Life cycle approach in sustainability assessment for petroleum refinery projects with fuzzy-AHP

Abstract

Infrastructure projects, which include a wide range of construction and energy projects, play an important role amid other industrial projects. In this regard, petroleum refinery industry projects are one of the leading manufacturing industries in the world and have the most notable position in the energy industry projects. Developing petroleum refinery industry projects are one of the principal contributors to economic and social development. In spite of the necessity of country development, this development has to be sustained at least in economic, social, and environmental matters (pillars of sustainable development) particularly after the Brundtland Commission Report in 1987. In this paper, it is proposed to simplify the evaluation process of life cycle sustainability versus life cycle stages. Thus, an indicator-based approach is used in order to evaluate the sustainability along different stages of petroleum refinery industry projects. Also, a multi-level hierarchy of criteria decision making is defined by using Analytic Hierarchy Process (AHP), combined with a fuzzy set theory to enhance the reliability of the results. The outputs of this paper will be helpful for decision makers in many ways such as the most important stage with regard to sustainable development matters; or the most important pillars (economic, social, and environmental) of sustainability in each life cycle stage. Also, other valuable outputs based on the results are consequently discussed.

Keywords

Petroleum refinery sustainability assessment sustainable development life cycle stages fuzzy-AHP

Introduction

Development of a society and its economy depends highly on infrastructural development.^1,2 Hence, for developing countries, in particular, it is essential to invest in infrastructure development projects. This is why 30% of the world bank investment had been assigned in developing countries for implementing various infrastructural projects between 1970 and 2005.³ As an indicator of the vitality of infrastructure in developing countries, it is estimated that a 1% increase in infrastructure stocks, would lead to 1% growth in GDP.⁴ From a social perspective, the infrastructure of a city or town serves the society in different ways and its function would deeply impact the society in different ways. From the environmental standpoint, the contribution of infrastructure to mankind’s footprint on Earth is significant and is a key determinant of the quantity and type of pollutants (including greenhouse gases) released into the natural environment.⁵ Among various infrastructure sectors, the energy sector is among the most important in the eyes of citizens and governments⁶; and within the energy sector, oil and gas industry (OGI) is of paramount importance in supplying energy demands. In spite of its positive role in economic development, OGI is responsible for its negative environmental footprint and adverse long-term social effects. Consequently, it is apparent that petroleum refineries with their strategic products have profound influences, not only from the economic standpoint but also in terms of social and environmental aspects.

Petroleum refining is the physical, thermal, and chemical separation of crude oil into its fractions. Petroleum refinery products can be categorized into three major groups: fuels, finished nonfuel products (such as lubricating oil), and chemical industry feedstock.⁷ These petroleum products comprise about 32.9% of the total energy consumed in the world in 2015.⁸ Although petroleum refineries are inherently against the sustainable environment, improvements in energy efficiency result in higher quality products and increased processing of oil.⁹

Human needs should be at the core if we want to develop strategies to transition society toward more sustainable forms of development.¹⁰ The idea of sustainability as an objective in the development process, has a hierarchy of goals, starting from pillars of sustainability, that is, social, economic, and environmental. At the bottom of this hierarchy, we deal with sustainability indicators which are defined for each pillar. These indicators provide the necessary tools to gauge or measure the level of sustainability achieved under each pillar. Having said that, the process of translating strategic sustainability objectives into concrete action at project-specific levels is still a difficult task.¹¹ With this inherent complexity in mind, there are many researchers working on sustainability indicators in infrastructure projects^2,11,12 as well as energy projects.^13–15 Various research works on sustainability indicators in OGI projects have been reviewed in Heravi et al.¹⁶

Sustainability is not solely limited to its pillars sparely^17,18 or to construction or operation phases of a project, but it has to be considered for its entire lifetime as well as the lifetime of its constituting materials and projects. That is why one needs to perform life cycle assessment (LCA) in order to determine what the environmental footprint of the project will be during its lifetime. The lifetime could have different definitions, ranging from cradle-to-gate to cradle-to-cradle, in case certain products are recycled and reused. Among relevant publications, LCA has been applied to future electricity systems by using techno-economic, environmental, and social sustainability indicators.¹⁹ Also, sustainability has been assessed for the UK electricity scenarios by 2070 by a life cycle approach.²⁰

