Process yield optimisation — Chemicals
Models that recommend set-points to lift yield and cut scrap. Delivered for a chemicals client in Delhi NCR.
- Location
- Delhi NCR
- Year
- 2024
- Duration
- 10 weeks
- Team
- 8 engineers
- +6.7%
- yield
- −16%
- scrap
- 729
- batches modelled
The challenge
Data existed but lived in spreadsheets and legacy systems, so decisions were made on instinct rather than evidence.
What we built
We owned the whole stack — hardware, firmware, models and dashboards — using XGBoost, Bayesian optimisation, OPC UA so there was one accountable team.
Delivery timeline
Phase 1
Discovery sprint and site survey
Phase 2
Prototype built and tested in the Motera lab
Phase 3
Pilot on site with before/after measurement
Phase 4
Scale-out, training and handover
Highlights
- Runs on-premises; no data leaves the site unless agreed
- Designed, built and supported from Motera, Ahmedabad
- Before-and-after measurements agreed with the client up front
- Documentation, drawings and source handed over at close
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