| 城市固废焚烧过程风量智能优化设定方法 |
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| 引用本文： | 崔莺莺,蒙西,乔俊飞.城市固废焚烧过程风量智能优化设定方法[J].控制与决策,2023,38(2):318-326. |
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| 作者姓名： | 崔莺莺 蒙西 乔俊飞 |
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| 作者单位： | 北京工业大学 信息学部,北京 100124;北京工业大学 智慧环保北京实验室,北京 100124;北京工业大学 计算智能与智能系统北京市重点实验室,北京 100124;北京工业大学 智能感知与自主控制教育部工程研究中心,北京 100124 |
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| 基金项目： | 国家自然科学基金项目(61890930-5,62021003,61903012,62073006). |
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| 摘 要： | 城市固体废物焚烧(municipal solid wastes incineration,MSWI)技术由于其高效的减容效果逐渐成为了生活垃圾处理的主要方式.MSWI过程产生的氮氧化物(nitrogen oxides,NOx)是大气中的主要污染物之一.为了在控制NOx排放的同时保证燃烧效率,提出一种基于多目标粒子群算法的MSWI过程风量智能优化设定方法.首先,结合最大相关最小冗余算法及前馈神经网络,建立燃烧效率和氮氧化物排放浓度预测模型;然后,提出分阶段多目标粒子群优化算法(staged multi-objective particle swarm optimization,SMOPSO),获得一次风流量和二次风流量的Pareto优化解集;此外,设计效用函数,确定一次风流量和二次风流量的最优设定值;最后,基于国内某城市固废焚烧厂的实际运行数据,验证所提方法的有效性.
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| 关 键 词： | 城市固体废物焚烧 氮氧化物 燃烧效率 风量智能优化设定 分阶段多目标粒子群优化 |
The intelligent optimization setting method of air flow for municipal solid wastes incineration process |
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| CUI Ying-ying,MENG Xi,QIAO Jun-fei.The intelligent optimization setting method of air flow for municipal solid wastes incineration process[J].Control and Decision,2023,38(2):318-326. |
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| Authors: | CUI Ying-ying MENG Xi QIAO Jun-fei |
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| Affiliation: | Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Laboratory of Smart Environmental Protection,Beijing University of Technology,Beijing 100124,China; Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Laboratory of Smart Environmental Protection,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing University of Technology,Beijing 100124,China;Engineering Research Center of Intelligent Perception and Autonomous Control,Ministry of Education,Beijing University of Technology,Beijing 100124,China |
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| Abstract: | Municipal solid waste incineration (MSWI) has gradually become the main technology of waste treatment because of its efficient capacity reduction. However, the nitrogen oxides (NOx) produced in the MSWI process are one of the main pollutants. In order to control NOx emissions while ensuring combustion efficiency, an intelligent optimization setting method of air flow for MSWI process based on multi-objective particle swarm optimization is proposed. Firstly, by the combined minimal-redundancy maximal-relevance criterion and the feedforward neural network, the prediction models of combustion efficiency and NOx emission are established. Then, an improved staged multi-objective particle swarm optimization algorithm (SMOPSO) is presented to obtain the Pareto optimal solutions of primary air flow and secondary air flow. In addition, the utility function is designed to determine the optimal setting value of the primary air flow and the secondary air flow. Finally, the simulation experiments verify the validity and feasibility of the proposed method based on the practical operation data. |
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