With mounting pressure to reduce carbon emissions—and still meet critical production targets—today’s energy and chemical companies are challenged to stay on top of changing operating conditions and respond in a timely, efficient and coordinated manner.

Machine learning technology can now predict equipment failures weeks in advance. But how do you decide when and how to perform the repair to maximize productivity and minimize risk? Learn how Aspen Mtell and Aspen Fidelis enable you to quickly evaluate hundreds of possible future scenarios, and understand the financial consequences and risk involved in different operational decisions.

For decades, first principle models have been used for process design and optimization in the chemical industry. Recent innovations employ machine learning techniques—enriched with first principle constraints—to create a hybrid model that closely represents real plant behavior. Fast, easy access to industrial AI is helping organizations make accurate decisions to select an optimum design quickly and improve operations, maximizing productivity and minimizing energy usage.

With more focus on sustainability, frequent changes in global gas markets and evolving processing technologies, gas plants are always looking to improve operations by accurately simulating and predicting increasingly complex processes.

See how AspenTech DataWorks is improving our best-in-class data management solutions with new capabilities and synergies.

Learn how to migrate your industrial data from a variety of sources to the AWS cloud in this on-demand webinar featuring experts from ARC, AWS and AspenTech.

The upstream oil and gas exploration and production industry is looking for ways to reduce costs while minimizing emissions, water use and other environmental impacts. Near-field exploration and development enables producers to leverage already depreciated costs in operating infrastructure and extend the life of a declining field by accessing new or previously bypassed reservoirs.

Industrial data from a variety of sources and locations has the potential to help organizations meet energy efficiency goals, avoid raw materials/feedstock shortages, decrease downtime, increase profitability and improve ESG reporting. But the chemical industry, like other asset-intensive industries, generates large amounts of data that never gets collected, contextualized and used to its full potential.

The successful execution of engineering projects requires intense collaboration among multi-discipline teams, but manual data handoffs often lead to errors, inconsistencies and delays that can derail a project.

During the question and answer portion of the AspenTech Inmation Conventional and Unconventional Use Cases webinar, attendees had the opportunity to pose their questions to the panel of experts presenting. This FAQ document provides the answers.