The Evolution of Refineries: Navigating the Shift from Crude to Chemicals
In the face of sustainability efforts, the refining industry is encountering the challenge of heightened complexity, necessitating the adoption of sophisticated Process Analysis (PA), Artificial Intelligence (AI), and Machine Learning (ML) technologies. A recent report by the International Energy Agency (IEA) suggests that global oil demand is projected to rise annually until 2026. However, a combination of policy reforms and behavioral shifts could signal a peak in demand sooner than anticipated. The COVID-19 pandemic, alongside a global push for a low-carbon future, has significantly altered oil demand forecasts, presenting oil producers and companies with tough decisions regarding the exploitation of resources and capacity expansions.
Dr. Fatih Birol, Executive Director of the IEA, emphasizes that while the COVID-19 crisis led to a historic drop in oil demand, the trend might not persist without substantial policy and behavioral changes.
The goal of transitioning away from oil smoothly is crucial for achieving climate objectives, but it hinges on proactive measures from governments and society at large.
The current landscape poses significant challenges for the crude oil refining sector, which must pivot from fossil fuels towards renewable energy sources and the production of light olefins from existing feedstocks. This transition is complicated by the intricate nature of process control and supply chain management, a complexity that modern PA, AI, and ML technologies under the Industry 4.0 framework aim to simplify.
Drawing parallels with internet companies that leverage data analytics to optimize consumer market assets, Modcon.AI solutions generate value by enhancing process analytics and utilizing operational data. Artificial neural networks (NNs) implemented in these solutions dynamically model physical properties and chemical compositions of various process streams, recommending optimal set points to achieve these predictions.
Online analysis of crude oil quality is vital for producing cost-effective blends with optimal refining margins. By leveraging process analyzers, refineries can enhance their profitability through savings in production costs, reduced product giveaways, minimized operating manpower, and energy conservation. Modcon Systems’ MOD-4100 crude analyzer exemplifies innovation in online crude oil analysis by measuring critical parameters such as distillation profiles, salt concentration, and more, thus improving refining margins.
The Modcon.AI suite equips process engineers with cutting-edge optimization tools for connectivity, validation, and prediction of key performance indicators (KPIs), enabling informed decision-making for efficient process management. Moreover, reinforcement learning, a subset of AI that learns through trial and error without relying solely on historical data, is ideally suited for optimizing hydrocarbon processing. This approach allows Deep Reinforcement Learning (DRL) agents to adapt to their environments by selecting actions that maximize rewards or achieve goals, offering a promising avenue for refining process optimization.
Investing further in PA, AI, and ML technologies represents Modcon’s commitment to sustainable development, adopting business strategies and advancing technologies that not only meet current needs but also safeguard and enhance the resources required for the future.
