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Energy & Materials

The Century of India: Engineering AI-Driven Energy Growth

A CHEMICAL TODAY feature

Debdas Sen, CEO of TCG Digital and Executive Director at Lummus Digital, shares his perspectives on how AI is shaping the future of energy and industrial operations. Featured in Chemical Today Magazine (March 2026), the interview highlights how AI-driven technologies enable refineries and plants to operate in a more predictive and insight-driven manner.
AI helps improve operational efficiency, optimize throughput and margins, and identify potential disruptions early. Solutions built on mcube™ combine deep process expertise with machine learning, generative AI, and large language models to convert operational data into actionable intelligence at scale.

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India’s First-of-its-Kind LC-Max® Digital Suite Powered By mcube™ Goes Live at HPCL’s Visakh Refinery

January 5, 2026

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Mumbai, India [January 5, 2026]

Hindustan Petroleum Corporation Limited (HPCL) has successfully deployed India’s first first-of-its-kind LC-Max® Digital Suite at its Visakh Refinery, marking a milestone in the digital transformation of India’s downstream refining sector. The deployment strengthens operational reliability, energy efficiency, and profitability across HPCL’s Residue Upgradation Facility (RUF)—a complex unit that upgrades low-value residue into high-value distillates.

Developed and delivered by Lummus Digital and powered by mcube™, the digital suite integrates real-time monitoring, predictive analytics, and AI-driven optimization. mcube™ securely ingests, contextualizes, and analyzes complex plant data to drive operational performance, energy efficiency, and sustainability.

Key capabilities powered by mcube™ include:

  • Real-time plant data ingestion and streaming

  • Secure contextualization of complex operational data

  • Advanced analytics and predictive intelligence enablement

  • Economic performance visualization across revenue, cost, and margins

  • Energy and carbon data monitoring aligned with ESG priorities

  • Hydrogen, utilities, and asset data orchestration at the enterprise level

  • A scalable and secure Enterprise AI foundation for LC-Max applications

Shri Vikas Kaushal, Chairman and Managing Director, HPCL, called the deployment transformative, noting, “As the country’s first LC-MAX® unit, this deployment positions HPCL at the forefront of technological and operational innovation. By integrating advanced intelligence, we are setting a new benchmark”

Debdas Sen, Joint Executive Director, Lummus Digital and CEO of TCG Digital, added, “With hybrid modelling and an integrated digital backbone built on mcube™, the LC-MAX® Digital Suite will be a valuable operational asset for HPCL—delivering sustained operational value.”

Ujjal Mukherjee, Joint Executive Director, Lummus Digital and CTO of Lummus Technology, remarked, “By combining decades of proven process technology from Lummus with AI-assisted optimization, this deployment represents exactly the kind of integrated innovation the downstream refining industry needs to drive margins.”

Arun Arora, CTO, Chevron Lummus Global, adds, “CLG is proud to be part of the successful deployment of advanced Digital Suite for the world’s first LC-MAX® unit at HPCL, designed for bottom-of-the-barrel upgrading of vacuum residue. This cutting-edge solution seamlessly integrates CLG’s proprietary process technology know-how and optimization tools, delivering real-time insights to maximize throughput, enhance reliability, and drive superior operational efficiency”

Introduction: From Proof-of-Concept to Enterprise Performance

Across the Process Industries, Artificial Intelligence (AI) has evolved from an emerging idea to a proven driver of efficiency and insight. Leading organizations have demonstrated that AI can predict equipment failures, optimize energy use, and improve plant reliability. Yet for most, these achievements remain trapped within pilot programs, valuable in isolation but limited in scale. 

The true opportunity lies in translating these local proofs into enterprise-wide performance. Doing so requires more than algorithms; it demands structure, governance, and a deep connection between data, operations, and financial outcomes. Only then can AI move from the lab to the control room and from experimentation to measurable enterprise value. 

The Challenge:Why AI Pilots Stall Before Scale

Despite significant investment, most industrial AI initiatives remain confined to the pilot stage. A recent study found that 74% of companies say they struggle to scale AI and turn pilots into full-value operations. This highlights a persistent gap between proof-of-concept success and enterprise-level impact — a challenge that continues to constrain digital transformation across the Process Industries.

