Aspen Mtell introduces exciting new features, including Alert Manager, industry-specific asset templates, custom models (BYOM) and intuitive dashboards and KPIs for improved visibility, health insights and recommended actions. This infographic provides a comprehensive overview of the capabilities now available in Aspen Mtell.

Aspen Mtell® combines the power of data and technology to enable reliability and maintenance engineers to monitor the performance of assets, providing real-time visibility into asset health and actionable insights for faster decision-making. Discover why companies like LG Chem, Evolution Mining, OCP Ecuador and others have opted for AspenTech´s prescriptive maintenance solution to improve their operational efficiency and achieve carbon mitigation and sustainability goals.

AspenTech provides over 140 easy-to-use application models that you can adapt to your own initiatives, along with the supporting documentation to help you get started. Models cover areas such as bio-based feedstocks, carbon capture, emissions management, hydrogen economy, materials circularity and more.

Leverage machine learning to perform quick and accurate amplitude inversions and rock property estimations when short project timelines exist. Aspen SeisEarth’s Neural Network Inversion feature provides a step-by-step workflow available for interpreters and non-specialists.

The hydrogen economy presents an attractive pathway for companies seeking to decarbonize their operations. However, developing the necessary global-scale infrastructure for green hydrogen poses various challenges and trade-offs across the value chain. Aspen Fidelis assists companies in managing this uncertainty, quantifying risks and ensuring that their capital investments have the highest probability of delivering desired returns throughout the asset's lifespan. See how your company can realize green hydrogen and ammonia solutions at scale.

Reducing energy use and GHG emissions remains a priority for the process industries. However, when designing more energy efficient plants or revamping existing sites, a high level of expertise is required to perform Pinch analysis. Engineers have limited time available for design, and the use of separate tools by multiple disciplines also brings the risk of errors due to manual data transfer.

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A recent study conducted by ARC Advisory Group analyzed how leading manufacturing companies are leveraging artificial intelligence (AI) and machine learning tools to improve asset performance. While condition-based maintenance is most prevalent, the study found that leaders have successfully built AI capabilities to enhance asset performance practices through in-house platforms, commercial solutions and a combination of both.

In this article, legacy 3D seismic data from the KG deep water basin underwent broadband reprocessing using state-of-the-art-software.