20T3 with increase in engine load. All the test samples show an increase in smoke with an increase in engine load. As the diesel fuel possesses lower cetane number incomplete combustion takes place in which the smoke opacity increases with an increase in engine load.¹⁰⁶ Due to the complete burning of biodiesel using rich oxygen molecules, reduction of smoke takes place. But when the engine load increases, the injection of biodiesel inside the combustion chamber also increases reducing the air fuel ratio resulting in elevated smoke.⁹⁵ When compared to diesel, B20 and B20T3 show an average diminish of 14.93% and 10.7% in smoke emission level. The drop in B20 blend, when compared to diesel, is due to the blending of the oxygen molecule in diesel, leading to better combustion.¹⁰⁷ While comparing B20 and B20T3 blend, B20T3 shows a hike of 4.97% in smoke emission level due to the addition of TBHQ antioxidant which diminishes the formation of oxidation chain radical leading to lower combustion.¹⁰⁰

Figure 21.

Smoke opacity levels for diesel, B20 and B20T3.

Nitrogen oxide (NO_x) emission analysis

The emission of NO_x for diesel, B20, and B20T3 with hike in engine load is shown in the Figure 22. For all test samples, the NO_x emission level increases for an increase in engine load. NO_x formation in a diesel engine is generally due to the elevated oxygen molecules, elevated combustion temperature, and elevated combustion period. The NO_x emission level of B20 and B20T3 shows a hike of 25.56% and 13.61% when compared to diesel. As the diesel fuel gets rich oxygen by blending 20% of biodiesel, there occurs premature combustion and belated ignition time leading to climbing of NO_x emission in B20 when compared to diesel.^96,108 While adding an antioxidant, B20T3 shows an average decrease of 9.51% when compared to B20. By blending antioxidants, the temperature of combustion is lowered and combustion response time is improved leading to lower NO_x emission levels when compared to B20.⁸⁵

Figure 22.

Nox emission levels for diesel, B20 and B20T3.

Global fuel market cost analysis

According to India’s best biodiesel production protocol, the complete cost analyses from Alexandrian laurel and Poison nut feedstock collection to biodiesel blend with TBHQ has been shown in Table 25. Table 26 shows that at 30°C, the samples B100, BP20, and B20T3 could be kept for 0.04, 0.23 and 0.48 years respectively.

Table 25.

Cost of biodiesel production and blend preparation (as per may 2021).

Requirements	Measure	Price
Feedstock collection (1 Kg each)	₹/ kg	₹ 7
1 liter bio oil extraction (3 kg each)	₹/ lit	₹ 20
Bio oil overall production	₹/ lit	₹ 27
Bio oil storage and package	₹/ lit	₹ 5
Glycerin, Feedstock cake (By products)	₹/ kg	₹ −9
Transesterification	₹/ lit	₹ 22
Value Added Tax on dealer commission (60%)	₹/ lit	₹ 43.2
Biodiesel (Inclusive of tax)	₹/ lit	₹ 115.2
Diesel (Inclusive of tax)	₹/ lit	₹ 92.58
B20 = 80% of Diesel + 20% of B100	₹/ lit	₹ 97.104
TBHQ	1000 ppm	₹ 0.00392
B20T3 = B20 with TBHQ at 1000 ppm	₹/ lit	₹ 97.108

Table 26.

Global fuel market cost analysis for storage.

	B100	B20	B20T3
Commercial Cost of fuel per liter for preparing one time	₹ 115.2	₹ 97.104	₹ 97.108
Extrapolated IP at 30°C in years	0.04	0.23	0.48
The number of times the fuel must be prepared in order to store one liter of fuel without properties deterioration.	25	5	2
The expense of storing one liter of fuel for a year	₹ 2880	₹ 485.52	₹ 194.22

The storage cost of B20 and B20T3 is ₹ 485.52 and ₹ 194.22 respectively for sustaining one liter of fuel for a year, which shows that the addition of scientific antioxidants reduces the storage cost The tax included cost of dual biodiesel with antioxidants (B20T3) is just around 5% higher than diesel which improves the economic viability for implementing the proposed bio fuel in the global fuel market.

Uniqueness of current study

The following Table 27 represents the uniqueness of current research when compared to other biodiesel studies.

Table 27.

Uniqueness of current study.