Several structural challenges explain why:

Siloed data and infrastructure

Operational data (OT), maintenance systems, and enterprise IT remain fragmented, limiting visibility and preventing a unified operational view.

Limited interoperability

AI models developed in isolation often fail to connect seamlessly with plant control systems (DCS/APC) or data historians.

Undefined value metrics

Many pilots focus on model accuracy rather than business outcomes such as yield, energy efficiency, or uptime.

Lack of ownership or lack of a practical companywide AI implementation plan

Without clear governance or accountability, AI efforts remain in academic experiments rather than operational tools.

The outcome is predictable — dozens of isolated AI initiatives that look promising on paper but fail to move the EBIT needle in any meaningful way.

A Composite Scenario: From Isolated Success to Scalable Impact

These structural challenges—where operational, maintenance, and enterprise IT data remain trapped in isolated silos, and AI models are unable to interoperate with foundational systems like DCS, APC, and plant historians—surface in operations as fragmented intelligence, uneven performance, and a systemic inability to propagate successful pilots across the enterprise.

Recognizing this fragmentation, it is important to set a clear objective: to build a unified AI framework that could deliver reliability and profitability at scale.

To support this shift, we use mcube™, TCG Digital’s Integrated AI Platform, which acts as a common intelligence layer across plants. At its core is an ontology-driven semantic layer that gives every data element—from sensor tags to lab results—a consistent, unambiguous meaning. By mapping all incoming data to a canonical vocabulary, mcube™ creates a unified knowledge graph that strengthens governance and ensures AI models operate on trusted, context-rich information.

Building on this semantic foundation, mcube™ serves as an autonomous AI fabric that layer intelligence over existing systems without requiring rip-and-replace modernization. It continuously integrates and contextualizes data from DCS/APC, historians, LIMS, ERP, EAM, and MIS, combining real-time and batch inputs into a single, actionable view of operations. Its data-source-agnostic design allows seamless connectivity with any IT or OT system, bridging gaps between operations, maintenance, and business functions.

mcube™ supports traditional machine learning, hybrid physics-ML models, generative AI, and agentic AI for decision support and autonomous action. Secure, standardized interfaces ensure that the platform enhances existing digital investments while progressively adding intelligence across sites. Deployable on cloud, on-premises, or hybrid environments, mcube™ provides scalable governance and democratized access to insights, enabling plants to transition from reactive operations to predictive and prescriptive performance—ultimately improving reliability, energy efficiency, and profitability.

Evolving Metrics: From OEE to Financially Linked Performance Indicators

While unified data and interoperability address the technical barriers to scaling AI, success ultimately depends on measuring what truly drives enterprise value. OEE has long been the standard for plant performance, but it reflects equipment efficiency—not margin improvement, financial risk reduction, or EBIT contribution. In today’s environment of volatile energy costs, variable feedstocks, and increasing reliability demands, OEE offers only a partial view.

To scale AI beyond isolated pilots, organizations must shift toward EBIT-linked performance metrics that capture real financial impact. Metrics such as EBIT per unit of throughput, cost-to-serve by product grade, predictive reliability value, energy margin contribution, adaptability to market conditions, and carbon intensity per EBIT dollar reveal how operational decisions influence profitability and resilience.

Just as importantly, AI pilots must be evaluated against these financially grounded KPIs. Without this alignment, pilots may show technical improvement without demonstrating business value.

When plants measure outcomes through an EBIT-focused lens, AI moves from experimentation to a scalable driver of margin growth and operational excellence.

