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。

Unplanned downtime has wide-ranging impacts that affect more than just a plant’s productivity and profitability. Forced shutdowns also have a major impact on plant and personnel safety, as well as greenhouse gas emissions and environmental compliance. But what if you could actually plan for downtime?

AspenTech® experts have put together this document on top questions and answers to help you get the most value from your process simulation tools including tips and tricks that you can use immediately to reduce batch cycle times and improve overall product quality.

En el mercado global actual, volátil, incierto, complejo y ambiguo (VUCA por sus siglas en inglés), todo está cambiando, y en los entornos de producción, el cambio significa un mayor riesgo. ¿Cómo pueden las organizaciones intensivas en capital minimizar su riesgo, especialmente cuando la cobertura de seguro está disponible fácilmente?

This valuable resource provides additional insights from our technical experts on how users can use historical plant data to maximize model accuracy.

資産集約型の業界では、IIoTとデジタル化テクノロジーが収益性と信頼性の向上への道を開きます。このホワイトペーパーでは、これらのテクノロジーを適用して最も大きな影響を得る方法について説明し、デジタルアクセラレーションへの移行に成功するために重要な3つの鍵について概説します。

Many new improvements have been made to Batch Modeling technology in Aspen Plus, including the integration of the technology previously available with Aspen Batch Modeler and the introduction of new unit operations, including batch crystallization. This document covers our answers to some of the most commonly asked questions about this technology.

Aspen HYSYS has several options available for modeling pipes and pipelines. Use these approaches, correlations and analysis tools in Aspen HYSYS to solve problems around pipe flow hydraulics and optimize flow and uptime. This Top 8 Questions document recommends best practices from our technical experts.

Aspen Hybrid Models combine AI and first principles to deliver a comprehensive, accurate model more quickly without requiring significant expertise. Machine learning is used to create the model, leveraging simulation or plant data while using domain knowledge, including first principles and engineering constraints to build an enriched model without the need for modeling or AI expertise.

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