Heavy industries, such as cement, steel, and chemicals, are major contributors to greenhouse gas emissions, accounting for 25% of global CO2 emissions. These industries heavily rely on high temperature heat produced by fossil fuels, making it challenging to reduce their carbon footprint. However, the application of generative AI and foundation models can help optimize industrial processes, improve production yield, reduce energy consumption, and ultimately lower emissions.

One of the main sources of inefficiency in heavy industries is process variability, arising from factors like material inconsistency, varying weather conditions, and human error. By implementing artificial intelligence technology, it becomes possible to predict future variability and optimize processes to minimize energy consumption while maintaining quality. For instance, in cement production, a digital twin of the process can recommend the optimal fuel, air, kiln speed, and feed to minimize energy consumption and achieve the desired quality of clinker.

“Foundation models make AI more scalable by consolidating the cost and effort of model training by up to 70%.”

Foundation models, commonly used in natural language processing, can also be adapted for industrial processes. These models capture multivariate relationships between process variables, material characteristics, energy requirements, weather conditions, operator actions, and product quality, creating an accurate representation of the process. By simulating complex operating conditions, the models provide optimized operating set points, leading to increased efficiency and reduced energy emissions.

Optimizing Industrial Production with Foundation Models

Traditionally, AI models have been used to optimize processes in heavy industries by employing regression models to capture process behavior. However, these models often struggle to capture process dynamics and require significant effort to train and maintain. In contrast, using foundation models in conjunction with the transformer architecture allows for the encoding of time series data and the capture of long and short-term relationships between variables. This approach drastically reduces model training and deployment time, making it suitable for large-scale rollouts. Foundation models have been shown to be 7 times more accurate than regression models and can effectively capture process dynamics through multivariate forecasting.

The Future of Heavy Industry with Generative AI

The application of generative AI technology, powered by foundation models, holds great potential for transforming industrial production. By implementing these technologies, heavy industries can significantly reduce emissions and increase productivity without major capital investment. IBM has already begun collaborating with clients in various sectors, including steel and cement production, to implement generative AI and has observed up to a 5% increase in productivity and a 4% reduction in specific energy consumption and emissions. Effective collaboration and change management play a crucial role in ensuring the successful adoption of these technologies at the plant level.

By embracing AI and foundation models, heavy industries can achieve their sustainability goals while maintaining production capacities, paving the way for a better and healthier world for future generations.

FAQs

How can AI and foundation models benefit heavy industries?

AI and foundation models can optimize industrial processes, improve production yield, reduce energy consumption, and lower emissions. By accurately predicting process variability and optimizing operations, heavy industries can achieve significant efficiency improvements.

What are foundation models?

Foundation models consolidate the cost and effort of model training by up to 70%. They are commonly used in natural language processing but can be adapted to accurately model complex industrial processes. These models capture multivariate relationships between various process variables and enable simulations for optimized operating set points.

How do foundation models compare to regression models?

Foundation models have been shown to be 7 times more accurate than regression models. Unlike regression models, foundation models can capture process dynamics and drastically reduce training and deployment time, making them more suitable for large-scale implementation.

What is the future of heavy industry with generative AI?

Generative AI, powered by foundation models, has the potential to revolutionize industrial production. It allows heavy industries to significantly reduce emissions, increase productivity, and achieve sustainability goals with minimal capital investment. The collaboration between technology providers and industry stakeholders is crucial for successful implementation and adoption.

What are some real-world examples of implementing generative AI in heavy industries?

IBM has already deployed generative AI technologies in a large steel plant in India, a cement plant in Latin America, and CPG manufacturing in North America. These implementations have resulted in increased productivity and reduced energy consumption and emissions.