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There is convincing evidence to suggest that understanding the fundamental needs of human beings is essential if we are to develop strategies to transition society towards more sustainable forms of development (Hall, 2006). Besides, human needs satisfaction is likely to "make fewer demands on our environmental resources, but much greater demands on our moral resources" (Brown, 1982). As such, it is a moral obligation for governments, societies, industries, and individuals, to help fulfill human needs by enhancing health, safety, economy, and society, while preserving the environmental assets such as biodiversity and natural resources; i.e. to realize sustainable development.

Infrastructural projects are often non-profit projects, however, governments have to pursue them to improve living standards and to develop economies and societies. This is especially true for energy sector projects. However, these projects almost always have large negative influences on the environment. As a result, a comprehensive sustainability framework is crucial, whether developed by governments or other entities. This framework should cover the entire life-cycle of the project and should cater to various types of information needed by managers in their respective industries.

PRI projects supply a great share of world's demand for energy, and as such, could have great positive economic and social impacts. At the same time, they are notorious for their effects on environmental degradation and possible social harm. Because of these conflicting attributes, a fully connected sustainability assessment framework has to be developed for PRI projects, and the correlation between sustainability factors and life cycle phases and sub-phases should be clearly determined so that it can be used as a decision making (MADM) tool with wide applicability. To be consistent with MADM terminology, we will call these phases and sub-phases as 'Alternatives' henceforth.

One of the important attributes of PRI projects is the related technology and its quick development. Technology has a positive effect which is its ability to shape the world by influencing international conflicts, national politics, the distribution of wealth and power, gender equality, etc. On the other hand, technology has a negative influence as well. It is largely responsible for the degradation of the natural environment to the extent that ecosystems and wildlife have either been destroyed or put under serious stress (Carson, 1962). Another technology concern is the pace at which the technology is changing (Streeten, 2001). Governments should have the required flexibility to adapt to rapid technology changes. All in all, PRI projects are high-tech industries especially in the complementary treatments which occur in reformers and unifiers. Due to the inherent complexities that are caused by high tech equipment and processes, the full connectivity of the sustainability framework is of primary importance (Stjepcevic & Siksnelyte, 2017).

1. Literature Review

There is a great deal of literature on social sustainability (Missimer et al., 2017; 2016; Silajdzic et al., 2015), environmental sustainability (Ferreira et al., 2015; Felix & Gheewala, 2012; de Castro Hilsdorf et al., 2017; Shortall et al. 2015), economic sustainability (Ooba et al., 2015; Filipovic & Golusin, 2015), and sustainability integrated assessment (Stamford et al., 2014; Musango, 2011; Angelis-Dimakis et al., 2012; Halaby et al., 2017). Besides, developing infrastructural projects and achieving sustainable infrastructure is of paramount importance, and as such, it has been studied in many publications (Yao et al., 2011; Papajohn et al., 2016; Sierra et al., 2015). Sustainability of Oil and Gas Industry (OGI) companies and projects has been the subject of many studies as well (George et al., 2016; Silvestre & Gimenes, 2017; Heravi et al., 2015; Neelis, 2008; Ahmad et al., 2016). Among OGI projects, PRI projects have been the subject of many sustainability studies in particular (Hadidi et al., 2016; Holmgren et al., 2008; Ba-Shammakh, 2010; Jovanovic et al., 2010; Mahmoud & Shuhaimi, 2013). Sustainability indicators in all Triple bottom lines (TBL) have been considered in many of these studies (Hiremath et al., 2013; Shen et al., 2010; Shen et al., 2011; Chong et al., 2016).

Most of the previous studies track only one pass towards the development of sustainability indicators and this would increase the possibility of reduced accuracy of the framework and its application. In this study, a sustainability framework which was specifically developed for PRI projects (Hasheminasab et al., 2018), has been used as the basis to develop a connectivity model to enable practitioners and managers to assess the realization of sustainability factors and indicators in various life-cycle phases of petroleum refineries. To create this model, expert opinions have been utilized; therefore, this framework is based on available literature and at the same time relies on industry experts as well.

