Your partner in production optimization and digital twins.

Your plant drifts. Your model shouldn’t.

Live, self-learning digital twins that optimize in real time — simple to use, understand, and maintain.

Physics Where It Counts · ML Where It Helps
Oil & Gas
LNG
Refining
Petrochemicals
Gas Processing
Clean Energy

AspenTech Independent Solution Provider (ISP) · Presented at AspenTech Optimize 2026

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Live digital twins
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Instrument tags / digital twin
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Performance-monitoring / digital twin
Our Solutions · The Process Map

From the wellhead to refined products

Follow the hydrocarbon through the plant — and see the digital-twin solution that plugs in at every stage.

What makes our twins different: they're built from first principles (equation-oriented models of the real process), they self-learn (re-calibrating to live plant data every cycle), and they run closed-loop (sending optimized targets back to the plant continuously).

Anukoolan · Process Map Oil and Gas Value Chain · 6 stages

Not sure where to start? We scope a single-unit pilot anywhere along the chain.

Global Footprint

Live installations, worldwide

The full network on one map — live installations worldwide. Filter by fleet.

Anukoolan · Global Ops 25 projects · 10 countries ← Swipe the map to explore →
Regional Focus

India Region

8 sites · incl. HQ
Regional Focus

Middle East Region

6 client sites
Self-Learning Digital Twin

One model. Three jobs. Zero decay.

Built on equation-oriented Aspen HYSYS — the modelling approach that makes plant-wide optimization tractable. The twin runs an automated 20–30 minute cycle: reconcile, calibrate, optimize, publish — continuously.

  • Ingest — historian data (IP.21 / PI) validated and reconciled automatically.
  • Calibrate — self-learning reconciliation model configured with an innovative method that re-tunes parameters to the current plant state.
  • Optimize — the EO model finds the most profitable operating point within constraints.
  • Publish — targets and KPIs delivered to dashboards and APC every cycle.
  • What-if case studies — run scenario studies on the calibrated twin to evaluate feed, operating, or design changes before applying them in the plant.
Technology stack: Aspen Plus · Aspen HYSYS · Aspen Custom Modeler (ACM) · Aspen Online · MySep Engine
Model acceptance: Material balance: as good as client plant data · Uptime ≥90% · Temperature, Flow: as per RWMSD
Data & visualize: Data Historian · Dashboard technology of client’s choice
Industries

Industries we serve

Oil & Gas
LNG
Refining
Petrochemicals
Gas Processing
Clean Energy
Our Team

Decades of frontline process expertise

HS

Dr. Hiren Shethna

Founder & Optimization Lead
  • M.Tech IIT Bombay · PhD University of Manchester
  • 26 years at AspenTech and Saudi Aramco
  • Implemented 25+ digital twin models worldwide
KP

Dr. Karuna Potdar

Simulation & Modelling Lead
  • B.Chem.Eng + PhD, ICT Mumbai
  • 32 years at Reliance Industries
  • Led RIL's simulation & modelling team
  • 15+ plant models deployed across RIL
SS

Shailesh Sakarkar

Training & Simulation Lead
  • B.Tech, Laxmi Narayan Institute, Nagpur
  • 30 years in the process industry
  • 80+ simulation training programs delivered globally
AspenTech Optimize · Conference Paper

CDU Digital Twin Built with Equation-Oriented Modelling in Aspen HYSYS and Custom Modeling Technology Helps Refinery Improve Margins

Co-authored with AspenTech.

AspenTech Optimize 2026 · Conference Paper

Development of a High-Fidelity FCC Riser Model Through ACM Integration for INDMAX Technology

Co-authored with IOCL R&D Centre Faridabad and AspenTech — a proprietary 27-lump kinetic model validated against commercial plant data.

FAQs

Knowledge sharing

Contact

Talk to us

Unit type, the problem, and whether plant data or a model already exists — three lines are enough to start.

Reply within one business dayNDA-ready, no obligation
Direct

Email

info@anukoolan.com

Anukoolan Solutions Private Limited · Pune, India

Social

Find us online

Pilot

Start small

We can scope a single-unit pilot of the digital twin to demonstrate value before any wider rollout — with defined acceptance criteria from day one.