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News Article

Digital twin for refinery-wide emission and efficiency monitoring

World of Chemicals India - BPCL technical case

News Article

Digital twins in self-healing supply chains

Process China - Digital twins in self-healing supply chains

News Article

Digital Twins in supply chains help achieve business continuity amidst global pandemic uncertainty

Business Today Malaysia - Digital twins in supply chains

Executive Brief

計画外ダウンタイムを計画的ダウンタイムに変えることによる安全性、持 続可能性、および生産性の最大化

計画外ダウンタイムの影響は財務だけにとどまりません。強制シャットダウンはプラントや作業員の安全はもちろん、温室効果ガス排出量や環境コンプライアンスにも多大な影響を及ぼします。 もし、実際にダウンタイムに備えて計画できるとしたらどうでしょうか。

Case Study

Data-Driven Maintenance Planning Saves $1.8 Million USD Per Year in Shutdown Costs

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.

Case Study

Prescriptive Maintenance Software Helps Saras Improve Business Performance and Drive Operational Excellence

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.

Case Study

Digital Transformation with Predictive Maintenance Drives Cost Savings

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.

Case Study

Two Looming Failures Stopped Within Two Weeks of Monitoring

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

Video

Melhores Agentes, Mais Rapidez – Com Maestro para Aspen Mtell®

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.

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