Abstract
This work aims to address the problem of green supply chain planning in the petroleum industry. Our primary objective is to minimize the crude, refinery, and petrochemical sectors’ total cost and meet environmental regulations. It presents a deterministic mathematical programming model for planning the supply chain. Furthermore, the study examines the impact of incorporating investment decisions in different carbon emission reduction options and evaluating the supply chain performance based on the economic and environmental dimensions. A novel mixed-integer linear programming model is presented in this study to evaluate the impact of introducing a stringent environmental regulation limiting greenhouse gas emissions. Experiments based on the Libyan petroleum industry are analyzed and demonstrate model capabilities to deal with the trade-off between the total cost and the petroleum sector’s environmental issues. This study shows that it is possible to reduce carbon emissions by up to 32% if the carbon capture and storage projects are implemented in the different petroleum sectors.
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Appendix
Appendix
Crude oil Parameters
\(LC_{w}^{C}\) = Setup cost (Fixed cost) of location well \(w \in W_{{}}\)($)
\(Cap_{wt}^{C}\) = capacity in the well (bbl/y)
\(Cap_{wt}^{C} - Min\) = Minimum Capacity in the well (bbl/y)
\(EXC_{pwgt}^{C}\) = Variable extraction cost of \(p \in P\) at wells \(w \in W\) by using technology \(g \in G\) during period \(t \in T\) ($/bbl)
\(PRC_{pwit}^{C}\) = Transportation cost of \(p \in P\) transported from well \(w \in W\) to storage tanks \(i \in I\) at period \(t \in T\) ($/bbl)
\(PMC_{pijt}^{C}\): Transportation cost of \(p \in P\) transported from storage tanks \(i \in I\) to market \(j \in J\) at period \(t \in T\) ($/bbl)
\(PRR_{pwrt}^{C}\): Transportation cost of \(p \in P\) transported from well \(w \in W\) to refinery \(r \in R\) at period \(t \in T\) ($/bbl)
\(CSC_{pit}^{C}\) = Inventory cost of \(p \in P\) at storage tanks \(i \in I\) during period \(t \in T\) ($/bbl)
\(FCC_{pjt}^{C}\) = Selling price of crude oil \(p \in P\) to market \(j \in J\) at period \(t \in T_{{}}\) ($/bbl)
\(DC_{pjt}^{C}\) = Demand of crude oil product \(p \in P\) by crude market \(j \in J\) at period \(t \in T_{{}}\) (bbl/y)
\(SC_{pi}^{\max }\) = Overall storage capacity for product \(p \in P\) at storage tanks \(i \in I\) (bbl/y)
\(VC_{t}^{\max ,min}\) = Maximum and Minimum production level of crude production at period \(t \in T_{{}}\) (bbl/y)
\(CGW_{wgt}^{C}\) = Cost of technology \(g \in G\) at Wells \(w \in W\) at the period \(t \in T\) ($)
\(EFC_{pwg}^{C}\) = Emission factor associated with extracting \(p \in P\) with technology \(g \in G\) at wells \(w \in W\) (kg CO2/bbl)
\(EFLC_{1}^{C}\) = Emission factor using pipeline transportation crude products to storage tanks and refinery (Kg CO2/bbl·km)
\(EFSC_{1}^{C}\) = Emission factor using ship transportation for crude products from storage tanks to market and petrochemical products to market (Kg CO2/bbl·km)
Refinery Parameters
\(LR_{r}^{R}\) = Setup cost (fixed cost) of refinery location \(r \in R\) ($)
\(Cap_{rt}^{R}\) = capacity in the refinery (bbl/y)
\(\gamma_{rep}^{R}\) = Yield of refinery product produced from processing crude product