Many corporate world problems stem from the complexity of dealing with many criteria and stakeholders in the process of decision making (DM). Hence, during the past decades, a wide variety of DM techniques have been developed in Operations Research field, Multi-criteria Decision Making (MCDM), by which the aforementioned problems would be simplified and solved (Ferreira et al., 2017). MCDM techniques are divided into Multi-Objective Decision Making (MODM) and Multi-Attribute Decision Making (MADM) (Zavadskas et al., 2014). MADM was considered as a multidisciplinary methodology for solving problems and is used as a decision making tool in many fields and studies (Mardani et al., 2015; Wang et al., 2014).

Life-cycle sustainability assessment has a life-long approach towards relevant issues. Therefore, it is a valuable tool for long-term decision making and strategic management (Stelzer et al., 2015). To use life-cycle concepts, various phases, i.e. cradle to refinery gate, refinery gate to product gate, and product gate to the grave, will be considered separately, and to be consistent with MADM terminology, these phases will be called 'Scenarios'. Although scenarios are qualitative in nature, MADM techniques are used to quantify them. Therefore, MADM based Scenarios will be used to model the life-cycle of PRI projects (Hashemkhani Zolfani et al., 2016).

2. Methodology

The methodology used in this study will comprise a chain of methodologies that will be used as represented in Tab. 1.

2.1 Sustainability Framework

So many frameworks are developed for sustainability assessment in order to be used for different projects and fields. Among these, a framework that was developed recently by the authors (Hasheminasab et al., 2018) for PRI projects, in particular, will be utilized in this study. This framework contains pillars, indicators, and factors which are going to be utilized in this study. For more information about the details of indicators and factors, the reader can refer to the aforementioned reference.

2.2 Petroleum Refinery Life-Cycle Modeling

A petroleum refinery is a complex, multidisciplinary facility which contains millions of components. In this study, in order to use a life-cycle model of a refinery with high accuracy and efficiency, a panel of highly experienced experts was formed from various related disciplines so that a comprehensive accurate model of a petroleum refinery with life-cycle focus could be developed. This panel comprised 5 experts, and during the Concept Mapping session, a model was developed which is presented in Fig. 1 and Tab. 2. In subsequent calculations, a petroleum refinery's processes and scenarios will be based on this model.

2.3 Calculate Connectivity

The connectivity between indicator-based sustainability framework which was developed by the authors in a previous publication (Hasheminasab et al., 2018) and petroleum refinery life-cycle model, which was developed from the above Concept Mapping approach, was determined by a panel of experts by using a Focus Group technique. These experts had at least 15 years of related experience in their petroleum refinery-related fields. The connectivity matrix showed whether a specific sustainability factor was related to a specific alternative (according to MADM terminology). The subsequent stages of this study will only be focusing on the non-zero cells of the connectivity matrix; i.e. the zero connectivity between factors and alternatives will not be pursued any further.

2.4 Life-Cycle Sustainability Criteria Assessment

Based on the aforementioned steps, the problem is modeled with MADM based scenarios. In this regard, three scenarios are considered as petroleum refinery life-cycle phases (Cradle-to-Gate, Gate-to-Gate, and Gate-to-Grave), and the criteria are the sustainability factors. Moreover, in this decision model, every scenario has some alternatives which are evaluated through different criteria set based on the connectivity outputs. The base methodology for this stage was taken from (Hashemkhani et al., 2016) and developed and modified in accordance with the problem at hand, as follows:

Step 1: Normalized decision making table

Normalized decision matrix is calculated as follows. These equations are used if the extremum value is max or min respectively.

[mathematical expression not reproducible] (1)

[mathematical expression not reproducible] (2)

Step 2: Calculate WASPAS weights

There are two types of WASPAS weights which are based on normalization and multiplication and are represented as follows respectively.

