Maximized Yield with 62% Fewer Experiments—Electrochemical R&D, Reinvented

Quick summary

Explore how AI is helping optimize freeze drying experiments and process conditions in pharmaceutical research.

Industry

Industrial Chemical R&D

Client

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.

Challenge

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.

Goal

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.

Approach & Solution

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.

Results & Metrics

• Yield increased from 74% to 93% — a 19 percentage point improvement
• Number of experiments reduced from 24 to 9 — a 62.5% reduction
• Time to results shortened from 6 weeks to 1.5 weeks — 75% faster
• Identified ideal operating ranges for:
  • Current density
  • DMP concentration
  • Flow rate
• ML-guided predictions honed in on global maximum with minimal experimentation

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.

The Sunthetics Edge

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.

Key Takeaways

• 62% fewer experiments to reach optimal conditions
• 75% time reduction from problem to solution
• Bayesian Optimization efficiently navigates complex chemical landscapes
• Enables lean, agile R&D—ideal for constrained or high-cost environments