| 引用本文: | 高梦泽,郁达伟,郑利兵,魏源送.基于多头自注意力机制模型的再生水厂泥线预测[J].哈尔滨工业大学学报,2026,58(6):90.DOI:10.11918/202512179 |
| GAO Mengze,YU Dawei,ZHENG Libing,WEI Yuansong.Prediction of sludge line in wastewater reclamation plant based on 〖JZmulti-head self-attention mechanism model[J].Journal of Harbin Institute of Technology,2026,58(6):90.DOI:10.11918/202512179 |
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| 基于多头自注意力机制模型的再生水厂泥线预测 |
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高梦泽1,2,3,郁达伟1,2,3,4,郑利兵1,2,3,魏源送1,2,3
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(1.区域环境安全全国重点实验室环境水质学重点实验室(中国科学院生态环境研究中心),北京 100085; 2.水污染控制实验室(中国科学院生态环境研究中心),北京 100085; 3.中国科学院大学 资源与环境学院, 北京 100085; 4.国家黄河流域生态保护和高质量发展联合研究中心,北京 100012)
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| 摘要: |
| 再生水厂泥线在实际工程中存在参数敏感性高、漂移非线性、时滞性大等问题,给泥线的运行调度带来较大的不确定性,对减污降碳协同能力形成制约。传统机理模型预测需要大量、高质量且标注规范的输入数据,难以对泥线参数进行准确、高效的预测。为增强再生水厂泥线的减污降碳协同,以北京某再生水厂水线、泥线运行数据为基础,采用经验模型基线残差预测的混合建模策略,构建基于多头自注意力机制（Transformer）的预测模型,先通过经验模型线性拟合生成基线,再利用Transformer模型对残差进行精准预测,以水线进出水水质指标和运行参数为输入,同步预测脱水污泥产量和污泥含水率,并与LightGBM、块状循环神经网络BlockRNN(LSTM)、时域卷积网络（TCN）模型进行性能对比。结果表明:依托多头自注意力机制的Transformer模型的全局时序关联捕捉能力,可有效适配泥线运行参数的非线性波动特征,综合性能最优,脱水污泥产量预测 R2达0.778 8,污泥含水率预测R2达0.679,均显著高于各对比模型,同时回测机制可提升模型的运行稳定性,持续优化模型预测精度,可适配实际工程中长时序预测场景。该模型可实现泥线关键参数的高精度预测,为再生水厂泥线工艺实时调控、药剂精准投加及设备智能调度等场景提供可靠技术支撑,助力泥线减污降碳协同治理。 |
| 关键词: 再生水厂 泥线 多头自注意力 预测模型 运行优化 |
| DOI：10.11918/202512179 |
| 分类号:X703 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2022YFC3203102)；黄河流域生态保护和高质量发展联合研究项目 |
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| Prediction of sludge line in wastewater reclamation plant based on 〖JZmulti-head self-attention mechanism model |
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GAO Mengze1,2,3,YU Dawei1,2,3,4,ZHENG Libing1,2,3,WEI Yuansong1,2,3
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(1.State Key Laboratory of Regional Environment and Sustainability (Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences), Beijing 100085, China; 2.Laboratory of Water Pollution Control (Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences), Beijing 100085, China; 3.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100085, China; 4.National Joint Research Center for Ecological Conservation and High Quality Development of the Yellow River Basin, Beijing 100012, China)
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| Abstract: |
| In practical engineering, the sludge line of wastewater reclamation plants faces challenges including high parameter sensitivity, drift nonlinearity, and significant time lag, which introduce considerable uncertainty into the operation and scheduling of the sludge line and restrict the synergistic capacity of pollution and carbon reduction. Conventional mechanism-driven models require a large amount of high-quality and well-annotated input data, making it difficult to predict sludge line parameters accurately and efficiently. To enhance the synergy of pollution and carbon reduction for the sludge line in wastewater reclamation plants, this paper established a prediction model based on the multi-head self-attention mechanism (Transformer) using a hybrid modeling strategy of "empirical model baseline and residual prediction", based on the operational data of the water line and sludge line of a wastewater reclamation plant in Beijing. Specifically, this paper first generated a baseline through linear fitting of an empirical model and then utilized the Transformer model to accurately predict the residual. Taking the inlet and outlet water quality indexes and operating parameters of the water line as inputs, the model simultaneously predicted the dewatered sludge output and sludge moisture content and compared its performance with LightGBM, block recurrent neural network BlockRNN (LSTM) and temporal convolutional network (TCN) models. The results indicate that relying on the global temporal correlation capture capability of the Transformer model based on multi-head self-attention mechanism, it can effectively adapt to the nonlinear fluctuation characteristics of sludge line operating parameters and achieve the optimal comprehensive performance: R2 of dewatered sludge output prediction reaches 0.778 8, and that of sludge moisture content prediction reaches 0.679, both of which are significantly higher than those of the comparative models. Meanwhile, the backtesting mechanism can improve the operational stability of the model, continuously enhance its prediction accuracy, and thus adapt to the long-term time series prediction scenarios in practical engineering. This model can realize high-precision prediction of key parameters in the sludge line, provide reliable technical support for scenarios such as real-time regulation of the sludge line process, precise chemical dosing, and intelligent equipment scheduling in wastewater reclamation plants, and facilitate the synergistic governance of pollution and carbon reduction for the sludge line. |
| Key words: wastewater reclamation plant sludge line Multi-Head Self-Attention prediction model operation optimization |
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