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Licensed Unlicensed Requires Authentication Published by De Gruyter July 20, 2020

A mixed integer nonlinear programming approach for integrated bio-refinery and petroleum refinery topology optimization

Ahmed Mahmoud and Jaka Sunarso


The conversion of biomass into gasoline and diesel in bio-refinery process is an attractive process given its carbon neutral and sustainable nature. The economics of bio-refinery can be improved via integration with petroleum refinery, whereby bio-refinery intermediates can be processed into gasoline and diesel in the well-established petroleum refinery processing units, i. e., hydrocracking (HC) and fluidized catalytic cracking (FCC) units. However, the integration of the new bio-refinery into the existing petroleum refinery may not give the optimum solution given the capacities constraints of the existing petroleum refinery upgrading units such as FCC and HC units. Thus, this work proposed a superstructure comprising new bio-refinery and new petroleum refinery block diagrams. The superstructure was formulated into mixed integer nonlinear programming (MINLP) model. The model was coded into general algebraic modeling system (GAMS) platform and solved using global optimum solver, LINDOGLOBAL. The model application was demonstrated using representative case study. The model results showed that the optimum integrated bio-refinery and petroleum refinery topology favors the upgrading of bio-refinery intermediates using petroleum refinery HC unit under one-through operation mode with a marginal increase in the profit of about 0.39% compared to the second optimum case of upgrading bio-refinery intermediate using petroleum refinery FCC unit under gasoline operation mode. Thus, the decision in selecting the most suitable topology can be made based on the market demand for gasoline and diesel as the topology that uses FCC maximizes gasoline production and the topology that uses HC maximizes diesel production.

Corresponding author: Ahmed Mahmoud, Research Centre for Sustainable Technologies, Faculty of Engineering, Computing and Science, Swinburne University of Technology, Jalan Simpang Tiga, 93350, Kuching, Sarawak, Malaysia, E-mail:

Funding source: Swinburne Sarawak Research Grant (SSRG)

Award Identifier / Grant number: SSRG No. 2-5540


This work was enabled by the financial support from Swinburne Sarawak Research Grant (No. 2-5540).


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Received: 2019-11-19
Accepted: 2020-04-01
Published Online: 2020-07-20

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