Operational Resilience and Revalidation for Reproducible Evidence in Electrochemical Production: Lifecycle Evidence and Accountable Decisions

Abstract

Electrochemical production links material batches, catalyst surfaces, equipment settings, sensor histories, quality measurements, and maintenance actions over a long lifecycle. This review asks how AI-assisted monitoring can remain reproducible and trustworthy as that chain changes. Statistical process monitoring, probabilistic prediction, and anomaly detection are integrated with a digital evidence thread that records samples, calibration, preprocessing, model checkpoints, operator interventions, and release decisions. The resilience design distinguishes physical process drift from data-pipeline or software failure and assigns graded responses: recalibration, shadow validation, restricted operation, rollback, or shutdown. Revalidation is triggered by material substitution, equipment maintenance, measurement change, model update, unexplained residuals, or a new decision context with different error costs. The synthesis emphasizes that rerunning code is insufficient if the physical evidence cannot be reconstructed. It reports no new production experiment. Its contribution is a lifecycle AI assurance framework in which reproducibility supports recovery, uncertainty reaches accountable decision makers, and automated control resumes only after the relevant scientific and operational evidence has been renewed.