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.

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.

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.

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