Petroleum refinery industry (PRI) projects are multidisciplinary and highly complex due to millions of tasks and work packages. Also, PRI projects are mega-projects, because apart from the requirement of a huge investment, many industries such as manufacturers, vendors, designers, constructors, and so on are involved. Hence, it will result in an economic boom in countries. Besides, assessment of life cycle stages is inherently complicated and its application in these projects makes the assessment process more difficult, time-consuming, and less accurate. In this paper, petroleum refinery projects are divided into three major stages during their life cycle, which makes a basis for the sustainability assessment tool and related analysis. These stages include preconstruction, design and construction, and operation and consumption of products. Hence, with regard to the importance of LCA in the sustainability assessment and the inherent complexity of the petroleum refineries, in this study, life cycle phases are evaluated so that the managers only need to consider the least items with the highest importance in their decisions.

Literature review

Multi-criteria decision making (MCDM) techniques are highly recommended to be used in complex circumstances such as PRI projects. A review paper records 22 different MCDM methods which have been used in construction projects.²¹ Among different methods, AHP in most of the cases either is the main method or one of the methods applied in papers. AHP is eminently suitable for a hierarchy of goals just like the sustainability three level objectives mentioned above. In this regard, a weighing-AHP of the necessary factors to develop a life cycle benefit-cost assessment is proposed.²² Also, AHP is utilized to weight the environmental impacts associated with a sustainable analysis of different flooring systems.²³ AHP is also used to enable the integration of life cycle cost and LCA in civil structures.²⁴ Hence, it is recommended to use AHP in complex decision-making processes and it is suitable for LCA and sustainability assessment problems.²¹ MCDM methods have some complements especially suitable for decision-making problems, such as fuzzy sets or gray numbers. In this paper, in order to enhance the accuracy of linguistic measurements, fuzzy set theory is combined with AHP as the proposed methodology.

Indicators can provide crucial guidance for decision-making in a variety of ways²⁵:

Translation of physical and social science knowledge into manageable units of information.

The process of measurement and calibration toward sustainable development goals.

Providing early warnings in order to prevent economic, social, and environmental damages.

Communication of ideas, thoughts, and values.

Due to the inherent complexity in quantifying the level of sustainability in all its three pillars, an indicator-based framework is largely used toward sustainability assessment.^26–31 Sustainability indicators simplify, quantify, analyze and communicate the complex and complicated information.³² Sustainability integrated assessment and sensitivity analysis for energy projects are studied in the previous papers.³³

The previous studies in the OGI projects, focused on the sustainability requirements of the OGI companies,³⁴ supply chain goals in the OGI,³⁵ or concentrate mainly on the operation phase.³⁶ In this study, the sustainability indicators are assessed against the life cycle phases with a novel viewpoint which will result in some useful tools to help managers in their decisions. The most important sustainability indicators in each PRI phases and the most influential phase for the sustainability pillars are of paramount importance. In this study, proposed indicators are primarily based on established United Nations (UN) sustainability indicators and goals.^37,38 Then a comprehensive literature review has been performed to complete the process of sustainability indicators identification. Finally, a panel of experts in OGI projects field would approve the consistency and compatibility of the proposed indicator list. Table 1 summarizes the indicators listed in some of the most important papers considered in the literature review.

Table 1.

Proposed sustainability indicators.

Pillars of sustainability	Sustainability indicators	UN³⁸	UNCSD³⁷	Ugwu et al.¹¹	Labuschagne et al.³⁹	Shen et al.²	Heravi et al.¹⁶	Sierra et al.⁴⁰
Social	Poverty and equity	X	X		X		X	X
	Health	X	X		X	X		X
	Safety and security	X	X	X	X	X		X
	Education	X	X		X			X
	Welfare	X			X	X	X	X
Environmental	Atmosphere	X	X	X	X	X	X
	Water (fresh water, ocean, sea, coast)	X	X	X	X	X	X
	Land and soil pollution	X	X	X	X	X	X
	Natural resource	X		X	X	X	X
	Biodiversity	X	X	X		X
Economic	Energy consumption	X
	Financial	X			X	X	X
	Economic performance	X	X	X	X	X	X
	Occupation	X					X
	Earning	X					X

