National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, 400067, China; Chongqing Water Group Co., Ltd., Chongqing, 400015, China.
National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, 400067, China.
Environ Res. 2022 Aug;211:113054. doi: 10.1016/j.envres.2022.113054. Epub 2022 Mar 9.
Carbon neutrality has been received extensive attention in the field of wastewater treatment. The optimal management of wastewater treatment plants (WWTPs) has great significance and urgency since the serious energy and materials waste. In this study, a full-view management method based on artificial neural networks (ANNs) for energy and material savings in WWTPs was established. More than 5 years of historical operating data from two typical plants (size 40,000 t/d and 10,000 t/d) located in Chongqing, China, were obtained, and public data in the service area of each plant were systematically collected from open channels. These abundant historical and public data were used to train two ANNs (GRA-CNN-LSTM model and PCA-BPNN model) to predict the inlets/outlets wastewater quality and quantity. The overall average prediction accuracy of inlets/outlets wastewater indicators are greater than 92.60% and 93.76%, respectively. By combining the two models, more appropriate process operation strategies can be obtained 2 weeks in advance, with more than 11.20% and 16.91% reduction of energy and material costs, respectively. This proposed method can provide full-view decision support for the optimal management of WWTPs and is also expected to support carbon emission control and carbon neutrality in the field of wastewater treatment.
污水处理领域受到了广泛关注。由于严重的能源和材料浪费，优化污水处理厂（WWTP）的管理具有重要意义和紧迫性。在这项研究中，建立了一种基于人工神经网络（ANNs）的 WWTP 节能和节材全景管理方法。从位于中国重庆的两个典型工厂（规模为 40000t/d 和 10000t/d）获得了超过 5 年的历史运行数据，并从每个工厂的公开渠道系统地收集了服务区的公共数据。这些丰富的历史和公共数据用于训练两个 ANN（GRA-CNN-LSTM 模型和 PCA-BPNN 模型）来预测进水/出水污水的质量和数量。进水/出水污水指标的总体平均预测精度均大于 92.60%和 93.76%。通过结合这两个模型，可以提前 2 周获得更合适的工艺运行策略，分别减少 11.20%和 16.91%的能源和材料成本。该方法可为 WWTP 的优化管理提供全景决策支持，有望为污水处理领域的碳排放控制和碳中和提供支持。
