As industrial organizations seek to scale digital transformation initiatives, the ability to develop custom business applications fed by real-time operational data helps operational technology (OT) and data teams support enterprise and on-site use cases.

As AI and digitalization transform industrial operations, disconnected systems and siloed data can limit visibility, slow decisions and hinder innovation. Unlocking their potential requires secure, accessible data with operational context.

Process engineers are under pressure to optimize production while managing variable feedstock and shifting process conditions. Aspen Hybrid Models™ simplifies the use of machine learning in process simulation, delivering faster, more accurate models that provide timely insights to guide operations decisions.

In chemical manufacturing, reliability teams must manage critical machinery and instrumentation in a fast-paced, complex environment.

In a world where supply chains, energy costs, feedstock availability and project economics can shift overnight, industrial leaders need the agility to act with confidence.

As industrial organizations accelerate AI, advanced analytics and digital transformation initiatives, traditional point-to-point data architectures are becoming a major barrier to innovation.

Industrial organizations are under increasing pressure to connect data from legacy systems, modern applications and distributed assets to support optimization, resilience and AI-driven operations.

Aspen Fidelis helps decision-makers evaluate capital investments, maintenance strategies and operational improvements by forecasting performance, cost and risk across complex industrial systems.

Hunt LNG, a Peruvian company and operator of the PERU LNG Project, is transforming reliability by using AI and machine learning to turn operational data into actionable decisions.

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