Overview and Classification of Online Process Optimization Approaches

Abstract

This workshop discusses various approaches to online process optimization, where the objective is usually to minimize an economic cost. Steady-state real-time optimization (RTO) has been around for more than 25 years, but still it is not used much in practice. One reason is the steady-state wait time, because one has to wait for a new steady state before the process is re-optimized. In this workshop, a number of alternative approaches are discussed, given here in the order from most complex (and model-based) to most simple (and data based): 1.Economic (nonlinear) model predictive control (EMPC) / Dynamic real-time optimization (DRTO) 2.Hybrid RTO - Steady-state RTO with dynamic model update (new method) 3.Feedback-based Hybrid RTO (new method) 4.Conventional steady-state RTO 5.Self-optimizing control 6.Methods based on directly estimating the true plant gradient such as extremum seeking control, NCO tracking and Modifier adaptation. 7.Optimal operation using conventional advanced control With the recent developments of various approaches to online process optimization with varying degrees of complexity and flexibility, different methods work in different timescales and can handle different kinds of uncertainty. This workshop will give an overview and classification of the different approaches available in the RTO “toolbox” and discusses the advantages and disadvantages of the different methods.