	Current work	¹⁰⁹	¹¹⁰	¹¹¹	¹¹²	³⁸
Nature of Blend	Binary biodiesel blend	Binary biodiesel blend	Binary biodiesel blend	Binary biodiesel blend	Mono biodiesel blend	Mono biodiesel blend
Composition of blend	Alexandrian laurel + Poison nut	Algae biodiesel + Biogas	Jatropha carcus + Karanja	Waste Cooking oil + CNG	Pomace Olive Oil & Soybean	Waste Cooking Oil
Scientific antioxidants	TBHQ, PG, PY, BHA and BHT	-	-	-	BHA, BHT, TBHQ, PY, PG & TBC	TBHQ, BHT & PY
Antioxidant concentration	500, 750, 1000, 1250 and 1500 ppm	-	-	-	50, 200, 1000 mg/kg	375, 750, 1000, 1125 & 1500
Oxidation stability analysis	Increase in stability has been observed using Rancimat equipment	-	-	-	Increase in stability has been observed using Rancimat equipment	Increase in stability has been observed using Differential Scanning Calorimetry equipment
Storage stability analysis	Storage stability has been increased when stored for 100 days	-	-	-	Storage stability has been increased when stored for 30 days	-
Thermal stability analysis	Loss in sample weight with increase in temperature has been analyzed using TGA equipment	-	Loss in sample weight with increase in temperature has been analyzed using TGA equipment	-	-	-
Molecular arrangement analysis	Arrangement of biodiesel molecules has been analyzed using FTIR equipment	-	Arrangement of biodiesel molecules has been analyzed using FTIR equipment	-	-	Arrangement of biodiesel molecules has been analyzed using FTIR equipment
Weightage and Optimization techniques involved	CRITIC, EDAS and TOPSIS	-	-	-	-	-
NO_x and combustion process correlation	Infrared Visual Imaging Technique	-	-	-	-	-
Fuel as reference	Diesel (D), B20 & B20T3	Algae biodiesel & Diesel (D)	Diesel (D)	Diesel, B20	-	-
CO	D > B20, D > B20T3 & B20 < B20T3	B20 + Biogas > B20 & D	Average < D	B20 + CNG < BX + CNG & B20 + CNG > B20	-	-
CO₂	D < B20, D > B20T3 & B20 > B20T3	-	Average > D	B20 + CNG < BX + CNG & B20 + CNG > B20	-	-
HC	D > B20, D > B20T3 & B20 < B20T3	B20 + Biogas > B20 & D	-	B20 + CNG < BX + CNG & B20 + CNG > B20	-	-
Smoke	D > B20, D > B20T3 & B20 < B20T3	B20 + Biogas < B20 & D	-	-	-
NO_x	D < B20, D < B20T3 & B20 > B20T3	B20 + Biogas < B20 & D	Average > D	B20 + CNG < BX + CNG & B20 + CNG > B20	-	-

Validation of objective accomplishment

Important issues	Validation
Bio oil and biodiesel yield percentage	Alexandrian laurel bio oil – 68.8%
Poison nut bio oil - 51.2%
Alexandrian laurel biodiesel – 84.2%
Poison nut biodiesel - 74.6%
Unbalanced biodiesel chemical compounds due to oxidation reaction	Using scientific antioxidants like pyrogallol (PY), pyrogallate (PG), butylated hydroxyanisole (BHA), butylated hydroxytoulene (BHT) and tert butyl hydrquinone (TBHQ) at 500, 750, 1000, 1250 and 1500 ppm respectively all the biodiesel samples shows elevated stability results
Optimizing best stability blend from triple stability outcomes	Through EDAS and TOPSIS optimization techniques, B100T3 (pure dual biodiesel with 1000 ppm TBHQ concentration) shows highest appraisal score of 1.000 and performance score of 0.998 respectively when compared to other dual biodiesel blends
Oxidation stability	B100T3 shows 83.11% higher Induction period when compared to pure biodiesel without antioxidant (B100)
Storage Stability	On hundredth day, B100T3 shows an average decrement of 50.25% lower KV and 47.07% lower AV
Thermal Stability	B100T3 shows an average increment of 29.74% onset temperature when compared to pure biodiesel without antioxidant (B100)
Justifying B100T3 as best blend	Due to the presence of elongated carbon chain structure in tert-butyl hydroquinone antioxidant there happens a sturdy C-H bonding between antioxidant and biodiesel. This C-H bonding encircles the O-H bonding, preventing the free radical strike which stabilizes the biodiesel sample. The presence of O-H at 3400 cm⁻¹, C-H at 2860.29 cm⁻¹ and C-H at 2825.37 cm⁻¹ wavelengths in B100T3 sample have been increased to 12.38%, 71.56% and 55.03% when compared to B100.
Revealing the relationship between NO_x and Combustion Chamber Temperature (CCT) & Exhaust Gas temperature (EGT) in a effective and novel way	High oxygen molecule in biodiesel enhances the chemical reaction between nitrogen and oxygen molecules inside the combustion chamber which obliquely indicates that in-cylinder temperature plays a key role in NO_x formation. The CCT and EGT emission decreases with increase in synthetic antioxidant concentrations which can be visualized in Figure 14 and 15. B100T3 shows 23.19% CCT and 25.72% lower CCT and EGT when compared to B100. As the CCT and EGT decreases with increase in antioxidant concentration, the NO_x emission also decreases.
Emission characteristics	B20T3 shows an average of 23.52% lower CO, 31.77% higher CO₂, 16.84% lower HC, 10.70% lower smoke and 13.61% higher NO_x emission when compared to diesel.
Biodiesel commercialization cost	The storage cost of B20 and B20T3 is ₹ 485.52 and ₹ 194.22 respectively for sustaining one liter of fuel for a year, which shows that the addition of scientific antioxidants reduces the storage cost The tax included cost of dual biodiesel with antioxidants (B20T3) is just around 5% higher than diesel which improves the economic viability for implementing the proposed bio fuel in the global fuel market.

