Drift-Aware Digital-Twin Readiness for Two-Stage Reverse Osmosis: Plant-Scale Time-Aware Machine Learning and Operating-Regime Validation

Reverse osmosis (RO) digital twins require predictive models whose accuracy remains credible as plant operating conditions change. We evaluated the readiness of predictive models for a full-scale, two-stage RO plant using operational spreadsheets and temporally structured validation. An audit of 70 legacy worksheets identified heterogeneous layouts. A normalized source provided 476 train-day records from 17 operating months between June 2022 and November 2023, while 55 records from April and May 2024 formed an independent later-period holdout. Four supervised regression algorithms (Ridge Regression, Random Forest, Extra Trees, and Gradient Boosting) were evaluated for second-stage permeate flow and conductivity. Under a random split, Random Forest achieved R2 values of 0.876 and 0.833, with root mean square errors of 8.48 m3/h and 63.26 µS/cm, respectively. Chronological validation reduced R2 to 0.042 and 0.118. On the later holdout, R2 fell to −1.173 and −0.918, with corresponding root mean square errors of 11.14 m3/h and 218.65 µS/cm. Rolling-origin tests revealed substantial variability among successive periods. Distributional diagnostics identified changes in feed pressure, first-stage permeate conductivity, second-stage flow, and second-stage conductivity; the available data could not establish their physical causes. These findings show that performance under random splitting is insufficient to establish readiness for future use. A membrane-process digital twin requires traceable data, chronological evaluation, operating-regime surveillance, controlled model updates, and human-supervised advisory operation.