A Structured Path to Scalable AI: From Pilot to Autonomy in 90 Days

Resolving the technical fragmentation is only half the challenge — organizations also need a clear, disciplined path that builds ownership, governance, and workforce readiness to ensure AI scales. The journey from pilots to autonomous operations begins not with machines, but with mindsets. While technology defines what’s possible, it is people — their decisions, discipline, and collaboration — that determine what scales. For most organizations, the first 90 days represent the critical inflection point between experimentation and execution. It’s the period where vision becomes action — aligning leadership, enabling the workforce, and embedding AI into the rhythms of daily plant performance. TCG Digital helps enterprises navigate this transition through a structured 90-day roadmap designed to accelerate progress toward self-optimizing operations. The approach blends strategic alignment, AI enablement, and human transformation — ensuring that every technical milestone is matched by organizational readiness and measurable business value.

TCG Digital works alongside clients and their AI partners to connect strategy with execution:

  • Leadership Alignment:

    Executive workshops to unite business vision with operational priorities.

  • Data & Pilot Readiness:

    Joint maturity scans, pilot selection, and success metric definition.

  • Workforce Enablement

    Training programs and copilots that empower operators with AI-assisted decision-making.

  • Integration & Governance

    Linking pilot workflows with plant control systems under supervised automation, supported by MLOps frameworks for model monitoring and retraining.

  • Change Management

    Preparing teams for human-in-the-loop autonomy through continuous coaching and KPI-linked incentives.

  • Executive Review:

    Consolidating results, measuring impact, and setting up a 6–12 months roadmap for scaled deployment.

Conclusion: The Path Forward

Industrial AI has reached an inflection point. The real differentiator is the capability to scale with intent — bringing data, intelligence, and people together under a unified operational vision. Success comes from structured execution that connects technology with measurable business impact.

We help enterprises make this transformation real — embedding AI into the fabric of plant operations, control systems, and decision-making. The outcome is a smarter, more resilient operation that continuously learns, adapts, and optimizes performance.

The path forward is clear: move beyond pilots, scale with purpose, and let AI drive sustainable, enterprise-wide value.

WEBINAR

webinar-logo-mcube

AI in Industrial Processes:

Optimizing processes & reducing OPEX with AI-Plant Control

  • November 18th | 10:00 - 10:45 AM CST
Watch webinar replay

Industrial operations are entering a new phase, where control systems not only respond but learn, adapt, and optimize in real time. Traditional Model Predictive Control (MPC) has reached its limits in handling today’s process variability and complexity. The next step forward is AI-Based Plant Control (AIPC) — a proven approach delivering measurable gains in reliability, yield, and OPEX.

Hear discussion on how industry leaders are using Linde’s AOPS™ AIPC powered by mcube™ to build autonomous, self-optimizing plants. The session includes a real-world case study – from implementation challenges to measurable results—and the success factors behind scaling AI-based control.,

Meet our Speakers

Arunava Mitra

Senior Vice President, TCG Digital

Rajeev Limaye

Director, Head of Advanced Ops Services, Linde Engineering

Daniel Neal

Advanced Operations Consultant, Linde Engineering

The audience represented core process industries, with refinery/petrochemical/chemical professionals forming the largest segment and engineering roles dominating participation.

Most attendees were aware or knowledgeable about MPC, but still early in hands-on experience, highlighting the need for simpler, scalable optimization solutions.

Notably, 47% expressed interest but uncertainty, while 47% said they would “absolutely” or “most likely” consider AI-based closed-loop control, signaling strong openness toward adopting AIPC in real operations.

AIPC: Proven Performance Gains in Full-Scale Industrial Operations

The webinar highlighted how AI-Based Plant Control (AIPC) is delivering measurable impact across high-throughput production units. Real plant deployments have shown up to an 80% reduction in control-application maintenance effort, a 2–3% increase in product yield, and a 1–2% improvement in energy efficiency beyond traditional MPC. Plants also reported faster, smoother load transitions and consistently stable 24/7 performance across multiple sites. 

Watch webinar replay

mcube™: The Architecture Enabling AI in Industrial Plants.

AI fails without the right foundation. This teaser captures how mcube™ delivers the data throughput, AI capability, and industrial-grade engineering needed to run AIPC in real production environments.
Watch webinar replay

Q&A Session Highlights

In our webinar on AI-based plant control, the Q&A session sparked some of the most practical and forward-looking discussions.

This short teaser brings together a few standout moments—ranging from data-processing speed on the mcube™ platform to how AIPC handles plantwide optimization in complex operating environments.

