On Optimization of Paper Machines using Economic Model Predictive Control

摘要

In this paper we consider applying economic model predictive control (EMPC) for economic optimization of a paper machine. EMPC is used to optimize overall process targets, e.g., the economy, directly in the control layer. The basic idea in EMPC is that by combining a dynamic process-model with an economic model, it is possible to predict and optimize the future economic outcome with respect to the manipulated process variables. Periodically solving such an optimization problem with updated information from measurements corresponds to a feedback controller. The results presented here are based on simulations, using a grey-box model with parameters estimated from real data, that reveal that EMPC may improve several aspects of the economic performance of a paper machine. First, EMPC may automatically prioritize among an excessive number of inputs to determine which combinations of inputs to use in order to counter disturbances in the most economically efficient manner. Also, since EMPC makes use of dynamic optimization, it may utilize control inputs with zero steady-state gain which are not used for traditional set-point tracking. Second, since EMPC is predictive in nature, it may plan ahead and prepare the process for known changes such as grade-changes, hence reducing the transition-time with a significant reduction in production loss, and thereby significant improvements in profitability, especially for machines where grade-changes are frequent. Finally, we note that EMPC typically operates the process with constraints active, as is typical for economic optimization problems in general. This may cause problems with robustness since even small exogenous disturbances or unmodelled dynamics may cause constraint violations. We therefore suggest using an adaptive approach where a constraint margin is introduced in the EMPC optimization problem to ensure that the operating point is backed off from the actual constraints relevant for production, thereby improving the robustness.