Las empresas líderes en uso intensivo de activos están utilizando el mantenimiento prescriptivo, impulsado por inteligencia artificial y machine learning, para desbloquear el valor y la productividad que se encuentran sin utilizar en los activos. Al emplear una solución de mantenimiento prescriptivo escalable y fácil de implementar, las empresas pueden mejorar la precisión de la detección de fallas, aumentar el período de notificación anticipada de los paros de planta y reducir los costos de mantenimiento.

Leading asset-intensive companies are using prescriptive maintenance—powered by AI and machine learning—to unlock the value and productivity lying uncaptured in assets. Using a scalable, easy to implement prescriptive maintenance solution, companies can improve the accuracy of failure detection, increase the advance notification period of asset downtime events and reduce maintenance spend.

The race to a zero-carbon future is on. Global oil and gas, petrochemical and chemical companies have announced ambitious carbon mitigation, plastics re-use and water conservation targets. Carbon capture and storage (CCS) and carbon capture and utilization (CCU) are key technologies to help companies reach their sustainability goals.

As sustainability targets drive the energy industry towards a net zero future and energy security concerns intensify, companies are challenged to meet these competing demands within financial and workforce restraints. Owners and EPCs must cater to short-term energy demands while simultaneously positioning themselves for the long-term energy transition projects gaining traction.

Chemical manufacturers are facing a dual challenge with global demand increasing. On one hand, chemical production has to increase to address the growing demands of a growing global population. On the other hand, the industry is facing a climate and an environmental crisis.

Discover how to predict equipment breakdowns and perform prescriptive maintenance using a new approach to asset performance management. In this white paper, learn how nine early adopters of prescriptive analytics have reduced unplanned downtime and improved asset reliability. Download the paper to read real stories about the bottom-line results that have been achieved—in as little as 2 ½ weeks.

Electrification is accelerating across all economic sectors as the world increases demand for resources, energy and sustainability. The shift from fossil-based systems to electric will drive change within the existing power infrastructure and beyond with new microgrids and self-generation at industrial sites.

实现未来零碳排放的比赛已经拉开序幕。全球油气、石化和化工公司纷纷宣布了宏伟的碳减排、塑料再利用及节水目标。 碳捕获和封存技术（CCS），以及碳捕获与利用技术（CCU）是现阶段帮助企业切实实现这些目标的关键路径。

碳捕获仍然是能源和化工公司在能源转型过程中的首要任务，但实现减排目标的最佳方法是什么？准确的工艺建模是选择正确工艺的关键要求。在本白皮书中，您将了解预测建模技术的进步如何使您能够设计最可操作、节能和可持续的解决方案。

领先的资产密集型公司正在使用人工智能和机器学习支持的规范性维护，以释放资产中未被捕获的价值和生产力。使用可扩展、易于实施的规定性维护解决方案，公司可以提高故障检测的准确性，延长资产停机事件的提前通知期，并减少维护开支。