Conclusion

This research proposed novel biodiesel blend using two step transesterification process from feedstock combination called Alexandrian laurel and Poison nut. The reason behind the selection of dual feedstock is to avoid dependence on one feedstock which may leads to extinction stage like crude oil. By blending dual feedstock, encouraging result has been obtained on significant physiochemical properties. As the study involves dual novel biodiesel blend, unstable oxidation, storage and thermal properties has been obtained for pure biodiesel (B100) when analyzed using Rancimat, viscometer, TGA and FTIR equipment while comparing with diesel. To stabilize the biodiesel properties, synthetic antioxidants like pyrogallol (P) and tert butyl hydroquinone (T) have been blended at five concentration ratios.

As this research involves the analysis of three stability criteria mutually, each criterion shows best results in each sample parameters which stretches the output as multiple results. For selecting the best stability blend, the multi objective optimization techniques have been involved. Through the optimization techniques, T at 1000 ppm sample (B100T3) has been obtained as the best stability blend from various blends. By using Infrared Imaging Technique, a novel visual imaging analysis of antioxidant effect on reducing the NOx emission and combustion chamber temperature has been proposed in this study. High noxious emissions from automobile engines have been a great threat to the environment. For this purpose, using Infrared Imaging Technique (IIT) a novel visual imaging analysis of antioxidant effect on reducing the NOx emission and combustion chamber temperature has been proposed in this study. Pure biodiesel with T dosed samples has shown more action with an average hike of 14.56% when compared with P antioxidant dosed samples in reducing the NOx emission of pure biodiesel.

As pure biodiesel cannot be used directly in diesel engines, the blending of 20% biodiesel with diesel has been prepared and emission characteristics like CO, CO₂, HC, Smoke opacity and NO_x have been analyzed for diesel, B20, and B20T3. By adding T antioxidant, the levels of CO, HC, and smoke opacity have been reduced to an average of 23.52%, 16.85%, and 10.7% when compared with diesel. Meanwhile, an average increase of about 31.51% and 13.72% for CO₂ and NO_x emission level respectively when compared with diesel has been observed. Moreover B20T3 has shown its capability to get commercially viable in global fuel market. On the whole, by adding antioxidants, the emission level of biodiesel has been reduced when compared to biodiesel without antioxidants. Based on the future energy need and eco-friendly environment, the cleaner energy has been produced by blending a novel binary biodiesel with synthetic antioxidant and proposing B100T3 as the best stabilized green energy source.

Footnotes

Acknowledgements

The authors would like to convey their gratitude to the Centre of Excellence for Environmental Studies (COE-Es), Government College of Technology, Coimbatore- 641013, Tamil Nadu state, India and Government College of Engineering, Tirunelveli- 627007, Tamil Nadu state, India for the successful completion of this research with their support.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mohamed Nishath Peer

References

Sezer

. A review study on using diethyl ether in diesel engines: effects on fuel properties, injection, and combustion characteristics. Energy Environ 2020; 31: 179–214.

Gabriel

Vieira

Soletti

, et al. Empirical modeling of different viscosity and density behavior of biodiesel from chicha (sterculiastiata) with diesel versus temperature variation. J King Saud Univer Sci 2020; 32: 628–635.

Yury

Vladimir

Konstantin

. Forecast of energy consumption and greenhouse gas emissions by road transport in Russia upto 2050. Trasnp Res Procedia 2020; 50: 698–707.

Moazeni

Chen

Y-C

Zhang

. Enzymatic transesterification for biodiesel production from used cooking oil, a review. J Cleaner Prod 2019. https:// doi.org/10.1016/j.jclepro.2019.01.181

Gui

Lee

Bhatia

. Feasibility of edible oil vs. Non-edible oil vs. Waste edible oil as biodiesel feedstock. Energy 2008; 33: 1646–1653.

KadirYesilyurt

Cesur

. Biodiesel synthesis from Styraxofficinalis L. Seed oil as a novel and potential non-edible feedstock: a parametric optimization study through the Taguchi technique. Fuel 2020; 265: 117025.

Kumar

Yadav

. Comparative experimental investigation of preheated thumba oil for its performance testing on a CI engine. Energy Environ 2018; 29: 533–542.

Karmakar

Samanta

Halder

. Delonixregia heterogeneous catalyzed two-step biodiesel production from pongamiapinnata oil using methanol and 2-propanol. J Cleaner Prod 2020; 255: 120313.

