控制理论与应用2024，Vol.41Issue(3)：484-495,12.DOI:10.7641/CTA.2023.20622
数据驱动的城市固废焚烧过程烟气含氧量预测控制
Data-driven predictive control of oxygen content in flue gas for municipal solid waste incineration process
摘要
Abstract
The accurate control of oxygen content in flue gas is of great significance to the stable and efficient op-eration of the municipal solid waste incineration plant.However,it is difficult to achieve effective control performance of oxygen content in flue gas due to the inherent nonlinearity and uncertainty of the municipal solid waste incineration process.Therefore,a data-driven predictive control scheme of oxygen content in flue gas is proposed for municipal solid waste incineration process.Firstly,the prediction model based on the self-organizing long short-term memory(SOLSTM)network is designed.The structure of the hidden layer is dynamically adjusted by integrating the activity and significance of neurons,and then the prediction accuracy of oxygen content in flue gas is improved.Secondly,the gradient descent method is utilized to obtain the control law,and the optimization efficiency is guaranteed.Thirdly,the stability of the pro-posed control scheme is analyzed based on the Lyapunov theory.Finally,the effectiveness of the proposed control method is verified based on the industrial data.Compared with other methods,the proposed method achieves stable and efficient control performance for oxygen content in flue gas.关键词
城市固废焚烧/烟气含氧量控制/模型预测控制/自组织长短期记忆网络Key words
municipal solid waste incineration/oxygen content in flue gas control/model predictive control/self-organizing long-short term memory network引用本文复制引用
孙剑,蒙西,乔俊飞..数据驱动的城市固废焚烧过程烟气含氧量预测控制[J].控制理论与应用,2024,41(3):484-495,12.基金项目
国家自然科学基金项目(61890930-5,62021003,62273013),科技创新2030-"新一代人工智能"重大项目(2021ZD0112301)资助.Supported by the National Natural Science Foundation of China(61890930-5,62021003,62273013)and the National Key Research and Development Program of China(2021ZD0112301). （61890930-5,62021003,62273013）
