Why more sensors and more alerts aren't the answer — and what mining operators should focus on instead.

AspenTech CTO Claudio Fayad joins Hydrocarbon Processing's The Main Column podcast to discuss where Industrial AI is headed for refining and petrochemicals.

Electric utilities are under increasing pressure from regulators, customers and stakeholders to integrate renewable energy resources while maintaining reliability, power quality and operational flexibility.

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.

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.