Liu

Shen

, et al. Effect of oxidation of Jatrophacurcas-derived biodiesel on its lubricating properties. Energy Sustain Dev 2019; 52: 33–39.

10.

Kapilan

Chandra

. Improving the oxidation stability of Karanja oil biodiesel. Energy Environ 2014; 25: 1481–1487.

11.

Kumar Sunil

Alok Jain

. Post Copenhagen summit scenario: attainment of sustainable energy regime in India by Jatropha biodiesel. Energy Environ 2011; 22: 877–889.

12.

Narendranathan

Sudhagar

Karthikeyan

. Optimization of engine operating parameters suitable for punnai oil application in CI engine using grey relational method. Energy Environ 2018: 0958305X1881360. doi:10.1177/0958305x18813603

13.

Atabani

Mahlia

Badruddin

, et al. Investigation of physical and chemical properties of potential edible and non-edible feedstocks for biodiesel production, a comparative analysis. Renew Sustain Energy Rev 2013; 21: 749–755.

14.

Muthukumaran

Saravanan

Prasanna Raj Yadav

, et al. Synthesis of cracked Calophyllum inophyllum oil using fly ash catalyst for diesel engine application. Fuel 2015; 155: 68–76.

15.

Ramesh

Ashok

Nanthagopal

, et al. Influence of hexanol as additive with Calophyllum Inophyllum biodiesel for CI engine applications. Fuel 2019; 249: 472–485.

16.

Ashok

Nanthagopal

Jeevanantham

, et al. An assessment of Calophyllum inophyllum biodiesel fuelled diesel engine characteristics using novel antioxidant additives. Energy Convers Manage 2017; 148: 935–943.

17.

Mofijur

Masjuki

Kalam

, et al. Prospects of biodiesel from Jatropha in Malaysia. Renew Sustain Energy Rev 2012; 16: 5007–5020.

18.

Pankaj

Rajesh

. Effects of dual bio-fuel (Jatropha biodiesel and turpentine oil) on a single cylinder naturally aspirated diesel engine without EGR. Appl Therm Eng 2017; 115: 1137–1147.

19.

Roy

Dabhi

Brahmbhatt

, et al. Effect of emulsified fuel based on dual blend of Castor-Jatropha biodiesel on CI engine performance and emissions. Alexandria Eng J 2021; 60: 1981–1990.

20.

Manickam

Kadambamattam

Abraham

. Combustion characteristics and optimization of neat biodiesel on high speed common rail diesel engine powered SUV. SAE Tech Pap 2009; 1: 2786.

21.

Mardhiah

Ong

Masjuki

, et al. A review on latest developments and future prospects of heterogeneous catalyst in biodiesel production from non-edible oils. Renew Sustain Energy Rev 2017; 67: 1225–1236.

22.

Zhao

Niu

, et al.

Experimental investigation on biodiesel production through transesterification promoted by the la-dolomite catalyst

Fuel 2019; 257: 116092.

23.

Krishnamoorthi

Malayalamurthi

. Combined effect of compression ratio, injection pressure and injection timing on performance and emission of a DI compression ignition engine fueled with diesel- aegle marmelos oil-diethyl ether blends using response surface methodology. Energy Fuel 2017; 31: 11362–11378.

24.

Atabani

Silva César

. Calophyllum inophyllum L. – A prospective non-edible biodiesel feedstock. Study of biodiesel production, properties, fatty acid composition, blending and engine performance. Renew Sustain Energy Rev 2014; 37: 644–655.

25.

Aransiola

Daramola

Ojumu

, et al. Nigerian Jatropha Curcas oil seeds: prospect for biodiesel production in Nigeria. Int J Renew Energy Res 2012; 2: 317–325.

26.

Devi

Das

Deka

. A green approach for enhancing oxidation stability including long storage periods of biodiesel via Thuja oreantalis L. As an antioxidant additive. Fuel 2019; 253: 1264–1273.

27.

Dunn Robert

. Effect of temperature on the oil stability Index (OSI) of biodiesel. Energy Fuels 2008; 22: 657–662.

28.

Karavalakis

Stournas

Karonis

. Evaluation of the oxidation stability of diesel/biodiesel blends. Fuel 2010; 89: 2483–2489.

29.

Mohamed Shameer

Ramesh

. FTIR Assessment and investigation of synthetic antioxidant on the fuel stability of Calophyllum inophyllum biodiesel. Fuel 2017; 209: 411–416.

30.

Chen

Y-H

Chen

J-H

Luo

Y-M

, et al. Property modification of jatropha oil biodiesel by blending with other biodiesels or adding antioxidants. Energy 2011; 36: 4415–4421.

31.

Fazal

Suhaila

Haseeb

ASMA

, et al. Sustainability of additive-doped biodiesel: analysis of its aggressiveness toward metal corrosion. J Cleaner Prod 2018; 181: 508–516.

