Digital twins and process optimization
Mathematical models of process units and stages: regime forecasting, setpoint recommendations, optimization of energy consumption and reagent use. The effect is a 3–8% gain in process-stage efficiency with no capital expenditure. We work in operator-advisor mode — the model recommends, the operator decides — with read-only access to the historian and no intervention in the process control loop.
What problems it solves
Pain
Regime quality varies between shifts by 2–5% in target-product yield at the same feed rate
Solution
Real-time quality soft sensors plus a recommender model of optimal setpoints built on the historical “best shifts”
All shifts brought up to the best shift's regime: +0.5–1.5% yield
Pain
Energy and reagent overconsumption: regimes are chosen by procedure and experience rather than by the actual state of the process
Solution
A hybrid of a physical model and ML: a digital twin of the unit forecasts the process response to setpoint changes (what-if analysis)
−3–7% energy and reagent consumption
Pain
Full APC systems cost $1–3 million per unit and require months of step tests
Solution
Operator advisor (open-loop): recommendations on a separate screen, read-only access to the historian — no closed loop and no shutdowns
~80% of the APC effect at a fraction of the price, with no intervention in the control system
Pain
Waiting 4–8 hours for a laboratory analysis: the operator runs the unit “by yesterday's sample”
Solution
Soft sensors: real-time prediction of the quality parameter from process parameters
Control by actual values, not by a lagging laboratory
Pain
Heat-exchanger fouling: cleanings by the calendar, 1–3% of fuel consumption lost
Solution
Heat-transfer coefficient calculation + ML forecast of degradation + cleaning-schedule optimization
−10–20% fuel costs in feed-preheat sections
What data we work with
- Process control system historian (PI, PHD) — 2–3 years of tags
- Laboratory analyses (LIMS)
- Process procedures and regime charts
- Feedstock quality data
What the customer gets
- Shift-variability audit on a historian export — potential quantified in money
- Soft sensors for key quality parameters
- Operator advisor screen with setpoint recommendations
- A/B assessment of the effect across shifts over the pilot period
What a digital twin is in industry
It is not a 3D visualization of the plant but a working model of the process: a hybrid of physical equations and machine learning that answers in real time “what happens if we change this setpoint” and “which regime is optimal for the current feedstock”. On a typical process unit, regime quality varies between shifts by 2–5% — the twin brings every shift up to the level of the best one.
How the approach has been proven in Kazakhstan
Industry benchmarks are public. The “digital advisor” on a jigging machine at Donskoy GOK (ERG) cut chromium losses by 2.4% — about 123 million tenge (KZT) a year from a single machine. Samruk-Kazyna Ondeu in Stepnogorsk deployed a digital twin of its sulfuric acid plant with a 5–8% cost reduction. The combined effect of AI solutions across the ERG group in 2025 was 55.7 billion tenge. This is the same “advisor” architecture we build for customers without in-house IT teams.
How the solution works
- Data: 2–3 years of process control system historian data (PI, PHD, WinCC), laboratory analyses (LIMS), feedstock quality
- Models: quality soft sensors (gradient boosting/neural networks), a process-response model, a setpoint optimizer (Bayesian optimization / offline RL on historical “best regimes”)
- Operating mode: advisor — read-only connection to the historian, recommendations on a separate operator screen and in a report to the unit manager. A closed loop only as phase 2, at the customer's decision
- Deployment: on-premise. A read-only connection removes 90% of information-security and metrology objections
Why an advisor rather than full APC
Classical APC (Honeywell Profit Controller, AspenTech DMC3) costs $1–3 million per unit and requires months of step tests that disturb the process. An advisor built on historical data delivers 70–80% of the APC effect without step tests or shutdowns, in months rather than years — and applies to units where full APC is not economically justified.
FAQ
Frequently asked questions
What chief engineers, security and procurement teams usually ask — with straight answers.
Next step
Request a shift-variability audit
Tell us about your task — we'll come back with a data audit plan and an effect estimate within two business days.
- 1A 30-minute call: the task, the data, who signs off on security
- 2Data audit or a retrospective analysis on an anonymised export
- 3An 8–12 week pilot with success criteria agreed before the start
NDA from the first contact
Reply within one business day