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城市生活垃圾焚烧过程二次风量智能优化设定方法

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垃圾焚烧过程二次风量通常是依据人工经验设定，具有主观随意性，使污染物排放浓度不达标。针对此问题，提出一种二次风量智能优化设定方法。首先，建立二次风量的案例推理预设定模型、设定值的评价与学习模型;其次，建立工艺指标的随机配置网络预测模型;接着，建立基于径向基神经网络自学习模糊推理的智能补偿模型;最后，将二次风量预设定模型、工艺指标预测模型、智能补偿模型以及设定值的评价与学习模型有机集成，设计二次风量智能优化设定方法的结构与功能，并给出算法实现。采用某垃圾焚烧厂历史数据进行实验，结果表明，所提方法获得的二次风量设定值波动程度更小，按此设定值运行的控制系统可以减少污染物排放浓度，促进垃圾焚烧过程运行优化目标的实现。
Intelligent optimal setting method of secondary air flow in municipal solid waste incineration process
Aiming at the secondary air flow of waste incineration process are usually set according to manual experience,which is subjective and arbitrary,so that the pollutant emission concentration does not meet the standard,an intelligent optimal setting method is proposed.Firstly,a case-based reasoning pre-set model,and an evaluation and learning model of secondary air flow setpoint are constructed.Secondly,a stochastic configuration network process index prediction model is established.Then,an intelligent compensation model based on the RBF neural network self-learning fuzzy inference is constructed.Finally,the pre-set model,process index prediction model,intelligent compensation model and evaluation and learning model of setpoint are organically integrated,the structure and function of the intelligent optimal setting method are designed,and algorithm implementation is given.The experimental results based on historical data of a waste incineration plant show that the fluctuation degree of setpoint obtained by this method is less,and the control system running according to the setpoint can reduce the pollutant emission concentration,which can promote the realization of operation optimal goal in the incineration process.

waste incinerationoptimal settingcase-based reasoningstochastic configuration networkintelligent compensationevaluation and learning

丁晨曦、严爱军、王殿辉

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北京工业大学信息学部,北京 100124

数字社区教育部工程研究中心,北京 100124

城市轨道交通北京实验室,北京 100124

中国矿业大学人工智能研究院,江苏徐州 221116

东北大学流程工业综合自动化国家重点实验室,沈阳 110004

拉筹伯大学计算机科学与信息技术系,墨尔本VIC 3086

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垃圾焚烧 优化设定 案例推理 随机配置网络 智能补偿 评价与学习

国家自然科学基金项目国家自然科学基金项目北京市自然科学基金项目国家重点研发计划项目

618730096207300642120322018AAA010030

2024

10.13195/j.kzyjc.2022.0349

控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子：1.227
ISSN：1001-0920
年,卷(期)：2024.39(1)
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