Exploring lignocellulosic biomass for bio-methane potential by anaerobic digestion and its economic feasibility

Abstract

Anaerobic digestion is a process to convert organic biomass into bio-methane. Plenty of produced waste in Pakistan is enough to compensate energy thirst of country and have potential to replace costly fossil fuels. The lignocellulosic biomass such as wheat straw, almond shell, sugarcane bagasse, maize straw and corn cob were subjected to bio-methane potential assay after proximate, ultimate and chemical analysis. These chemical fractions provide better understanding about theoretically predicating bio-methane potentials such as neutral detergent fibre, acid detergent fibre, acid detergent lignin, cellulose, hemicellulose, carbohydrates, proteins and elemental analysis. Experimental bio-methane potentials were found, 267.74 (wheat straw), 255.32 (almond shell), 222.23 (corn cob), 247.60 (sugar cane bagasse) and 293.12 ml/g (maize straw) volatile solids and was much less than predicted methane potential. The energy content on dry basis and methane potential has been assessed to find economic feasibility of biomass. The biodegradability and methane potential inversely related to the lignin content of biomass. Bioenergy production from biomass is economically favourable. The volatile fatty acids were produced in the percentage of 53–58% acetic acid, 30–35% butyric acids and 6–13% propionic acid and showed same metabolic pathway and types of bacteria involved in digestion.

Keywords

Anaerobic digestion acid detergent lignin wheat straw almond shell corn cob sugarcane bagasse maize straw

Introduction

First-generation biomethane are derived from food crops and lignocellulosic biomass.¹ Presently, Pakistan is facing energy crisis, and is producing plenty of wastes from agricultural fields and can be utilized to quench energy crisis. Major crop is cotton, sugarcane, wheat, maize and rice. Total sum of crop waste is ≈28m-ton/year (http://fp.brecorder.com/2013/12/201312071262105/). Technically and theoretically, all these agricultural waste substrates (AWSs) can be subjected to anaerobic digestion (AD) for biomethane production and also considered to be an ideal solution of wastes. AD is an anaerobic process for the conversion of organic materials into biogas via hydrolysis, acidogenesis, acetogenesis and methanogenesis. Initially higher organic compounds are converted to simple monomers products, progressively convert into volatile fatty acids (VFAs) and CO₂/H₂ via acetogenesis and methanogenesis_. Ultimately in the last stage, it is converted to CH₄. Main composition of biogas is 55–80% CH₄ and 20–45% CO₂, with other impurities such as H₂ and H₂S (traces) and provide as alternate, potential source of renewable energy, replaced biofuel, heat/electricity sources.²

Agricultural waste substrates ( AWSs) have been recently got more attention due to its treasured aspects such as high yield, cheap energy sources.² Literature review provides immediate knowledge of bio-methane potential (BMP) of AWSs and are cited in the range of from 200 to 450 ml/g VS.¹ This anaerobic digestion (AD) product can be used without modification in power gas engines i.e. combined heat and power.^2,3 Natural gas has a calorific value (CV) of 37.5 to 43.0 MJ/m³, but reported CV for bio-methane is 37.78 MJ/m³.⁴ After understanding of AWSs digestion as renewable energy source, objective of this research is decided to explore the BMP and access the economic feasibility of substrates for small-sized digester. The wheat straw (WS), almond shell (AS), sugarcane bagasse (SB), maize straw (MS) and corn cob (CC) were subjected to pursue objectives.

Materials and methods

Analytical methods

Indigenous agricultural substrates such as WS, AS, SB, MS and CC were collected, dried and ground with a laboratory blender to fine powder. Total solids (TS), volatile solids (VS) and ash were determined by gravimetric method.⁵ The elemental composition (C, H, O, N, and S) of substrates was analysed by an elemental analyser Carbon, Hydrogen, Nitrogen, Sulphur/Oxygen (CHNS/O) analyser, PE 2400 Series II, Perkin Elmer, USA. Neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), hemicellulose (HC) and cellulose was determined by modified methods of Van Soest. Crude fibre content (CF) was determined by modified Maynard methods of food analysis.⁶ Crude protein (CP) was calculated by multiplying nitrogen content (determined by CHNS/O analyzer) with 5.7 i.e. protein factor (N ×5.7).⁷ Total carbohydrate was measured by anthrone method.⁶ Theoretical CH₄ yields have been calculated by using Buswell and Mueller equations 1 and 2.⁸ Organic fraction composition (OFC) i.e. carbohydrate and CP values were used to calculate theoretical CH₄ yields (BMP_OFC) by using general equation 3.⁹ Elemental anaerobic biodegradability (BD_ele) of the substrate has been calculated based on experimental BMP_exp and BMP_CHNO by equation 4.¹⁰ Similar the experimental biodegradability (BD_exp) by using equation 5 based on VS initial and final values.¹¹

