摘要
针对污水处理过程中出水生化需氧量(BOD)难以在线准确测量的问题,提出一种基于随机森林(RF)重要性评估与改进海鸥算法(ISOA)优化长短期记忆神经网络(LSTM)相结合的软测量方法对出水BOD进行预测.利用随机森林算法对影响出水BOD的预测因子进行重要性评估,筛选出重要性评分较高的预测因子作为软测量模型的输入变量;设计一种基于改进海鸥优化算法(ISOA)优化LSTM网络的软测量模型,引入混沌映射与新的搜索机制克服海鸥优化算法多样性差、易陷入局部最优等问题,利用改进的海鸥优化算法对LSTM网络的迭代次数、隐含层节点数、初始学习率、学习率下降因子4个超参数进行优化.将软测量模型运用于实际污水处理过程,结果表明:经随机森林筛选变量以及改进海鸥算法优化之后,模型预测误差变小,预测精度有明显提高,能够实现对出水BOD的精准预测.
Aimed at the problem that the biochemical oxygen demand(BOD)of effluent in the sewage treatment process is difficult to accurately measure online,a soft measurement method based on the combination of random forest(RF)importance assessment and improved seagull optimization algorithm(ISOA)optimized long short-term memory neural network(LSTM)is proposed to predict the effluent BOD.The predictors affecting the effluent BOD were evaluated by the random forest algorithm,and the predictors with high importance scores were screened out as input variables of the soft measurement model.Secondary,an improved seagull optimization algorithm is presented for optimizing the LSTM network.The chaos mapping and new search mechanisms are introduced to overcome the problems of poor diversity and easy to fall into local optima of the seagull optimization algorithm.The ISOA is employed to optimize four hyperparameters of the LSTM network:the network iteration number,the number of nodes in the hidden layer,initial learning rate,and learning rate degradation factor.Finally,the proposed soft measurement models are applied to actual wastewater treatment processes.The results show that the prediction error of the model has been reduced and the prediction accuracy has been improved,achieving the accurate prediction of effluent BOD.
作者
杜先君
柴俊伟
DU Xian-jun;CHAI Jun-wei(College of Electrical and Information Engineering,Lanzhou Univ.of Tech.,Lanzhou 730050,China)
出处
《兰州理工大学学报》
CAS
北大核心
2024年第6期85-91,共7页
Journal of Lanzhou University of Technology
基金
国家自然科学基金(61963025)
甘肃省高等学校创新基金(2021A-027)。
作者简介
通讯作者:杜先君(1979-),男,江苏杭州人,博士,副教授.Email:xdu@lut.edu.cn。
