Advancing Resource Recovery from Wastewater

Abstract

This PhD thesis advances resource recovery from wastewater by focusing on two key technologies: Purple Phototrophic Bacteria (PPB) raceway reactors and anaerobic digesters (ADs). To address challenges such as process variability, monitoring limitations, and operational inefficiencies, this research employs three complementary approaches: mechanistic modeling, hybrid system identification, and adaptive predictive control. Mechanistic models offer detailed insights into microbial interactions and process dynamics; hybrid system identification develops low-order models for practical data reconciliation and forecasting; and adaptive model predictive control dynamically optimizes operations to enhance performance. Key contributions include a novel mechanistic model for PPB cultivation in raceway reactors, a temperature-dependent extension for the anaerobic digestion model no.1, a hybrid system identification method to approximate complex mechanistic models, and an adaptive hierarchical process-oriented MPC framework for PPB reactors and ADs to manage variable operations and unknown disturbances. These approaches advance wastewater resource recovery in their intended perspectives, providing efficient solutions for both case studies.