Methodology

AHP was developed by Saaty⁴¹ and has been being used in a wide variety of decision-making problems since then. Besides, the concept of fuzzy set theory which was introduced by Zadeh⁴² is highly suitable for the complex environment of problems particularly when the answers aren’t clear and can’t be expressed in quantitative terms. As such, researchers are always seeking new methods by which fuzzy set theory can be utilized in AHP.^43–46

MCDM methods such as AHP, always deal with a set of criteria and a set of alternatives. The goal in this regard is to reach the best alternative based on the criteria in a comprehensive manner. In this study, sustainability indicators are perceived as criteria and life cycle stages are dealt with as alternatives in order to determine the most important stage as well as the most crucial indicator.

The hierarchy of criteria is proposed in Figure 1 in which there are three levels of objects. Hence, criteria (indicators) are categorized into three groups and three FAHP need to solve the problem. This paper is based on Chang’s extent analysis⁴⁷ method which can be detailed as follows.⁴⁸

Figure 1.

Hierarchy of criteria.

Let O = {o₁, o₂, … , o_n} denote an object set and G = {g₁, g₂, … , g_m} be a goal set. According to fuzzy extent analysis, the method can be performed with respect to each object for each corresponding goal, $g_{i}$ , resulting in m extent analysis values for each object, given as $M_{g i}^{1}, M_{g i}^{2}, \dots, M_{g i}^{m}$ , and $i = 1, 2., \dots, n$ . Every $M_{g i}^{j} for (j = 1, 2, \dots, m)$ is a triangular fuzzy number (TFN). The decision table is proposed in Table 2.

Table 2.

MCDM decision table.

		Pillars of sustainability
		Social		Environment		Economy
		indicators	…	indicators	…	indicators	…
LCA Phases	Preconstruction
	Design and construction
	Operation and consumption

In this study, object and goal sets in the fuzzy extent analysis literature are the hierarchy of the criteria which was specified in Figure 2 and presented in Table 2. Besides, linguistic variables defined by the panel of experts in the comparison processes is Linkert scale of the fuzzy numbers with triangular membership function as specified in Figure 2.

Figure 2.

Linguistic Linkert scale.

Let’s assume TFN of $M 1 = (l . m . u)$ is assigned to criterion “i” when compared to criterion “j.” Thus, criterion “j” in comparison to criterion “i” will be $M 1^{- 1} = (\frac{1}{u} . \frac{1}{m} . \frac{1}{l})$ .

Pairwise comparison of performance criteria

In this part, we have three different tables for each pillar of sustainability (Tables 3 to 5).

Table 3.

Pairwise comparison of performance criteria.

Social sustainability	X1	X2	X3	X4	X5
Poverty and equity (X1)	1, 1, 1	1/3, 1, 1	1/5, 1/3, 1	1/9, 1/7, 1/5	1, 1, 3
Health (X2)	1, 1, 3	1, 1, 1	1/3, 1, 1	1/5, 1/3, 1	1, 3, 5
Safety and security (X3)	1, 3, 5	1, 1, 3	1, 1, 1	1, 1, 3	1, 3, 5
Education (X4)	5, 7, 9	1, 3, 5	1/3, 1, 1	1, 1, 1	3, 5, 7
Welfare (X5)	1/3, 1, 1	1/5, 1/3, 1	1/5, 1/3, 1	1/7, 1/5, 1/3	1, 1, 1
Environmental sustainability	Y1	Y2	Y3	Y4	Y5
Atmosphere (Y1)	1, 1, 1	1, 3, 5	3, 5, 7	1/3, 1, 1	1, 1, 3
Water (Y2)	1/5, 1/3, 1	1, 1, 1	3, 5, 7	1/3, 1, 1	1, 1, 3
Land and soil pollution (Y3)	1/7, 1/5, 1/3	1/7, 1/5, 1/3	1, 1, 1	1/5, 1/3, 1	1/5, 1/3, 1
Natural resource (Y4)	1, 1, 3	1, 1, 3	1, 3, 5	1, 1, 1	1/3, 1, 1
Biodiversity (Y5)	1/3, 1, 1	1/3, 1, 1	1, 3, 5	1, 1, 3	1, 1, 1
Economic sustainability	Z1	Z2	Z3	Z4	Z5
Energy consumption (Z1)	1, 1, 1	1/5, 1/3, 1	1/3, 1, 1	1, 1, 3	3, 5, 7
Financial (Z2)	1, 3, 5	1, 1, 1	1, 1, 3	3, 5, 7	5, 7, 9
Economy performance (Z3)	1, 1, 3	1/3, 1, 1	1, 1, 1	3, 5, 7	5, 7, 9
Occupation (Z4)	1/3, 1, 1	1/7, 1/5, 1/3	1/7, 1/5, 1/3	1, 1, 1	1, 1, 3
Earning (Z5)	1/7, 1/5, 1/3	1/9, 1/7, 1/5	1/9, 1/7, 1/5	1/3, 1, 1	1, 1, 1

