The pH of a solution has a large influence on the ion removal efficiency of the membrane capacitive deionization (MCDI) process, an electrochemical ion separation process. We developed a convolutional neural network linked with a long short-term memory (CNN-LSTM) model based on an artificial intelligence algorithm to predict the effluent pH of MCDI, as effluent pH is difficult to predict using conventional numerical modeling. The model accurately predicted effluent pH (R²≥0.998) based on the analysis of five input variables (current, voltage, influent conductivity and pH, and effluent conductivity) under standard operating conditions of MCDI using either constant-current or constant-voltage conditions. The developed model predicted effluent pH using only limited input variables, current and voltage, with high accuracy (R²≥0.997). Thus, the CNN-LSTM model can be used in practical applications as only the current and voltage of MCDI cells are often monitored in field applications.

溶液的 pH 值对膜电容去离子 (MCDI) 过程（一种电化学离子分离过程）的离子去除效率有很大影响。我们开发了一个与基于人工智能算法的长短期记忆 (CNN-LSTM) 模型相关联的卷积神经网络，以预测 MCDI 的出水 pH 值，因为使用传统的数值建模很难预测出水的 pH 值。该模型准确预测出水 pH 值 (R ²≥0.998) 基于在使用恒流或恒压条件的 MCDI 标准操作条件下对五个输入变量（电流、电压、进水电导率和 pH 值以及出水电导率）的分析。开发的模型仅使用有限的输入变量、电流和电压来预测出水 pH 值，并具有很高的准确度 (R ² ≥0.997)。因此，CNN-LSTM 模型可用于实际应用，因为在现场应用中通常只监测 MCDI 单元的电流和电压。