Lummus Digital

LUMMUS DIGITAL

Proof Points

Outcomes our customers have defended in the P&L. Production deployments. Day-one results. Sustained intelligence — across refining, petrochemicals and process industries.

CASE / 01

Hindustan Petroleum Corporation Limited (HPCL)

Visakh Refinery

EFINING · INDIA · LC-MAX DIGITAL SUITE

Turning residue into margin

A leading Indian refinery commissioned one of the world’s first 3.55 MMTPA LC-MAX® residue upgrading units — converting up to 93% of vacuum residue to high-value distillates at one of the industry’s highest complexity levels (NCI 11.6).
To manage the risks of a first-of-its-kind startup, the refinery deployed India’s first LC-MAX® Digital Suite — enabling end-to-end operational intelligence from day one. Real-time data integrated with licensor process models established a trusted digital foundation for safe commissioning, faster stabilisation, and sustained operational performance.

$2.5/bbl

LC-MAX® GRM uplift

Equivalent to ~$275M annual value at 110 KBPD capacity.

+10%

Distillate yield gain

Versus baseline. Optimisation decisions now simulated before execution.

$1–2M/yr

Phase 2 digital upside

Incremental margin leverage from continued hybrid-model optimisation.

The challenge

  • High startup & safety risk during pre-startup and feed cut-in
  • Fragmented visibility across critical LC-MAX® process systems
  • Inconsistent catalyst, inventory and mass-balance tracking
  • Limited visibility into early feed characterisation and yield estimates
  • Manual, time-intensive readiness and performance calculations
  • Cross-functional misalignment during a critical startup phase
The solution

The LC-MAX® Digital Suite, deployed in two phases. Phase 1: a production-ready digital foundation live before feed cut-in — integrating real-time plant data with proprietary licensor process models, delivering a unified operational view and automating critical engineering calculations.


Phase 2 builds on this foundation with advanced hybrid models combining first-principles knowledge with data-driven learning, driving ongoing optimisation in yields, energy performance, and operational efficiency.

The impact
  • System production-ready before feed cut-in
  • 2,000+ automated calculations every 2 hours
  • Real-time monitoring of catalyst health, reactors, hydrogen systems
  • Trade-offs (yield vs. energy) quantified before each decision
  • Single, trusted operational view from pre-startup through stabilised operation

The challenge

“India’s first LC-MAX® Digital Suite developed by Lummus Digital goes live at HPCL’s Visakh Refinery.”
CASE / 02

Hydrocracker Performance Monitoring

KBPD · Full conversion
REFINING · HYDROCRACKER PERFORMANCE MONITORING

Reinventing hydrocracker performance

A 400,000 bpd full-conversion refinery set out to strengthen hydrocracker reliability, preserve catalyst health, and improve energy efficiency — under tight operating windows, at high severity, and with limited real-time visibility into unit health.

By deploying Lummus Digital’s integrated performance monitoring and advisory platform, the operator delivered measurable gains across unit stability, energy recovery and catalyst lifecycle — establishing a scalable digital foundation for continuous, sustained performance improvement.

$4M+/yr

Annual value identified

From energy recovery, stability gains and reduced downtime risk.

2–3mo

Catalyst cycle extension

Through earlier detection of thermal excursions and WABT drift — deferring $15–25M in reload costs.

2–3%

Energy efficiency improvement

Across critical heat exchange and fired-heater equipment.

The challenge

  • Limited real-time visibility into hydrocracker and reactor health
  • Delayed detection of catalyst degradation and thermal issues
  • 2–5% throughput gap vs. design from hidden constraints
  • Excess energy consumption — $3–5M/year in recoverable value
  • Each day of unplanned downtime = $1–2M in lost margin
The solution

An integrated digital performance-monitoring and advisory platform purpose-built for hydrocracker operations. Real-time data from control, historian and engineering systems consolidated into a single operational view — supported by advanced engineering models and normalised KPIs.



150+ structured KPIs unified from disparate systems. Threshold-based and predictive alerts. Centralised dashboards for operators, engineers, and management on a single shared view.

The impact

  • 40–60% faster issue detection — hours, not days
  • Improved reactor stability through early WABT detection
  • 60–70% reduction in manual reporting effort
  • Better asset utilisation through constraint visibility
  • Scalable foundation for AI APC and predictive maintenance
By integrating data, insights and deep process expertise, the refinery established a scalable foundation for more reliable, efficient and intelligent hydrocracker operations.
CASE / 03

Olefins Complex Digital Twin

TROUBLE SHOOTING . WHAT-IF ANALYSIS

OLEFINS • ASIA-PACIFIC • DIGITAL TWIN PROGRAMME

Turning resilience into value.

One of the largest integrated petrochemical operators in the Asia-Pacific region deployed unit-wide digital twins across an olefins complex, integrating troubleshooting, what-if analysis, and equipment failure prevention. The solution provides strategic capability for collaborative analysis, market flexibility, and resource allocation demonstrated through major production-impact case studies.

$49M

COMPRESSOR INCIDENT SAVING

Saved approximately 30 days of lost production via automated surge response modelling.

100%

CAPACITY PRESERVED

Maintained full production during a month-long reflux pump maintenance outage without product loss to vent.

>$4M

FLARE OPTIMISATION SAVINGS

Realistic temperature profiles identified expensive mitigation could be minimised during scheduled turnaround.

The challenge

  • Significant production loss risk from compressor reverse rotation events.
  • Vibration issues on 2 of 3 demethanizer reflux pumps requiring a month of off-reflux operation.
  • Overly conservative steady-state flare analysis suggesting multi-million dollar complex header mitigation options.
  • Requirement for unified “what-if” actions to avoid plant trips and vent losses.
The solution
  • Digital twin replicated compressor incidents to model surge response and recommend scenario actions.
  • Validated operational actions to maintain ethylene product specs (0.78 mol% H2-rich stream) without reflux.
  • Predicted realistic temperature profiles along the flare header system to optimize mitigation design.
  • Unified platform for collaborative analysis and flexibility.

The impact

  • $49M saved by avoiding 30 days of production loss during the compressor rotation event.
  • 100% throughput maintained throughout repair with ethylene product loss to vent completely avoided.
  • >$4M in CAPEX avoided by utilizing higher fidelity temperature modeling vs. standard steady-state.
  • Validated model performance allowed the plant to operate without trips during the outage.

MORE PROOF POINTS

Engagements across the value chain

Production deployments at major operators across India, the Middle East, and the Americas — each engagement with measurable outcomes defended in the P&L.

Look, Listen & Feel (LLF)-
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Transform your Asset Management from a Cost Center To a Revenue Generator

HPL’s Look, Listen, and Feel (LLF) automation transformed operations and plant reliability with real-time asset monitoring. By reducing manual intervention and ensuring precise geo-location tracking, LLF optimizes asset management and streamlines decision-making.

ePOD Electronic Proof of Delivery-
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Smart Logistics Management Solution for Efficiency, Transparency & Control

Haldia Petrochemicals (HPL), implemented the Electronic Proof of Delivery (ePOD), to enhance logistics and supply chain management. The solution provides near real-time tracking, automates delivery documentation, and ensures secure blockchain transactions.

Catalyst R & D

Faster Hydrocracking Catalyst Discovery With Agentic AI

Petrochemical R&D is being transformed by Agentic AI through accelerated discovery and optimization of hydrocracking catalysts using a semi-autonomous, human-in-the-loop workflow. By combining AI-generated hypotheses, multi-agent reasoning, and predictive screening, the approach reduces trial-and-error experimentation while balancing selectivity, activity, lifecycle, and cost.

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