首页|融合SVMD和IGJO-LSTM的污水处理曝气量预测

融合SVMD和IGJO-LSTM的污水处理曝气量预测

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污水处理过程中,曝气量数据波动大、周期性不明显,现有模型难以对曝气量进行准确的预测.因此,提出了一种优化模型,此模型利用逐次变分模态分解提取曝气数据特征,并采用改进的金豺算法优化长短期记忆网络的超参数,以提升模型预测能力.首先,针对实际污水数据复杂的问题,利用逐次变分模态分解算法分解重构原始曝气数据序列.其次,用长短期记忆网络分别对每个序列依次预测,并采用柯西反向学习混合变异策略改进金豺算法对长短期记忆网络参数进行优化.最后,将各个序列预测结果进行重组,得到最终预测值.利用实际污水水质数据对该模型进行验证,结果表明该模型有效提高了曝气量的预测精度,具有很好的应用前景,能很大提升污水处理厂的经济效益.
Prediction of Waste water Treatment Aeration by Integrating SVMD and IGJO-LSTM
In the process of wastewater treatment,the aeration amount data exhibits large fluctuations and lacks clear periodicity,making it challenging for existing models to accurately predict aeration levels.Therefore,an optimized model was proposed,which utilized successive variational mode decomposition to extract features from the aeration data.Additionally,an improved Jackal algorithm was employed to optimize the hyperparameters of LSTM(long short-term memory)network,thereby enhancing the model's predictive capabilities.To address the complexity of real wastewater data,the successive variational mode decomposition algorithm was applied to decompose and reconstruct the original aeration data sequence.Subsequently,the LSTM networks were used to predict each sequence sequentially,and a Cauchy backpropagation learning mixed mutation strategy was implemented to enhance the Jackal algorithm for optimizing the parameters of the LSTM network.Finally,the predicted results of each sequence were recombined to obtain the ultimate prediction.Validation of this model using actual wastewater quality data demonstrates its effectiveness in improving the accuracy of aeration volume prediction.This model has promising applications and has the potential to significantly enhance the economic efficiency of wastewater treatment plants.

wastewater treatmentprediction of aeration quantitysuccessive variational modal decompositiongolden jackal optimization algorithmlong-term and short-term memory networks

侯登云、南新元、夏斯博、陈浩辉、李海龙

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新疆大学电气工程学院,乌鲁木齐 830017

中南大学自动化学院,长沙 410083

污水处理 曝气量预测 逐次变分模态分解 金豺优化算法 长短期记忆网络

国家自然科学基金新疆维吾尔自治区自然科学基金

520650642022D01C694

2024

10.12404/j.issn.1671-1815.2400138

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子：0.338
ISSN：1671-1815
年,卷(期)：2024.24(26)