\begin{matrix} C_{n} H_{a} O_{b} N_{c} + (n - \frac{a}{4} - \frac{b}{2} + \frac{3 c}{4}) H_{2} O \to (\frac{n}{2} + \frac{a}{8} - \frac{b}{4} - \frac{3 c}{8}) \\ C H_{4} + (\frac{n}{2} - \frac{a}{8} + \frac{b}{4} + \frac{3 c}{8}) C O_{2 +} c N H_{3} \end{matrix}

(1)

\begin{matrix} B M P_{C H N O} (m l / g V S) = \frac{22.4 \times 1000 \times (n / 2 + a / 8 - b / 4 - 3 c / 8)}{12 n + a + 16 b + 14 c} \end{matrix}

(2)

\begin{array}{l} B M P_{C H N O} (O F C) = 415 \times % c a r b o h y d r a t e s + 496 \times % p r o t e i n s + 1014 \times % l i p i d s \end{array}

(3)

B D e l e = \frac{B M P e x p}{B M P C H N O} \times 100

(4)

B D e x p V S = \frac{V S i - V S f}{V S i} \times 100

(5)

BMP assay

For assessment of BMP_exp, bottles of 250 ml volume (Schott Duran, Germany) were taken and added 100 ml prepared media in each bottles. Media was prepared according to Angelidaki and Sanders.¹² A calculated amount of substrate was added (2% VS) in bottles and 10 ml inoculum active and developed inoculum was taken from anaerobic pilot plant digester of NIBGE, Faisalabad, Pakistan) was added. Each bottle was flushed with N₂ and CO₂ mixture (80/20% by volume) for 5 min for ensuring maintaining anaerobic condition. These bottles were placed in incubator at 37°C for 35–40 days and vortexed twice a day. The methane production was measured on daily basis during first week, alternate days in second week and then after two or three days in third, fourth and fifth weeks of AD.

Biogas analysis

Biogas volume was measured through water displacement methods by using alkaline solution (2% w/v NaOH). A gas composition analysis was measured using gas analyser (GFM 4XX series). The individual VFAs after 6 days, 15 days and 30 days of incubation by APHA standards,¹³ total VFAs (tVFAs) were determined by APHA standards¹⁴ by equation 6 with regular interval of time.

t V F A \frac{m g}{L} = \frac{N \times V \times 5000}{A}

(6)

here, A is volume of sample in ml, N is normality of acid used (0.1 N H₂SO₄) and V is volume of acid used in attaining end point pH=4.3.

Biodegradability

Lignin BD (BD_LB) is calculated by modelled described by Chandler¹⁵ using equation 7.

L B % = (0.83 - (0.028 \times X i)) \times 100

(7)

where B is the biodegradable fraction of VS lies in between 0 < B < 1, Xi is initial lignin concentration by percentage of VS and lies in between for accurate applying model 0 < X1 < 20%.

Statistical analyses

All statistical analysis was performed using excel of MS office 2016.

Results and discussion

Table 1 shows the ultimate and proximate analysis of AWSs. In proximate analysis, VS is more important because only VS fractions are digestible and found in the rage of 61.23 to 70.33% with the order of SB>WS>MS>CC>AS and these results supported by previous study. These results of proximate and ultimate analysis are in line and supported by literature,^16,17 however, few variation was observed in literature might be because of biomass growth parameters variations like growing and environmental (soil, plant maturity stage and seasonal difference) conditions as reported variation parameters in literature.¹⁸ These ultimate data are used for balancing C/N ratio and help in predicting BMP_CHNO. The chemical composition was determined and presented in Table 2. The chemical compositions are best criteria to evaluate AWSs for bioenergy production and extent of biodegradability. As reported, CP, NDF, ADF, ADL, HC and cellulose of dry matter of AWSs are in the range of reported data.¹⁷ Normally, chemical composition of agricultural crop residues’ substrates varies from species to species or even changes within species. MS and CC are components of same crops with a lot of variations in chemical compositions. The reason of variation in chemical composition is similar to variation as discussed for proximate and ultimate variations.¹⁸ The BMP_exp of AS and CC were not studied previously, however, its results 255.3 and 222.2 ml/g VS, respectively, can be compared with lignocellulosic biomass range of BMP and it is found in range of from 200 to 450 ml/g VS.¹ The BMP_exp of MS WS and SB is found in the range of reported data shown in Table 3.

