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Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm

Panda, Debashish, Ramteke, Manojkumar
Applied energy 2019 v.235 pp. 68-82
algorithms, distillation, energy, models, petroleum, profit maximization, profitability, uncertainty
Crude oil supply about 33% of the total energy consumption worldwide and is predominantly (>50%) processed in marine access refineries. Therefore, crude oil scheduling which enables the efficient processing of the crude to increase the profitability is an important problem. It often becomes challenging due to the presence of combinatorial constraints, discrete variables and uncertainties. This study addresses the crude oil scheduling under commonly present demand uncertainty. A proactive two-stage approach has been developed to solve such optimization problems. A discrete-time model is used with structure adapted genetic algorithm (SAGA) for solving single- and multi-objective scheduling optimizations. In the first stage, an initial schedule is generated with the nominal parameters provided a priory. The same schedule is then checked and accepted in the second stage only if it is robust with respect to demand uncertainty. This method is applied to four different industrial size problems with scheduling horizon of 3, 7, 14 and 20 days. The objective function in the single objective formulation is the maximization of profit. However, in multi-objective optimization, an additional objective of minimization of fluctuation in crude oil supply to crude distillation units is used as it provides a better control and operability of the plant. The proposed method is successfully implemented for the above four different crude oil scheduling problems to generate the robust schedule.