山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (4): 42-50.doi: 10.6040/j.issn.1672-3961.0.2023.111
杜睿山1,2,井远光1,孟令东2,张豪鹏1
DU Ruishan1,2, JING Yuanguang1, MENG Lingdong2, ZHANG Haopeng1
摘要： 为减少储气库不合理注气导致的微震次数,保证储气库注气量最大,构建基于双向长短期记忆(bi-directional long short-term memory, BiLSTM)神经网络预测代理模型,降低微震次数和储气库有效应力的预测误差,提出一种精英进化多目标粒子群优化(elite-evolved multi-objective particle swarm optimizer, EMPSO)算法。采用基于排序分组策略对种群进行分组,并在每个分组内进行随机精英竞争学习,提高算法的多样性;引入精英聚集的思想,加快算法的收敛速度。基于BiLSTM模型和EMPSO算法对储气库注气过程进行优化,与其他3种多目标优化算法进行对比,将EMPSO算法应用于实际配产优化。结果表明,改进后的算法具有更好的Pareto前沿、更快的收敛速度,优化后微震次数和有效应力分别降低了9.78%和10.12%,对保障储气库安全和提高储气库储气量具有重要意义。
中图分类号:
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