A global provider of knowledge-based maintenance, modifications and asset integrity services wanted to take a more data-driven approach to planned maintenance and reduce unplanned downtime to optimize lifecycle costs.

As part of an effort to drive reliability in its refinery operations, Saras turned to Aspen Mtell® prescriptive maintenance to improve equipment uptime and decrease maintenance costs.

When this large energy company launched its digital transformation initiative, it turned to Aspen Mtell® to execute an online predictive maintenance pilot on a hydrogen compressor.

Executives at this mining company were looking for a new approach to proactively handle reliability issues for critical equipment.

Una tarea o situación de análisis de datos se puede entorpecer dramáticamente por el tiempo, recursos y habilidades necesarias para la identificación, selección y preparación de datos. Estas tareas consumen el 50% o más del tiempo dedicado al análisis. Ahora, con Maestro para Aspen Mtell®, puede automatizar gran parte de esa preparación de datos. A través de flujos de trabajo automatizados, el tiempo y el esfuerzo se minimizan al mismo tiempo que se reducen las habilidades y la experiencia requeridas por los usuarios finales. Maestro también aborda la selección de los hiperparámetros que controlan el algoritmo de machine learning y automatiza la selección de características para reducir aún más la necesidad de habilidades de ciencia de datos. Vea este video para aprender cómo esta poderosa tecnología de IA puede ayudarle a crear mejores agentes en una fracción del tiempo.

A análise de dados pode se complicar na identificação, seleção e preparação de dados. Estas tarefas consomem 50% ou mais do tempo gasto nas análises. Agora, com o Maestro para o Aspen Mtell®, você pode automatizar grande parte desta preparação de dados. Através de fluxos de trabalho automatizados, o tempo e o esforço são minimizados, reduzindo as habilidades e a experiência exigidas dos usuários finais. Maestro também aborda a seleção dos hiper parâmetros que controlam o algoritmo de machine learning e automatiza a seleção de recursos para reduzir ainda mais a necessidade de habilidades em ciência de dados. Assista este vídeo para saber como essa poderosa tecnologia de IA pode ajudá-lo a construir agentes melhores em pouco tempo.

Data analysis can get bogged down in identifying, selecting, and preparing data. These tasks consume 50% or more of the time spent doing analysis. Now, with Maestro for Aspen Mtell®, you can automate much of that data preparation. Through automated workflows, time and effort is minimized while reducing the skills and experience required by the end users. Maestro also tackles the selection of the hyperparameters that control the machine learning algorithm and automates feature selection to further reduce the need for data science skills. Watch this video to learn how this powerful AI technology can help you build better Agents in a fraction of the time.

This podcast is part of a series featured on “In the Oil Patch” with Shale Oil & Gas Business Magazine radio show. In this podcast, host Kym Bolado, interviewed Zafar Ali, AspenTech Senior Director of Petroleum Supply Chain Products, and frequent guest Ron Beck, AspenTech Market Strategy Director, on how refineries are adapting to new challenges presented by the pandemic, and how Artificial Intelligence is will impact planning in the near future.

Accenture’s high value consultancy combined with AspenTech's innovative technology solutions offer energy, chemical and mining companies a path to greater resilience and agility.

Aspen Technology Announces Financial Results for the Fourth Quarter and Fiscal 2020