Watch webinar replay

AOPS™ AI Plant Control (AIPC) is a closed-loop self-learning, model predictive control (MPC). With a deep learning model retraining built on TCG Digital’s mcube™ platform, AIPC model learns from the operating data and adapts without constant reconfiguration. Unlike traditional MPCs, AIPC excels in non-linear or large operational envelopes. In today’s climate, economic headwinds continue to drive organizations to do more with less and optimizing plant operations in real time reducing OPEX.

Our solution is a result of a strategic collaboration between:

Linde, a global leader in industrial gases and engineering, with 64,000+ employees and $33B in 2024 sales, operates 1000+ plants in 80+ countries from seven Remote Operation Centers located globally. Advanced automation software, AOPS IGNITE platform, developed in-house by Linde over 30+ years and operating in 300+ plants has made it possible to run these plants in autonomous mode. AIPC, part of the AOPS IGNITE suite is commercially available.

mcube™, the enterprise and Agentic AI platform by TCG Digital delivers real time intelligence and optimization that continuously enhance process performance, leveraging process digital twins and optimizers to simulate, predict, and fine-tune outcomes. Built to be enterprise-ready and scalable, the platform ensures seamless integration across plant systems and global sites, while maintaining the highest IT security standards.

Introduction:
Reliability Beyond Conventional Predictive Maintenance

In asset-intensive industries such as refining, petrochemicals, and continuous processing, the financial, safety, and reputational implications of unplanned downtime are significant. Traditional predictive maintenance systems have improved reliability, yet they often fall short in critical areas:

  • Weak signals of failure are missed until they escalate into major incidents.
  • Operators face alert fatigue from excessive, non-prioritized notifications.
  • Troubleshooting relies heavily on manual fault tree analysis, extending recovery times.
  • Maintenance planning is decoupled from financial impact, limiting business alignment.

A leading petrochemical operator confronted precisely these issues within its high-pressure (HP) and depressurized (DP) operations. The organization required a framework that not only detected anomalies but also contextualized risks, accelerated resolution, and linked reliability directly to business outcomes.

The Challenge

The operator’s monitoring systems produced vast amounts of data but lacked the intelligence to distinguish critical events from background noise. Failures within HP instrumentation and DP process systems frequently progressed unnoticed until they triggered costly outages.

The core challenges were:

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Delayed or no anomaly detection in complex mechanical and process domains.
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Unclear alert prioritization, leading to resource misallocation.

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Manual, time-intensive root cause analysis that slowed recovery.

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Limited financial visibility, making it difficult to align reliability initiatives with business priorities.

The Reliable AI Intervention

To address these challenges, the operator deployed Reliable AI, an agentic framework leveraging Generative AI and Large Language Models (LLMs). The solution integrated anomaly detection, predictive modeling, and intelligent retrieval to provide actionable, plant-specific insights.

The following framework illustrates how Reliable AI was applied across HP and DP operations to detect anomalies and generate actionable insights.

How GenAI Strengthened Reliability

From unstructured data to insights

Maintenance logs, operator shift notes, and incident reports were ingested by LLMs, transforming unstructured text into structured signals that complemented sensor data.

Contextual diagnostics

GenAI connected new anomalies with past incidents, highlighting likely causes and recommended fixes based on plant-specific history.

Adaptive learning loop

As engineers validated or rejected AI recommendations, the model continuously refined its accuracy, improving predictive reliability over time.

Natural-language interaction

Engineers could query the system in everyday language “Show me previous DP compressor failures with similar vibration patterns” and receive precise, context-aware answers.

Key components included

Anomaly Detection

Isolation Forest models continuously monitored HP and DP units to detect subtle deviations in real time.

RAG-Based Retrieval

Plant-specific historical incidents and resolutions were embedded into the model, allowing rapid recall of relevant precedents.

Financial Overlay

Reliability risks were evaluated not only technically but also in terms of cost, downtime impact, and production losses.

Predictive Modeling with Tagged Data

Systematic use of historical attributes (anomaly status, cause, temporary and permanent fixes) enabled forecasting of failures.