32.

Singh

Sharma

Soni

, et al. A comprehensive review of physicochemical properties, production process, performance and emissions characteristics of 2nd generation biodiesel feedstock: Jatrophacurcas. Fuel 2021; 285: 119110.

33.

Meng

Fashe

. Effect of TEPA on oxidation stability and metal ion content of biodiesel. Renew Ener 2019; 143: 352–358.

34.

Navdeep

Gyanendra

Ajay

. Evaluation of physicochemical characteristics of Mahua (Madhuca indica) and Jatropha (Jatropha curcas) dual biodiesel blends with diesel. J King Saud Univ Eng Sci 2021; 33: 424–436.

35.

Patil

Gude

Reddy

, et al. Biodiesel production from waste cooking oil using sulfuric acid and microwave irradiation processes. J Environ Prot (Irvine, Calif) 2012; 3: 107–113.

36.

Atabani

Shobana

Mohammed

, et al. Integrated valorization of waste cooking oil and spent coffee grounds for biodiesel production: blending with higher alcohols, FT–IR, TGA, DSC and NMR characterizations. Fuel 2019; 244: 419–430.

37.

Saifuddin

Refal

. Spectroscopic analysis of structural transformation in biodiesel degradation. Res J Appl Sci Eng Technol 2014; 8: 1149–1159.

38.

Uğuz

Atabani

Mohammed

, et al. Fuel stability of biodiesel from waste cooking oil: a comparative evaluation with various antioxidants using FT-IR and DSC techniques. Biocatal Agric Biotechnol 2019; 101283. doi.org/10.1016/j.bcab.2019.101283

39.

Chen

Guo

, et al. Synthesis of MgO/ZSM-5 catalyst and optimization of process parameters for clean production of biodiesel from spirulinaplatensis. J Cleaner Prod 2020; 276: 123382.

40.

Fabio

Marko

Ante

. Test method for determination of different biodiesels (fatty acid alkyl esters) content in diesel fuel using FTIR-ATR. Renew Ener 2019; 133: 1231–1235.

41.

Justine

Joy Prabu

Johnson

, et al. Synthesis and characterizations studies of ZnO and ZnO-SiO₂ nanocomposite for biodiesel applications. Mater Today Proc 2021; 36: 440–446.

42.

Mahmood Khan

Iqbal

Haider Ali

, et al.

Sustainable biodiesel production from waste cooking oil utilizing waste ostrich (Struthiocamelus) bones derived heterogeneous catalyst

Fuel 2020; 277: 118091.

43.

Krishnamoorthi

Malayalamurthi

. Engine characteristics analysis of chaulmoogra oil blends and corrosion analysis of injector nozzle using scanning electron microscopy/energy dispersive spectrometry. Energy 2018; 165: 1292–1319.

44.

Kumar

Rana

Tripathi

. Performance evaluation of diesel–additives ternary fuel blends: an experimental investigation, numerical simulation using hybrid entropy–TOPSIS method and economic analysis. Therm Sci Eng Prog 2020; 20: 100675.

45.

Mishra

Mardani

Rani

, et al. A novel EDAS approach on intuitionistic fuzzy set for assessment of health-care waste disposal technology using new parametric divergence measures. J Cleaner Prod 2020; 272: 122807.

46.

Aggarwal

Choudhary

Mehrotra

. Evaluation of smartphones in Indian market using EDAS. Procedia Comput Sci 2018; 132: 236–243.

47.

Zhao

Wang

, et al. Comprehensive evaluation of national electric power development based on cloud model and entropy method and TOPSIS: a case study in 11 countries. J Cleaner Prod 2020; 277: 123190.

48.

Anwar

Rasul

Ashwath

. The efficacy of multiple-criteria design matrix for biodiesel feedstock selection. Energy Convers Manage 2019; 198: 111790.

49.

Chen

. Evaluation of city innovation capability using the TOPSIS-based order relation method: the case of Liaoning province, China. Technol Soc 2020; 63: 101330.

50.

Abdel-Basset

Mohamed

. A novel plithogenic TOPSIS- CRITIC model for sustainable supply chain risk management. J Cleaner Prod 2020; 247: 119586.

51.

Asante

Adjei

, et al. Exploring the barriers to renewable energy adoption utilising MULTIMOORA- EDAS method. Energy Policy 2020; 142: 111479.

52.

Erdoğan

Aydın

Balki

, et al. Operational evaluation of thermal barrier coated diesel engine fueled with biodiesel/diesel blend by using MCDM method base on engine performance, emission and combustion characteristics. Renew Energy 2020; 151: 698–706.

53.

Sakthivel

Ilangkumaran

Gaikwad

. A hybrid multi-criteria decision modeling approach for the best biodiesel blend selection based on ANP-TOPSIS analysis. Ain Shams Eng J 2015; 6: 239–256.

