Keywords
materials
systems
evidence synthesis
reliability
provenance
Abstract
Carbon-nanotube desalination systems face abrupt feed changes and gradual membrane aging, either of which can invalidate an AI model while leaving its software apparently healthy. This review develops an operational resilience and revalidation strategy that links laboratory characterization to recoverable field control. Mechanism-based residuals test whether pressure, flux, and rejection remain consistent with expected transport; streaming drift detectors monitor changes in sensor distributions, cleaning response, and defect signatures. The framework assigns graded actions to evidence loss: recalibration for bounded sensor change, shadow evaluation for model updates, restricted operation for uncertain membrane condition, and safe shutdown when water-quality risk cannot be contained. Batch provenance, model registries, rollback, and operator decision logs support reconstruction and learning after incidents. The review also requires revalidation after material, module, preprocessing, or control-policy changes rather than relying on a calendar alone. No new plant experiment is reported. Its contribution is an AI assurance architecture in which resilience means detecting invalid assumptions early, preserving safe fallback modes, and rebuilding evidence before full autonomy resumes.
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