城市固废焚烧过程风量智能优化设定方法
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作者单位:

北京工业大学

作者简介:

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中图分类号:

TP273

基金项目:

国家自然科学基金项目(61890930-5, 62021003, 61903012, 62073006)


The Intelligent Optimization Setting Method of Air Flow for Municipal Solid Wastes Incineration Process
Author:
Affiliation:

Beijing University of Technology

Fund Project:

National Science Foundation of China(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. 然而, 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. 为了控制NOx 排放的同时保证燃烧效率, 文中提出了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 multiobjective 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, 验证了所提方法的有效性.

    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 multiobjective particle swarm optimization is proposed in this paper. Firstly, by combined minimalredundancy maximalrelevance criterion and feedforward neural network, the prediction models of combustion efficiency and NOx emission are established. Secondly, an improved staged multiobjective 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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历史
  • 收稿日期:2021-07-06
  • 最后修改日期:2021-10-18
  • 录用日期:2021-10-27
  • 在线发布日期: 2021-12-01
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