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

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.

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.

A multinational company focused on the energy and material industries transformed its cost estimating process by replacing manual, Excel-based workflows with Aspen Capital Cost Estimator (ACCE).

OCP Ecuador operates a critical 485-kilometer heavy crude pipeline that transports approximately 30% of Ecuador’s oil production, making reliability essential to safe, efficient operations.

Developing projects in emerging industries often requires making critical decisions with limited information. Engineering teams must evaluate multiple design alternatives early in the project lifecycle to identify the optimal configuration that maximizes return on investment while minimizing risk.