[mathematical expression not reproducible] (3)

[mathematical expression not reproducible] (4)

Step 3: weighted normalized matrix

Final weights are calculated by the following equation:

[mathematical expression not reproducible] (5)

Step 4: Final evaluation and rankings

The calculated weights from Step 3 are further modified in this step, based on the number of times that a criterion is repeated in various scenarios. In other words, more weight is given to criteria that are repeated more frequently, compared to the criteria that have a lower frequency:

[[bar.W].sub.i] = [W.sub.i] x [I.sub.i]/[[summation].sub.i]([W.sub.i] x [I.sub.i]) (6)

[mathematical expression not reproducible] (7)

where [I.sub.i] represents the frequency of the ith criteria. [W.sub.i]: primary weights that signify the absolute importance of various criteria (sustainability factors), without regard to their influence on various alternatives. [[bar.W].sub.i] : modified weights. [A.sub.i]: primary ranks. [[bar.A].sub.i] : modified ranks.

3. Case Study

A real refinery is investigated as a case study for this research. A panel of 15 experts with related experiences was selected for this study. Tab. 3 shows their competencies according to their years of experience and education level.

Based on the connectivity outputs, life-cycle sustainability criteria assessment is evaluated for the petroleum refinery of the case study. A decision table (Tab. 4) was created as follows and was subsequently filled by each of the aforementioned experts.

As the end result, mean value of significance of criteria are calculated, averaged across various criteria belonging to each indicator, and presented in Tabs. 5-7 for various scenarios.

To elaborate further on the steps that were taken to reach the results of Tabs. 5-7, intermediate results are partially presented in Tabs. 9-15. To be brief, in the following tables, only the first two sustainability factors are presented. Tab. 9 lists the absolute weights for the first two factors (criteria) that is the outcome of the focus group exercise. Note that, in cells that no connectivity is found (see Tab. 10) the weight is set as 0.

In the next step, the connectivity between various criteria and alternatives is assessed during the Focus Group exercise. Tab. 10 shows a sample of the connectivity matrix.

In the next step, the significance of each criterion for each alternative is assessed. Tab. 11 shows the average values (taken from experts' opinions during the Focus Group exercise) for the first two factors (criteria). Where there is no connectivity, no value is provided.

Normalized values are calculated for every factor based on the related extremum values (Tab. 12).

Following the stated WASPAS methodology, the normalized weighted sum values are calculated and presented in Tab. 13.

Normalized weighted multiplied values are calculated for every sustainability factor as well (Tab. 14).

Finally, according to the mentioned calculation chain, scenarios and alternatives are primarily and Anally weighted and ranked as is illustrated in Tab. 15.

Based on the outputs, poverty, safety, and health are important indicators for the social sustainability, respectively. Moreover, Atmosphere, natural resource, and water, respectively, are the most prominent indicators in the environmental category. Finally, performance of the economy, financial matters, and consumption of energy are three pivotal economic indicators for the case refinery.

Also, desulfurization and reformer which are the most expensive units with the highest influence on the sustainability parameters, and play a prominent role in enhancing the quality of the refinery products, are at the top in the final ranking. Besides, based on the outcome the most important scenario is the Gate-to-Gate or main refinery phase in the life-cycle of the PRI projects. Second to that, comes the Cradle-to-Gate phase which covers the processes from crude oil excavation, transportation, and procurement services.


Sustainability evaluation frameworks are widely developed in the industry (building sustainability standards, sustainability reports, etc.) as well as in the academia and research (as is presented in the literature review section). Having said that, a comprehensive integrated framework covering various decision making levels would have a great influence and would be a useful tool for managers in their decision making process.

This study is a part of a chain of studies in the sustainability assessment of petroleum refineries. A sustainability framework based on the indicators and quantitative factors was developed earlier by the authors. In this study the said sustainability framework is applied to PRI projects' life-cycle model. Furthermore, this multistep process is tested for a real refinery case study.

PRI projects are mega-projects and can be categorized and modeled with a life-cycle viewpoint in many ways. However, in this study, modeling the project is developed in a concept mapping session so that the model is suitable for sustainability assessment purposes. Afterward, a highly experienced group of experts have detailed the connectivity of the sustainability framework and the PRI life-cycle model during a focus group session. Finally, a real refinery is taken through this methodology via a new scenario based technique.

