Industrial AI
As Willie K Chan, CTO, AspenTech states – 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.
Today’s industrial organizations, and especially those in capital-intensive industries, stand at a crossroads for opportunity. They recognize the need to reinforce their industrial operations and complex value chains with greater resiliency, flexibility, and agility to respond to shifting market conditions. At the same time, they’re investing in autonomous and semi-autonomous artificial intelligence (AI) capabilities to realize their vision of the digital plant of the future—the “Self-Optimizing Plant.”
Source – AspenTech
Market Forces as the Driving Factors for Industrial AI
Digitalization in industrial facilities is critical to achieving new levels of safety, sustainability, and profitability—and AI is a key enabler for that transformation. While the wariness typically associated with implementing any new technology may be a stumbling block for AI adoption, there are three pivotal needs driving capital-intensive industries to digitize and implement purpose-built AI systems:
- Compelling need for knowledge automation – Generational shifts in the workforce are creating a loss of operational expertise. Veteran workers with years of institutional knowledge are retiring, replaced by younger employees fresh out of school, taught on technologies and concepts that don’t match the reality of many organizations’ workflows and systems. This dilemma is fuelling the need for automated knowledge sharing and intelligence-rich applications that can close the skills gap.
- Data value superseding data volume – Industrial organizations are accumulating massive volumes of data but deriving business value from only a small slice of it. Transient repositories like data lakes often become opaque and unstructured data swamps. Organizations are switching their focus from mass data accumulation to strategic industrial data management, homing in on data integration, mobility, and accessibility—with the goal of using AI-enabled technologies to unlock value hidden in these unoptimized and underutilized sets of industrial data. The rise of the digital executive (chief technology officer, chief data officer, and chief information officer) as a driver of industrial digital transformation has been a key influence on this trend.
- Competitors are digitally transforming – Adopting new technologies unlocks new business models that are integral to sustainability, market competitiveness, and new corporate strategies. The more that competitors digitally transform to reap these advantages, the more that organizations that don’t transform will be left behind.
Framework for Industrial AI
Industrial Internet viewpoints. Source: IIC IIRA
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 considers the design characteristics, capacity limits, and safety and regulatory guidelines crucial for real-world industrial operations.
- 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 operations—realizing 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 itself—drawing on domain knowledge to optimize industrial processes, make easy-to-execute recommendations, and automate mission-critical workflows.
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.
- A process plant may deploy an advanced class of Industrial AI-enabled Hybrid Models, drawing on deeper collaboration between domain experts and data scientists, machine learning, and first principles for more comprehensive, accurate, and performant models. These hybrid models can be used to optimally design, operate, and maintain plant assets across their lifecycles. Because they are reliably relevant for a longer period, they also provide a better representation of the plant.
- A chemical plant could leverage Industrial AI for yielding real-time insights from integrated industrial data from the edge to the cloud, using the Artificial Intelligence of Things (AIoT) to enable agile decision-making across the organization. Using richer, dynamic workflows, supply chain and operations technologies are seamlessly linked together to detect changes in market conditions and automatically adjust the operating plan and schedule in response.
- A refinery can use Industrial AI to evaluate thousands of oil production scenarios simultaneously, across a diverse set of data sources, to quickly identify optimal crude oil slates for processing. Combined with AI-rich capabilities, enterprise-wide insights, and integrated workflows to improve executive decision-making, this approach empowers workers to allocate their time and efforts to more strategic, value-driving tasks.
- A next-generation industrial facility could apply Industrial AI as the plant’s “virtual assistant” to validate the quality and efficiency of a production plan, in real time. AI-enabled cognitive guidance ultimately helps reduce reliance on individual domain experts for complex decision-making, and instead institutionalizes historical decisions and best practices to eliminate expertise barriers.
Today, AI has progressed from lines of code to 2nd and 3rd waves of AI involving more integrated and complex neural networks that are enabling deeper learning and insights. The 2nd wave is happening today, where industrial data is used to train algorithms to help improve decision-making around how to operate and maintenance industrial products and processes. The 3rd wave is about adding the human senses of sight, hearing and others that give machines the ability to perceive and interact in decision making more directly with humans. Alexa and Google Home are good examples of consumer 3rd Wave AI applications.
