AI in Industrial Processes: Optimizing processes & reducing OPEX with AI-Plant Control to unlock hidden value!
WEBINAR
AI in Industrial Processes:
Optimizing processes & reducing OPEX with AI-Plant Control
- November 18th | 10:00 - 10:45 AM CST
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.
mcube™: The Architecture Enabling AI in Industrial Plants.
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.
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.