Batch Quality Optimization
Optimization of Chemical Processes
Today, most petrochemical manufacturing companies rely on lab results to determine product quality. This typically delays lab responses by 10 to 40 minutes because the products cannot be released until the lab results are available. Due to the inefficiency of this process, chemical manufacturers are seeking batch process improvements that allow them to release their products more quickly.
Through the use of the manufacturer’s historical data, oPRO.ai can generate machine learning models that will help chemical and oil and gas manufacturers modify the conditions as necessary for the next batch. This application of process optimization in chemical engineering enables the customer to predict the batch quality (lab results), understand the correlation among the process variables, and prescribe the optimal cycle time within the given constraints.
oPRO.ai’s Batch Process Optimization Accelerator helps maximize throughput of the batch reactor while maintaining the quality of the batch. It does this by predicting the batch quality before the batch ends, and then prescribing the batch cycle time to increase throughput.
This accelerator can:
- Predict the batch quality based on historical data
- Prescribe optimal process values based on the constraints and predicted batch quality
- Supervise-steer the process values to the optimal path to ensure good batch quality — without manual intervention