Adaptive Monitoring and Control for Bernoulli-Driven Water Separation: Measurement and Comparator Design

Abstract

Adaptive control is useful only when a Bernoulli-driven separation system can distinguish genuine process change from noisy measurement. This review connects pressure-flow physics with AI monitoring by treating pressure differential, velocity, salinity, temperature, fouling indicators, and actuator state as a time-dependent evidence set. Bayesian state estimation, online anomaly detection, and physics-informed digital twins are examined as tools for updating the estimated operating regime, but every adaptation is evaluated against a fixed and documented comparator. The synthesis separates detection, diagnosis, and intervention so that a model does not silently learn from its own control actions. It highlights delayed outcomes, sensor dropout, feedback-induced bias, and the need for confidence-aware policies that request another measurement or human review before changing set points. Comparator choice includes non-adaptive control, mechanism-only control, and AI-assisted control under the same disturbances. No operational dataset or effect estimate is introduced. Instead, the article provides a measurement and evaluation design for adaptive AI that makes gains attributable, preserves a stable safety envelope, and records why each control change was authorized.