Resin Quality Robust Estimation And Control For High Impact Copolymerization In Gas Phase Process

In the industry of polypropylene, we always measure the performance of products through resin qualities. For the high impact polypropylene we are concerned about in our research, the melt index and the quality of ethylene fraction which in another word is impact strength is the target. For actual production, in order to decrease the influence in process and increase economic efficiency, it is necessary to do real-time precise control for melt index and ethylene fraction. However, it is a pity that the resin quality can’t be on-line measured, as a result we can not get the true value in time. In real-time application, the value of melt index and ethylene fraction are generally obtained by sample test in laboratory, but the test value has2to4hours delay for real-time production process. The absence of real-time polymer property information, large time delay and frequent grade transition could induce unstable production, even particle agglomeration and the process would be forced to shut down. Therefore, it is very important to develop the online polymer property estimator in industrial polyepropylene process. First of all for Spheripol process, considering complexity of real-time estimate, the predictive model of melt index and ethylene fraction which means the high impact ability is finally obtained. On the basis of a mount of actual production data, we can get the unknown parameters in the predictive model by the particle swarm optimization algorithm.Secondly, to guarantee the accuracy of model prediction, model calibration is needed. Considering the nonlinear of model and the continuity of online estimation, two methods of design the robust filtering are proposed to estimate states and updating factor simultaneously. The application result of the proposed method to an industrial gas-phase polypropylene process has verified its feasibility and effectiveness.Meanwhile, we use the model predictive control algorithm to control the polypropylene production process in real-time. In the results of simulation and real industry application by control method, it proves that the predictive model aftering robust filtering is highly accurate again. The application of the device has shown that it can minimize the product fluctuations, improve the quality product rate and increase the production rate.