AspenTech Inmation™ OT Data Fabric unifies OT data into a stateful, persistent enterprise namespace and continuously contextualizes it into trusted, AI-ready intelligence. It connects plants, assets and applications across edge, cloud, and on-premises to establish a consistent, real-time, query-ready operational picture that scales from a single site to the global enterprise.

Explore real-world results from YPFB Andina and AspenTech, demonstrating how an integrated, asset-wide approach can uncover hidden production opportunities and improve economic performance—without relying solely on new capital investment.

Create feasible, up-to-date production schedules that bridge the gap between planning and operations. Enable faster decisions and more profitable operations by combining traditional scheduling with optional crude and blend optimization.

This layered application automates crude receipt, tank transfer and CDU feed scheduling—replacing manual workflows with fast, constraint-aware optimization to maximize refining margins.

In this episode of The Main Column Podcast, Nithiya Parameswaran, VP of Product Management at Aspen Technology, joins Lee Nichols, Editor-in-Chief of Hydrocarbon Processing, to explore how Industrial AI is transforming Asset Performance Management.

Natively embedded in Aspen Unified Scheduling, Multi-Blend Optimization (MBO) optimizes blend recipes and executable schedules in a single, real-time environment to maximize refinery margins.

Optimal design and operation of heat exchangers (HX) are critical for reducing energy usage, lowering emissions and ensuring proper plant performance.

Industrial organizations are increasingly focused on making operational data accessible across plants, business units and cloud initiatives—yet many data platforms struggle to scale without introducing complexity, cybersecurity risk and high operational overhead.

Bharat Petroleum Corporation Limited (BPCL), one of India’s leading energy and refining companies, has relied on Aspen Petroleum Supply Chain solutions for more than a decade.

What happens when a single enterprise is running on dozens of incompatible systems, speaking dozens of different data dialects? Our team set out to find out, taking on a data fragmentation challenge that spanned multiple sites, systems, vendors and protocols.