Table 1.

The proximate analyses of substrate along with physical properties.

Components	WS	AS	SB	MS	CC
Components	Mean (±SD)	Mean (±SD)	Mean (±SD)	Mean (±SD)	Mean (±SD)
Moisture %	11.4 (1.48)	8.7 (1.18)	22.7 (2.52)	12.2 (2.1)	9.2 (0.80)
VS % (VM)	68.9 (3.78)	61.2 (3.04)	70.3 (1.53)	65.8 (3.83)	62.7 (2.07)
FS% (Ash)	3.4 (1.06)	9.9 (1.05)	2.9 (0.25)	6.4 (2.52)	7.9 (0.57)
TS%	72.3 (4.31)	71.2 (4.03)	73.3 (1.28)	72.3 (5.60)	70.6 (2.41)
COD (g)	1.4 (0.31)	1.3 (0.12)	1.3 (0.12)	1.3 (0.17)	1.4 (0.19)
C%	50.3 (1.70)	45.6 (2.12)	45.2 (3.82)	47.2 (2.30)	47.9 (2.74)
H%	5.8 (0.35)	4.9 (0.25)	5.9 (0.23)	6.8 (0.06)	5.7 (0.58)
N%	0.67 (0.31)	0.28 (0.03)	0.33 (0.15)	0.53 (0.21)	0.60 (0.10)
S%	0.13 (0.06)	0.13 (0.06)	2.37 (0.42)	0.13 (0.06)	0.13 (0.06)
O%	43.2 (2.35)	49.1 (2.27)	46.2 (4.09)	45.4 (2.05)	45.7 (2.14)
Empirical	C_103.5H_144.2	C_190.1H_243.3	C_158.08H_249.3	C_103.8H_178.7	C_93.1H_132.3
formula	O_66.7N₁	O_153.4N₁	O_121.3N₁	O_74.9N₁	O_66.7N₁

C: carbon; COD: chemical oxygen demand; FS: fixed solids; H: hydrogen; N: nitrogen; O: oxygen; S: sulphur; SD: standard deviation; TS: total solids; VS: volatile solids.

Table 2.

The chemical composition of AWSs.

Component	WS	AS	SB	MS	CC
Component	Mean (±SD)	Mean (±SD)	Mean (±SD)	Mean (±SD)	Mean (±SD)
NDF%	70.2 (2.04)	67.9 (2.85)	85.2 (4.59)	67.1 (3.34)	61.9 (3.35)
ADF%	41.1 (3.46)	46.3 (1.54)	40.9 (1.77)	35.8 (0.63)	37.5 (2.28)
ADL%	6.3 (1.50)	9.5 (1.34)	10.8 (1.41)	5.2 (1.15)	10.9 (1.61)
HC%	29.2 (2.02)	21.6 (2.17)	44.3 (4.10)	31.2 (3.47)	24.5 (1.16)
Cellulose%	34.7 (3.64)	36.8 (0.67)	30.1 (2.91)	30.7 (0.58)	26.6 (2.29)
CF %	1.3 (0.10)	1.6 (0.45)	1.3 (0.10)	1.6 (0.45)	1.9 (0.41)
CP%	3.2 (1.74)	1.6 (0.15)	1.9 (0.87)	3.1 (1.19)	3.4 (0.57)
Carbohydrates%	63.9 (0.38)	64.4 (0.38)	74.4 (0.38)	67.9 (0.38)	57.1 (0.38)

ADF: acid detergent fibre (which consists of cellulose and lignin); ADL: acid detergent lignin (consist only lignin); AS: almond shell; AWSs: agricultural waste substrates; CC: corn cob; CF: crude fibre; CP: crude protein; HC: hemicellulose; MS: maize straw; NDF: neutral detergent fibre (which consist of cellulose, hemicellulose and lignin); SB: sugarcane bagasse; WS: wheat straw.

