Afan Haitham Abdulmohsin, Melini Wan Mohtar Wan Hanna, Khaleel Faidhalrahman, Kamel Ammar Hatem, Mansoor Saif Saad, Alsultani Riyadh, Ahmed Ali Najah, Sherif Mohsen, El-Shafie Ahmed
Upper Euphrates Basin Developing Center, University of Anbar, Iraq.
Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia.
Heliyon. 2024 Aug 28;10(18):e36940. doi: 10.1016/j.heliyon.2024.e36940. eCollection 2024 Sep 30.
Monitoring and managing wastewater treatment plants (WWTPs) is crucial for environmental protection. The presection of the quality of treated water is essential for energy efficient operation. The current research presents a comprehensive comparison of machine learning models for water quality parameter prediction in WWTPs. Four machine learning models presented in MLP, GFFR, MLP-PCA, and RBF were employed in this study. The primary notion of this study is to apply the proposed models using two distinct modeling scenarios. The first scenario represents a straightforward approach by utilizing the inputs and outputs of WWTPs; meanwhile, the second scenario involves using multi-step modeling techniques, which incorporate intermediate outputs induced by primary and secondary settlers. The study also investigates the potential of the adopted models to handle high dimensional data as a result of the multi-step modeling since more data points and outputs are progressively integrated at each step. The results show that the GFFR model outperforms the other models across both scenarios, specifically in the second scenario in predicting conductivity (COND) by providing higher correlation accuracy (R = 0.893) and lower prediction deviations (NRMSE = 0.091 and NMAE = 0.071). However, all models across both scenarios struggle to predict the other water quality parameters, generating significantly lower prediction correlations and higher prediction deviations. Nonetheless, the innovative multi-step technique in scenario two has significantly boosted the prediction capacity of all models, with improvement ranging from 0.2 % to 157 % and an average of 60 %. The implementation of AI models has proven its ability to accomplish high accuracy for WQ parameter prediction, highlighting the impact of leveraging intermediate process data.
监测和管理污水处理厂对环境保护至关重要。对处理后水质的预判对于高效节能运行至关重要。当前的研究全面比较了用于污水处理厂水质参数预测的机器学习模型。本研究采用了多层感知器（MLP）、广义回归森林（GFFR）、多层感知器 - 主成分分析（MLP - PCA）和径向基函数（RBF）这四种机器学习模型。本研究的主要理念是使用两种不同的建模场景来应用所提出的模型。第一种场景是一种直接的方法，利用污水处理厂的输入和输出；同时，第二种场景涉及使用多步建模技术，该技术纳入了初次和二次沉淀池产生的中间输出。该研究还探讨了由于多步建模导致采用的模型处理高维数据的潜力，因为在每个步骤中会逐渐整合更多的数据点和输出。结果表明，在两种场景下，GFFR模型均优于其他模型，特别是在第二种场景中预测电导率（COND）时，具有更高的相关精度（R = 0.893）和更低的预测偏差（归一化均方根误差NRMSE = 0.091和归一化平均绝对误差NMAE = 0.071）。然而，在两种场景下，所有模型在预测其他水质参数时都存在困难，产生的预测相关性显著较低且预测偏差较高。尽管如此，第二种场景中的创新多步技术显著提高了所有模型的预测能力，提升幅度从0.2%到157%，平均为60%。人工智能模型的实施已证明其能够实现水质参数预测的高精度，突出了利用中间过程数据的影响。
