西部人居环境学刊2025，Vol.40Issue(5)：16-24,9.DOI:10.13791/j.cnki.hsfwest.20250918002
面向"双碳"目标的膜法水处理系统污染预警与碳管控平台研究
Study on a pollution early-warning and carbon management platform for membrane-based water treatment systems under the"Dual-Carbon"goals
摘要
Abstract
Under"Dual Carbon"strategic goals,the membrane bioreactor(MBR)process encounters substantial challenges related to membrane fouling and high energy consumption,which significantly impede the green transformation of wastewater treatment plants.This research develops an intelligent early-warning platform integrating membrane fouling prediction and carbon emission accounting within a smart water management framework.The study employs advanced machine learning algorithms,including Random Forest(RF),Gradient Boosting Decision Tree(GBDT),Light Gradient Boosting Machine(LightGBM),and Multilayer Perceptron(MLP),to establish accurate prediction models.Feature selection is conducted using Recursive Feature Elimination with Cross-Validation(RFECV),identifying seven critical parameters strongly correlated with membrane fouling:transmembrane pressure(TMP),water temperature,operational duration,oxidation-reduction potential(ORP)in anaerobic tanks,mixed liquor suspended solids(MLSS)in aerobic tanks,permeate flow rate,and membrane scouring aeration intensity.The platform architecture comprises three integrated layers:a perception layer for real-time monitoring of 31 operational parameters encompassing influent quality indicators(COD,NH3-N,TN,TP),biological tank conditions(dissolved oxygen,pH,ORP),membrane tank status(TMP,flux,aeration intensity),and effluent quality parameters;a platform layer responsible for sophisticated data processing,advanced model computation,and continuous system optimization;and an application layer providing comprehensive visualization interfaces,dynamic early-warning functionalities,and practical decision-support tools.The carbon accounting system adopts a rigorously standardized emission factor-based methodology with clearly defined boundaries that encompass direct emissions from microbial degradation processes(N2O and CH4)and indirect emissions derived from electricity consumption,chemical usage,and other operational inputs,strictly following the technical guidelines for carbon accounting and emission reduction pathways in urban water systems to ensure methodological consistency and computational accuracy.Through a comprehensive 273-day field validation conducted at a full-scale wastewater treatment plant in Hebei Province,China,the platform demonstrated exceptional performance robustness and operational reliability.The majority of prediction models achieved accuracy scores exceeding 0.7,with the LightGBM model exhibiting particularly outstanding performance metrics:R2=0.992,RMSE=0.016,and MAE=0.010,while maintaining superior computational efficiency with prediction times of only 0.1-0.3 seconds per computation.Carbon emission analysis revealed a system carbon intensity of 0.9357 kg CO2/m3,with electricity consumption identified as the predominant contributing factor accounting for 63.9％of total emissions,followed by chemical consumption(18.2％)and direct emissions(14.1％).The application of the NSGA-II multi-objective optimization algorithm elucidated complex trade-off relationships among three key objectives:carbon emissions,effluent quality standards,and operational expenditures.Detailed analysis demonstrated that improving effluent quality by 1.3％would necessitate a 3.4％increase in carbon emissions coupled with a 5.3％rise in operational costs,highlighting the critical importance of balanced decision-making and optimized operational strategies in practical wastewater treatment applications.The platform incorporates several innovative features including automated data processing capabilities handling 31,680 validated data points with comprehensive preprocessing protocols for outlier detection and missing value imputation,self-updating models that maintain prediction accuracy through continuous machine learning adaptation,and dynamic early-warning systems that support proactive maintenance decisions and operational adjustments.The system successfully addressed various practical challenges including data transmission interruptions and abnormal value occurrences through improved data cleaning strategies,such as optimizing the transmembrane pressure threshold from 80 kPa to 110 kPa to enhance data integrity and processing accuracy.While the carbon accounting module demonstrated significant practical utility in identifying emission hotspots and supporting carbon reduction strategies,further development is required to achieve full automation,particularly in integrating real-time electricity consumption monitoring and chemical dosage tracking systems.This research establishes a comprehensive technical framework for achieving synergistic pollution control and carbon emission reduction in wastewater treatment systems,providing both innovative machine learning methodologies and valuable practical implementation experience for the industry's low-carbon transition.The study presents an effective approach for intelligent decision support in wastewater treatment management,contributing substantially to the achievement of Dual Carbon goals through technological innovation in membrane bioreactor operations.Future research directions will focus on expanding carbon accounting boundaries to include sludge treatment processes and equipment embedded carbon,enhancing model adaptability to extreme operating conditions such as low-temperature environments,developing integration frameworks with urban drainage systems and sponge city infrastructure,and advancing multi-scale carbon management strategies from process-level optimization to system-wide sustainability assessment.关键词
膜污染预警/碳排放核算/多目标优化/机器学习/智慧水务Key words
membrane fouling early-warning/carbon emission accounting/multi-objective optimization/machine learning/smart water management分类
土木建筑引用本文复制引用
刘鸿霞,谭瑶,韩正朋,李鹏,皇甫小留..面向"双碳"目标的膜法水处理系统污染预警与碳管控平台研究[J].西部人居环境学刊,2025,40(5):16-24,9.基金项目
国家自然科学基金项目(52530003) （52530003）
