| In Recent years, environment protection draws international attention. Volatile Organic Compounds (VOCS), as one of the major sources of air contaminations, can not only cause direct pollution, but also lead to secondary pollution, such as photochemistry smog. Thus, it is very important to control the VOCS in the air. In this area, the reverse flow catalytic combustion technique is a unique solution. The reversed flow catalytic combustion reactor highly integrates the catalytic combustion and the recovery of the reaction heat, which cut down the operation cost and save the investment. With the developing of the control theory and the computer application technique, it is highly demanded to apply advanced process control algorithm and optimal operation for the reversed-flow catalytic combustion process.The composition and flow rate of industrial waste gas may vary frequently. In this case, maintaining the reactor in normal operation (e.g. the reactor will not go into temperature runaway or extinguish state) and high VOCs conversion by adjusting the reversal cycle duration or gas flow is the ultimate goal of the advanced control application. The shape of the thermal wave and the characteristic parameters, such as the maximum of the catalyst bed temperature, the average temperature and the shifting speed, determine the performance of the catalytic reactor system, and also provide lots of information regarding the reaction and the heat flow. Thus, they are important guidelines to maintain the reversed flow reactor in normal operation and must be timely and accurately predicted and be affectively controlled. Aimed at a pilot scale reverse flow reactor for catalytic combustion of VOCS in contaminated air, the advanced monitoring system was designed and built, with rebuilding the former manual data-collections and control mode. The quasi-steady state model of temperature profile for the reversed flow reactor was developed by the improved RBFNN (Radial Basis Function Neural Networks). A valid approach was proposed to enhance the data integrity and orthogonality of the neural network's training samples. The RBFNN and the regressive least square method (RLS) with forget factor were adopted to develop the dynamic model for predicting temperature profiles. With developing a series of control experiments, a feedback control strategy for the flow reversal reactor was examined. The variable that was chosen to control average and maximum bed temperatures was the cycle period. A nonlinear internal model control (NIMC) strategy based on RBFNN was proposed by adjusting the gas flow, and the neural networks was trained online using RLS Algorithm, in order to realize adaptive control. The main conclusion may be summarized as follows:The controlling part of the monitoring system was mainly composed of industrial controlling computer, cards and the software written with MCGS configuration software. The system has been used in experimental environment, and the application of the system shows that the monitoring system is high reliability, real-time and intelligent.The application performance of Neural-network model is highly constrained by the selected training samples, it lacks the extropolability. The deep knowledge repository of temperature profile was yielded based on the determinant mathematical model, which increased the 'extrapolative ability' and 'reliability'. Simulation results have proved that the model presented in this paper is simple, and can satisfy the control requirements.In order to predict and control, A real-time prognosticate model of temperature profile for a reverse flow reactor was built based on dynamic RBFNN with online correcting model parameters. Simulation results have proved that the model is highly accurate and can meet real-time control demand.As an important means that assures normal operations, the cycle period can not only control the bed temperatures effectively, thus adjusting reactive capacity in a wide range, but also be altered easily.In the NIMC strategy proposed, t...
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