Automated Fault Tree Traversal

The AI streamlined diagnostic workflows, significantly reducing time-to-resolution.

The Results

Within six months of deployment, the operator achieved measurable and business-critical improvements across its HP and DP operations.

These outcomes demonstrated that the integration of GenAI-driven intelligence into reliability workflows improved operational stability, safety, and financial performance simultaneously.

Strategic Implications

Building on the success of the initial deployment, the operator is extending Reliable AI across additional facilities, embedding reliability as a core driver of operational performance. The broader implications are both immediate and forward-looking:

Operational Relevance

Plant-specific insights reduced downtime and alert fatigue, proving that GenAI can be integrated into daily engineering workflows without overwhelming operators.

Business Alignment

By overlaying financial metrics on reliability risks, maintenance actions were directly tied to profitability, capital efficiency, and risk reduction.

Autonomous Reliability Agents

Future versions will be capable of executing routine maintenance decisions with minimal human intervention.

Deeper Financial Integration

Asset reliability metrics will be directly correlated with profitability, risk exposure, and shareholder value.

Cross-Sector Scalability

The framework shows strong applicability in power, utilities, and discrete manufacturing, extending benefits beyond petrochemicals.

Evolving Engineering Roles

As automation reduces routine analysis, engineers will shift from reactive troubleshooting to proactive optimization and reliability strategy.

Interested in what lies ahead? You can watch our webinar, “Case Discussion: Asset Reliability and Operational Excellence using Gen-AI and AI in Process Industries,” on demand. Register to access the full recording and explore these future developments in detail.

Register here

Conclusion

The case of this petrochemical operator underscores how Reliable AI redefines predictive maintenance. By bridging anomaly detection, contextual reasoning, and financial visibility, the framework reduced downtime, optimized maintenance, and strengthened safety.

This demonstrates that the future of plant reliability lies not in more data or dashboards, but in intelligent, adaptive systems that augment human expertise with real-time, context-rich, and business-aligned guidance.

Reliable AI moves beyond monitoring, providing a practical framework for achieving higher reliability and operational resilience.

Webinar

Case Discussions:

Gen-AI and LLM’s for Reliable, Efficient Process Operations

27th August 2025 6:30 PM-7:00 PM IST

Watch the Recording

About the Webinar

Operational efficiency and asset reliability remain top priorities for
process industry leaders navigating complex market and operational conditions.

In this session, industry experts from TCG Digital discussed how leading organizations are using Generative AI and Large Language Models (LLMs) to address these challenges improving performance, reducing variability, and strengthening decision-making across operations.

Through real-world case studies, the discussion highlighted digital solutions delivering tangible results in areas such as throughput, yield, and equipment uptime. You will gain practical insights into what successful implementation looks like, and how to align digital initiatives with broader business and operational goals.

Speakers:

Sayan Basu,
Senior Director at TCG Digital

Nimit Arora,
Senior Director at TCG Digital

Watch the Recording

Key Topics Covered

Intelligent Operations in Practice

Real-Time Optimization

Predictive Asset Management with Generative AI

Who Will Benefit

This on-demand session is designed for industrial leaders and practitioners focused on driving reliability, efficiency, and performance through digital transformation. It will be especially valuable for professionals in:

  • Operations & Production: Optimizing throughput and yield with AI-driven decisions.
  • Maintenance & Reliability: Advancing from reactive to predictive asset management.
  • Process Engineering & Control Systems: Integrating AI with plant control systems for real-time optimization.
  • Digital Transformation & Innovation: Enabling enterprise-wide adoption of Generative AI and LLMs.
  • Industrial IT / OT & Data Science: Building connected data ecosystems that turn insights into outcomes.