Table 4.

Pairwise comparison of alternatives (LC stages) based on social criterion.

	(W1)	(W2)	(W3)	Priority weight (%)
Poverty and equity
Preconstruction (W1)	1	0.22	0.33	21
Construction (W2)	4.5	1	2.5	53
Operation and consumption (W3)	3	0.4	1	26
Health
Preconstruction (W1)	1	0.15	0.2	8
Construction (W2)	6.5	1	2.5	61
Operation and consumption (W3)	5	0.4	1	31
Safety and security
Preconstruction (W1)	1	0.13	0.2	7
Construction (W2)	7.5	1	3	65
Operation and consumption (W3)	5	0.33	1	28
Education
Preconstruction (W1)	1	0.29	0.22	11
Construction (W2)	3.5	1	0.4	30
Operation and consumption (W3)	4.5	2.5	1	59
Welfare
Preconstruction (W1)	1	0.25	0.29	12
Construction (W2)	4	1	1.5	51
Operation and consumption (W3)	3.5	0.67	1	37

Table 5.

Pairwise comparison of alternatives (LC stages) based on environmental criterion.

	(W1)	(W2)	(W3)	Priority weight (%)
Atmosphere
Preconstruction (W1)	1	0.2	0.12	6
Construction (W2)	5	1	0.22	23
Operation and consumption (W3)	8.5	4.5	1	71
Water
Preconstruction (W1)	1	0.25	0.22	11
Construction (W2)	4	1	0.5	39
Operation and consumption (W3)	4.5	1	1	50
Land and soil pollution
Preconstruction (W1)	1	0.29	0.2	10
Construction (W2)	3.5	1	0.33	27
Operation and consumption (W3)	5	3	1	63
Natural resource
Preconstruction (W1)	1	2	0.13	17
Construction (W2)	0.5	1	0.29	14
Operation and consumption (W3)	7.5	3.5	1	69
Biodiversity
Preconstruction (W1)	1	1.5	0.29	22
Construction (W2)	0.67	1	0.4	19
Operation and consumption (W3)	3.5	2.5	1	59

Fuzzy numbers based on the mentioned approach should be considered with the consensus among a group of experts.

Step 1: fuzzy synthetic extent value calculates as follows

S_{i} = \sum_{j = 1}^{m} M_{g i}^{j} ⊙ {[\sum_{i = 1}^{n} \sum_{j = 1}^{m} M_{g i}^{j}]}^{- 1}

where S_i is a representation of normalized criteria weights,

M_{g i}^{j}

denotes members of the criteria comparison matrix, j denotes number of columns,

j \in {1, 2, \dots, 5}

, and i denotes number of rows,

i \in {1, 2, \dots, 5}

Addition and multiplication operators in the above formula are fuzzy operators; for more detail refer to Chang.⁴⁵ Decision table containing $S_{i}' s$ is the normalized table and will be used in upcoming steps.

