(163c) Ready for Digitalization - Don't Miss the Train | AIChE

(163c) Ready for Digitalization - Don't Miss the Train


Whether a brownfield or greenfield facility, a company manufacturing chemicals or food supplements or a 10 person startup versus a Fortune 500 company, businesses of all sizes and sectors can achieve a simple entry into the world of digital transformation. Depending on the degree of digitalization, users are able to implement either the full scope of the Digital Enterprise or simply pick individual digital applications that can get them started on their digital journey.

One of the focal points of digitalization is the ability to increase the availability of plant assets and to give on-site personnel quick and meaningful decision-making tools for their work. The objective is to make processing plants more reliable through proven products and innovative services and to be able to more dependably predict the condition of assets. With the dawn of new and innovative analytical methods powered by secure, open, cloud-based IoT operating systems, significant amounts of data is collected, put into a meaningful context and intelligently linked in data models. Smart data applications provide support for all levels of the organization. They enable potential asset or component defects to be predicted, as well as helping to minimize plant downtime.

The overall goal of the presentation will be to provide a solid understanding of the power of digitalization and gain insights into how to get onboard the digitalization train. Some of the concepts to be discussed in more detail include:

  • The digital twin, 3D and simulation and how it impacts automation and digitalization across engineering, operations and maintenance
  • The increasing importance of cybersecurity
  • Innovative ways of gathering data at the field level to be used for gaining new insights into the process
  • How to bring plant and tag data into the field and to make optimal use of remote service capabilities to enable faster and more efficient maintenance
  • Provide specific examples of how to use existing asset data to provide insights that provide value in terms of improved predictive maintenance, less downtime and faster maintenance