An optimization framework for parameter estimation of large-scale systems

Abstract

Model-based applications like, e.g. optimization or NMPC are more and more used in the chemical industry. The basis of these applications is a detailed process model and measurements from the plant to determine the actual state of the process and to increase the accuracy of the model with parameter estimation techniques. However, these measurements usually contain random as well as gross errors which have to be identified and eliminated before the measurements are used. In this contribution, a framework for data reconciliation and parameter estimation which is able to handle large-scale problems is presented. The approach was integrated into an online optimization framework for the ammonia hydrogen sulfide circulation scrubbing process. To increase the accuracy of the model, we estimated several process parameters using a sequential parameter estimation approach. Data reconciliation was performed based on simple component balances to achieve model-consistent data and to identify measurement biases. The model was then validated online on a pilot plant by connecting the estimation package through the process control system. Based on the online measured data, operating cost minimization was carried out and the computed optimal set-points realized real-time. A satisfactory agreement between measured data and optimization was achieved.