Mobile Navigation

Chemical Engineering

View Comments PDF

Digitalization progress and hurdles

| By Scott Jenkins, Chemical Engineering magazine

At the 7th Connected Plant Conference, held June 25–28 in New Orleans, (CPC;, attendees heard about significant progress made toward the digital transformation in the chemical process industries (CPI) along several fronts. However, complex challenges remain as organizations seek to take full advantage of available digitalization tools.

Engaging in digital transformation activities has become imperative for the CPI as they grapple with modern issues, such as decarbonization and environmental sustainability. The constant improvement, aggregation and re-combination of existing technologies, along with the advent of new technologies, will give rise to new opportunities to improve processes and workflows. Here are some of the topic areas discussed at CPC that we will be watching closely over the coming year.

People. The importance of company culture and worker buy-in framed many of the conversations at CPC. This year’s conference theme “the intersection of technology, process and people,” acknowledged the critical role that humans play in the way that digitalization tools are tested, implemented and scaled.

Data engineering. Turning available data into actionable insights is difficult. Contextualizing data and ensuring data quality are among the key challenges in any digital transformation initiative, because quality data sets form the foundation for the success of further analytics and artificial intelligence (AI) efforts. Questions around data quality and data ownership will continue to be important.

Cybersecurity. A holistic approach to maximizing efficiency across a whole process, plant or fleet requires the continued convergence of operational technology with information technology (IT) infrastructure. This increases the exposure of plant data to potential cyberattacks. Approaches to cybersecurity continue to evolve with the threat landscape.

Generative AI. Generative AI could have a massive impact on industrial operations if the industry can properly incorporate domain-specific knowledge into machine-learning models and effectively contextualize data for queries using natural language models.

Quantum computing. Computational chemistry will be an innovation driver in sustainability, and quantum computing, which uses quantum-physics concepts, such as superposition and entanglement, to approach computation probabilistically, rather than in a binary way, could change the way simulations are carried out. Quantum computing will not replace traditional computers, but will augment the capabilities of classical computers.

Digital twins.The use cases for digital twins, in which a digital version of a physical asset is used to gain optimization insights, are continuing to expand, and will have an even larger impact in the future.

Interoperability. Having control-system vendor software and hardware adhere to standardized, open control-system architectures will allow operators to access the best features and approaches for their specific digital transformation needs, and enhance the user experience for their workers. CPI workers of the present and future expect a positive and intuitive user experience, and approaches to pursuing the digital worker are increasingly people-focused.

Democratization. Machine-learning and data-analytics workflows can be time-consuming, so products are proliferating that allow partial automation of programming and data science, to allow a wider contingent of workers to use digitalization tools. ■

Scott Jenkins, senior editor