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针对燃料热值控制的非线性、大滞后特性,该文提出了一种用于燃用高炉煤气联合循环发电系统的燃料热值优化控制方法,分析了引起混合燃料热值波动的不确定扰动因素的影响。基于混合燃料热值的神经网络预测模型,并结合实际工况和专家经验,建立了基于智能预测模型的模糊专家控制器,实现对燃料热值的优化控制。仿真结果表明提出的智能优化控制方法能有效减少混合燃料热值的波动,实际热值保持在设计波动范围3.165~3.245 MJ/m~3之间的比率为100%,具有一定的实际工业应用价值。
Aiming at the nonlinear and large hysteresis characteristics of fuel calorific value control, an optimal control method of fuel calorific value for coal gasification combined cycle power generation system is proposed in this paper. The uncertain disturbance factors causing calorific value fluctuations of mixed fuel are analyzed. Impact. Based on the neural network prediction model of the heating value of mixed fuel and the actual working conditions and expert experience, a fuzzy expert controller based on intelligent predictive model is established to optimize the control of fuel heating value. The simulation results show that the proposed intelligent optimization control method can effectively reduce the fluctuation of the heating value of the mixed fuel, and the actual heating value is kept within 100% of the designed fluctuation range of 3.165 ~ 3.245 MJ / m ~ 3, which has certain practical industrial application value .