PRI projects have large impacts on various sustainability attributes in different phases. A comprehensive assessment of these impacts for project's life-cycle is of great importance. The proposed methodology has created the required framework for such assessment by using Concept Mapping, Focus Group, and Multi-Attribute Decision Making Techniques. As an example, the proposed methodology has been applied to a real refinery case. Based on the results, the most important phase in the petroleum refinery life-cycle with regards to all three pillars of sustainability is found to be the operation phase. As such, implementation of various operation phase sustainability aspects should be the first priority of stakeholders during design, procurement, construction, and operation phases. To ensure the continued sustainable performance of the refinery, periodic monitoring and control of the sustainable operation of the refinery are warranted.


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Prof. Yaghob Gholipour, PhD

University of Tehran

College of Engineering

Engineering Optimization Research Gr.


Hamidreza Hasheminasab

University of Tehran

College of Engineering

School of Civil Engineering


Mohammad Kharrazi, PhD

Amir Kabir University of Technology

Office of Sustainable Development


Justas Streimikis

Lithuanian Institute of Agrarian Economics

Division of Farms and Enterprises Economics


Caption: Fig. 1: Life-cycle model of a petroleum refinery
Tab. 1: Methodology map

Methodology                                 Phase

Literature review      Sustainability indicator framework
Concept mapping        Petroleum refinery life-cycle modeling
Focus group            Indicator-refinery connectivity
MADM based scenario    Life-cycle sustainability criteria assessment
Case study             Real petroleum refinery

Source: own

Tab. 2: Life-cycle modeling for the petroleum refinery project

Cradle-to-Gate              Gate-to-Gate

Raw material                Pretreatment       Distillations

Crude oil    Procurement      De-salter     Atmospheric    Vacuum

Cradle-to-Gate                      Gate-to-Gate           Gate-to-

Raw material                Enhancers                      Products

Crude oil    Procurement    Desulfurization    Reformer      Light

Cradle-to-Gate                         Gate-to-Grave

Raw material                              Products

Crude oil    Procurement      Middle        Heavy       Further
                            Distillate    Distillate    Products

Source: own

Tab. 3: Experts' information

                           Education              Experience(year)

                     BSc.    MSc.    Ph.D.    0-10    10-15    Over 15

Number of experts      8       5       2        3       7         5

Source: own

Tab. 4: Decision table for MADM Based Scenarios

                                                Life-Cycle Phases
Proposed                          ext()            (Scenarios)
Factor               Weight                  Scenario1     Scenario2

                                  Max/Min      A1, A2     A3, ..., A7
F1, ..., F101    W1, ..., W101

                 Life-Cycle Phases
Factor             Scenario3

                 A8, ..., A11
F1, ..., F101

Source: own

Note: F1, ..., F101 are the sustainability factors (see Hasheminasab
et al. (2018) for more information). A1, ..., A11 are the scenarios'
alternatives based on Tab. 2. W1, ..., W101 are assigned weights to
the absolute importance of various factors (criteria as per MADM

Tab. 5: Social criteria evaluation for different scenarios


             [C.sub.1.sup.1]    [C.sub.2.sup.1]    [C.sub.3.sup.1]

Scenario1          2.33               2.71               2.00
Scenario2          1.83               1.57               3.20
Scenario3          2.67               1.29               2.80
mean               2.28               1.86               2.67
Rank                1                  3                  2


             [C.sub.4.sup.1]    [C.sub.5.sup.1]

Scenario1          2.20               1.50
Scenario2          2.20               1.67
Scenario3          2.00               0.83
mean               2.13               1.33
Rank                4                  5

Source: own

Tab. 6: Environmental criteria evaluation for different scenarios


             [C.sub.1.sup.2]    [C.sub.2.sup.2]    [C.sub.3.sup.2]

