Towards better process management in wastewater treatment plants : Process analytics based on SHAP values for tree-based machine learning methods
| Autor: | Mats Tysklind, Lili Jiang, Dong Wang, Ulrika Lindberg, Sven Thunéll, Johan Trygg |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Environmental Engineering
Mean squared error Process (engineering) Computer science media_common.quotation_subject Wastewater treatment Management Monitoring Policy and Law Overfitting Machine learning computer.software_genre Water Purification Interpretable AI Machine Learning Feature (machine learning) Quality (business) Waste Management and Disposal SHapley additive exPlanations Total suspended solids media_common Sweden business.industry General Medicine Miljövetenskap Process analytics Random forest Tree (data structure) Artificial intelligence business computer Environmental Sciences |
| Popis: | Understanding the mechanisms of pollutant removal in Wastewater Treatment Plants (WWTPs) is crucial for controlling effluent quality efficiently. However, the numerous treatment units, operational factors, and the underlying interactions between these units and factors usually obfuscate the comprehensive and precise understanding of the processes. We have previously proposed a machine learning (ML) framework to uncover complex cause-and-effect relationships in WWTPs. However, only one interpretable ML model, Random forest (RF), was studied and the interpretation method was not granular enough to reveal very detailed relationships between operational factors and effluent parameters. Thus, in this paper, we present an upgraded framework involving three interpretable tree-based models (RF, XGboost and LightGBM), three metrics (R2, Root mean squared error (RMSE), and Mean absolute error (MAE)) and a more advanced interpretation system SHapley Additive exPlanations (SHAP). Details of the framework are provided along with a demonstration of its practical applicability based on a case study of the Umeå WWTP in Sweden. Results show that, for both labels TSSe (Total suspended solids in effluent) and PO4e (Phosphate in effluent), the XGBoost models are optimal whereas the RF models are the least optimal, due to overfitting and polarized fitting. This study has yielded multiple new and significant findings with respect to the control of TSSe and PO4e in the Umeå WWTP and other similarly configured WWTPs. Additionally, this study has produced two important generic findings relating to ML applications for WWTPs (or even other process industries) in terms of cause-and-effect investigations. First, the model comparison should be carried out from multiple perspectives to ensure that underlying details are fully revealed and examined. Second, using a precise, robust, and granular (feature attribution available for individual instances) explanation method can bring extra insight into both model comparison and model interpretation. SHAP is recommended as we found it to be of great value in this study. EcoChange |
| Databáze: | OpenAIRE |
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