Dynamic Hybrid Modeling: Incremental Identification and Model Predictive Control

2025-06-23

AI Summary

Addressing challenges in dynamic hybrid modeling of chemical processes—including high computational cost, elevated development effort, and poor identifiability under data scarcity—this paper proposes an incremental decoupled identification method. It separates mechanistic and data-driven components during modeling; the latter is independently and interpretable identified via regularized dynamic parameter estimation and input–output correlation analysis. Subsequently, machine learning models (e.g., LSTM, SVR) are tightly coupled with the mechanistic structure to enable co-optimization. This approach significantly enhances modeling flexibility and structural verifiability. Validation across three representative chemical process case studies demonstrates that the proposed method reduces modeling time by over 40%, improves modeling accuracy by 15–28% under small-sample conditions, and enhances closed-loop control performance.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Overcoming computational and conceptual difficulties in dynamic hybrid model identification
• Integrating data-driven models within mechanistic structures for chemical processes
• Accelerating hybrid model development with incremental identification approach

Innovation

• Incremental identification decouples mechanistic and data-driven components
• Regularized dynamic parameter estimation optimizes flux variables
• Advanced machine learning identifies data-driven model components