Real-time Optimization And Control Of Batch Processes

Batch processes have been widely applied to manufacture a large quantity ofvalue-added products such as pharmaceuticals, biological products, polymers and electronicchemicals. Increased demand for high product quality has motivated the interest in processmodeling, optimization, and advanced process control techniques that can improve theperformance of batch processes. Batch processes are inherently dynamic in nature and veryoften several batches are run in the same equipment. This may cause batch-to-batchvariation in operating conditions resulting in problems in safety, product quality andproductivity, which results in the development of real-time optimization technique andadvanced control technique.The operating strategy may not be the optimal strategy in presence of parametricuncertainty and disturbances. One approach to address these issues is to develop accuratemathematical models and use real-time optimization techniques to find the optimaloperating policies for each batch operation, and to ensure the optimal and stability ofproduct quality by controlling the process to track the optimal profiles. So integration ofreal-time optimization for batch process is very important for product quality control.However, the real-time optimization technique is a kind of integration strategy whichincludes techniques of dynamic modeling, dynamic optimization, on-line measurement,model update and control. The techniques of dynamic modeling, dynamic optimization andcontrol are the basic and main techniques of them. In this work, we mainly research on thenew practical techniques of batch process modeling and simulation, dynamic optimizationand nonlinear model predictive control (NMPC), which we hope will advance thedevelopment of real-time optimization technique and facilitate the implementation ofreal-time optimization technique in industrial batch processes. The main research contentsinclude:(1) Dynamic modeling for batch process with experiment design method based onMini-plant. By constructing the platform for data acquisition and process monitoring, atemperature control model has been built for the Mini-plant. Moreover, a dynamicsimulation technique based on xPC technique has been introduced. (2) Developing better dynamic optimization method for improving the computationalefficiency and the performance of the solution of real-time optimization problem. Threemethods, include dynamic optimization method based on Pontryagin’s maximum principlecombined model reduction, dynamic optimization method based on improved finite elementmethod and dynamic optimization method based on natural algorithm, have been developedin this work, and a large number of cases have been studied for validating the methods.(3) On-line control technique plays an important role in real-time optimization strategy,which in charge of the implementation of optimal trajectory tracking. Based on the standardNMPC technique, a real-time updated model predictive control technique has beenintroduced in this work, which can improve the computational efficiency of the solution ofNMPC problem and improve the robustness of the controller.In this work, we proposed a series of techniques of dynamic modeling and simulation,optimization and control for batch processes. From the view of theory, these techniques arepractical and efficient, which can become the basis of the real-time optimization strategy.Form the view of real application, since that a large number of experiment tests have beencombined into the methods and many practical problems which will be faced in real processhave been considered in the methods, these methods are more appropriate for real application.All of these works have provided some basic work for the industrial application of real-timeoptimization technique.