Management of complex dynamic systems based on model-predictive multi-objective optimization

over the past two decades, model predictive control and decision-making strategies have estabilished themselves as powerful methods for optimally managing the behavior of complex ynamic 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 techniues. 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 ptimization and control software environment has been deployed to dynamically optimize emissions nd efficiency while simultaneously meeting load demands and other operational constraints in a omplex real-world powerplant. While this approach is described in the context of power plants, the ethod is adaptable to a wide variety of industrial process control and management applications.