基于EHO优化的COBDP预神测经模网型络污水处理出水COD预测模型
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Prediction Model for Effluent COD in Sewage Treatment Based on BP Neural Network Optimized by EHO
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    摘要:

    为了改善在污水处理环节中对有关化学需氧量预测效果的问题,提出了一种经象群算法优化的 BP 神经网络预测模型。 首先通过将象群算法中的分离操作与改进权重的粒子群算法相结合,有效去除了种 群中适应度较差的个体,进一步提高算法寻找最优值的能力;首次利用改进后的象群算法优化 BP 神经网络 对预测数据进行更好的逼近,提高预测模型的预测准确度;最后,通过仿真结果清晰表明:改善后的 BP 神经 网络相对于传统 BP 神经网络以及一般的小波神经网络有着更高的预测精度。 改进后的象群算法结合 BP 神经网络所建立的预测模型在一定程度上可以对污水处理中的出水化学需氧量进行比较准确的预测,能满 足预测出水化学需氧量的一般要求,具有一定的研究价值。

    Abstract:

    In order to improve the prediction effect of chemical oxygen demand (COD) in sewage treatment, a BP neural network prediction model optimized by elephant herding optimization (EHO) is proposed. By combining the separation operation in the elephant herding optimization algorithm with the particle swarm optimization (PSO) algorithm with improved weight, the individuals with poor fitness in the population can be effectively removed to further improve the algorithm’s ability to find the optimal value. The improved elephant herding optimization algorithm is used to optimize the BP neural network for the first time to better approximate the data and improve the prediction accuracy of the prediction model. Finally, the simulation results clearly show that the improved BP neural network has higher prediction accuracy than the traditional BP neural network and the general wavelet neural network. The prediction model based on the improved elephant herding optimizlation algorithm and BP neural network can accurately predict the chemical oxygen demand of effluent in sewage treatment to a certain extent, which can meet the general requirements of predicting the chemical oxygen demand of effluent and which has certain research value.

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朱琳, 李明河, 陈园.基于EHO优化的COBDP预神测经模网型络污水处理出水COD预测模型[J].重庆工商大学学报(自然科学版),2022,39(3):26-32
ZHU Lin, LI Ming-he, CHEN Yuan. Prediction Model for Effluent COD in Sewage Treatment Based on BP Neural Network Optimized by EHO[J]. Journal of Chongqing Technology and Business University(Natural Science Edition),2022,39(3):26-32

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  • 在线发布日期: 2022-05-12