This video is from the ARC Industry Forum 2020: Predictive Maintenance Fact and Fiction - Learnings from Real Implementations Workshop presented by Chris Williams, Director, Customer Success, Aspen Technology, Inc.

Ron Beck speaks to the power of a model based estimating system providing insight to its attributes like reusable templates and saleable features. How does ACCE differ from my proprietary Excel-based spreadsheets?

With Recipe-based modeling, chemists and engineers can work together to develop new processes faster, remove equipment and schedule limitations, and transfer technology smoothly between sites and departments.  With Aspen Batch Process Developer use recipe-based modeling solutions to develop your batch process from lab to plant using models that grow with the scale of your batch process.  Watch now to learn more.

業界リーダーは、持続可能性目標を達成する革新的な方法を模索しています。水素は、炭素削減とエネルギーコスト削減の実現に役立つ貴重な資源です。このホワイトペーパーをダウンロードして、なぜデジタルテクノロジーが水素経済の実現に不可欠な要素であり、どのようにして以下のことを実現するのかを学んでください。

在全球范围内，各领先组织正在接纳并实施先进的数字化技术。数字化转型之旅将改变资产密集型行业（尤其是能源和化工企业）的性质。在这种情况下，数字孪生（实物资产及其操作行为的虚拟副本）将发挥关键作用。对于今天我们创建的数字孪生，一个关键概念是人工智能在提供虚拟数据相关见解和建议方面的作用。

多くの企業は、この変化する状況の中で、安全性、資産の健全性、排出量管理、廃棄物削減など、持続可能性の目標をビジネス目標に組み込んでいます。デジタルテクノロジーによって、ポリマー製造会社は、より大きなリターンを獲得し、新しいレベルの最適化を推進する機会を得ることができます。

In this white paper, plant managers, engineers and supervisors can learn the different ways simulations models can be used throughout the lifecycle of their plant. It focuses on seven areas you can use process simulation software (like Aspen Plus or Aspen HYSYS) for troubleshooting, process monitoring, and real time performance monitoring or real time optimization (RTO).

There is a challenge for many refinery and chemical companies to impove the analysis proess and raise performance. The inside story of operational intelligence lies in rich thin visualization and analytics capabilities that improve production execution, enabling process manufacturers to quickly identify and resolve operational issues.

在白皮书里，我们探讨先进过程控制(APC)如何提高生产处理量，降低能耗，改善石油化工厂和聚合物工厂的产品质量。

Traditional preventive maintenance alone cannot solve the problems of unexpected breakdowns. With asset performance management powered by low-touch machine learning, it’s now possible to extract value from decades of process, asset and maintenance data to optimize asset performance. This white paper describes five best practices for driving state-of-the-art reliability management to predict breakdowns months in advance—increasing production and profitability.