Towards Intelligent Membrane Systems: A Critical Analysis of Digital Twin–ML Integration for Real–Time Process Optimization, Fouling Management, and Autonomous Design

Membrane separation is central to potable water production, wastewater reclamation, desalination, gas separation, and decarbonization, yet its economics remain constrained by fouling and by a gap between process data and timely operating decisions. Digital twins (DTs) offer live, bidirectionally coupled virtual representations of physical assets, while machine learning (ML) extracts predictive and prescriptive insights from operational data. Together, they are promising computational approaches for membrane systems. However, their applications have largely evolved independently across fouling prediction, DT development, and ML-based materials discovery. This review argues that these are converging strands of a single trajectory toward self-sensing, self-predicting, and eventually self-designing membrane systems, and that treating them separately obscures key interdependencies. The contributions are threefold. First, applying the digital model/digital shadow/digital twin taxonomy, it shows that most published membrane DTs are digital shadows (DSs). Second, it proposes a five-level autonomy framework, inspired by vehicle automation taxonomies, providing a unified scale for comparing applications ranging from LSTM-based fouling forecasting and reinforcement learning dosing control to autonomous materials laboratories. Third, it integrates autonomous materials design, including self-driving laboratories, inverse design, generative models, and Bayesian search, into the upper levels of this ladder. Evidence is synthesized across reverse osmosis, nanofiltration, ultrafiltration, microfiltration, membrane bioreactors, membrane distillation, and gas separation membranes, with emphasis on critically assessing model performance and generalizability. The study concludes with a roadmap for overcoming data limitations, poor interpretability, interoperability gaps, cybersecurity risks, techno-economic barriers, and regulatory trust deficits that block autonomous membrane systems.