Scenario1          3.67               1.13               1.29
Scenario2          3.67               2.00               1.57
Scenario3          3.50               1.50               1.14
mean               3.61               1.54               1.33
Rank                1                  3                  4


             [C.sub.4.sup.2]    [C.sub.5.sup.2]

Scenario1          2.25               1.67
Scenario2          2.38               1.67
Scenario3          1.25               0.00
mean               1.96               1.11
Rank                2                  5

Source: own

Tab. 7: Economic criteria evaluation for different scenarios


             [C.sub.1.sup.3]    [C.sub.2.sup.3]    [C.sub.3.sup.3]

Scenario1          4.50               3.92               2.54
Scenario2          4.50               4.50               2.54
Scenario3          4.50               4.50               2.54
mean               4.50               4.31               2.54
Rank                3                  2                  1


             [C.sub.4.sup.3]    [C.sub.5.sup.3]

Scenario1          2.20               2.20
Scenario2          2.20               2.20
Scenario3          1.40               2.20
mean               1.93               2.20
Rank                5                  4

Source: own

Note: [C.sup.j.sub.i], is the ith indicator for the jth sustainability
pillar (j = 1 for social, 2 for environmental, 3 for economical) as
per Tab. 8 below. This value is computed by calculating the mean of
its sustainability factor (criteria) weights.

Tab. 8: Sustainability indicators

Sustainability Pillars           Sustainability indicators

Social                    [C.sub.1.sup.1]   Poverty & Equality
                          [C.sub.2.sup.1]   Health
                          [C.sub.3.sup.1]   Safety & Security
                          [C.sub.4.sup.1]   Education & Training
                          [C.sub.5.sup.1]   Welfare
Environmental             [C.sub.1.sup.2]   Atmosphere
                          [C.sub.2.sup.2]   Water(Fresh Water, Ocean,
                                            Sea, Coast)
                          [C.sub.3.sup.2]   Land & Soil Pollution
                          [C.sub.4.sup.2]   Natural Resource
                          [C.sub.5.sup.2]   Biodiversity
Economical                [C.sub.1.sup.3]   Energy consumption
                          [C.sub.2.sup.3]   Financial
                          [C.sub.3.sup.3]   Economy Performance
                          [C.sub.4.sup.3]   Occupation
                          [C.sub.5.sup.3]   Earning

Source: own

Tab. 9: Weights and extrema definition

No.   Pillar     Indicator
                                    Proposed Factor / EN

1     Social     Poverty &     Proportion of project human
                 Equality      resource living below national
                               poverty line

2                              Fraction of project human
                               resource protected against
                               impoverishment by out-of-pocket
                               health expenditures

               Weight             ext()
       Sce1     Sce2     Sce3    Max/Min

1       5        0        5        min

2       4        4        4        max

Source: own

Tab. 10: Connectivity measurement

          Cradle-to-Gate                     Gate-to-Gate

           Raw material          Pretreatment   Distillations
      Crude Oil    Procurement    De-salter     Atmospheric   Vacuum

1         1             1             0              0          0
2         1             1             1              1          1

              Gate-to-Gate          Gate-to-Grave

               Enhancers              Products
      Desulfurization   Reformer   Light Distillate

1            0             0              1
2            1             1              1


      Middle Distillate   Heavy Distillate   Further Products

1             1                  1                  1
2             1                  1                  1

Source: own

Tab. 11: Mean value of significance of each criteria for each

          Cradle-to-Gate                  Gate-to-Gate

           Raw material         Pretreatment      Distillations
      Crude Oil   Procurement    De-salter     Atmospheric   Vacuum

1       1.154        0.615           --            --          --
2       2.231        1.154         1.538          1.769      1.385

              Gate-to-Gate          Gate-to-Grave

               Enhancers              Products
      Desulfurization   Reformer   Light Distillate

1           --             --           2.154
2          1.538         1.615            2


      Middle Distillate   Heavy Distillate   Further Products

1           2.462              2.154              1.923
2           2.231              2.308              1.769

