Dynamic Controlled Variables Based Dynamic Self-Optimizing Control
For process control engineers, this work addresses the limitation of self-optimizing control to steady-state problems by enabling dynamic optimization, though the method is incremental as it builds on existing concepts with a neural network approach.
This paper extends self-optimizing control to dynamic optimization problems by introducing dynamic controlled variables (DCVs) and an implicit control policy. A data-driven approach using deep neural networks is proposed to design DCVs, validated on three case studies showing superiority in handling multi-valued/discontinuous functions and non-fixed horizon problems.
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.