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Over the past 40 years, maintenance best practices have greatly improved, but equipment continues to break down. So how can asset reliability be improved? With Aspen Mtell prescriptive maintenance software, you can predict breakdowns weeks in advance and take action to protect your machines, business investments, the environment, and most importantly, people.

電力

アスペンテックは、第一原理に基づくプロセスシミュレーションモデルと専門知識をAIおよびアナリティクスアルゴリズムと統合するための手法を考案しました。その結果生まれたアプリケーションソフトウェアは、第一原理モデリングだけ、あるいはAIだけの場合よりも多くのことを達成できるハイブリッドモデリングシステムです。

With current shortages in on-site staffing, many companies struggle to prevent unexpected breakdowns. AI and machine learning technology leverage precise pattern recognition from manufacturing assets to provide weeks, or even months, of advanced warning on imminent degradation and failures.

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.

ARC Advisory Group conducted research to gain a better understanding of the current state of sustainability and “green initiatives” in the chemical and energy industries. Research showed that:

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Aspen Mtell Video Library

Use AspenTech’s Operational Analytics to improve operator effectiveness, avoid production losses, and improve profits.