Source: own

Tab. 12: Normalized values

           Cradle-to-Gate                 Gate-to-Gate

            Raw material        Pretreatment      Distillations
      Crude Oil   Procurement    De-salter     Atmospheric   Vacuum

1       0.533          1             --            --          --
2       0.967         0.5          0.667          0.767       0.6

               Gate-to-Gate         Gate-to-Grave

                Enhancers             Products
      Desulfurization   Reformer   Light Distillate

1           --             --           0.286
2          0.667          0.7           0.867


      Middle Distillate   Heavy Distillate   Further Products

1           0.250              0.286              0.320
2           0.967                1                0.767

Source: own

Tab. 13: Normalized weighted sum values

           Cradle-to-Gate                  Gate-to-Gate

            Raw material        Pretreatment      Distillations
      Crude Oil   Procurement    De-salter     Atmospheric   Vacuum

1      0.0053        0.010           --            --          --
2      0.0077        0.004         0.0052         0.006      0.005

              Gate-to-Gate          Gate-to-Grave

               Enhancers              Products
      Desulfurization   Reformer   Light Distillate

1           --             --           0.0032
2         0.0052         0.0055         0.0077


      Middle Distillate   Heavy Distillate   Further Products

1          0.0028              0.0032             0.0036
2          0.0086              0.0089             0.0068

Source: own

Tab. 14: Normalized weighted multiplied values

           Cradle-to-Gate                  Gate-to-Gate

            Raw material        Pretreatment      Distillations
      Crude Oil   Procurement    De-salter     Atmospheric   Vacuum

1      0.4938       0.5000           --            --          --
2      0.4997       0.4945         0.4968        0.4979      0.496

              Gate-to-Gate           Gate-to-Grave

               Enhancers               Products
      Desulfurization   Reformer   Light Distillate

1           --             --           0.4863
2         0.4968         0.4972         0.4987


      Middle Distillate   Heavy Distillate   Further Products

1          0.4848              0.4863             0.4875
2          0.4997              0.5000             0.4976

Source: own

Tab. 15: Scenarios and their alternatives evaluation

                               Life-Cycle Phases

                           Raw Material        Pretreatment

                     Crude Oil   Procurement    De-salter

                        A1           A2             A3

Scenario 1            0.4013       0.4132
Scenario2                                        0.42423
Primary Ranking          2            1             3
Participation         0.90099
Secondary Weight      0.36159      0.37237       0.40743
Normal Weight         0.08888      0.09153       0.10015
Secondary Ranking        7            6             3

                              Life-Cycle Phases

                         Distillations          Enhancers

                     Atmospheric    Vacuum    Desulfurization

                         A4           A5            A6

Scenario 1
Scenario2              0.42404     0.42277        0.42492
Scenario3                          0.40602
Primary Ranking           4           5              1
Participation                      0.96039
Secondary Weight       0.40724     0.40602        0.40809
Normal Weight          0.10010     0.09980        0.10031
Secondary Ranking         4           5              1

                                     Life-Cycle Phases

                     Enhancers               Products

                    Reformer    Light Distillate   Middle Distillate

                        A7             A8                 A9

Scenario 1
Scenario2            0.42483
Scenario3                           0.40806             0.40669
Primary Ranking         2              3                   4
Participation                       0.79207
Secondary Weight     0.40800        0.32322             0.32213
Normal Weight        0.10029        0.07945             0.07918
Secondary Ranking       2              10                 11

                              Life-Cycle Phases


                     Heavy Distillate   Further Products

                           A10                A11

Scenario 1
Scenario3                0.40965            0.41340
Primary Ranking             2                  1
Secondary Weight         0.32447            0.32744
Normal Weight            0.07976            0.08049
Secondary Ranking           9                  8

Source: own
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Title Annotation:Business Administration and Management/Ekonomika a management; Multiple criteria decision making
Author:Gholipour, Yaghob; Hasheminasab, Hamidreza; Kharrazi, Mohammad; Streimikis, Justas
Publication:E+M Ekonomie a Management
Geographic Code:7IRAN
Date:Jul 1, 2018

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