DuPont Selects Aspen Technology to Modernize its Industrial Data Foundation and Minimize Implementation Costs
DuPont Selects AspenTech
Celebrating International Day of Women and Girls in Science at AspenTech
At AspenTech, we are fortunate to have a prominent contingent of women in STEM. In recognition of this day, we highlight women scientists and engineers who make up the AspenTech family.
Digitalization and Sustainability Perspectives from 2023 ARC Forum
Operational excellence, digitalization and sustainability were all key topics of discussion at the 27th Annual ARC Forum. AspenTech CMO, Lawrence Schwartz details his experience.
Novozymes Uses Process Modeling to Optimize and Develop Biodiesel Processes
Novozymes A/S, a global biotechnology company based in Denmark, was looking to support the development and optimization of biodiesel processes due to increasing biodiesel market demand, rising materials costs and more stringent industry regulations.
Insights from ARC Forum 2023: Navigating the Path to a Sustainable Future
At the annual ARC Forum meeting in Orlando, we highlight the dual challenge of meeting the growing demand for resources while addressing sustainability goals.
Taming the Downtime Scheduling Beast
Achieve supply chain resilience with downtime scheduling that leverages prescriptive maintenance and advanced scheduling optimization to minimize impact on production.
Aspen Technology Combines inmation Software and AIoT Hub to Advance Customers’ Digital Transformation Strategies
A global leader in industrial data management from the shopfloor to the boardroom, we accelerate data-driven value creation in asset-intensive industries through robust data software offerings.
Exploring the African Heritage in our Daily Cuisine
To celebrate Black History Month, Byron Gautier, a member of the AspenTech Black Leadership Forum, explores how African culture has shaped our food, drink and American popular culture.
Introducing the AspenTech DataWorks Business Unit
AspenTech DataWorks is purpose-built to be the global leader in industrial data management.
Unsupervised Machine Learning for Seismic Facies Classification Applied in Presalt Carbonate Reservoirs of the Búzios Field, Brazil
Seismic data can provide useful information for prospect identification and reservoir characterization. Combining seismic attributes helps identify different patterns, for improved geological characterization. Machine learning applied to seismic interpretation is very useful in assisting with data classification limitations.