Key Takeaways

  • A clearer understanding of how AI can fit into your operational strategy
  • A practical pathway for moving beyond automation to predictive and autonomous operations
  • Real-world examples of how digital tools are delivering measurable value
  • Insights into what's working today and what's next in the digitalization journey
Watch the Recording
Discover how mcube™ supports process manufacturing businesses in driving efficiency, optimizing yield, and improving profitability through practical automation and real-time, data-driven decisions.
Download Brochure

TCG Digital at
NOG Energy Week 2025

29 June - 3 July 2025

ICC, Abuja, Nigeria

In collaboration with PANA Holdings, Strategic Partner
for Oil & Gas in Nigeria

Schedule a meeting

TCG Digital is proud to join hands with PANA Holdings—a business platform (based in Nigeria) that has set sail to become a reference point of unmatched enterprise standards in the Oil and Gas, Mining, Power, Process Industries, Agriculture, and Industrial Real Estate sectors.


As their exclusive AI partner at NOG Energy Week 2025, we’re bringing TCG Digital’s mcube™ –powered autonomous solutions for O&G downstream to the biggest energy conversation in Africa.

NOG Energy Week: A Global Platform for Regional Action

The NOG Energy Week Conference & Exhibition is the official meeting ground for leaders across Sub-Saharan Africa’s energy landscape. The 24th edition will bring together visionaries, policymakers, and innovators to discuss investment, sustainability, and digital innovation across oil, gas, renewables, and infrastructure.

As part of PANA Holdings’ expansive booth at the event, TCG Digital will showcase its data and advanced intelligence platform —mcube™ and how it’s reshaping the Oil & Gas downstream value chain, from powering intelligent operations to transforming profitability.

Experience mcube™ in Action — at the PANA Holdings Booth

We’re not just presenting a product—we’re demonstrating a vision. mcube™, TCG Digital’s flagship Data-AI platform, has been built to help enterprises protect and grow EBIT through intelligent automation, AI-driven optimization, and real-time decision support.


At the event, our experts will be showcasing how mcube™ is revolutionizing downstream operations with:

Automated Plant Control

Self-regulating feedback loops that adjust pressure, flow, and temperature with minimal intervention.

Real-Time Process Optimization

Live tuning for catalyst efficiency, yields, and energy based on fluctuating feedstock or market conditions.

Digital Twins for Process Units

Predict failures, simulate stress scenarios, and validate changes before implementation.

GenAI-Driven Predictive Maintenance

Use LLMs to foresee critical failures and auto-generate SOPs based on real-time asset condition.

24/7 Remote Monitoring & Advisory Hubs

Intelligent diagnostics, event correlation, and risk-scored alerts—available anytime, anywhere.

Why mcube™ is built for Oil & Gas:
From Intelligence to EBIT

mcube™ isn’t just another analytics tool—it’s an advanced intelligence platform engineered with deep industrial logic and shaped by our ecosystem of partners.

 

Rooted in real-world process innovation, large-scale operations, and industrial expertise, our approach blends hybrid process modelling, AI-driven automation, and domain knowledge to help companies not just adapt and survive, but thrive.

Velocity to Value: mcube™’s Business Impact

From real-time economic optimization to multi-million-dollar inventory savings, mcube™ delivers measurable impact fast:

7% Yield
$80/MT

Margin Improvement

100% Accurate

Allocation | $10M Raw Material Savings

$5.5M Saved

in Logistics Optimization

Smart Turnarounds

with 20x Faster Insights

ePOD-based Tracking

↓ Pilferage, ↑ Billing Accuracy

And that’s just the beginning. We’re continuously scaling use cases across lab analytics, maintenance, pricing, procurement, and beyond.

“VELOCITY TO VALUE” is not just our mantra but what we
achieve for enterprises – rapid ROI realization

Intelligent Operations:
Achieving Excellence and Reliability in Process Industries through AI

SPEAKER
SAYAN BASU

Senior Director, Analytics - Europe, TCG Digital

DATE: 30 June 2025
TIME: 02:00 pm (WAT)

Discover how TCG Digital is redefining operational excellence and asset reliability in process industries through hybrid AI models and generative intelligence.

Watch Sayan Basu unveil a dual approach—real-time optimization and AI-powered anomaly detection—that empowers plants to maximize yield, minimize risk, and elevate decision-making.

