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
- Water treatment
- Input modalities
- Time-series process data
Problem
The workflow challenge
Water treatment operations needed better forecasting support for dosing decisions instead of relying on slower manual interpretation alone.
AI system built
What got implemented
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.
Proof
Metrics and proof points
- 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.
Connected capabilities