Hybrid Modeling: Asset Optimization through the Fusion of AI and Expertise

Mitsubishi Chemical was facing difficulties in detecting and resolving product quality issues on schedule in specific polymer processes. Therefore, they found an opportunity to address these issues by collaborating with AspenTech. By using Aspen Hybrid Models, the company's process engineers were able to create robust and high-performance models that accurately predict quality issues and support preventive measures. 【Value Created by Adopting AspenTech Products】 • Detection and avoidance of potential quality issues • Significant reduction in on-site sampling work • Shortened time to realize operator value For more details, please refer to the related catalog.

Nissan Chemical Corporation is considering modeling an ammonia plant and conducting a study on cost reduction, initially focusing on steam reforming. The company has struggled to accurately model processes and assets using existing rigorous reactor simulations. This requires temperature profiles of process fluids, and accurately estimating or measuring the temperature distribution within the reactor has been challenging. Due to such constraints in conventional models, the company wanted to try a different approach. Ultimately, they chose Aspen Hybrid Models and Aspen Plus based on first principles. 【Twice the speed】Build models that surpass conventional ones more quickly 【Up to 1%】Cost reduction rate achievable through optimization of steam input 【Improved accuracy and sustainability】Enhance predictive insights through the use of AI For more details, please refer to the related catalog.