Some Studies Of Process Control And Optimization Based On Intelligent Computation

Intelligent computation has a long history. Many of the theories have been put into successful applications. With the development of intelligent computation, classical intelligent computation combines other biological theories of the life science, which makes the intelligent computation have big progress and hence leads to form the modern intelligent computation theories. Now there are many new intelligent tools in the modern intelligent computation region such as support vector machines, kernel method, particle swarm optimization algorithm and iterative learning control theory. In the article, some of them are applied for the process control and optimization and hence acquired successful achievements.The main original points of this paper lie below:1. A run-to-run method is presented for the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because Support Vector Machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models are developed for end-point optimal control of batch processes. To reach the desired end-point properties, a run-to-run control method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy. Quadratic programming (QP) is employed to solve the optimal control problem. The run-to-run control method is proved convergent and robust even when model mismatches and disturbances exist. Therefore the run-to-run optimal control method based on support vector regression model is a comprehensive method which sufficiently utilizes the intelligence of support vector machine's modeling and the characters of the run-to-run control that can move away the model mismatches and overcome the disturbances. Hence we can see the run-to-run optimal control based on support vector regression model is a reliable optimal control method.2. Online monitoring and fault diagnosis of industry process is extremely important for operation safety and product quality. In the paper, an integrated method is applied for process monitoring and fault diagnosis, which combines kernel principle component analysis (KPCA) for fault feature extraction and multiple support vector machines (MSVMs) for identification of different fault sources. For the algorithm, the kernel primary component model according to the normal system is constructed firstly. Second, the new acquired data are mapped to the kernel primary component model to reconstruct the data. Then the reconstructed data are analyzed by multiple statistical indexes such as T2 or SPE to determine if the supervised process exceeds the normal control limits. If fault occurs, then the supervising program will alarm to prompt that the process has abnormal states. Because it is hard to get the inverse mapping from the kernel space to the original space after the original data are nonlinear mapped by the KPCA method, hence it is difficult to diagnose how the fault happened. The paper adopts the learning method of MSVMs to classify the faults, which avoids the mathematical solution for the inverse mapping and acquires the fault information through intelligent method directly. Such an integrated method provides a new way for process monitoring and fault diagnosis.3. As far as iterative learning control concerned, the paper summarized and classified the iterative learning control theories. On the basis, the paper analyzed the robust performances of two kinds of feedforward-feedback iterative learning control based on inverse model. Then robust convergent conditions are presented respectively. The theoretically acquired robust convergent regions are the sufficient conditions for the two kinds of iterative learning control schemes'global convergences in the learning space. The acquired theoretical results can provide the references for the design of iterative learning controllers.4. To solve optimization problems of batch processes without state independent and end-point constraints, the article combined the iteration method and the particle swarm optimization algorithm together and proposed an iterative particle swarm algorithm. For the algorithm, the control variables are discretized firstly and the standard particle swarm optimization algorithm is used to search for the best solution of the discretized control variables. Next, the benchmark is moved to the acquired optimal values in the subsequent iterations and the searching space contracted at the same time, hence the optimization performance index and control profile could achieve the best value gradually through iterations. The algorithm is simple, feasible and efficient. It avoided the problem solving large-scale differential equation group. The algorithm is especially practical when the system's gradient information is unavailable. In the case, it is hard for general mathematical method to acquire the optimal solutions. However, the iterative particle swarm algorithm can acquire the satisfying results based on its intelligent researching. By discretizing the control variables, a continuous dynamic optimization problem is transformed to a discrete problem. The algorithm has the characters of parallel computations which make the control profile at all time stage optimized simultaneously. The algorithm has the advantages of iteration methods and intelligent algorithms. Nevertheless, it don't need discretization of state variables and only search the optimal control profile in the solution space randomly, hence overcome the disadvantages of iteration methods and intelligent algorithms. The algorithm is of more advantage when solve the robust optimization of batch process which make the program simple and can reduce the computation largely.