A cloud-based infrastructure to deploy supervisory forecast models for predictive coagulant dosing control

• Published in: Water practice and technology Vol. 19; no. 4; pp. 1307 - 1314
• Main Authors: Nair, Abhilash, Yavorska, Viktoria, Hykkerud, Aleksander, Ratnaweera, Harsha
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.04.2024
• Subjects: Algorithms; Chemical oxygen demand; Coagulants; control; Control algorithms; Control equipment; Control systems; Control theory; Data acquisition; digitalization; Dosage; Downtime; Effluents; Feedback loops; hybrid model; Infrastructure; Internet of Things; iot; Maintenance; Nutrient removal; performance forecast; Phosphates; Predictive control; process optimization; Programmable logic controllers; Python; Sedimentation & deposition; Sensors; Sludge; Supervisory control and data acquisition; Water treatment; Norway
• ISSN: 1751-231X; 1751-231X
• DOI: 10.2166/wpt.2024.091

Advanced optimal dosing control based on multiple online sensor data is operational in several treatment facilities in Norway. The benefits of the dosing control system in maintaining stable phosphate/solids removal and saving coagulant usage are documented in the literature. The dosing algorithm is currently implemented in a programmable logic controller (PLC) connected to the treatment plant's Supervisory Control and Data Acquisition System (SCADA) system. The PLC receives online sensor data from the plant's SCADA, calculates the optimal dosing values, and transmits optimal dosage values back to the SCADA system. The dosing algorithm is frequently updated to keep in sync with the process and equipment upgrades of the treatment plant and advances in control algorithm schemes. The upgrades include new regulatory feedback loops structural changes to the dose equation, and the addition of conditional setpoints. Each maintenance and upgrade routine entails operational downtime where the dosing algorithm is set to a sub-optimal flow-proportional dose. This paper presents a non-intrusive Internet of Things (IoT) infrastructure to implement a predictive/forecast component to an existing dosing control algorithm. The benefits of the new cloud-based system in improving nutrient removal, increasing operational flexibility, and reducing maintenance downtime are presented in this work.