Beijing Key Lab for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing, China.
Institute of Environment Engineering and Research, University of Engineering and Technology, Lahore, Pakistan.
Water Environ Res. 2024 Aug;96(8):e11099. doi: 10.1002/wer.11099.
In this study, we employed the response surface method (RSM) and the long short-term memory (LSTM) model to optimize operational parameters and predict chemical oxygen demand (COD) removal in the electrocoagulation-catalytic ozonation process (ECOP) for pharmaceutical wastewater treatment. Through RSM simulation, we quantified the effects of reaction time, ozone dose, current density, and catalyst packed rate on COD removal. Then, the optimal conditions for achieving a COD removal efficiency exceeding 50% were identified. After evaluating ECOP performance under optimized conditions, LSTM predicted COD removal (56.4%), close to real results (54.6%) with a 0.2% error. LSTM outperformed RSM in predictive capacity for COD removal. In response to the initial COD concentration and effluent discharge standards, intelligent adjustment of operating parameters becomes feasible, facilitating precise control of the ECOP performance based on this LSTM model. This intelligent control strategy holds promise for enhancing the efficiency of ECOP in real pharmaceutical wastewater treatment scenarios. PRACTITIONER POINTS: This study utilized the response surface method (RSM) and the long short-term memory (LSTM) model for pharmaceutical wastewater treatment optimization. LSTM predicted COD removal (56.4%) closely matched experimental results (54.6%), with a minimal error of 0.2%. LSTM demonstrated superior predictive capacity, enabling intelligent parameter adjustments for enhanced process control. Intelligent control strategy based on LSTM holds promise for improving electrocoagulation-catalytic ozonation process efficiency in pharmaceutical wastewater treatment.
在这项研究中，我们采用响应面法（RSM）和长短期记忆（LSTM）模型来优化操作参数，并预测电絮凝-催化臭氧氧化工艺（ECOP）处理制药废水的化学需氧量（COD）去除率。通过 RSM 模拟，我们量化了反应时间、臭氧剂量、电流密度和催化剂填充率对 COD 去除的影响。然后，确定了达到超过 50%COD 去除效率的最佳条件。在优化条件下评估 ECOP 性能后，LSTM 预测 COD 去除率（56.4%）接近实际结果（54.6%），误差为 0.2%。LSTM 在 COD 去除预测能力方面优于 RSM。针对初始 COD 浓度和出水排放标准，智能调整操作参数变得可行，基于此 LSTM 模型实现对 ECOP 性能的精确控制。这种智能控制策略有望提高 ECOP 在实际制药废水处理场景中的效率。
本研究利用响应面法（RSM）和长短期记忆（LSTM）模型进行制药废水处理优化。LSTM 预测的 COD 去除率（56.4%）与实验结果（54.6%）非常接近，误差仅为 0.2%。LSTM 表现出卓越的预测能力，能够进行智能参数调整，以增强过程控制。基于 LSTM 的智能控制策略有望提高电絮凝-催化臭氧氧化工艺在制药废水处理中的效率。
