Data-driven predictive control of oxygen content in flue gas for municipal solid waste incineration process
SUN Jian
MENG Xi
QIAO Jun-fei
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.
Keywords：municipal solid waste incinerationoxygen content in flue gas controlmodel predictive controlself-organizing long-short term memory network
Publication Date：2024-03-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 484-495 )