AI is increasingly recognized as a critical enabler for chemical process optimization, yet widespread adoption in the sector remains modest. According to recent surveys from IBM and McKinsey, while up to 80% of chemical executives acknowledge AI's importance, fewer than half have implemented comprehensive strategies, and exposure to generative AI remains among the lowest across industries. Despite this lag, pioneering firms are now demonstrating tangible value from AI integration - especially through hybrid modeling frameworks that combine first-principles physics with machine learning.

For instance, Isu Chemicals developed a hybrid reactor model using six years of operational data, achieving a predictive accuracy of 99.7% in yield estimation. This enabled significant improvements in process tuning and output quality. Another notable case involves The International Group Inc. (IGI), which collaborated with AVEVA and Lityx to deploy AI-powered process optimization across its wax production network. The system identified inefficiencies and opportunities across thousands of variables, resulting in $10M annual profit gain, 49% reduction in crude waste, and elimination of 20 hours of off-spec production every two weeks—yielding a 67x ROI.

Beyond hybrid simulation, AI applications are expanding into predictive maintenance (e.g., SCG Chemicals' Digital Reliability Platform) and AI-driven material discovery using generative models and deep learning. These advances collectively represent a shift toward smarter, more sustainable, and autonomous chemical manufacturing, where hybrid AI frameworks are pivotal in unlocking efficiencies, reducing emissions, and shortening R&D cycles.

Source: AVEVA, July 8, 2025