54.

Xiaoqian

Fei

Haiyan

, et al. TOPSIS Method for quality credit evaluation: a case of air-conditioning market in China. J Comput Sci 2014; 5: 99–105.

55.

Zeldovich

YHG

Sunyaev Rashid

. Selected works of Yakov Borisovich Zeldovich, volume I (chemical physics and hydrodynanics). In: Sunyaev

Barenblatt

(eds) 26. Oxidation of nitrogen in combustion and explosions. New Jersey: Princeton University Press, 1992.

56.

İleri

Koçar

. Experimental investigation of the effect of antioxidant additives on NOx emissions of a diesel engine using biodiesel. Fuel 2014; 125: 44–49.

57.

Sharma

Duraisamy

Cho

, et al. Production, combustion and emission impact of bio-mix methyl ester fuel on a stationary light duty diesel engine. J Cleaner Prod 2019; 233: 147–159.

58.

Shameer

Ramesh

. Experimental evaluation on performance, combustion behavior and influence of in-cylinder temperature on NOx emission in a D.I diesel engine using thermal imager for various alternate fuel blends. Energy 2017; 118: 1334–1344.

59.

Mofijur

Atabani

Masjuki

, et al. A study on the effects of promising edible and non-edible biodiesel feedstocks on engine performance and emissions production: a comparative evaluation. Renew Sustain Energy Rev 2013; 23: 391–404.

60.

Karthickeyan

. Effect of cetane enhancer on Moringaoleifera biodiesel in a thermal coated direct injection diesel engine. Fuel 2019; 235: 538–550.

61.

Srithar

Arun Balasubramanian

Pavendan

, et al. Experimental investigations on mixing of two biodiesels blended with diesel as alternative fuel for diesel engines. J King Saud Univ Eng Sci 2017; 29: 50–56.

62.

Prabhakar

Annamalai

Lalvani Isaac

. Experimental study of using hybrid vegetable oil blends in diesel engine. J Sci Ind Res 2012; 71: 612–615.

63.

Shahabaldin

Bahareh

Junboum

, et al. Review on transesterification of non-edible sources for biodiesel production with a focus on economic aspects, fuel properties and by-product applications. Energy Convers Manage 2019; 201: 112155.

64.

Mahmudul

Hagos

Mamat

, et al. Production, characterization and performance of biodiesel as an alternative fuel in diesel engines – A review. Renew Sustain Energy Rev 2017; 72: 497–509.

65.

Z-h

F-s

Wang

, et al. Antioxidative performance and oil-soluble properties of conventional antioxidants in rubber seed oil biodiesel. Renew Energy 2020; 145: 93–98.

66.

Costa Kênia de

de Viveiros Bárbara

Schmidt

JMAS

, et al. Chemical transformations in technical cashew nut shell liquid and isolated mixture of cardanols, evaluation of the antioxidant activity and thermal stability of the products for use in pure biodiesel. Fuel 2019; 235: 1010–1018.

67.

Zhou

Xiong

Gong

, et al. Analysis of the oxidative degradation of biodiesel blends using FTIR, UV–vis, TGA and TD-DES methods. Fuel 2017; 202: 23–28.

68.

Inayat

Nassef

Rezk

, et al. Fuzzy modeling and parameters optimization for the enhancement of biodiesel production from waste frying oil over montmorillonite clay K-30. Sci Total Environ 2019; 666: 821–827.

69.

Jaliliantabar

Ghobadian

Najafi

, et al. Multi-objective NSGA-II optimization of a compression ignition engine parameters using biodiesel fuel and exhaust gas recirculation. Energy 2019; 187: 115970.

70.

Saravanan

Krishnamoorthy

. Investigation on reduction in consequences of adding antioxidants into the algae biodiesel blend as a CI engine fuel. Fuel 2020; 276: 117993.

71.

Rizwanul Fattah

Masjuki

Kalam

, et al. Effect of antioxidants on oxidation stability of biodiesel derived from vegetable and animal based feedstocks. Renew Sustain Energy Rev 2014; 30: 356–370.

72.

de Guzman

Tang

Salley

, et al. Synergistic effects of antioxidants on the oxidative stability of soybean oil- and poultry fat-based biodiesel. J Am Oil Chem Soc 2009; 86: 459–467.

73.

Dunn

. Effect of antioxidants on the oxidative stability of methyl soyate (biodiesel). Fuel Process Technol 2005; 86: 1071–1085.

74.

Obadiah

Kannan

Ramasubbu

, et al. Studies on the effect of antioxidants on the long-term storage and oxidation stability of Pongamia pinnata (L.) pierre biodiesel. Fuel Process Technol 2012; 99: 56–63.

75.

Almeida

Portela

Sousa

RMF

, et al. Behaviour of the antioxidant tert-butylhydroquinone on the storage stability and corrosive character of biodiesel. Fuel 2011; 90: 3480–3484.

