Abstract
Operators in chemical plants are confronted with several different measured process variables and parameters. Although the precision of measuring rose, individual measurements remained uncertain. This might affect real measurements such as temperature or pressure, where the measured value is more an expected value, with the real value within a range around it or also process dynamics, which hold exactly only under certain circumstances. Within the process monitoring and control, the operator has to take such uncertainties into account; on the one hand to not risk the violation of safety regulations, on the other hand to not use a too conservative control and give away product or quality. Even though an experienced and skilled operator might be able to handle single uncertain parameters and variables quiet efficiently, the outcome of multiple uncertain parameters is difficult. To handle multiple uncertain parameters simultaneously in optimisation, the concept of chance-constrained optimisation has been developed and extended over the last years. In this work, we present developed techniques of chance-constrained optimisation for process monitoring and control. It will allow to calculate potential key performance indicators out of uncertain variables and parameters, which can help operators in the decision making process. However, one drawback of using chance-constraints techniques is the required computation time for calculation. It requires a significant amount of individual calculations. Therefore, algorithmic improvements were required to meet the requirements of online monitoring and control. The talk will present the application of the developed chance-constrained approach on uncertain parameters in process monitoring and control, give an insight how the computing time improvements were fulfilled and show results of a practical evaluation.