تجربه صنعتی با کنترل کیفیت محصول در فرایندهای نیمه دسته ای

Two practical approaches to the control of final product quality in semi-batch reactors are illustrated with an industrial experience. The first is an orthodox approach to the control of final quality which is achieved through a high degree of automation of all the reactor charging operations, good temperature and pressure control, and sequence of steps implemented throughout the course of the batch. The second approach to the control of final quality is to use mid-course correction policies. Both approaches to the control of final quality are illustrated with an industrial example. Through our experience with this example, the first approach, although very basic, is shown to be very important to reduce the variability in the final product quality. The second approach is useful to compensate for the new disturbances that are affecting the batch being run at the current time. Good practice is that mid-point control policies should be used after this basic automation step has been implemented as efficiently as possible.

The control of final product quality in an industrial semi-batch reactor is a difficult problem for various reasons. Foremost among them is the fact that robust on-line sensors for monitoring the progress of product quality development during the batch are almost never available. Rather, quality variables are usually measured only on the final product after the batch is completed. Furthermore, batch reactors exhibit highly nonlinear dynamic behavior, and the complex nonlinear kinetic and dynamic models needed for model-based inferential and nonlinear control are rarely available for industrial processes. As a result, most batch and semibatch reactors are operated in an open loop manner with respect to product quality.The basic approach to control final quality is achieved through a high degree of automation of all the reactor charging operations, the start-up sequence, and sequence of steps that must be implemented throughout the course of the batch. This automation eliminates the variability that might otherwise arise from the batch-to-batch variations in the implementation of these steps. Good temperature and pressure controls are used to minimize any effect of variations in these variables on final quality. In spite of using such advanced automation, some batch-to-batch variations in quality will still be present. If this quality variability exhibits predictable trends (i.e. autocorrelation) due to persistent trends in raw material properties, impurities, catalyst activities, etc., then batch-to-batch feedback control schemes can be used to eliminate it. They involve making small recipe adjustments to each new batch based on the recent history of quality measurements from past batches. Although this batch-to-batch feedback control can eliminate the predictable component of quality variation between successive batches, it does nothing to compensate for the new disturbances that are affecting the batch being run at the current time. If adequate on-line sensors are available to detect the presence of disturbances arising from sources such as raw materials, impurity and catalyst variations or from charging or start-up variations, and if models can be developed to predict the effects of these detected disturbances on the final product quality, then model-based within-batch feedback control is possible. This within-batch control will lead to a further reduction in the product quality deviations from target for each batch. Various approaches to this within-batch control problem can be taken depending upon the type of on-line information available and upon the complexity of models available. Kozub and MacGregor (1992) present an approach to the within-batch control of multiple product quality variables in the semi-batch emulsion polymerization of styrene-butadiene rubber (SBR), using nonlinear control and state estimation. However, this approach requires a good fundamental dynamic model of the SBR emulsion polymerization process, and a set of good on-line sensors (e.g. for residual monomer concentrations and particle size) to ensure observability of the state variables throughout the time history of the batch. Neither of these is usually available in an industrial setting. Data-based quality prediction and control approaches to the within-batch control problems have been proposed. These approaches explore a more realistic approach to within-batch product quality control that is based on simple and readily available on-line measurement and some off-line analyses. Multivariate statistical process control (MSPC) has been introduced for batch and semi-batch processes based on the concept of multiway PCA or multiway PLS (Nomikos & MacGregor, 1994, 1995). This statistical monitoring is done using on-line process measurements rather than off-line quality measurements. With this technique, an abnormal batch can be detected quickly, providing an opportunity to stop the batch or make compensatory adjustments. Mid-course control adjustment policies with an empirical models (based on neural network or PLS) have been proposed for reducing predict deviations of final product quality variables from their target values (Tsen, Jamg, Wong and Joseph (1996), Yabuki and MacGregor (1997)).

Two practical approaches to the control of final product quality in semi-batch reactors are illustrated with an industrial example. Through our experience with this example, the first approach, although very basic, is shown to be very important to reduce the variability in the final product quality. The mid-point correction approach is shown to be useful to occasionally compensate for the new disturbances that are affecting the batch being run at the current time. Good practice is that mid-point control policies should be used after this basic automation step has been implemented as efficiently as possible.