Multimodal Measurement and Diagnosis for Bernoulli-Driven Water Separation: A Mechanism-to-Decision Synthesis

Abstract

Bernoulli-driven water separation is usually judged by pressure, flow, and salt-rejection measurements considered in isolation, even though operational failure often appears first as disagreement among them. This critical review develops a mechanism-to-decision synthesis in which hydraulic variables, salinity signals, membrane condition, imaging, and maintenance observations are treated as complementary inputs to an AI-assisted diagnostic pipeline. Physics-informed feature construction preserves the Bernoulli mechanism, while multimodal representation learning and uncertainty-aware anomaly detection expose local deviations that a global efficiency score may hide. The review compares sensor alignment, missing-data handling, calibration, and fusion strategies and asks when an automated diagnosis should trigger additional measurement, operator review, or shutdown. It also connects model outputs to data provenance, versioned evidence, and explicit validity domains so that a decision remains traceable after sensors, feedwater, or equipment change. No new experiment is reported; the contribution is a source-grounded architecture for using multimodal AI without allowing apparent agreement among channels to suppress rare but consequential evidence.