Step 2: The degree of possibility of $M_{1} \geq M_{2}$ is defined as follows

V (M_{1} \geq M_{2}) = \sup_{x \geq y} [min (μ_{M_{1}} (x) . μ_{M_{2}} (y))]

where SUP is the supremum function;

μ_{M}

is the membership function of the fuzzy number M; x, y are two crisp values in the fuzzy number domain, where a pair (x, y) exists which

x \geq y

and

μ_{M_{1}} (x) = μ_{M_{1}} (y) = 1

then, we have

V (M_{1} \geq M_{2}) = 1

In other words, M is a TFN and can be written as (l, m, u). Then, this formula as is presented in Figure 3, can be simplified to

V (M_{1} \geq M_{2}) = {\begin{cases} 1 & if m_{1} \geq m_{2} \\ h g t (M_{1} \cap^{​} M_{2}) = μ_{M_{2}} (d) = \frac{l_{2} - u_{1}}{(m_{1} - u_{1}) - (m_{2} - l_{2})} & Otherwise \end{cases}

where d is the ordinate of the highest intersection point between

μ_{M_{1}}

and

μ_{M_{2}}

and hgt is the height of the fuzzy set.

Figure 3.

Supremum function.

Step 3: The degree of possibility

The degree of possibility of a convex fuzzy number to be greater than k convex fuzzy numbers $M_{i}$ can be defined as follows

V (M \geq M_{1} . M_{2} . \dots . M_{k}) = V [(M \geq M_{1}) and (M \geq M_{2}) . \dots . and (M \geq M_{k})] = \min (V [M \geq M_{i}]) . i = 1, 2, \dots, k .

Hence, degree of possibility can be defined for the normalized matrix of criteria as follows

d^{′} (A_{i}) = \min (S_{i} \geq S_{k})

where

k = 1, 2, \dots, n; k \neq i

And weighted vector will be $W^{′} = {(d^{′} (A_{1}), d^{′} (A_{2}), \dots, d^{′} (A_{n}))}^{T}$

Step 4:- Normalized weight vector

Normalization would be done by the following equation

\begin{matrix} d (A_{i}) = \frac{d^{′} (A_{i})}{\sum_{i} (d^{′} (A_{1}))} \\ W = {(d (A_{1}) . d (A_{2}) . \dots . d (A_{n}))}^{T} \end{matrix}

Pairwise comparison of alternatives (LC stages) based on criteria

Due to the number of questions in this part, reaching a consensus is difficult and will reduce the accuracy of the calculations. Hence, comparison matrix of alternatives has to be calculated by a weighted average rounded to 0.5 (to clarify and make it more sensible for decision makers) by the following equation

X_{i j} = r o u n d (\sum_{k} (\int^{​} x_{i j}^{k} \times μ (x_{i j}^{k}) \times d (x_{i j}^{k})) . 0.5) . \forall i . j

where

X_{i j}

is the crisp value based on the weighted average which is used in the following calculations.

$μ (x_{i j}^{k})$ is the TFN membership function based on kth expert opinion for the ith alternative (LC stage) in comparison to jth alternative.

This calculation has to be repeated for every criterion (sustainability indicators).

Step 1: Normalization of the decision matrix

Let’s assume that the pairwise comparison matrix of decision maker is ${[X_{i j}]}_{r \times r}$ . The decision matrix has to be formed for every criterion separately. In this study, i and j are life cycle stages and equal to 3.

\bar{{[X_{i j}]}_{r \times r}} = [\frac{X_{i j}}{\sum_{i = 1}^{r} X_{i j}}] \forall i . j \in {1.2. \dots . r}

Step 2: Calculation of average

V_{i}^{c} = \underset{j}{Average} (X_{i j})

where

V_{i}^{c}

denotes relative weight of alternative “i” with regard to criteria “c.”

Step 3: Calculate the alternative’s relative importance index (RII) related to sustainability pillar “p” and ranking

R I I_{i}^{p} = V_{i}^{c} \times W_{c}

For each alternative (project life cycle stage), RII will be calculated and based on the relative importance, they will rank.

Case study

A real-world case in the HEDCO Company is chosen to be assessed by the proposed methodology. The selected private company is a prominent design, engineering, research, and development company in OGI field. Consequently, sustainability is part of HEDCO’s strategic mission. In this study, with the assistance of HEDCO’s leadership, the extensive experience and expertise of company’s senior researchers were used along with project-specific information to carry out the case study in a real petroleum refinery project. In this regard, a questionnaire includes mentioned criteria and the alternative was designed and distributed in a meeting with the project teams. They perform the comparative analysis of criteria and alternatives by using the Linkert fuzzy scale. The project team reached a consensus via Delphi technique in some rounds and proposed decision table as is represented in Table 6. In this regard, separated answers could be result in misleading outputs. Thus, in this study, the project team is asked to fill the decision table and by using Delphi technique in three rounds the consensus is reached.

