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基于活性污泥数学模型(ASM_s)的污水处理系统不确定性分析研究进展 被引量:6

Research and development on uncertainty analysis in wastewater treatment system based on activated sludge model(ASM_s)
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摘要 活性污泥数学模型(ASM_s)在应用过程中由于未能充分考虑污水处理系统的不确定性而降低了其可靠性和决策的准确性,目前国内外对基于ASM_s的不确定性分析尚处于研究的初级阶段。本文概述了不确定性分析的两种常用方法,介绍了污水处理系统中基于ASM_s的不确定性源的识别与分类、不确定性指标量化的研究现状,以及不确定性分析在污水处理厂优化设计、工艺改造等方面的应用。指出不确定性源的识别与分类目前仍然没有规范统一的分类机制和识别方法,不确定性指标的量化是模型应用的关键环节,尚需开展深入研究和分析;污水处理系统的不确定性分析将是今后ASM_s应用和发展的研究重点,不确定性分析可以帮助研究者更好地了解和把握污水处理系统模拟预测结果的不确定性范围,从而可以进行有效的风险评估和提高决策支持过程。 For complex wastewater treatment systems,uncertainty is their basic attribute.Due to not fully consider the actual system uncertainty,the reliability and decision accuracy of Activated sludge model(ASMs)in the application process was decreased.The complete model application should include the uncertainty analysis of the model.At present,the uncertainty analysis based on ASMs is still in its infancy.Based on the limited literature data,the research status on the identification and classification of uncertainty sources in wastewater treatment system and the quantification of model uncertainty index were analyzed.the application of uncertainty analysis in optimization design,upgrading and control strategy evaluation of wastewater treatment plant were introduced.It was pointed out that there is still no standard classification and identification of uncertainty sources.The quantification of uncertainty index needs further study and analysis.The uncertainty analysis of wastewater treatment system will be the focus of future research and application development of ASMs.Uncertainty analysis can help researchers better understand and grasp the uncertainty range of the prediction results,which can perform risk assessment and improve decision support process effectively.
出处 《化工进展》 EI CAS CSCD 北大核心 2017年第12期4651-4657,共7页 Chemical Industry and Engineering Progress
基金 江西省教育厅科学技术研究项目(GJJ150662) 江西理工大学博士启动基金项目(3401223198)
关键词 废水 活性污泥数学模型 不确定性分析 参数识别 蒙特卡罗模拟 wastewater activated sludge model(ASMs) uncertainty analysis parameter identification Monte Carlo simulation
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