Adaptive Monitoring and Control for Carbon-Nanotube Desalination Membranes: From Laboratory Evidence to Operational Control

Abstract

Moving carbon-nanotube membranes into continuous desalination changes the evidence problem from measuring peak transport to managing a drifting physical system. This review examines how laboratory findings can inform an AI-enabled operational loop without being overextended. A digital twin couples nanotube transport and module hydraulics with online pressure, flux, conductivity, temperature, and cleaning histories. Bayesian filtering estimates latent membrane condition, while anomaly models look for fouling, defects, or sensor faults; a constrained controller may adjust set points only within a validated envelope. The review emphasizes shadow testing, delayed outcome labels, causal effects of maintenance, and separate alarms for process change and data-pipeline failure. Human operators retain override authority, and every adaptation is linked to the evidence and model version that justified it. The synthesis does not present a new control trial or numerical performance claim. Instead, it supplies a staged AI translation design—from laboratory characterization through commissioning, monitored release, and revalidation—that protects water quality while allowing adaptive operation to learn from trustworthy field evidence.