76.

Crane

Aurore

Joseph

, et al. Composition of fatty acids triacylglycerols and unsaponifiable matter in Calophyllum calaba L. Oil from Guadeloupe. Phytochemistry 2005; 66: 1825–1831.

77.

Jain

Sharma

. Application of thermogravimetric analysis for thermal stability of Jatropha curcas biodiesel. Fuel 2012; 93: 252–257.

78.

Herman

. Polymerization of drying oils. Chem Rev 1964; 64: 591–611.

79.

Knothe

Dunn

. Dependence of oil stability index of fatty compounds on their structure and concentration and presence of metals. J Am Oil Chem Soc 2003; 80: 1021–1026.

80.

Georgios

Stamos

. Impact of antioxidant additives on the oxidation stability of diesel/biodiesel blends^†. Energy Fuels 2010; 24: 3682–3686.

81.

Constantino

Lenzi

Franco

, et al. Thermal lens temperature scanning technique for evaluation of oxidative stability and time of transesterification during biodiesel synthesis. Fuel 2017; 202: 78–84.

82.

Kudre Tanaji

Bhaskar

Sakhare Patiram

. Optimization and characterization of biodiesel production from rohu (Labeo rohita) processing waste. Renew Energy 2017; 113: 1408–1418.

83.

Shameer

Ramesh

Sakthivel

, et al. Experimental evaluation on oxidation stability of biodiesel/diesel blends with alcohol addition by rancimat instrument and FTIR spectroscopy. J Mech Sci Technol 2017; 31: 455–463.

84.

Anand

Gobalakrishnan

Maruthupandy

, et al. Biodiesel production from mixed crude oil of non-edible marine fishes. Energy Environ Focus 2015; 4: 47–53.

85.

Kurechi

Aizawa

Kunugi

. Studies on the antioxidants XVIII: oxidation product of tertiary butyl hydroxyquinone (TBHQ) (I). J Am Oil Chem Soc 1983; 60: 1878–1882.

86.

Kurechi

Kunugi

. Studies on the antioxidants XIX: photooxidation products of tertiary butyl hydroquinone (TBHQ) (bdII). J Am Oil Chem Soc 1983; 60: 1882–1887.

87.

Knothe

Sharp

Ryan

. Exhaust emissions of biodiesel, petrodiesel, neat methyl esters, and alkanes in a new technology engine^†. Energy Fuels 2006; 20: 403–408.

88.

Shrivastava

Verma

Pugazhendhi

. An experimental evaluation of engine performance and emisssion characteristics of CI engine operated with Roselle and Karanja biodiesel. Fuel 2019; 254: 115652.

89.

Sathiyamoorthi

Sankaranarayanan

. Effect of antioxidant additives on the performance and emission characteristics of a DICI engine using neat lemongrass oil–diesel blend. Fuel 2016; 174: 89–96.

90.

Ghadikolaei

Wei

Cheung

, et al. Effects of engine load and biodiesel content on performance and regulated and unregulated emissions of a diesel engine using contour-plot map. Sci Total Environ 2019; 658: 1117–1130.

91.

Gharehghani

Asiaei

Khalife

, et al. Simultaneous reduction of CO and NOx emissions as well as fuel consumption by using water and nano particles in diesel–biodiesel blend. J Cleaner Prod 2019; 2010: 1164–1170.

92.

Sun

Caton

Jacobs

. Oxides of nitrogen emissions from biodiesel-fuelled diesel engines. Prog Energy Combust Sci 2010; 36: 677–695.

93.

Palash

Kalam

Masjuki

, et al. Impacts of NOx reducing antioxidant additive on performance and emissions of a multi-cylinder diesel engine fueled with Jatropha biodiesel blends. Energy Convers Manage 2014; 77: 577–585.

94.

Banapurmath

Tewari

Hosmath

. Performance and emission characteristics of a DI compression ignition engine operated on Honge, Jatropha and sesame oil methyl esters. Renewable Energy 2008; 33: 1982–1988.

95.

Aydin

Bayindir

. Performance and emission analysis of cottonseed oil methyl ester in a diesel engine. Renew Energy 2010; 35: 588–592.

96.

Gad

El-Araby

Abed

, et al. Performance and emissions characteristics of C.I. engine fueled with palm oil/palm oil methyl ester blended with diesel fuel. Egypt J Pet 2018; 27: 215–219.

97.

Efe

Akif Ceviz

Temur

. Comparative engine characteristics of biodiesels from hazelnut, corn, soybean, canola and sunflower oils on di diesel engine. Renew Energy 2018: 119: 142–151.

98.

Mofijur

Rasul

Hassan

NMS

, et al. Investigation of exhaust emissions from a stationary diesel engine fuelled with biodiesel. Energy Procedia 2019; 160: 791–797.

99.

