LNG plants are complex and highly energy-intensive operations. Gaining deep operational insights is essential to optimize performance and reduce energy consumption.

The primary challenge in modern asset reliability isn’t a lack of data; it’s a lack of context and scale. Industrial operators are often inundated with equipment data, but without understanding asset behavior within its specific operational environment, that data remains unexploited. Furthermore, while many organizations have established effective reliability plans for critical units, scaling those practices across the entire enterprise remains an uphill battle; leaving companies vulnerable to significant operation and financial losses from unplanned downtime.

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

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.

Engineering, procurement and construction (EPC) firms are under increasing pressure to deliver projects faster, at lower cost and with greater confidence amid volatile market conditions and growing project complexity.

Geomodels are foundational for understanding subsurface assets and informing field development strategies and drilling plans.

Designing a distillation column requires balancing thermodynamics, hydraulics, internals, energy use and cost—often with limited information early in a project.

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.

Refinery scheduling is more complex than ever. Volatile crude markets, shifting demand and constant operational changes can make it difficult to respond quickly while protecting margins.