Management of Complex Dynamic Systems based on Model-Predictive Multi-objective Optimization

Abstract

Over the past two decades, model predictive control and decision-making strategies have established themselves as powerful methods for optimally managing the behavior of complex dynamic industrial systems and processes. This paper presents a novel model-based multi-objective optimization and decision-making approach to model-predictive decision-making. The approach integrates predictive modeling based on neural networks, optimization based on multi-objective evolutionary algorithms, and decision-making based on Pareto frontier techniques. The predictive models are adaptive, and continually update themselves to reflect with high fidelity the gradually changing underlying system dynamics. The integrated approach, embedded in a real-time plant optimization and control software environment has been deployed to dynamically optimize emissions and efficiency while simultaneously meeting load demands and other operational constraints in a complex real-world power plant. While this approach is described in the context of power plants, the method is adaptable to a wide variety of industrial process control and management applications