Abstract
The global energy transition requires technologies that can be scaled rapidly to decarbonise both power and chemical production. Microbial electrolysis cells (MECs) convert wastewater into renewable hydrogen; however, their commercial adoption is hindered by highly nonlinear dynamics, complex microbial interactions, and inadequate process control. This review traces the evolution of five control paradigms, including voltage/proportional-integral-derivative (PID), model-based, adaptive, fuzzy logic, and artificial-intelligence (AI) systems, as well as hybrid intelligent controllers, and benchmarks each against scalability, dynamic responsiveness, and energy-recovery performance. Recent hybrid approaches that embed AI learners within mechanistic-based models and real-time feedback loops show the greatest gains in predictive accuracy and robustness. To translate these advances beyond laboratory volumes, a modular design framework is introduced that (i) separates sensing, decision-making, and actuation into plug-and-play units; (ii) supports incremental scale-up from bench reactors to pilot-scale stacks without re-engineering the core algorithms; and (iii) leverages distributed edge-computing hardware to execute computationally intensive tasks close to the reactor, reducing latency and cloud-dependence. By directly targeting the bottlenecks of controller portability, computational load, and integration with industrial supervisory systems, this framework provides a practical roadmap for deploying intelligent MEC control across large-scale wastewater-to-hydrogen facilities.
