Energy Consumption Optimization for Short-Term Scheduling of Crude Oil Operations Based on an Improved NSGA-Ⅲ
In order to further improve the solution quality and optimization effectiveness of the short-term crude oil scheduling problem,a two-stage optimization strategy for addressing such problems is proposed.First,Through the analysis of the assignment process from charging tanks to distillers,a crossover operator that can preserve segmentally parent genes and a mutation operator that adaptively changes mutation probabilities are given.Additionally,the NSGA-III-ACMO algorithm is introduced to solve the short-term crude oil scheduling problem,which ensures good convergence and population diversity while optimizing five objectives:crude oil mixing cost in pipeline and in charging tanks,tank-switching cost in distillers,tank usage cost,and energy consumption cost.To address the issue of incomplete optimization of energy consumption cost,a new mixed integer linear programming model is proposed for further optimization.The advantage of this model is that,for a given detailed schedule,it can minimize the energy consumption without affecting other objectives.A case study demonstrates that comparing the schedule obtained by the NSGA-III-ACMO algorithm with the results of existing literature,the optimization of individual objectives is improved by 9%to 45%.On this basis,the proposed model can further reduce energy consumption cost by 6.8%.Overall,the NSGA-III-ACMO shows the obvious superiority in both solution quality and optimization effectiveness.
short-term crude oil schedulingadaptive operatorenergy consumption optimizationmixed integer linear programming
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