Table 6.

Pairwise comparison of alternatives (LC stages) based on economic criterion.

	(W1)	(W2)	(W3)	Priority weight (%)
Energy consumption
Preconstruction (W1)	1	0.5	0.18	12
Construction (W2)	2	1	0.4	25
Operation and consumption (W3)	5.5	2.5	1	63
Financial
Preconstruction (W1)	1	0.14	2.5	23
Construction (W2)	7	1	1.5	56
Operation and consumption (W3)	0.4	0.67	1	21
Economic performance
Preconstruction (W1)	1	0.22	0.13	7
Construction (W2)	4.5	1	0.4	29
Operation and consumption (W3)	7.5	2.5	1	63
Occupation
Preconstruction (W1)	1	0.25	0.33	12
Construction (W2)	4	1	1.5	52
Operation and consumption (W3)	3	0.67	1	36
Earning
Preconstruction (W1)	1	2.5	0.33	26
Construction (W2)	0.4	1	0.29	14
Operation and consumption (W3)	3	3.5	1	60

Also, calculations in Table 3 can be summarized in Table 7.

R I I_{i}^{S o c i a l} = V_{i}^{c} \times W_{c} = [\begin{array}{l} 0.21 & 0.08 & 0.07 & 0.11 & 0.12 \\ 0.53 & 0.61 & 0.65 & 0.30 & 0.51 \\ 0.26 & 0.31 & 0.28 & 0.59 & 0.37 \end{array}] \times [\begin{matrix} \begin{matrix} 0.08 \\ 0.21 \end{matrix} \\ 0.29 \\ \begin{matrix} 0.4 \\ 0.01 \end{matrix} \end{matrix}] = [\begin{matrix} 0.1 \\ 0.48 \\ 0.41 \end{matrix}]

R I I_{i}^{E n v i r o n m e n t a l} = V_{i}^{c} \times W_{c} = [\begin{array}{l} 0.06 & 0.11 & 0.10 & 0.17 & 0.22 \\ 0.23 & 0.39 & 0.27 & 0.14 & 0.19 \\ 0.71 & 0.50 & 0.63 & 0.69 & 0.59 \end{array}] \times [\begin{matrix} \begin{matrix} 0.29 \\ 0.25 \end{matrix} \\ 0.01 \\ \begin{matrix} 0.23 \\ 0.22 \end{matrix} \end{matrix}] = [\begin{matrix} 0.13 \\ 0.24 \\ 0.63 \end{matrix}]

R I I_{i}^{E c o n o m i c a l} = V_{i}^{c} \times W_{c} = [\begin{array}{l} 0.12 & 0.23 & 0.07 & 0.12 & 0.26 \\ 0.25 & 0.56 & 0.29 & 0.52 & 0.14 \\ 0.63 & 0.21 & 0.63 & 0.36 & 0.60 \end{array}] \times [\begin{matrix} \begin{matrix} 0.23 \\ 0.39 \end{matrix} \\ 0.36 \\ \begin{matrix} 0.02 \\ 0 \end{matrix} \end{matrix}] = [\begin{matrix} 0.15 \\ 0.39 \\ 0.46 \end{matrix}]

Table 7.

Summarize the criteria outputs.

