神经网络预测控制在SCR烟气脱硝系统中应用

以自某热电厂350 MW燃煤机组的选择性催化还原(SCR)反应系统所采集的数据为依托,使用神经网络预测控制方法,研究电厂尾气中氮氧化物排放的预测及控制问题.利用神经网络的方法进行模型辨识,利用预测控制的思想对喷氨量进行控制,既可使尾气达到限排标准,亦能减少用氨量,提升经济效益的同时减少氨逃逸.采用最速梯度方法进行控制器的优化,并通过性能函数来约束控制量,达到预期输出.最后将仿真结果与现场所测数据进行对比,结果表明神经网络预测控制方案可以较准确地预测出未来有限时刻所需的喷氨量.

Application of Neural Network Predictive Control in SCR Flue Gas Denitration System

Based on the data collected from selective catalytic reduction (SCR) reaction system of a 350MW coal-fired unit in a thermal power plant， neural network predictive control method was used to study the prediction and control of nitrogen oxides emission in power plant tail gas. Firstly， the non-linear model of SCR denitrification system was modeled and nonlinear autoregressive model was used to estimate the model. Then， by using the predictive control method to control the ammonia injection， the tail gas could achieve the standard of discharge limitation， and the amount of ammonia and ammonia escape could also be reduced， resulting in the enhancement of economic efficiency. The controller was optimized by the steepest gradient method and the control variable was constrained by the performance function to achieve the expected output. Finally， compared with the measured date in the field， the simulation results show that the neural network predictive control scheme can predict the amount of ammonia sprayed in the future at a finite time.