数据驱动的城市固废焚烧过程多变量自适应预测控制OA

To address the issue of difficulty in precisely controlling key process parameters such as furnace temper-ature and flue gas oxygen content in the municipal solid waste incineration process,this paper proposes a data-driv-en multivariable adaptive predictive control method.Firstly,by introducing operational variable constraints and designing a weighted barrier function,an unconstrained form of the objective function is constructed to reduce the complexity of the optimization solution.Secondly,a sparsified online Newton algorithm is proposed,which achieves efficient solution of the control law through sparse decomposition of the Hessian matrix.Meanwhile,a dynamic weighting coefficient allocation strategy for multivariable control is designed,which can adaptively adjust the weights of furnace temperature and flue gas oxygen content according to the actual operating state,further enhan-cing the control performance.Additionally,the feasibility and stability of the proposed control method are ana-lyzed to ensure its reliability in practical applications.Finally,the feasibility and effectiveness of the proposed con-trol method are verified using actual operation data from a municipal solid waste incineration plant.