Data-Driven Modeling, Order Reduction and Control of Anaerobic Digestion Processes

Abstract

This research delves into the realms of data-driven modeling, order reduction, and control strategies within the context of Anaerobic Digestion (AD) processes. The study is centered on addressing pivotal challenges in this domain and delivering innovative contributions to the field. The primary objectives encompass streamlining the complexity of the Anaerobic Digestion Model No.1 (ADM1) for the specific purpose of control, as well as the exploration of suitable data-driven techniques to achieve precise modeling and prediction of AD systems. Furthermore, the research endeavors to extract kinetics reactions from simulated time-series AD data, develop robust predictive models for Chemostat dynamics under both Monod and Haldane kinetics through data-driven methodologies, and employ the Koopman Operator theory to enable data-driven modeling and control of the Chemostat system, relying solely on substrate measurements. By adopting a data-driven approach, this research aims to provide profound insights into the intricacies of AD processes, thereby shedding light on their complex dynamics and advancing our comprehension beyond conventional models. It introduces an alternative modeling perspective exclusively grounded in data, augmenting our analytical capabilities within the realm of AD processes. The research rigorously evaluates and tests a variety of data-driven techniques, yielding intriguing results. Notably, the application of the Koopman Operator theory represents a significant contribution, particularly in scenarios where measurement resources are limited. This innovation holds the potential to pave the way for robust control strategies within AD systems, ultimately enhancing their sustainability and efficiency.