Learn how YPFB Andina was able to increase their gas production using an integrated model solution that was able to provide a $280M increase in revenue in 1 year.

Find out how a major Energy Company in South America uses Aspen Exchanger Design & Rating™ and Aspen HYSYS® to model to simulate the reboiler circuit and regenerating column. The Digital Twin model revealed hydraulic instability in the thermosiphon system and allowed the customer to save $6M USD per year, prevent damage to the column and related equipment and much more.

Conozca cómo Tupras utilizó el análisis de columnas en Aspen HYSYS en conjunto con Aspen Exchanger Design & Rating para incrementar en un 40% la capacidad de columna y al mismo tiempo cumplir con las especificaciones de calidad del producto.

Learn how Tupras used the column analysis capability in Aspen HYSYS and its integration with Aspen Exchanger Design & Rating to significantly increase the column capacity and meet the required product specifications.

To remain competitive, energy companies increasingly look to artificial intelligence and machine learning to boost their advantage. But AI is everywhere now and everybody knows about its potential... so how do you create a real competitive advantage?

最近のGartnerの調査によると、製造業の回答者の85％が、スマート製造戦略に関連する最大の課題は他のサプライチェーン機能との統合であると報告しています。 本ホワイトペーパーでは、業界のリーダーがバリューチェーン全体を調整し、顧客中心、収益性、需要と供給の変化への対応を強化した事例をご紹介しております。

Learn how Westlake assesses time sensitive supply chain opportunities, such as accepting a modified customer order, in a matter of minutes using Aspen Plant Scheduler rather than four hours using previous spreadsheet methods.

Learn how SK Engineering & Construction improved the speed and accuracy of their capital project cost estimates with Aspen Capital Cost Estimator (ACCE).

Learn how Saudi Aramco used Aspen HYSYS to analyze feasibility of refinery reconfiguration plans by developing plant digital twins of multiple units. The new reconfiguration plan projects a 100,000 barrels/day increase in the refinery’s processing capacity, a substantial reduction in fuel oil production together with a significant boost in diesel production capacity.

Because traditional diagnostic methods weren’t preventing equipment failures or identifying root causes of historic failures, a U.S. refinery turned to Aspen Mtell prescriptive maintenance to improve internal data science resources. Download this case study to learn how this refinery's pilot program with Aspen Mtell was able to predict failures with nearly one month of lead time, enabling planning for maintenance and rescheduling production.