The force is strong with Industrial AI driving business transformation
By Adi Pendyala, Senior Director, Aspen Technology
Resistance is futile, as the force is strong with Industrial Artificial Intelligence (Industrial AI) transforming businesses, one company at a time. To embark on this journey, an enterprise-wide framework is needed to capture the essence of the AI paradigm shift and the resulting transformation of all business processes in an organization. Many capital-intensive organizations are evolving their industrial AI and sustainability strategies to lead the way in creating the plant of the future. AI and sustainability are mutually catalytic and synergistic, as both concepts share the same underlying business drivers to create a better plant of the future - by enabling safer, greener, longer and faster operations.
Industrial AI use cases for asset-intensive companies
To overcome AI adoption hurdles, there is an increased emphasis on democratizing the application of AI to targeted industrial challenges with a focus on business outcomes. This is where we get to the paradigm of Industrial AI, which combines data science and AI with software and domain expertise to deliver comprehensive business outcomes for the specific business needs of capital-intensive industries.
In Industrial AI Market Report 2020 – 2025 from IoT Analytics, the team identified 33 different use cases that employ AI tools and techniques on connected data sources and assets of industrial enterprises. This study estimates the global industrial AI market size will reach $72.5B by 2025, up from over $11B in 2018. The firm has identified three top industrial AI use cases.
In the lead, predictive maintenance represents over 24% of the total market in 2019 – making use of advanced analytics and machine learning to determine the condition of a single asset or an entire set of assets. The business goal is to predict when maintenance should be performed. Quality, reliability and assurance is the second-largest industrial AI use case category at 20.5%. A key challenge is to enable decision-makers to maximize the economics of business decisions – by going beyond the equipment level, and accurately predicting future asset performance of the whole system.
In third position, process optimization is perhaps, the most obvious and compelling use case but still one of the most difficult to implement. This involves multiple AI-based capabilities across the system, automating repeat human tasks; enabling real-time decisions across various applications; augmenting the asset lifecycle and optimizing the value chain across different business dimensions. This use case employs advanced machine learning methods, including reinforcement learning and sophisticated deep learning neural networks, to infer information and intelligence from different data sources, assets and processes.
A formidable duo – AI and sustainability
As we envision industrial operations in the new normal, safety and sustainability are two important business dimensions for asset-intensive industries. For example, the use of predictive analytics can substantially reduce unplanned “flaring”. The World Bank estimates that flaring contributes more than 350 million tons of CO2 emissions globally every year, the equivalent of approximately 90 coal-fired power plants. These emissions could be significantly reduced by increasing equipment reliability to eliminate unplanned shutdowns and the flaring that comes with them. Predictive maintenance can dramatically improve safety, as the Chemical Safety Board (CSB) assets that unplanned startups and shutdowns contribute to 50% of safety incidents in the refining industry.
Recently, China National BlueStar (Group) chose AspenTech to accelerate their digitalization via embedded AI. This partnership will enable BlueStar to achieve significant production improvements throughout its specialty chemicals business. Early prediction of process deviations means that the company can avoid product quality issues and mitigate unplanned downtime via predictive and prescriptive analytics on all their critical equipment. By accelerating their digital transformation journey, BlueStar is positioned to capitalize on global market opportunities in a volatile, uncertain, complex and ambiguous (VUCA) world.
Environmental regulations, energy and water conservation, air quality and climate change are prime concerns for industries. The circular economy of plastics requires a holistic approach to production and extended use to conserve resources and protect the environment. With solutions leveraging insights enabled by AI and machine learning, companies can pursue renewable energy projects, such as bioethanol, biodiesel, carbon capture, solar and wind initiatives. In doing so, companies have the ability to improve profitability and reliability, while reducing capital investments. Carbon capture from industrial operations to mitigate climate change is another area of focus.
Industrial AI all set for take off
Traditionally, cost savings drove much of the effort to be more efficient. However, companies are now looking towards more specific process metrics considering emissions and resource use. Companies are also focusing on waste and discharge reduction from production units, as well as efficiency enhancements through digital technology. In the process industries, real-life behaviors of complex interconnected assets, processes and systems are defined by the design characteristics and capacity (limits) of the asset, which are captured in the model of the asset dictated by the physics and chemistry of the process.
Industrial AI, like previous multivariable and adaptive control capabilities, is used to gain greater insights to operate the asset within the physics and chemistry of the process and process design limitations. The International Energy Agency (IEA) has found that Industrial AI and digital solutions can help boost energy efficiency as much as 30% for industrial operations. The next-generation asset optimization solutions will provide the visibility, analysis and insight needed to address the challenges inherent in meeting sustainability goals.
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