INNOptimizer™ and AI | Smarter and faster solutions

CHEMICALS
& MATERIALS

Smarter and faster solutions using INNOptimizer^TM and AI

Stage 1

Define optimization target

Stage 2

Initial experimental design

Stage 3

Bayesian optimization with INNOptimizer^TM

Stage 4

Validation of optimal conditions (QbD)

Stage 5

Scale-up to production

Case Study – Flow Reaction

INNOptimizer^TM dominates in efficiency (faster convergence), quality (higher yield), lower impurity), and cost (lower price). Early-stage performance is dramatically better with INNOptimizer^TM (+176% yield in first 5 iterations).

Resource Efficiency:

Bayesian approach required ~70% fewer iterations to reach 90% of optimal performance than Design of Experiments (DOE) Translates to significant savings in experimental costs and development time

Scalability Considerations:

Bayesian optimization’s high initial performance enables faster time-to-market More granular parameter exploration means better fine-tuning potential Reduced experimental variance translates to more confident scale-up decisions.