02 آذر 1403
يوسف كاظم زاده

یوسف کاظم زاده

مرتبه علمی: استادیار
نشانی: دانشکده مهندسی نفت، گاز و پتروشیمی - گروه مهندسی نفت
تحصیلات: دکترای تخصصی / مهندسی نفت
تلفن: 07731222604
دانشکده: دانشکده مهندسی نفت، گاز و پتروشیمی

مشخصات پژوهش

عنوان Design of a sustainable supply chain network of biomass renewable energy in the case of disruption
نوع پژوهش مقالات در نشریات
کلیدواژه‌ها
Biomass supply chain, Genetic algorithm, Simulated annealing algorithm, Stability, Disruption
مجله Scientific Reports
شناسه DOI https://doi.org/10.1038/s41598-024-64341-9
پژوهشگران لیلا اصلانی (نفر اول) ، عاطفه حسن زاده (نفر دوم) ، یوسف کاظم زاده (نفر سوم) ، امیرحسین شیخ احمدی (نفر چهارم)

چکیده

Non-renewable energy sources, including fossil fuels, are a type of energy whose consumption rate far exceeds its natural production rate. Therefore, non-renewable resources will be exhausted if alternative energy is not fully developed, leading to an energy crisis in the near future. In this paper, a mathematical model has been proposed for the design of the biomass supply chain of field residues that includes several fields where residue is transferred to hubs after collecting the residue in the hub, the residue is transferred to reactors. In reactors, the residue is converted into gas, which is transferred to condenser and transformers, converted into electricity and sent to demand points through the network. In this paper, the criteria of stability and disturbance were considered, which have been less discussed in related research, and the purpose of the proposed model was to maximize the profit from the sale of energy, including the selling price minus the costs. Genetic algorithm (GA) and simulated annealing (SA) algorithm have been used to solve the model. Then, to prove the complexity of the problem, different and random examples have been presented in different dimensions of the problem. Also, the efficiency of the algorithm in small and large dimensions was proved by comparing GA and SA due to the low deviation of the solutions and the methods used have provided acceptable results suitable for all decision-makers. Also, the effectiveness of the algorithm in small and large dimensions is proven by comparing the genetic algorithm and simulated annealing, and the genetic algorithm's values are better, considering the deviation of 2.9%.and have provided solution methods suitable for all decision makers.