Coagtech
Coagtech provides AI-driven coagulation optimization for water treatment plants, delivering model recommendations that reduce chemical use by up to 20% and improve treated water reliability. The company follows a three-phase process—system evaluation, custom hardware integration with existing SCADA systems, and annual maintenance—with each phase summarized in an engineering report. The solution is designed with operators in mind, reducing staff workload and environmental impacts from treatment residuals.
- Artificial Intelligence
- Industrial Automation
- Clean Technology
- Hardware
Funding
Founders
Founder details are not available yet.
Product
Problem
Water treatment plants face challenges in optimizing coagulation chemical dosing, leading to excessive chemical use, inconsistent treated water quality, and increased environmental impacts from treatment residuals. Operators lack decision-support tools that can adapt to changing source-water conditions and process dynamics, resulting in reliance on manual judgment and increased staff stress.
Solution
Coagtech provides AI-driven coagulation optimization for water treatment plants, delivering model recommendations that lower chemical usage by up to 20% and improve treated water reliability. The company follows a three-phase process: on-site system evaluation to identify process blind spots and data integrity issues, custom hardware integration with existing SCADA systems—without adding internet connectivity or cybersecurity risk—and annual maintenance that retrains models to adapt to process and source-water changes. Each phase is summarized with an engineering report detailing the findings. The solution is designed with operators in mind, reducing staff workload and providing assurance to dosing decisions while increasing reliability of treated water quality.
Target Audience
Primary customers are water treatment plant operators and managers seeking to optimize coagulation processes, reduce chemical costs, and improve treated water quality reliability.
Features
- AI-driven model recommendations that reduce coagulation chemical use by up to 20%
- On-site system evaluation including identification of process blind spots and data integrity issues, with preliminary model development to assess feasibility
- Customized hardware with no internet connectivity integrated within existing SCADA systems, introducing no additional cybersecurity risks
- Annual maintenance that evaluates model performance and re-trains with additional data to improve accuracy and prevent model deterioration
- Engineering reports summarizing findings at each phase of the process
- Designed to reduce stress on operational staff and provide assurance to dosing decisions