Aspentech

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. To strengthen asset availability and operational resilience, the company has deployed machine learning across critical assets—including turbo compressors, gas turbine generators and a cryogenic heat exchanger—to detect and diagnose anomalies before they become operational failures.

LNG plants worldwide face unique operational challenges due to their complex process configurations, including refrigeration systems, compressor surge risks, lengthy startup and shutdown sequences, emergency operating scenarios and stringent safety requirements.

Moving Past the Hype: How AI and Predictive Maintenance Are Delivering Real ROI in Mining For years, digital transformation has been one of the mining industry's favorite buzzwords. Yet for many operations, it remained a broad concept – promising value but often lacking a clear path to measurable results. Today, the conversation has changed. Mining companies are no longer debating the need for digital transformation.

AspenTech OSI monarch Express - a Fit for Small and Mid-Size Utilities Electric and Gas Utility Operations Technology (OT) investments such as Supervisory Control and Data Acquisition(SCADA), big data historian functionality and advanced visibility of critical data are all key in enabling operators to achieve key objectives in safety, reliability, economics and customer service.

As North American utilities approach the one-year mark following the July 2025 compliance milestone for FERC Order 881, many are reflecting on the value that adopting ambient-adjusted rating (AAR) functionality has provided to grid reliability and how to expand further use and adoption. The objective of FERC 881 is to improve the fidelity of transmission line limits, enabling both more reliable system operation and the identification of additional line capacity when conditions permit.

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. In this session, Stefan Secker, VP of Software Development at AspenTech and co-founder of inmation, introduces a new generation of AspenTech Inmation™: the AspenTech Inmation OT Data Fabric.

As electrical networks continue to grow in complexity and scale, a strong IT/OT data strategy is becoming critical to reliable and efficient system operations. Aging infrastructure, combined with electrification and the rapid adoption of distributed energy resources (DERs), is fundamentally reshaping how transmission and distribution networks operate. Today’s grids are no longer defined by predictable, one‑way power flows.

AspenTech Digital Grid Management (DGM) is actively advancing data interoperability across the utility ecosystem by participating in the Electric Power Research Institute (EPRI) Grid Model Data Management Forum. This is a collaborative initiative that brings together utilities, software providers and consultants to standardize the exchange of electrical network models.

Optimal design and operation of heat exchangers (HX) are critical for reducing energy usage, lowering emissions and ensuring proper plant performance. However, using disparate software for process simulation, thermal design and mechanical design leads to missed opportunities, increased risk of design errors from manual data transfer and higher engineering effort.

Periods of market volatility test every part of the refining and chemicals value chain. Rapid price movements, disrupted trade flows and shifting supply–demand dynamics compress decision windows and expose operational weaknesses. At the same time, these conditions create opportunities for companies that can respond quickly and confidently.