[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123336-en":3,"doc-seo-123336-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123336,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning and genetic algorithm for effluent quality optimization in wastewater treatment","This paper integrates a genetic algorithm with a fine tree machine learning model to optimize wastewater effluent quality by tuning both controller parameters and operating setpoints. The study targets the Benchmark Simulation Model no.1 (BSM1), where weather and load uncertainties make proportional-integral (PI) tuning difficult and where pollutant constraints must be respected. Thirty rain and storm scenarios expand the testbed, and the fine tree model quantifies effects of key parameters on the effluent quality index and constraint-violation time. Representative designs are iteratively evaluated on BSM1 with surrogate model updating until the computational budget ends. The proposed method outperforms conventional Bayesian optimization and improves effluent quality by 6–43 kg pollution unit per day.","Journal of Water Process Engineering 71 (2025) 107294  \nContents lists available at ScienceDirect  \nJournal of Water Process Engineering  \njournal [homepage: www.elsevier.com/locate/jwpe](homepage: www.elsevier.com/locate/jwpe)  \n| Machine learning and genetic algorithm for effluent quality optimization in   wastewater treatment\u003Cbr>Chengyan Yea, Thu Thao Thi Tran b, Yu Yanga,*\u003Cbr>a Department of Chemical Engineering, California State University Long Beach, Long Beach, CA 90840, USA\u003Cbr>b Department of Chemical Engineering, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901, USA |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Editor: Guangming Jiang |  | This paper integrates the genetic algorithm (GA) with a fine tree machine learning model to simultaneously tune controller and setpoint for the effluent quality optimization while keeping the constraint violation time within an acceptable level for wastewater treatment Benchmark Simulation Model no.1 (BSM1). The weather and load uncertainties complicate the proportional-integral (PI) controller tuning in the BSM1 simulator. Conventional tuning approaches based on step test cannot take all possible weather scenarios into account, struggle with pollutant constraints, and fail to capture complex dynamics of the wastewater treatment process. Our approach resolves these issues by first generating 30 rain and storm scenarios with varying influent flow rates and duration for BSM1 to expand this testbed. Then, a fine tree model is developed to quantify the impact of integral constant, gain, anti-windup constant, dissolved oxygen and nitrate setpoints, on the effluent quality index and constraint violation. The GA is applied to yield a pool of potential solutions based on the machine learning model to optimize the effluent quality index subject to violation time constraint. We select representative designs from the solution pool by balancing the exploitation and exploration, then evaluate them on the BSM1 for surrogate model updating. This iterative process is repeated until the computational budget is reached. We compare the proposed approach with conventional Bayesian optimization to show its superiority in identifying the high-quality solution for BSM1 control system and improve the effluent quality by 6–43 kg pollution unit per day. |\n| Keywords:\u003Cbr>Benchmark simulation model no.1 Activated sludge process\u003Cbr>Genetic algorithm\u003Cbr>Regression model |  |  |\n\n1. Introduction  \nWastewater treatment plants (WWTPs) are non-linear and largescale systems that experience substantial variations in influent flow rates and pollutant loads, coupled with uncertainties regarding the composition of incoming wastewater. Numerous control strategies have been proposed in the literature for WWTPs [1–4], but evaluating and comparing their economic performance and robustness is challenging. This difficulty arises from the variability of influent, the complexity of underlying physical and biochemical processes, and the large time constants in the activated sludge process. Additionally, deploying, tuning, and maintaining control system in realistic WWTPs is often expensive and time-consuming. Hence, benchmark simulators, such asthe Benchmark Simulation Model No. 1 (BSM1), serve as an efficient tool to rapidly and fairly evaluate different control strategies [5]. The controller parameters of WWTPs need to be tuned to reject serious disturbances in practical applications. The default controller and setpoint in BSM1 designed for dry weather condition may not be suitable to rain  \nand storm weathers. When such wet-weather conditions occur, the influent flow rate, chemical oxygen demand (COD), ammonium, and total suspended solids (TSS) will vary significantly [6,7], and thus the WWTP’s controller and setpoint should be adaptively adjusted. Although we focus on the simulation study, the resulting synthetic data, controller parameters, and setpoint provide a fo","cbCaijHTzrIoB76z","https://ap.wps.com/l/cbCaijHTzrIoB76z","pdf",2006665,1,11,"English","en",105,"# Abstract\n# Introduction\n## Challenges in wastewater control under weather uncertainty\n## Role of BSM1 benchmark simulation model\n## Need for adaptive controller and setpoint tuning\n## Related work on PI/PID, stochastic optimization, and MPC","[{\"question\":\"What problem does the paper address in wastewater treatment?\",\"answer\":\"It addresses effluent quality optimization in wastewater treatment under weather and load uncertainties, where conventional controller tuning struggles to meet pollutant constraints.\"},{\"question\":\"How does the proposed method use genetic algorithms and machine learning together?\",\"answer\":\"It uses a fine tree machine learning model to quantify parameter impacts, then applies a genetic algorithm to generate candidate solutions that optimize the effluent quality index while controlling constraint-violation time.\"},{\"question\":\"What benchmark and scenario setup are used to evaluate the approach?\",\"answer\":\"The approach is evaluated on the Benchmark Simulation Model no.1 (BSM1) using 30 rain and storm scenarios with varying influent flow rates and durations, with iterative surrogate model updating until the computation budget is reached.\"}]","Machine learning and genetic algorithm for effluent quality optimization in wastewater treatment | 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problem does the paper address in wastewater treatment?","Question",{"text":75,"@type":76},"It addresses effluent quality optimization in wastewater treatment under weather and load uncertainties, where conventional controller tuning struggles to meet pollutant constraints.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method use genetic algorithms and machine learning together?",{"text":80,"@type":76},"It uses a fine tree machine learning model to quantify parameter impacts, then applies a genetic algorithm to generate candidate solutions that optimize the effluent quality index while controlling constraint-violation time.",{"name":82,"@type":73,"acceptedAnswer":83},"What benchmark and scenario setup are used to evaluate the approach?",{"text":84,"@type":76},"The approach is evaluated on the Benchmark Simulation Model no.1 (BSM1) using 30 rain and storm scenarios with varying influent flow rates and durations, with iterative surrogate model updating until 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