{"title":"利用可解释的机器学习方法优化集碳、氮、磷和硫生物转化于一体的新型工程生态系统,以处理含盐废水。","authors":"Jinqi Jiang, Xiang Xiang, Qinhao Zhou, Lichang Zhou, Xinqi Bi, Samir Kumar Khanal, Zongping Wang, Guanghao Chen, Gang Guo","doi":"10.1021/acs.est.4c03160","DOIUrl":null,"url":null,"abstract":"<p><p>The denitrifying sulfur (S) conversion-associated enhanced biological phosphorus removal (DS-EBPR) process for treating saline wastewater is characterized by its unique microbial ecology that integrates carbon (C), nitrogen (N), phosphorus (P), and S biotransformation. However, operational instability arises due to the numerous parameters and intricates bacterial interactions. This study introduces a two-stage interpretable machine learning approach to predict S conversion-driven P removal efficiency and optimize DS-EBPR process. Stage one utilized the XGBoost regression model, achieving an <i>R</i><sup>2</sup> value of 0.948 for predicting sulfate reduction (SR) intensity from anaerobic parameters with feature engineering. Stage two involved the CatBoost classification and regression model integrating anoxic parameters with the predicted SR values for predicting P removal, reaching an accuracy of 94% and an <i>R</i><sup>2</sup> value of 0.93, respectively. This study identified key environmental factors, including SR intensity (20-45 mg S/L), influent P concentration (<9.0 mg P/L), mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio (0.55-0.72), influent C/S ratio (0.5-1.0), anoxic reaction time (5-6 h), and MLSS concentration (>6.50 g/L). A user-friendly graphic interface was developed to facilitate easier optimization and control. 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引用次数: 0
摘要
用于处理含盐废水的反硝化硫(S)转化相关强化生物除磷(DS-EBPR)工艺的特点是其独特的微生物生态学,它将碳(C)、氮(N)、磷(P)和 S 的生物转化融为一体。然而,由于参数繁多,细菌相互作用错综复杂,导致运行不稳定。本研究采用两阶段可解释的机器学习方法来预测 S 转化驱动的 P 去除效率,并优化 DS-EBPR 过程。第一阶段利用 XGBoost 回归模型,通过特征工程从厌氧参数预测硫酸盐还原(SR)强度的 R2 值达到 0.948。第二阶段采用 CatBoost 分类和回归模型,将缺氧参数与预测的 SR 值进行整合,以预测 P 的去除率,准确率分别达到 94% 和 0.93 的 R2 值。这项研究确定了关键的环境因素,包括 SR 强度(20-45 毫克 S/L)、进水 P 浓度(6.50 克/升)。为便于优化和控制,开发了一个用户友好型图形界面。这种方法简化了确定在 DS-EBPR 过程中提高 P 去除率的最佳条件的过程。
Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach.
The denitrifying sulfur (S) conversion-associated enhanced biological phosphorus removal (DS-EBPR) process for treating saline wastewater is characterized by its unique microbial ecology that integrates carbon (C), nitrogen (N), phosphorus (P), and S biotransformation. However, operational instability arises due to the numerous parameters and intricates bacterial interactions. This study introduces a two-stage interpretable machine learning approach to predict S conversion-driven P removal efficiency and optimize DS-EBPR process. Stage one utilized the XGBoost regression model, achieving an R2 value of 0.948 for predicting sulfate reduction (SR) intensity from anaerobic parameters with feature engineering. Stage two involved the CatBoost classification and regression model integrating anoxic parameters with the predicted SR values for predicting P removal, reaching an accuracy of 94% and an R2 value of 0.93, respectively. This study identified key environmental factors, including SR intensity (20-45 mg S/L), influent P concentration (<9.0 mg P/L), mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio (0.55-0.72), influent C/S ratio (0.5-1.0), anoxic reaction time (5-6 h), and MLSS concentration (>6.50 g/L). A user-friendly graphic interface was developed to facilitate easier optimization and control. This approach streamlines the determination of optimal conditions for enhancing P removal in the DS-EBPR process.
期刊介绍:
Environmental Science & Technology (ES&T) is a co-sponsored academic and technical magazine by the Hubei Provincial Environmental Protection Bureau and the Hubei Provincial Academy of Environmental Sciences.
Environmental Science & Technology (ES&T) holds the status of Chinese core journals, scientific papers source journals of China, Chinese Science Citation Database source journals, and Chinese Academic Journal Comprehensive Evaluation Database source journals. This publication focuses on the academic field of environmental protection, featuring articles related to environmental protection and technical advancements.