	$V (M_{r o w} \geq M_{c o l u m n})$
	(X1)	(X2)	(X3)	(X4)	(X5)	S_i	$d^{'}$	Weight (%)
Social sustainability
(X1)	—	0.74	0.56	0.2	1	0.04, 0.09, 0.26	0.2	8
(X2)	1	—	0.85	0.52	1	0.06, 0.16, 0.47	0.52	21
(X3)	1	1	—	0.73	1	0.08, 0.23, 0.73	0.73	29
(X4)	1	1	1	—	1	0.17, 0.44, 0.98	1	40
(X5)	0.87	0.57	0.38	0.03	—	0.03, 0.07, 0.18	0.03	1
	(Y1)	(Y2)	(Y3)	(Y4)	(Y5)
Environmental sustainability
(Y1)	—	1	1	1	1	0.11, 0.31, 0.79	1	29
(Y2)	0.86	—	1	1	1	0.10, 0.23, 0.60	0.86	25
(Y3)	0.04	0.1	—	0.22	0.3	0.03, 0.06, 0.12	0.04	1
(Y4)	0.82	0.94	1	—	1	0.08, 0.20, 0.60	0.82	23
(Y5)	0.78	0.93	1	1	—	0.06, 0.20, 0.51	0.78	22
	(Z1)	(Z2)	(Z3)	(Z4)	(Z5)
Economic sustainability
(Z1)	—	0.58	0.66	1	1	0.08, 0.18, 0.42	0.58	23
(Z2)	1	—	1	1	1	0.16, 0.37, 0.80	1	39
(Z3)	1	0.91	—	1	1	0.15, 0.32, 0.67	0.91	36
(Z4)	0.48	0.06	0.11	—	1	0.04, 0.07, 0.18	0.06	2
(Z5)	0.07	0	0	0.71	—	0.02, 0.05, 0.09	0	0

According to the results, social concerns play an important role in the design and construction phases and the operation phase is important afterward. Preconstruction phase in comparison to the other phases has a Negligible impact. Besides, with regards to the refinery highly pollutants in the operation phase, this phase is of primary importance in environmental effects. Finally, market demands are supplied in the operation phase. Thus, this phase has the highest impact among other phases in the sustainability economic concerns.

Conclusion

On the contrary to the position of PRI projects in the economic growth, with regard to their products and their high environmental footprint in the operation phase, they are categorized in the inherently anti-sustainable industries. Thus, developing a green PRI project with the minimum environmental, social, and economic adversely effects would have a great influence on the sustainability goals. Besides, as the PRI projects are multi-disciplinary, complicated projects, there are numerous considerations which should be taken in to the account in different phases. Thus, the outputs of this study would be so applicable in the decision making process in the projects of this industry.

All PRI projects can use the proposed methodology in order to respond to three major questions, which will assist managers in their decision making. This framework is such that, as the project progresses and more information becomes available, or as the plant operates and real data can be measured, the methodology can be repeated so that variation of those responses can be tracked. These three questions are:

Which life cycle stage is more important with regard to all three sustainability pillars and for each pillar separately (ranking of the alternatives in the methodology).

Which indicators have the most important position compared to others in each pillar (weight vector of the criteria).

Which life cycle stage is more important for each indicator or pillar of sustainability (normalized comparison matrix related to that criteria and weights).

For the real petroleum refinery project assessed in the case study, “operation and consumption” is the most important stage for environmental, “construction” is for social, and “operation and consumption” are for economic aspects. Hence, it is recommended to concentrate on environmental matters while the project is in its operation condition. Besides, social matters are important in construction phase and slightly afterwards during the operation phase. Economic parameters play a central role in operation and construction phases of the petroleum refinery project. Moreover, it is concluded from the output of this study that the significant criteria in social, environmental, and economic sustainability in this petroleum refinery project are education, atmosphere, and financial concerns, respectively.

Based on the PARETO principle, managers need to concentrate on the 20% of the most influential parameters which have 80% of the importance. Thus, the proposed methodology can definitely help managers in different ways; especially when the schedule is tight and they have to make fast decisions with regards to the most important concerns. Also, this study helps researchers and future studies to be carried out in proper fields at different stages.

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

Hamidreza Hasheminasab is PhD Candidate in Construction Engineering and Management, School of Civil Engineering, University of Tehran. Main area of research is Sustainability of Oil Refinery Projects.

Yaghob Gholipour is the Director of Engineering Optimization Research Group(EORG), College of Engineering, University of Tehran. The main area of research is Sustainability and Optimization in Construction Projects.

Mohammad Kharrazi is a senior research fellow at Office of Sustainability at Amir Kabir University of Technology in Tehran. His main research area is Sustainability Assessment Systems and Sustainable Construction.

Dalia Streimikiene is leading research associate at the Lithuanian Energy Institute. The main area of research is sustainable energy development and sustainability assessment in energy sector.

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