A Modular Intelligent Control Framework for Scalable Biohydrogen Production in Microbial Electrolysis Cells

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.