Griffin

Ward

Terrell

, et al. Diesel fumes do kill: a case of fatal carbon monoxide poisoning directly attributed to diesel fuel exhaust with a 10-year retrospective case and literature review. J Forensic Sci 2008; 53: 1206–1211.

100.

Mamilla

Mallikarjun

Lakshmi Narayana Rao

. Effect of combustion chamber design on a DI diesel engine fuelled with Jatropha methyl esters blends with diesel. Procedia Eng 2013; 64: 479–490.

101.

Chauhan

Kumar

Cho

. A study on the performance and emission of a diesel engine fueled with Jatropha biodiesel oil and its blends. Energy 2012; 37: 616–622.

102.

Alagu

Nagappan

Jayaraman

, et al. Impact of antioxidant additives on the performance and emission characteristics of C.I engine fuelled with B20 blend of rice bran biodiesel. Environ Sci Pollut Res 2018; 25: 17634–17644.

103.

Ozsezen

Canakci

Turkcan

, et al. Performance and combustion characteristics of a DI diesel engine fueled with waste palm oil and canola oil methyl esters. Fuel 2009; 88: 629–636.

104.

Ghobadian

Rahimi

Nikbakht

, et al. Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renew Energy 2009; 34: 976–982.

105.

Ryu

. The characteristics of performance and exhaust emissions of a diesel engine using a biodiesel with antioxidants. Bioresour Technol 2010; 101: S78–S82.

106.

Can

Çelikten

Usta

. Effects of ethanol addition on performance and emissions of a turbocharged indirect injection diesel engine running at different injection pressures. Energy Convers Manage 2004; 45: 2429–2440.

107.

Jindal

Nandwana

Rathore

, et al. Experimental investigation of the effect of compression ratio and injection pressure in a direct injection diesel engine running on Jatropha methyl ester. Appl Therm Eng 2010; 30: 442–448.

108.

Ndayishimiye

Tazerout

. Use of palm oil-based biofuel in the internal combustion engines: performance and emissions characteristics. Energy 2011; 36: 1790–1796.

109.

Ramesha

Bangari

Rathod

, et al. Experimental investigation of biogas-biodiesel dual fuel combustion in A diesel engine. J Middle Eur Constr Design Cars 2015; 13: 12–20.

110.

Sayyed

Das

Kulkarni

. Performance assessment of multiple biodiesel blended diesel engine and NOx modeling using ANN. Case Stud Therm Eng 2021; 28: 101509.

111.

Mohsin

Majid

Shihnan

, et al. Effect of biodiesel blends on engine performance and exhaust emission for diesel dual fuel engine. Energy Convers Manage 2014; 88: 821–828.

112.

Dodos

Tsesmeli

Zannikos

. Evaluation of the antimicrobial activity of synthetic and natural phenolic type antioxidants in biodiesel fuel. Fuel 2017; 209: 150–161.

Novel FTIR and visible infrared imaging assessment of binary biofuel stability and abated NO x for clean environment assisting energy approach

Abstract

Keywords

Introduction

Materials and methodology

Feedstock involved

Transesterification process for biodiesel extraction

Mutual stability analysis of Alexandrian laurel and poison nut biodiesel mixture

Synthetic antioxidants for stabilizing oxidation reaction of biodiesel

Stability analysis using standard equipment

Working of standard equipment

Oxidation stability analysis

Storage stability analysis

Thermal stability analysis

Analyzing uncertainty and accuracy

Weightage and optimization techniques

Criteria importance through intercriteria correlation (CRITIC)

EDAS - evaluation based on distance from the average solution

Technique for order preference by similarity to ideal solution (TOPSIS)

Infrared imaging technique (IIT) for combustion analysis

Antioxidants effect on diesel engine characteristics

Objectives of this study

Results and discussion

Fuel stability – oxidation stability analysis

Fuel stability - storage stability analysis

Fuel stability - Thermal stability analysis

FTIR – Correlating tool of stability outcome

Core motive for implementing optimization techniques

Weightage evaluation techniques

CRITIC technique

Optimization evaluation techniques

EDAS technique

TOPSIS technique

Focalization of EDAS and TOPSIS results

Nox emission and IIT assessment

Antioxidant effect on neat biodiesel Nox emission

Infrared imaging technique (IIT)

Emission characteristics - impact of diesel blending to biodiesel with and without antioxidants

Carbon monoxide (CO) emission analysis

Carbon dioxide (CO2) emission analysis

Hydrocarbon (Hc) emission analysis

Smoke opacity emission analysis

Nitrogen oxide (NOx) emission analysis

Global fuel market cost analysis

Uniqueness of current study

Validation of objective accomplishment

Conclusion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

ORCID iD

References

No_x emission and IIT assessment

Antioxidant effect on neat biodiesel No_x emission

Carbon dioxide (CO₂) emission analysis

Nitrogen oxide (NO_x) emission analysis