Integrating Artificial Intelligence (AI) into asset-intensive industries such as refining, petrochemicals, fertilisers, cement, and minerals promises far greater efficiency alongside gains in environmental sustainability.
The technologies that makeup AI have existed for decades, but breakthrough advances now enable new types of applications. AI, with its capability to analyse vast amounts of data, predict outcomes, and optimise processes, is emerging as a pivotal technology in this transformative era.
However, emissions from industrial processes remain sky-high, and organisations are far from achieving the efficiency gains AI applications enable. A report undertaken by the Energy Institute found fossil fuels continued to make up 82 per cent of the world’s total energy consumption in 2022, in line with the previous year.
This caused greenhouse gas (GHG) emissions to climb by 0.8 per cent as the world used more energy. Pressure is mounting to reduce emissions, enhance energy efficiency, and align with global net-zero targets. Boston Consulting Group states, “By scaling currently proven applications and technology, AI has the potential to unlock insights that could help mitigate 5 to 10 per cent of GHG emissions by 2030—and to significantly bolster climate-related adaptation and resilience initiatives.”
Adopting a targeted approach
The potential for AI to drive energy efficiency in asset-intensive sectors is immense.The technology’s ability to process and analyse large datasets can uncover insights that would be impossible for humans to discern manually. Many companies have set their sights on enhancing energy efficiency by 10-20%. But a closer look will reveal the opportunity to achieve more.
A targeted approach is required to achieve the true benefits of AI-driven energy efficiency. This includes identifying inefficiencies in energy use, predicting machinery failures before they occur, and optimising energy consumption in real time. Predictive and prescriptive maintenance has significant potential in all asset-intensive environments, reducing downtime by minimising unexpected failures and saving substantial costs.
AI systems can monitor and analyse emissions to identify areas for improvement in individual processes and optimise the integration of renewable energy resources. A company with its smart microgrid can use AI to improve its energy resilience, forecasting the availability of renewable power for an asset hours or days in advance.
Carbon capture and battery development
AI tools can customise and optimise the design of facilities capturing carbon. Companies might then use generative AI to create options for converting CO2 into methanol and then identify the best use case based on their specific challenges. Interpreting geological and topological information enables organisations to identify the best areas for renewable energy generation or to find subsurface reservoirs for storing CO2.
Mining companies can also employ AI to find rare earth minerals for use in electric motors or lithium for energy storage. Generative AI can even propose new materials optimised around a particular set of properties. This approach could allow companies to create more efficient batteries, improved solvents for carbon capture, etc.
Therefore, the deployment of AI technologies should focus on areas where they can deliver the greatest value. This involves a careful assessment of an industry’s unique challenges and opportunities and an understanding of the specific ways in which AI can contribute to sustainability goals.
Overcoming the complexities of industrial data for AI in energy efficiency
However, harnessing AI’s potential for enhancing energy efficiency in asset-intensive industries requires a nuanced approach. Guardrails are important to ensure safe and reliable AI use, and that data is used legally and does not infringe on the intellectual property of customers, partners, and other third parties.
The complexity of asset-intensive industries’ operations is also one of the biggest challenges when aiming to increase energy efficiency. These sectors are characterised by vast, interconnected systems of machinery and equipment, each with its own energy demands. In this setting, AI’s capacity to manage and make sense of complex datasets is invaluable.
By leveraging AI-driven planning models, companies can predict CO2 emissions, adjust operations in real time, and ensure adherence to decarbonisation targets. AI reduces time to insight, while AI modelling not only aids in energy management but also helps in strategic planning for future sustainability efforts.
Making the most of digital
For AI to have an immediate impact, asset-intensive industries must address the need for digital infrastructure to support advanced AI applications. This includes data acquisition systems capable of collecting high-quality, real-time data and computing resources to process this information effectively. Industrial data is often highly heterogeneous, making it important to have a simplified ingestion process that renders it usable at scale, providing context, ensuring integrity, and centralised management. Data needs to be accessible to all the relevant applications.
Many businesses are aware that these are important capabilities that they lack. In the Equinix 2023 Global Tech Trends Survey, 42 per cent of IT leaders said their existing IT infrastructure is not fully prepared for the demands of artificial intelligence (AI) technology. This is an area they should address as soon as possible. Developing such infrastructure is critical in enabling AI technologies to deliver on their promise of enhanced energy efficiency.
The human dimension
In addition, human expertise within organisations and from external partners cannot be overstated in this context. While AI can provide powerful tools for analysing data and optimising operations, human expertise is essential for interpreting AI-generated insights and making informed decisions.
Inside organisations, operations, sustainability, and IT experts need to work collaboratively to implement AI solutions effectively. They must ensure that AI applications are aligned with the company’s strategic goals and operational realities, bridging the gap with industry-specific knowledge.
On the other hand, the advantages of external expertise from a company with experience in industrial AI implementations are that it provides valuable insights into current best practices, identifies the most promising applications specific to the company, and supports the development of strategies for managing and scaling AI initiatives.
Capitalising on the opportunity
Integrating AI into asset-intensive industries represents a significant opportunity to enhance energy efficiency and advance sustainability goals. However, bringing this potential into reality requires a careful, strategic approach that considers the complexities of these industries and the capabilities of AI technologies.
Asset-intensive organisations can certainly harness the power of AI to drive significant improvements in energy efficiency and environmental performance if they focus on areas where they will deliver the greatest value. They must also develop the necessary digital infrastructure, with the capacity to ingest and make sense of vast amounts of diverse industrial data.
In conclusion, the journey towards integrating AI in asset-intensive industries for energy efficiency and sustainability is not just about technological innovation. It’s a more comprehensive endeavour that necessitates strategic planning, investment in digital infrastructure, responsible implementation, and a collaborative effort between AI experts and industry professionals.
As we advance, the synergy between AI and human insight will be crucial in navigating the challenges and unlocking the full potential of AI to contribute to a more sustainable and energy-efficient future.
