Dynamic Controlled Variables Based Dynamic Self-Optimizing Control

2026-05-07

AI Summary

This study addresses the limitations of traditional self-optimizing control, which is confined to steady-state optimization and struggles with dynamic processes—such as batch operations or grade transitions—characterized by variable time horizons and multi-valued or discontinuous objectives. The work proposes a novel dynamic self-optimizing control framework that introduces the concept of “dynamic controlled variables” (DCVs), reformulating dynamic optimization as an implicit control problem. A data-driven approach is employed to cast DCV design as a mapping identification task, parameterized using deep neural networks and theoretically linked to conventional controllers. Validation through three case studies demonstrates that the proposed method effectively approximates complex objective functions and significantly outperforms existing approaches in dynamic optimization over non-fixed time horizons.

Abstract

Self-optimizing control is a strategy for selecting controlled variables, where the economic objective guides the selection and design of controlled variables, with the expectation that maintaining the controlled variables at constant values can achieve optimization effects, translating the process optimization problem into a process control problem. Currently, self-optimizing control is widely applied to steady-state optimization problems. However, the development of process systems exhibits a trend towards refinement, highlighting the importance of optimizing dynamic processes such as batch processes and grade transitions. This paper formally introduces the self-optimizing control problem for dynamic optimization, termed the dynamic self-optimizing control problem, extending the original definition of self-optimizing control. A novel concept, "dynamic controlled variables" (DCVs), is proposed, and an implicit control policy is presented based on this concept. The paper theoretically analyzes the advantages and generality of DCVs compared to explicit control strategies and elucidates the relationship between DCVs and traditional controllers. Moreover, this paper puts forth a data-driven approach to designing self-optimizing DCVs, which considers DCV design as a mapping identification problem and employs deep neural networks to parameterize the variables. Three case studies validate the efficacy and superiority of DCVs in approximating multi-valued and discontinuous functions, as well as their application to dynamic optimization problems with non-fixed horizons, which traditional self-optimizing control methods are unable to address.

Problem

• dynamic self-optimizing control
• dynamic controlled variables
• process optimization
• batch processes
• grade transitions

Innovation

• dynamic self-optimizing control
• dynamic controlled variables
• data-driven control
• deep neural networks
• dynamic optimization