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

中文关键词  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

英文关键词  municipal solid waste incineration  oxygen content in flue gas control  model predictive control  selforganizing long-short term memory network

中文摘要

      烟气含氧量的精准控制对城市固废焚烧处理厂的稳定高效运行具有重要意义. 然而, 由于固废焚烧过程固 有的非线性和不确定性, 难以实现烟气含氧量的有效控制. 为此, 文中提出一种数据驱动的城市固废焚烧过程烟气 含氧量预测控制方法. 首先, 设计了一种基于自组织长短期记忆(SOLSTM)网络的预测模型, 结合神经元活跃度与 显著性动态调整隐含层结构, 提高了烟气含氧量的预测精度. 其次, 为了保证优化效率, 利用梯度下降法求解控制 律. 此外, 基于李雅普诺夫理论分析了所提方法的稳定性, 确保控制器在实际应用过程中的可靠性. 最后, 基于实际 工业数据对所提出的控制方法进行了验证, 结果表明, 提出的数据驱动预测控制方法能实现对城市固废焚烧过程烟 气含氧量的稳定高效控制.

英文摘要

      The accurate control of oxygen content in flue gas is of great significance to the stable and efficient operation 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 proposed 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.