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

Department

Advanced Solutions department — PFE 2026–2027

Project

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

Main topics

• Collect, contextualize and analyze plant operating data using SIOTH and Python, including feed-water quality, pressures, flows, permeate quality, energy and dosing data.
• Apply process knowledge and industry 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 RO performance normalization, including normalized permeate flow, salt passage and differential pressure.
• Optimize energy consumption and specific energy consumption under operating constraints.
• Optimize antiscalant, coagulant, acid/caustic and chlorine/SBS dosing.
• Schedule CIP 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.
• Build interactive SIOTH UHMI dashboards with process data, predictions, KPIs, alerts and recommendations.

Deliverables

Technical documentation, final report and presentation covering methodology, models, results and potential business impact.

Keywords

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