Linde Engineering North America monitors construction costs and progress using ACCE linked to 3D models driving down the cost of the estimate.

Learn how YPFB Andina was able to increase their gas production using an integrated model solution that was able to provide a $280M increase in revenue in 1 year.

This case study details the methodical approach a global supermajor took, utilizing best-in-class technology, to improve production planning in global refineries and chemical plants with Aspen PIMS-AO technology. Based on side-by-side testing of different technologies, Aspen PIMS-AO led to new insights, increased confidence in results and enhanced conversations with traders.

Graham Hart achieves 97% on time delivery of complex heat exchangers through use of HYSYS and Aspen EDR.

Aspen Mtell provided a nine-day advance warning of imminent kiln overheating, allowing the plant to change operating conditions and avoid an operational shutdown. Download this case study to learn more.

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.

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. In this white paper, learn how this disruptive technology deploys precise failure pattern recognition with very high accuracy to predict equipment breakdowns months in advance and advise on prescriptive maintenance. The paper also outlines five best practices for driving state-of-the-art reliability management to increase production and profitability.

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. In this white paper, learn how this disruptive technology deploys precise failure pattern recognition with very high accuracy to predict equipment breakdowns months in advance and advise on prescriptive maintenance. The paper also outlines five best practices for driving state-of-the-art reliability management to increase production and profitability.

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.

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。