Table 3.

Experimental and theoretical BMPs, biodegradability and energy contents.

Substrates		WS	AS	SB	MS	CC
BMP_CHNOa	(ml/g VS) This study	471.5	381.2	425.6	471.2	436.5
BMP_OFCb		281.2	275.3	318.2	296.8	253.7
BMP_expc		267.7	255.3	247.6	293.1	222.2
BMP _reported		269.0²²	N.R	278.0¹⁶, 230²³	338.0¹⁶, 216.0²⁴	N.R
CH₄/CO₂ (last two weeks)		2.2	2.1	2.1	2.3	2.1
% BD_eled (equation 4)		68.1	66.9	58.1	62.2	50.9
% BD _expe		70.2	67.0	61.2	71.0	57.0
% BD _LBf		65.2	56.5	52.8	68.5	52.5
gEC on dry basis (MJ/kg)		6.8	3.9	4.9	5.5	5.5
BMP m³/ton VS		267.0	255.0	248.0	293.0	222.0
hCV of methane (MJ/ton)		10,087.3	9633.9	9369.4	1103.0	8387.2
Electrical (kWhel)		453.9	433.5	421.6	498.1	377.4

BMP: bio-methane potential; AS: almond shell; CC: corn cob; CV: calorific value; MS: maize straw; SB: sugarcane bagasse; WS: wheat straw;

^aBMP_CHNO was calculated from CHNO values determined from CHNS analyser.

^bBMP_OFC was calculated from carbohydrates and protein contents only.

^cExperimentally determined BMP.

^dElemental BD.

^eExperimentally BD.

^fLignin-based BD.

^gEnergy content.

^hCalorific values N.R not reported.

Biodegradability

BD_ele actually explains the extent of degradation of biomass and divided into biodegradable and non-biodegradable. The BD_exp was measured from undigested VS and chemical oxygen demand. Similarly, lignin-based BD_LB has shown least BD_LB as compared to BD_exp and BD_ele as listed in Table 3. The BD order was MS>WS>AS>SB>CC which interpreted indirectly relationship to lignin content. The recalcitrance nature of lignin defines the extent of BD and methane/biogas yields. It also means biodegradable components other than lignin directly related to methane/biogas production. As noticed, BD_ele was lower than BD_exp and BD_LB, higher BD_exp is due to some part of digested VS also used for microbial growth and metabolism in addition to digestion for CH₄. BD and lignin relationship has observed 1% lignin reduces about 3% degradability of biomass. It is also observed slight variations in BD and BMPs results, it can vary due to the use of dried samples in this study, different crop conditions suited for AD i.e. inoculum properties, operating temperature, headspace volume of flasks, substrate to inoculum ratio (S/I).^9,16

Comparison of BMP_exp and theoretical BMPs

In this study, theoretical methods have been used for the predication of BMPs (BMP_CHNO, BMP_OFC). The BMP_exp was lower than theoretical BMPs, BMP_OFC prediction values were lower as compared to BMP_CHNO calculation as shown in Table 3. The less values of BMP_exp are because of considering lignin as non-biodegradable part of biomass, and whereas theoretical BMPs based complete biodegradation i.e. no discrimination in non-biodegradable and biodegradable components.¹ However, another limitation of these methods is not considering the conversion of substrates into cell growth, metabolites and protoplasm synthesis of microbes and simply deals with stoichiometric calculation of BMPs and not converted into biogas.¹⁰ However, these methods are useful for determination of BD extent and can’t be used as gauge of BMPs.¹

Composition of biogas

The cumulative methane graph as in Figure 1 did not show rate of methane production, actually about 80% VS was degraded in first 15 days of process with lower methane. The remaining VS was digested very slowly and completed in almost 40 days. The CH₄ was increased gradually started from 40 to 45% in first week, 45 to 55% in second week and 55 to 68% in third and fourth week of biogas. The CH₄ contents were raised up to 62–68% of biogas in last two weeks and CH₄/CO₂ was also reported in Table 3. Initially, the higher rate of biogas production with lower CH₄ was due to large amount of digestible VS and low number of methanogens. As methanogens requires acclimated period for attaining sufficient number, CH₄% increased after this period and remained constant until complete BD of substrates. This behaviour of percentage enhancement is in line with literature as reported.⁴ The attaining stability of percentage after two weeks is in line with other researcher findings, who concluded that it stabilized after two weeks of AD. The variation of percentage composition is mainly because of chemical composition of substrate as generally observed in these study substrates which have high BD of substrates, have high percentage of methane in biogas.

