AspenTech helps you to optimize production and rapidly develop new grades, polymer process optimization, polymerization reactions and modeling.

Create process models on a solid foundation. Start your modeling efforts with confidence with the most up-to-date and comprehensive portfolio of physical property data.

Increase visibility and responsiveness to production problems through real-time monitoring of business metrics via dashboards, alertsand pattern matching.

Select the optimum heat exchanger size and rating when both cost and performance must be balanced through rigorous up-to-date integrated modeling capabilities.

Improve transparency and lower CAPEX though use of a shared model-based estimating platform from conceptual engineering through construction.

AspenTech's Solid Process optimization delivers consistent quality of solids, process optimization, crystallization and granulation modeling.

Increase throughput and operating efficiency through data-driven decisions enabled by plant-calibrated predictive models in an operator-friendly interface.

A recent ARC Advisory Group survey of 365 industry practitioners revealed that two-thirds of respondents believe reliability-centered maintenance (RCM) isn’t improving reliability, and that operations has as much impact on plant asset performance as the maintenance organization.

Les raffineries du monde entier ont économisé des milliards de dollars en optimisant leur production et leurs chaînes d'approvisionnement en pétrole. Cependant, il existe encore plus d'opportunités, certaines en résolvant des problèmes opérationnels et d'autres en comblant les écarts entre la planification, l'ordonnancement et les opérations. Le marché mondial d’aujourd’hui continue de mettre les planificateurs au défi d’optimiser les opérations. La volatilité des prix et de la demande ont un impact sur les matières premières et la sélection des produits, tandis que les opportunités disponibles dans le marché nécessitent une prise de décision rapide et agile

Watch how Aspen Mtell analyzed 220 million sensor values to identify the top 10 maintenance cost failures at one world-class refinery, and predicted a compressor breakdown 49 days in advance.