Let’s Connect

Visit us at the PANA Holdings Booth
Schedule a meeting

AI-Powered Conversion Optimization for a Leading European Downstream Operator

~2%​

increase in Conversion Efficiency
Enhanced operational stability resulting in higher throughput
Better catalyst management reducing waste

Business Challenge​

A major European downstream oil & gas operator sought to improve conversion efficiency and operational reliability within its LC-FINING unit, which processes heavy feedstock into lighter, high-value products. Despite sophisticated DCS systems and historical data, the plant faced difficulties in detecting early performance deviations due to variability in feed composition, catalyst fluctuations, and changing process dynamics. The lack of real-time correlation between key parameters—such as viscosity, reactor firing, and feed pump behavior—and the absence of a structured optimization approach limited their ability to consistently maximize yield and maintain process stability.

Solutions Deployed

  • A remote monitoring framework was deployed, enabling real-time visibility into process behavior flagging developing anomalies, recommending corrective actions ahead of time.
  • ML-Based Conversion Optimization Engine – ML models were trained on DCS data, lab reports, and operating history to predict:
    • Short Heavy Fraction Tolerance (SHFT), Feed Operability Index, Conversion potential under multiple operating constraints (e.g., reactor exotherm, catalyst availability)
  • Real-Time Dashboard with live insights into:
    • Feed integrity and reactor behavior
    • Catalyst performance and SHFT trends
    • Optimization targets and operating recommendations
  • Correlated Alerting System – Instead of isolated alarms, alerts were configured based on the combined movement of multiple process KPIs, enabling faster identification of pattern-based anomalies.
  • What-If Simulation & Decision Support – During steady-state periods, the system proposed setpoint changes using real-time model outputs, allowing operators to take proactive decisions grounded in data and business logic.

Benefits

  • ~2% increase in conversion efficiency, driving significant bottom-line impact
  • Faster anomaly detection and resolution, reducing unplanned variability and interventions
  • Enhanced operational stability, resulting in consistent product quality and throughput
  • Better catalyst management, reducing waste and improving overall asset performance

Transforming Plant Reliability with Gen-AI: A Blueprint for Intelligent Operations in Specialty Chemicals

$5M

Cost Savings

30%

Earlier detection of reliability risks through multi-KPI anomaly correlation

Business Challenge​

Despite having advanced monitoring systems in place, the client—a global specialty chemicals leader—struggled with a persistent gap between data visibility and decision-making clarity. While their plants tracked production and equipment KPIs in real time, the insights were often generic, isolated, and lacked the context needed for timely intervention.

 

Operators were aware of abnormal readings, but lacked contextual guidance on what those deviations meant, how urgent they were, or whether they warranted intervention. The challenge became even more pronounced in situations where multiple KPIs were trending within acceptable limits, but collectively indicated a brewing anomaly. There was no clear visibility on the direct connection between these early warning signs and the financial impact of ignoring them — whether in lost production, equipment damage, or opportunity cost — issues often went unaddressed until they escalated into costly failures. The client needed a solution that could not only detect complex anomalies earlier, but also overlay them with financial and historical relevance to drive the right response, at the right time.

Solutions Deployed

  • Focused monitoring on assets most associated with production loss or reliability risk
  • Historical performance + SAP-PMS job orders and repair data augmented with OEM best practices for more holistic decision-making
  • GenAI-Powered Digital Process Advisor to provide contextualized fault trees and nudges to operators
  • Multi-Variable Correlation & Pattern-Based Anomaly Detection rather than isolated threshold breaches
  • Smart Alerting System and Remote Monitoring Dashboard for unified view of real-time plant behavior
  • ML based Stability & Reliability Index quantifying combined effect of anomalies on process and equipment health
  • Reinforcement Learning Loop to fine-tune recommendations and continuously improve the system’s intelligence

Benefits

Up to $5M in potential annual value through production loss avoidance, energy efficiency, and smarter maintenance

  • 30% earlier detection of reliability risks through multi-KPI anomaly correlation
  • 40% faster resolution time enabled by Gen-AI-guided fault trees and operator nudges
  • Reduction in maintenance cost overruns, by prioritizing financially significant issues