| 摘要: |
| 在“双碳”战略背景下，膜生物反
应器（Membrane Bioreactor，MBR）工艺因
膜污染和高能耗问题，制约污水处理厂的绿
色低碳转型。本研究基于智慧水务框架，集
成机器学习与多目标优化方法，构建了膜污
染智能预测与碳排放精准核算系统，并开发
了膜污染预警平台。该平台具备数据自动处
理、模型自主更新及动态预警功能，碳排放
核算系统则基于排放因子法建立独立计算体
系。经过273天实际试运行，预警平台绝大多
数模型精度高于0.7，表现出良好的预测可靠
性。通过非支配排序遗传算法（Nondominated
Sorting Genetic Algorithm II，
NSGA-II）解析表明，碳排放量、出水水质与
运行成本之间存在多重权衡关系。核算结果
显示，系统碳排放强度为0.9357 kg CO2/m3，
电力消耗为主要控碳环节。本研究为污水处
理系统实现减污降碳协同优化提供了有效的
智能决策支持与技术工具。 |
| 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 |
| DOI：10.13791/j.cnki.hsfwest.20250918002 |
| 分类号: |
| 基金项目:国家自然科学基金项目（52530003） |
|
| Study on a pollution early-warning and carbon management platform for membrane-basedwater treatment systems under the “Dual-Carbon” goals |
|
LIU Hongxia,TAN Yao,HAN Zhengpeng,LI Peng,HUANGFU Xiaoliu
|
| 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. |
| Key words: membrane fouling early-warning carbon emission accounting multi-objective optimization machine learning smart water management |