\(FRR_{est}^{R}\) = Selling price of \(e \in E\) to market \(s \in S\) at the period \(t \in T_{{}}\) ($/bbl)
\(PRM_{erst}^{R}\) = Transportation cost \(e \in E\) transported from \(r \in R\) to market \(s \in S\) at \(t \in T\), ($/bbl)
\(PRH_{erht}^{R}\) = Transportation cost of \(e \in E\) from refinery \(r \in R\) to \(h \in H\) at period \(t \in T\) ($/bbl)
\(DR_{est}^{R}\) = Demand of refinery product \(e \in E\) by the market \(s \in S\) at the period \(t \in T_{{}}\) (bbl/y)
\(VTR_{erxt}^{R}\) = Variable transformation cost \(e \in E\) at \(r \in R\) using technology \(x \in X\) at \(t \in T\) ($/bbl)
\(CGR_{rxt}^{R}\) = Variable transformation cost at refinery \(r \in R\) using technology \(x \in X\) \(t \in T\) ($/bbl)
\(EFR_{erx}^{R}\) = Emission factor for transformation \(e \in E\) at \(r \in R\) technology \(x \in X\) (kg CO2/bbl)
\(EFLR_{1}^{R}\) = Emission factor pipeline from refinery to petrochemical plants (kg CO2/bbl. km)
\(EFTR_{1}^{R}\) = Emission factor truck form refinery to the local market (Kg CO2/bbl·km)
Petrochemical Parameters
\(LH_{h}^{H}\) = Setup cost of petrochemical plant location \(h \in H\) ($)
\(Cap_{ht}^{H}\) = capacity in the petrochemical (bbl/y)
\(Cap_{ht}^{H} - Min\) = Minimum capacity in the petrochemical (bbl/y)
\(\gamma_{hen}^{H}\) = Yield of petrochemical products produced from processing refinery products
\(CSH_{nkt}^{H}\) = Unit inventory cost of \(n \in N\) at storage tank \(k \in K\) during the period \(t \in T\) ($/bbl)
\(DH_{nzt}^{H}\) = Demand of petrochemical product \(n \in N\) by market \(z \in Z\) at period \(t \in T_{{}}\) (bbl/y)
\(FHH_{nzt}^{H}\) = Selling price of the product \(n \in N\) to market \(z \in Z\) at period \(t \in T_{{}}\) ($/bbl)
\(SH^{\max }\) = Overall storage capacity for storage tank \(k \in K\) (bbl/y)
\(VH_{nht}^{\max } ,VH_{nht}^{\min }\) = Maximum & Minimum production level of petrochemical product \(n \in N\) at petrochemical plants \(h \in H\) at the period \(t \in T\) (bbl/y)
\(PHK_{nhkt}^{H}\) = Transportation cost of \(n \in N\) transported from petrochemical \(h \in H\) to storage tank \(k \in K\) at the period \(t \in T\) ($/bbl)
\(PKZ_{nkzt}^{H}\) = Transportation cost of \(n \in N\) transported from storage tank \(k \in K\) to market \(z \in Z\) at the period \(t \in T\) ($/bbl)
\(VTH_{nhqt}^{H}\) = Variable transformation cost of the product \(n \in N\) at the petrochemical plant \(h \in H\) using technology \(q \in Q\) during the time \(t \in T\) ($/bbl)
\(EFH_{nhq}^{H}\) = Emission factor associated with transformation petrochemical products \(n \in N\) with technology \(q \in Q\) at petrochemical (kg CO2/bbl)
\(EFLH_{1}^{H}\) = Emission factor using pipeline transportation petrochemical products to storage tanks (Kg CO2/bbl·km)
\(EFSH_{1}^{H}\) = Emission factor using ship transportation for petrochemical products from storage tanks to market (Kg CO2/bbl·km)
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Abdussalam, O., Fello, N., Chaabane, A. (2022). Carbon Abatement in the Petroleum Sector: A Supply Chain Optimization-Based Approach. In: Le Thi, H.A., Pham Dinh, T., Le, H.M. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. MCO 2021. Lecture Notes in Networks and Systems, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-030-92666-3_17
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