Research On The Applications Of Neural Dynamic Programming In Temperature Control Of Cement Decomposing Furnace

In cement manufacturing, pre-calcining process (PCP) technology is an added decomposing furnace between the preheater and the kiln. Since raw meal decomposition rate in the kiln is over 90%, optimal control is the necessary and sufficient condition for keeping temperature stable to ensure normal decomposition.Due to the diversity of raw meal composition and the consistent thermal transformation, temperature control in the decomposing furnace is a complex nonlinear control object. Many factors influence it, and coupling between various factors and uncertainties, it is difficult to solve the dynamic stability control problem for the temperature in decomposing furnace.Neural Dynamic Programming (NDP) is an on line control approach that based on actual data not with the exact mathematical model. This paper addresses temperature control in the decomposing furnace using dual heuristic dynamic programming (DHP) which a method of NDP. The approach is mainly composed of three parts: model network (MODEL), action network (ACTION) and critic network (CRITIC).Firstly, we establish the neural network model (NNM) of temperature control of decomposing furnace by the method of mechanism and neural network. Then, put NNM as a structure of the training module MODEL in DHP. Next follow training critic net and action net. All three parts use BP networks. Temperature changes Control with Function approximation methods. Finally, we compared to the real-time PID control in actual fieldwork, Simulation PID control and BP neural network control. Demonstrate the advantages of the NDP in control.