AspenTech conducted a survey of 240 downstream customers to uncover thoughts and opinions on digital optimization and industry trends for 2018 and beyond. This white paper details the results of the survey and provides the reader with insights on the focus of increased reliability, a top priority for many refinery organizations.

Las compañías mineras invierten fuertemente en activos para todas las etapas del proceso minero: extracción, procesamiento, refinería y distribución del mineral. Al monitorear la condición y el comportamiento de los activos y desarrollar perfiles de operaciones normales, anomalías y fallas, las herramientas de mantenimiento predictivo pueden notificar al personal sobre los problemas del equipo antes de que ocurra falla. Este documento describe cómo el mantenimiento predictivo proporciona a las organizaciones mineras la inteligencia necesaria para:

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.

Making big capital project management decisions shouldn’t be left to subjective perceptions or over-simplified analysis. Decision-makers need quantifiable, trustworthy answers to make the most profitable decisions possible. Aspen Fidelis Reliability is a robust RAM analysis tool that can handle the real-world challenges of today's process industries. In this paper, learn how Fidelis enables you to maximize the economics of business decisions and accurately predict future asset performance of the whole system.

En todo el mundo, las organizaciones lideres están adaptando e implementado tecnologías digitales avanzadas. La travesía de transformación digital cambiará la naturaleza de las industrias intensivas de activos, particularmente los negocios de energía y químicos. En ese contexto, Digital Twin — (copias virtuales de activos físicos y sus comportamientos operativos) tendrán papeles clave. Para los gemelos digitales que creemos hoy, un concepto clave es la inteligencia artificial que proporciona información y asesoramiento sobre los datos virtuales. Descargue este White Paper para conocer sobre estas claves esenciales en su estrategia de digital twin:

Mining companies invest heavily in equipment for all stages of mining, mineral processing, refining and distribution. By monitoring asset condition and behavior and developing profiles of normal operations, anomalies and failures, predictive maintenance tools can notify staff of equipment problems prior to failure. This paper outlines how predictive maintenance provides mining organizations the intelligence needed to:

特殊化学品メーカーの主なバリュードライバーにしっかりと整合化された実証済みの業界ベストプラクティスを導入した、製造パフォーマンスを変革するためのロードマップを示しています。また、製品品質を改善し、顧客への対応力を強化し、コスト効率を高める革新的な製造プラクティスを実現するために、どのような技術が活用できるのかについて説明しています。

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。

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。

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.