Optimize design and lower energy cost while meeting specifications with predictive, rate-based models and specialized physical properties.

Develop and transfer batch processes from lab to manufacturing by using the recipe as process mode.

Achieve real-time control and optimization of growing and increasingly complex energy networks. Improve safety, reliability, maintenance and efficiency of operations, while integrating new green energy sources for a reduced carbon footprint.

Explore strategies to modernize power infrastructure. This article covers the essential technological advancements—like digital twins—needed to manage a reliable and resilient grid.

CCREMC, an Electric Cooperative serving 25,400 members in southeast Indiana, recognized the need to adapt and scale to meet its growing member base and infrastructure demands.

Process engineers working with specialty chemicals often face the challenge of optimizing production systems—consisting of both batch and continuous processes. However, modeling these mixed processes can be complex and time-consuming. 

Synchronized intelligence is critical to closing the gap between planning and execution. It’s the blueprint that defines successful digital transformation in manufacturing today.

Aspen Mtell® is a groundbreaking predictive maintenance solution that harnesses the power of data and technology to ensure reliable equipment performance, while safeguarding our environment. Learn about the latest advancements to boost asset efficiency, reduce costs and ensure a sustainable future.

Refinery reactors are among the most complex and critical process units, directly impacting profitability. However, accurately predicting reactor yields and properties remains a major challenge due to constantly changing operating conditions and the complexity of reactor processes.

The process industry faces unique and complex transient operational challenges—especially during critical phases like start-up and shut-down. These conditions can introduce significant risks if not properly managed. Moreover, many engineers find it tedious and time-consuming to set up dynamic process simulation models to effectively study these scenarios.