Figure 1.

Cumulative CH₄ ml/g VS of AWSs.

VFA analysis

Result of individual analysis of VFA has shown acetic acids (HAc), propionic acid (HPr), butyric acids (HBu) and iso-butyric acid (HiB) production. The concentration of acetic was 50% (53–58% of tVFA) of tVFAs, propionic acid (6–13%), butyric acids (30–35% of tVFA), the higher concentration of HAc is because of other VFAs are also converted to HAc as reported in literature.¹⁹ The production of tVFAs is in the order of MS (2619) >CC (2450)>AS (2370) >BG (2326) >WS (1870) after the sixth day as shown in Figure 2. Actually individual acids are produced and consumed regularly and its concentration varied with time. Initially it is higher in production because of high rate of BD and sum of these VFAs was increased until methane forming started. It decreased with conversion of tVFAs into methane and also by decreasing the biodegradable components of substrates. The VFA production almost same for all digestion flasks it shows that all substrate was digested in similar pathways. Moreover, tVFAs were not produced to such extent that can inhibit the AD process e.g. tVFA concentration above 4000 mg/l inhibits AD process,²⁰ in this study, no such level of tVFA was observed as reported. In addition to controlled concentration of tVFAs, buffer (NaHCO₃) was also added in digestion medium that can maintain the neutral pH and ensured the complete digestion of biodegradable components. These observations of concentration ratios are similar and supported to literature.²¹

Figure 2.

Volatile fatty acids analysis of AWSs digestion.

Assessment of biogas plant

A model plant was assessed having capacity of 1 tonne of biomass waste daily as feeding material and able to generate electricity with genset. Approximate investment cost is divided into recurring (5000 PKR = Pakistani rupees) and non-recurring cost (1,500,000 PKR) indigenous estimations are given in Table 4. Recurring cost is variables due to transport distance and biomass weight and effort needs on collection of waste. The energy content calculated from Dulong modified formula,⁴ CVs and electricity potential of methane are also in Table 3. Price of unit electricity is 18 PKR in Pakistan, and average generation cost per unit by is less than 1.5 PKR. Overall benefit of biogas and electricity generation in rural areas is reducing global warming, new job and investment opportunities. This is more recyclable and efficient alternative process for human environment than others.

Table 4.

Recurring and non-recurring cost value of digester (1 tonne capacity).

Description	PKR (US$)	Total= PKR (US$)
Non recurring		1,500,000.0 (13,680.0)
Plant	950,000.0 (8664.0)
Electricity cables	320,000.0 (2918.4)
Genset	160,000.0 (1459.2)
Genset room	70,000.0 (638.4)
Recurring (variables cost)		5000.0 (45.6)
Manpower	2000.0 (18.2)
Biomass waste purchasing	1000.0 (9.1)
Transport	2000.0 (18.2)
Operating and maintenance	5000.0 (45.6)	5000.0 (45.6)

Conclusion

It is concluded from the study that BD and BMPs of AWSs are same and inversely proportional to lignin content. The methane contents in the biogas are directly proportional to number of methanogens in process_. The theoretical calculation of BMPs doesn’t consider the lignin content as recalcitrance material and predicted yields were higher than BMP_exp. These theoretical methods provide suitable knowledge about biodegradability extent and empirical potential of methane only. The VFAs analysis showed the same profile of individual VFAs in biogas analysis and indicated the same metabolic pathway for the production of CH₄. Moreover, VFAs concentration was much lower to inhibit the process. BMP_exp of AWSs is economically feasible as potential replacer of fossil fuel from electricity generation.

Footnotes

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors received financial support from PAK-US collaborative project for the research, authorship, and/or publication of this article.

Shehbaz Ali, Tawaf A Shah and Asifa Afzal are Ph.D student and researcher of solid waste biomass energy.

Romana Tabassum is an Deputy Chief scientist and supervisor of bio-energy project.

References

Wall

O’Kiely

Murphy

JD.

The potential for biomethane from grass and slurry to satisfy renewable energy targets. Bioresour Technol 2013; 149: 425–431.

Murphy

Power

NM.

An argument for using biomethane generated from grass as a biofuel in Ireland.

