End-to-End AI Application for RO Desalination: Energy, Chemical, CIP Optimization and Intelligent Diagnostics Using SIOTH

Department

Advanced Solutions department — Project 02/03 of the Integration Objects PFE Book 2026–2027. The topic is built around the SIOTH® Industrial IT-OT-IoT platform.

Project

Develop an end-to-end AI application for Reverse Osmosis desalination plants to optimize energy consumption, chemical dosing, and Cleaning-In-Place scheduling while supporting root-cause identification and AI-generated recommendations for major equipment.

Objectives and deliverables

• Collect, contextualize, and analyze plant operating data, including feed-water quality, pressures, flows, permeate quality, energy, and dosing data, using SIOTH and Python.
• Apply process knowledge and standards such as ASTM D4516 normalization, membrane manufacturer guidelines, and ISO 50001.
• Build predictive and optimization models for high-pressure pumps, energy recovery devices, RO trains, and pretreatment units.
• Integrate and preprocess plant data in SIOTH, including normalized permeate flow, salt passage, and differential pressure.
• Develop energy optimization models to minimize Specific Energy Consumption under operating constraints.
• Optimize antiscalant, coagulant, acid/caustic, and chlorine/SBS dosing based on feed-water quality and process conditions.
• Develop intelligent CIP scheduling based on membrane fouling/scaling prediction and normalized performance trends.
• Identify performance deviations and equipment anomalies using Machine Learning, statistical analysis, and process rules.
• Generate AI recommendations for operators and engineers.
• Develop SIOTH UHMI dashboards displaying process data, predictions, alerts, recommendations, and KPIs such as SEC, chemical cost per cubic meter, CIP intervals, recovery, and permeate quality.
• Produce technical documentation, a final report, and a presentation covering methodology, models, results, and potential business impact.

Keywords

AI, Python, Reverse Osmosis desalination, energy optimization, chemical dosing optimization, CIP scheduling, root-cause identification, predictive maintenance, SIOTH, industrial data analytics, SIOTH UHMI.