Increase Production Process Efficiency with Machine Learning
BHC3™ Process Optimization applies advanced analytic techniques on operational data to improve production yield, quality, and process efficiency by proactively identifying emerging process risks and opportunities.
Demonstrated Benefits
40%
Reduction in off-spec product
80%
Accuracy in detecting low-yield batches
15%
Increase in energy production
1%
Increase in process throughput
Next Generation AI-based Process Optimization
Product yield is variable over time
Product yields and quality forecasted through advanced AI techniques to reduce waste and maximize production
Quality issues emerge without sufficient warning
AI predictions enable operators to act before a product issue emerges
Lab testing and related data are not integrated with operational data
Unified data from process simulators, operational systems, ERP systems, and asset management systems provide a holistic view of production process
Existing solutions do not handle all process types
Support for discrete, bath, semi-batch, and continuous production processes
Unpredictable impact of reactive solutions on quality issues
Integration of process simulators and optimization frameworks to evaluate what-if scenarios
Inefficient collaboration and triage
Enable operators to focus on high-priority issues and leverage AI-based prescriptive insights to support intervention
Successful Deployments
Optimizing Yield by Predicting Product Quality in Real Time
Production Optimization for near real-time visibility into product quality
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