Biomass Bioenergy 2009; 33: 504–512.

Appels

Lauwers

Degrève

et al . Anaerobic digestion in global bio-energy production: potential and research challenges. Renew Sustain Energ Rev 2011; 15: 4295–4301.

Nizami

Korres

Murphy

JD.

Review of the integrated process for the production of grass biomethane.

Environ Sci Technol 2009; 43: 8496–8508.

Spellman

FR.

Handbook of water and wastewater treatment plant operations. Washington, D.C: CRC Press, 2013.

Faithfull

NT.

Methods in agricultural chemical analysis: a practical handbook. UK: Cabi, 2002.

Labconco

A guide to Kjeldahl nitrogen determination methods and apparatus. Houston, TX: Labconco Corporation, 1998.

Buswell

Mueller

Mechanism of methane fermentation.

Ind Eng Chemistry 1952; 44: 550–552.

Lesteur

Bellon

Gonzalez

et al . Alternative methods for determining anaerobic biodegradability: a review. Process Biochem 2010; 45: 431–440.

10.

Raposo

Fernández

De la Rubia

et al . Biochemical methane potential (BMP) of solid organic substrates: evaluation of anaerobic biodegradability using data from an international interlaboratory study. J Chem Technol Biotechnol 2011; 86: 1088–1098.

11.

Nielfa

Cano

Fdz-Polanco

Theoretical methane production generated by the co-digestion of organic fraction municipal solid waste and biological sludge.

Biotechnol Reports 2015; 5: 14–21.

12.

Angelidaki

Sanders

Assessment of the anaerobic biodegradability of macropollutants.

Rev Environ Sci Biotechnol 2004; 3: 117–129.

13.

Apha

and WEF. Standard methods for the examination of water and wastewater. American Public Health Association (APHA) Washington, DC 2005; 21: 258–259.

14.

Apha

Clesceri

Greenberg

AE.

Standard methods for the examination of water and wastewater. 20th ed. Washington, DC: American Public Health Association, 1998.

15.

Chandler

Jewell

Gossett

et al . Predicting methane fermentation biodegradability. Biotechnol Bioeng 1980; 22: 93–107. Ithaca, NY: Cornell Univ.

16.

Chandra

Takeuchi

Hasegawa

Methane production from lignocellulosic agricultural crop wastes: a review in context to second generation of biofuel production.

Renew Sustain Energ Rev 2012; 16: 1462–1476.

17.

Das

Kundu

Kumar

et al . Fractionation of carbohydrate and protein content of some forage feeds of ruminants for nutritive evaluation. Vet World 2015; 8: 197–204.

18.

Carpita

NC.

Structure and biogenesis of the cell walls of grasses.

Annu Rev Plant Biol 1996; 47: 445–476.

19.

Ali

Shah

Afzal

et al . Evaluating the co-digestion effects on chicken manure and rotten potatoes in batch experiments. Int J Biosci 2017; 10: 150–159.

20.

Siegert

Banks

The effect of volatile fatty acid additions on the anaerobic digestion of cellulose and glucose in batch reactors.

Process Biochem 2005; 40: 3412–3418.

21.

Wang

Kuninobu

Ogawa

et al . Degradation of volatile fatty acids in highly efficient anaerobic digestion. Biomass Bioenerg 1999; 16: 407–416.

22.

Lin

Solid-state anaerobic co-digestion of spent mushroom substrate with yard trimmings and wheat straw for biogas production.

Bioresour Technol 2014; 169: 468–474.

23.

Gunaseelan

VN.

Biochemical methane potential of fruits and vegetable solid waste feedstocks.

Biomass Bioenerg 2004; 26: 389–399.

24.

Song

Liu

Yan

et al . Comparison of seven chemical pretreatments of corn straw for improving methane yield by anaerobic digestion. PLoS One 2014; 9: e93801.

Exploring lignocellulosic biomass for bio-methane potential by anaerobic digestion and its economic feasibility

Abstract

Keywords

Introduction

Materials and methods

Analytical methods

BMP assay

Biogas analysis

Biodegradability

Statistical analyses

Results and discussion

Biodegradability

Comparison of BMPexp and theoretical BMPs

Composition of biogas

VFA analysis

Assessment of biogas plant

Conclusion

Footnotes

Declaration of conflicting interests

Funding

References

Comparison of BMP_exp and theoretical BMPs