Adaptive Monitoring and Control for Carbon-Nanotube Desalination Membranes: A Mechanism-to-Decision Synthesis

Abstract

Carbon-nanotube membranes couple nanoscale transport with system-level variables such as feed composition, pressure, flux, rejection, and fouling. This review synthesizes those layers into an AI-assisted monitoring and control architecture. Mechanistic descriptors of pore size, alignment, surface interaction, and transport are combined with streaming sensor data in a physics-informed state model; anomaly detection then identifies departures that may indicate blockage, leakage, or loss of selectivity. Rather than allowing a learned controller to optimize flux alone, the proposed decision layer balances water quality, energy use, membrane condition, and uncertainty. It distinguishes model updates supported by familiar operating data from novel conditions that require abstention or human review. The analysis also specifies provenance for membrane batches, preprocessing, sensor calibration, and model versions, enabling a diagnosis to be reconstructed after deployment. This is a critical review, not a report of a new membrane experiment. Its contribution is a mechanism-to-decision blueprint showing how AI can translate nanoscale evidence into adaptive yet accountable operation without treating high initial permeability as proof of system readiness.