The remarkable increment in the demand for freshwater in water-resource-stressed regions increases the necessity of saltwater desalination and the application of a brackish water treatment plant (BWTP). In that respect, model-based process analysis can play an essential role in optimizing BWTP operation and maintenance (O&M) and reducing costs. In modeling, it is challenging for either theoretical or numerical methods to sufficiently account for the complex causality and various correlations among the numerous process parameters or variables in the BWTP system. Contrastively, deep learning approaches are capable of modeling such a BWTP system as it can describe the complexity and nonlinearity of its variables with robust autonomous learning. In this study, we modeled an RO unit process of BWTP using conventional long short-term memory (Conv-LSTM) and dual-stage attention-based LSTM (DA-LSTM) based on hourly time-series data obtained from the actual BWTP operation during a one-year period. Hyperparameter optimization for Conv-LSTM and DA-LSTM was individually conducted to enhance the model prediction performance. The model prediction results demonstrated the superiority of DA-LSTM (R² > 0.99) over Conv-LSTM (0.531 ≤ R² ≤ 0.884). The sensitivity analysis offered straightforward interpretations of how the attention mechanisms of DA-LSTM used time-series data of the model input and output parameters for prediction.

在水资源紧张地区，对淡水的需求显着增加，增加了对海水淡化的必要性，并增加了咸水处理厂（BWTP）的应用。在这方面，基于模型的过程分析可以在优化BWTP运营和维护（O＆M）并降低成本方面发挥至关重要的作用。在建模中，无论是理论方法还是数值方法，要充分考虑BWTP系统中众多过程参数或变量之间的复杂因果关系和各种相关性，都具有挑战性。相反，深度学习方法能够对这样的BWTP系统进行建模，因为它可以通过强大的自主学习来描述其变量的复杂性和非线性。在这项研究中，我们根据一小时内从实际BWTP操作获得的每小时时间序列数据，使用常规的长期短期记忆（Conv-LSTM）和基于两阶段注意的LSTM（DA-LSTM）对BWTP的RO单元过程进行了建模。年期。分别进行了Conv-LSTM和DA-LSTM的超参数优化，以增强模型预测性能。模型预测结果证明了DA-LSTM（R²  > 0.99）在转化率-LSTM（0.531≤[R ²  ≤0.884）。敏感性分析为DA-LSTM的注意力机制如何使用模型输入和输出参数的时间序列数据进行预测提供了直接的解释。