Dynamic Hybrid Modeling: Incremental Identification and Model Predictive Control

TL;DR

This paper introduces an incremental method for developing dynamic hybrid models that combine mechanistic and machine learning components, improving efficiency and robustness in chemical process modeling.

Contribution

The paper presents a novel incremental identification approach that decouples model components, enabling faster and more reliable hybrid model development.

Findings

Effective handling of complex systems with limited data

Improved speed and reliability in hybrid model identification

Successful application across three case studies

Abstract

Mathematical models are crucial for optimizing and controlling chemical processes, yet they often face significant limitations in terms of computational time, algorithm complexity, and development costs. Hybrid models, which combine mechanistic models with data-driven models (i.e. models derived via the application of machine learning to experimental data), have emerged as a promising solution to these challenges. However, the identification of dynamic hybrid models remains difficult due to the need to integrate data-driven models within mechanistic model structures. We present an incremental identification approach for dynamic hybrid models that decouples the mechanistic and data-driven components to overcome computational and conceptual difficulties. Our methodology comprises four key steps: (1) regularized dynamic parameter estimation to determine optimal time profiles for flux variables, (2) correlation analysis to evaluate relationships between variables, (3) data-driven model identification using advanced machine learning techniques, and (4) hybrid model integration to combine the mechanistic and data-driven components. This approach facilitates early evaluation of model structure suitability, accelerates the development of hybrid models, and allows for independent identification of data-driven components. Three case studies are presented to illustrate the robustness, reliability, and efficiency of our incremental approach in handling complex systems and scenarios with limited data.