Project
Chemical Dosing Forecast for Water Treatment Plant
A forecasting workflow that supports dosing decisions with time-series analysis and predictive outputs.
Project snapshot
- Industry
- Engineering teams
- Input modalities
- Time-series process data
Situation
The situation
Water treatment operations needed better forecasting support for dosing decisions instead of relying on slower manual interpretation alone.
What I set up
What I recommended and set up
Rel-AI-able built a time-series forecasting workflow focused on chemical dosing decisions, feature analysis, and operational guidance.
Outcome summary
What changed after delivery
- Supported dosing decisions with predictive outputs.
- Applied time-series forecasting to plant operations.
- Used feature analysis to improve operational understanding.
Result
The result
- 5.9% MAPE in chemical dosing forecasting.
Architecture notes
Delivery shape
- Time-series forecasting.
- Optimized dosing decisions.
- Feature analysis.
Problem context
A fuller look at the operational context, workflow inputs, and business outcomes behind the build.
Problem context
Plant teams needed a more reliable way to anticipate dosing needs. The challenge was not just prediction for its own sake, but forecasting that could support day-to-day operational decisions.
AI system built
Rel-AI-able implemented a forecasting workflow for chemical dosing in a water treatment plant. The system combines time-series forecasting with feature analysis to make the outputs more useful for operations.
Inputs and workflow
- Time-series operational data is collected for the forecasting process.
- The model generates dosing forecasts from process history.
- Feature analysis helps explain what is driving the outputs.
- Results are used to support dosing decisions in the plant workflow.
Business outcomes
The forecasting workflow supported chemical dosing decisions with predictive outputs and feature analysis. It achieved 5.9% MAPE in chemical dosing forecasting.