化学需氧量(COD)的测量在污水处理过程中非常重要。COD值在一定程度上反映了污水处理的效果和趋势,但是获得准确的数据需要很高的成本和劳动强度。TO1解决这个问题,提出了一种基于卷积神经网络-双向长短期记忆网络-注意力机制(CNN-BiLSTM-attention)算法的COD在线软测量方法。首先,通过分析厌氧-缺氧-氧化(A2O)废水处理过程中好氧池阶段的机理,初步确定了输入变量的选择范围,并对采集的样本数据集进行相关性分析。最后,pH值,溶解氧(DO),电导率(EC),和水温(T)被确定为COD软测量预测的输入变量。然后,基于CNN的特征提取能力和BiLSTM能够捕获时间序列数据中的后向和前向依赖的优势,结合可以为关键数据分配更高权重的注意力机制,建立了CNN-BiLSTM-Attention算法模型对A2O污水处理过程好氧区出水COD进行软测量。同时,均方根误差(RMSE),平均绝对误差(MAE),平均绝对百分比误差(MAPE)和决定系数(R2)三个指标用于评估模型,结果表明,该模型能够准确预测COD值,具有较高的准确性。同时,与CNN-LSTM-Attention等模型相比,CNN-BiLSTM,CNN-LSTM,LSTM,RNN,BP,SVM,XGBoost,和RF等。,结果表明,CNN-BiLSTM注意力模型表现最好,证明了算法模型的优越性。Wilcoxon符号秩检验表明CNN-BiLSTM-注意力模型与其他模型之间存在显著差异。
The measurement of chemical oxygen demand (COD) is very important in the process of sewage treatment. The value of COD reflects the effectiveness and trend of sewage treatment to a certain extent, but obtaining accurate data requires high cost and labor intensity. To1 solve this problem, this paper proposes an online soft measurement method for COD based on Convolutional Neural Network-Bidirectional Long Short-Term Memory Network-Attention Mechanism (CNN-BiLSTM-Attention) algorithm. Firstly, by analyzing the mechanism of the aerobic tank stage in the Anaerobic-Anoxic-Oxic (A2O) wastewater treatment process, the selection range of input variables was preliminarily determined, and the collected sample dataset was subjected to correlation analysis. Finally, pH, dissolved oxygen (DO), electrical conductivity (EC), and water temperature (T) were determined as input variables for soft measurement prediction of COD.Then, based on the feature extraction ability of CNN and the advantage that BiLSTM is able to capture the backward and forward dependencies in time series data, combined with the attention mechanism that can assign higher weights to the key data, a CNN-BiLSTM-Attention algorithm model was established to soft measure COD in the effluent from the aerobic zone of the A2O wastewater treatment process. At the same time, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) were utilized Three indicators were used to evaluate the model, and the results showed that the model can accurately predict the value of COD and has a high accuracy. At the same time, compared with models such as CNN-LSTM-Attention, CNN-BiLSTM, CNN-LSTM, LSTM, RNN, BP, SVM, XGBoost, and RF etc., the results showed that the CNN-BiLSTM Attention model performed the best, proving the superiority of the algorithm model.The Wilcoxon signed-rank test indicates significant differences between the CNN-BiLSTM-Attention model and other models.