This Chemical Industry Digest article describes how to lower costs through improved productivity during Basic Engineering and FEED.

Robust heat exchangers designed and delivered on time and on budget are vital for the success of engineering projects. AspenTech offers an integrated design workflow that supports every major type of heat exchanger. The seamless integration between its solutions for process design and the thermal and mechanical design of heat exchangers reduces design time. Learn more.

この記事では AspenTech AIoT platform のIoTアナリティックスが、センサや稼働中デバイス の時系列データを使用し、設備資産のAsset Health スコアをどのように算出しているかを解説します。Asset Health スコアによって、サービスチームはリアルタイムで設備資産の健全性を把握で き、サービス業務やメンテナンスにかかる費用の削減につなげることができます。

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?

ACCE is central to your strategy to lower CAPEX for your bids or estimates by being one common bidding and estimating platform across the bidding cycle.

In the current extreme VUCA (volatile, uncertain, complex and ambiguous) environment, chemical producers are learning the importance of operating with insight and agility. At the same time, companies are being tested for the depth of their resilience, and learning how to navigate huge shifts in demand, supply, workforce and economics, and emerge from this crisis in a stronger position.

Aspen Mtell is a prescriptive maintenance solution that uses Agents to recognize asset failures earlier and with greater accuracy. Learn what an Agent is, what they do, how they are created, and how they work—to Safeguard equipment, people, and the environment.

This white paper considers the challenges power generation operators face today and the forces that are driving demand for more proactive maintenance strategies. Gain insight into predictive maintenance that is being used to create a more comprehensive, correlated, efficient and effective environment for the collection, management,analysis and presentation of data. Discover the critical differences that make predictive maintenance a much more efficient and effective alternative to planned and condition based maintenance methodologies.

单独的传统预防型维护无法解决非预期停机问题。凭借低接触式机器学习所驱动的资产绩效管理，现在可能会从数十种程序、资产和维护数据中抽取相关数值，从而优化资产绩效。在本白皮书中，将学习这种插断性技术如何部署精确性故障模式识别，其具有较高的准确性，可以提前预测设备停机月数，并就约定的维护提供相关建议。本白皮书亦列述了驱动先进可靠性管理的五个最佳实践，以期增产提盈。

In this brief video, Antonio Pietri discusses the concept of VUCA and what that means for Industry 4.0 and digitalization in the smart enterprise.