In today’s VUCA environment, organizations are turning to digitalization and Industrial AI to develop autonomous and semi-autonomous processes that enable new levels of safety, sustainability and profitability. This is where the journey to the Self-Optimizing Plant begins.

Uma empresa global de papel e celulose se propôs a prever riscos e aumentar capacidade usando o poder da automação.

Conozca cómo una importante compañía de energía en Sudamérica utiliza Aspen Exchanger Design & Rating™ y Aspen HYSYS® para modelar y simular el circuito de reboiler y la torre regeneradora. El modelo de Digital Twin identificó una inestabilidad hidráulica en el sistema termosifónico y le permitió ahorrar $6M USD anuales, evitar daños de la torre y sistemas asociados y mucho más.

Hear how industry leaders from ConocoPhillips, NOVA Chemicals, P66 and McKinsey view digitalization best practices and value generation, and how they are laying the foundation for the smart asset of the future. Below are a few of the highlights:

Industrial organizations require greater agility and flexibility in their operations as they face unprecedented market volatility and complexity along with increasing expectations on safety, sustainability and return on capital.

Neste estudo de caso, saiba como a Braskem Idesa usou o Aspen ProMV™ para identificar e corregir em forma proativa condições que históricamente levaram ao entumpimento dos reatores altos.

En este caso de estudio, conozca cómo Braskem Idesa utilizó Aspen ProMV ™ para identificar y corregir de manera proactiva y de acuerdo a las condiciones históricas que llevaron a una mayor velocidad de ensuciamiento.

In this case study, learn how Braskem Idesa used Aspen ProMV™ to proactively identify and correct for conditions that historically led to high reactor fouling.

As companies look to embrace AI, they are challenged with data collection and data integration – which are key to realizing AI’s full potential. For example, according to one study, between 60% and 73% of data within an enterprise goes unused. This requires a fundamental change in how organizations access, gather and analyze data. Fortunately, there are solutions to help manage industrial data and create more value from AI applications.

The confluence of AI and IIoT technological forces gives rise to a new digital solution category – the Artificial Intelligence of Things (AIoT) – that centers on unlocking the hidden business value in industrial data. This category describes the combination of AI technologies with the IIoT to enable the next-generation of Industrial AI infrastructure, allowing organizations to achieve more efficient IIoT operations, enable seamless human-machine workflows, harmonize industrial data management, and rapidly transform raw data into tangible business outcomes.