A U.S. pilot chemical plant conducting advanced electrochemical process development. An innovation-focused leader in industrial-scale R&D, operating at the intersection of sustainability and precision chemistry.
Traditional optimization was slow, resource-heavy, and unable to consistently reach optimal yield. With 24 experiments over 6 weeks, the best achievable performance plateaued at 74%. Time and materials were being consumed at a rate that hindered project velocity.
To reach the maximum possible yield in the anodic oxidation of DMP with fewer experiments, faster cycle times, and improved prediction accuracy to accelerate overall R&D timelines.
SuntheticsML deployed its small-data ML modeling framework using Bayesian Optimization. With just 9 experiments (down from 24), the model rapidly converged toward the global maximum, leveraging pattern recognition to guide decisions and narrow down ideal process conditions.
Additional insight: SuntheticsML recommended optimal ranges for current density, DMP concentration, and flow rate, identifying the sweet spot that would otherwise have been prohibitively time-consuming to find through traditional methods.
"SuntheticsML allowed to bypass months of experimentation. The model's suggestions led directly to a 93% yield—faster and with fewer resources than ever before."
