The future starts with Industrial AI – MIT Technology Review

Domain expertise is the secret sauce that separates Industrial AI from more generic AI approaches. Industrial AI will guide innovation and efficiency improvements in capital-intensive industries for years to come, said Willie K Chan, CTO of AspenTech. Chan was one of the original members of the MIT ASPEN research program that later became AspenTech in 1981, now celebrating 40 years of innovation.

Incorporating that domain expertise gives Industrial AI applications a built-in understanding of the context, inner workings, and interdependencies of highly complex industrial processes and assets, and takes into account the design characteristics, capacity limits, and safety and regulatory guidelines crucial for real-world industrial operations.

More generic AI approaches may come up with specious correlations between industrial processes and equipment, generating inaccurate insights. Generic AI models are trained on large volumes of plant data that usually does not cover the full range of potential operations. Thats because the plant might be working within a very narrow and limited range of conditions for safety or design reasons. Consequently, these generic AI models cannot be extrapolated to respond to market changes or business opportunities. This further exacerbates the productization hurdles around AI initiatives in the industrial sector.

By contrast, Industrial AI leverages domain expertise specific to industrial processes and real-world engineering based on first principles that account for the laws of physics and chemistry (e.g., mass balance, energy balance) as guardrails for mitigating risks and complying with all the necessary safety, operational, and environmental regulations. This makes for a safe, sustainable, and holistic decision-making process, producing comprehensive results and trusted insights over the long run.

Digitalization in industrial facilities is critical to achieving new levels of safety, sustainability, and profitabilityand Industrial AI is a key enabler for that transformation.

Talking about Industrial AI as a revolutionary paradigm is one thing; actually seeing what it can do in real-life industrial settings is another. Below are a few examples that demonstrate how capital-intensive industries can leverage Industrial AI to overcome digitalization barriers and drive greater productivity, efficiency, and reliability in their operations.

These use cases are by no means exhaustive, but just a few examples of how pervasive, innovative, and broadly applicable Industrial AIs capabilities can be for the industry and for laying the groundwork for the digital plant of the future.

Industrial organizations need to accelerate digital transformation to stay relevant, competitive, and capable of addressing market disruptors. The Self-Optimizing Plant represents the ultimate vision of that journey.

Industrial AI embeds domain-specific know-how alongside the latest AI and machine-learning capabilities, into fit-for-purpose AI-enabled applications. This enables and accelerates the autonomous and semi-autonomous processes that run those operationsrealizing the vision of the Self-Optimizing Plant.

A Self-Optimizing Plant is a self-adapting, self-learning and self-sustaining set of industrial software technologies that work together to anticipate future conditions and act accordingly, adjusting operations within the digital enterprise. A combination of real-time data access and embedded Industrial AI applications empower the Self-Optimizing Plant to constantly improve on itselfdrawing on domain knowledge to optimize industrial processes, make easy-to-execute recommendations, and automate mission-critical workflows.

This will have numerous positive impacts on the business, including the following:

The Self-Optimizing Plant is the ultimate end goal of not just Industrial AI, but the industrial sectors digital transformation journey. By democratizing the application of industrial intelligence, the digital plant of the future drives greater levels of safety, sustainability, and profitability and empowers the next generation of the digital workforcefuture-proofing the business in volatile and complex market conditions. This is the real-world potential of Industrial AI.

To learn more about how Industrial AI is enabling the digital workforce of the future and creating the foundation for the Self-Optimizing Plant, visit http://www.aspentech.com/selfoptimizingplant, http://www.aspentech.com/accelerate, and http://www.aspentech.com/aiot.

This article was written by AspenTech. It was not produced by MIT Technology Reviews editorial staff.

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The future starts with Industrial AI - MIT Technology Review