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Yi Zhang,Yanjun Ding,Zhansong Wu,Liang Kong,Tao Chou 한국화학공학회 2007 Korean Journal of Chemical Engineering Vol.24 No.6
empirical model to predict the boiler efficiency and pollutant emissions was developed with artificialneural networks based on the experimental data on a 360 MW W-flame coal fired boiler. The temperature of the furnacewas selected as an intermediate variable in the hybrid model so that the predictive precision of NOx emissions was en-hanced. The predictive precision of the hybrid model was operational objects were proposed in order to minimize the fuel and environmental costs. Based on the neural networkmodel and optimal objects, a genetic algorithm was employed to seek real-time solution every 30 seconds. Optimummanipulated variables such as excess air, primary air and secondary air were obtained under different optimal objects.The above algorithm interconnected with a distributed control system (DCS) formed the supervisory control and achievedreal-time coordinated optimization control of utility boilers.
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Liang Kong,Yi Zhang,Lichuan Yuan,Zhansong Wu,Yanjun Ding 한국화학공학회 2009 Korean Journal of Chemical Engineering Vol.26 No.2
A hierarchical gain scheduling (HGS) approach is proposed to model the nonlinear dynamics of NOx emissions of a utility boiler. At the lower level of HGS, a nonlinear static model is used to schedule the static parameters of local linear dynamic models (LDMs), such as static gains and static operating conditions. According to upper level scheduling variables, a multi-model method is used to calculate the predictive output based on lower-level LDMs. Both static and dynamic experiments are carried out at a 360MW pulverized coal-fired boiler. Based on these data, a nonlinear static model using artificial neural network (ANN) and a series of linear dynamic models are obtained. Then, the performance of the HGS model is compared to the common multi-model in predicting NOx emissions, and experimental results indicate that the proposed HGS model is much better than the multi-model in predicting NOx emissions in the dynamic process.
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