Are you collecting lots of data on your batch processes but not getting the information you need to detect and diagnose process failures?
Identifying process failures or process drift early enough could prevent costly re-work or even scrapping batches.
We’ve outlined how your historical data can be used for early fault detection so you can improve product quality, increase yield, and reduce operating costs.
Get a Positive ROI from Your Historical Batch Data
Your data can be used to model your process to establish the underlying relationship from raw materials and process conditions to final product quality. This statistical model can then be used for real-time monitoring using ProBatch Online to:
- Predict what your final product quality variables will be at any time point during the batch
- Detect which variables (initial conditions, raw materials, or process conditions) are contributing to predicted poor product quality
- Take corrective actions to steer your batch process towards optimal operating conditions
Predicting your final product quality at any time point during the batch is really beneficial for early fault detection as it essentially tells you if you keep operating under your current conditions, you’re destined for failure. Fault diagnosis then identifies which process variables are contributing to the process drift right now so you can steer it back to optimal operating conditions.
This means a batch that would have otherwise required rework or would’ve been scrapped can be brought back in-spec via timely and informed operator intervention. The return on investment would vary depending on the value of the product from each batch.
Automated Batch Control
You could go one step further and implement Multivariate Predictive Batch Control using ProBatch Control. Batch control usually involves 2-3 key decision points where the standard recipe is dynamically adjusted to steer the batch towards an optimal outcome, all without the need for operator intervention. Using ProBatch Control, our clients have seen a 50% reduction in quality variation and 20% increase in productivity because they’re producing better batches more frequently using the same resources.
How to Start Batch Monitoring and Control
Okay great, you can get better batches more frequently by (i) predicting final product quality, (ii) identifying the process conditions leading to process drift, and (iii) dynamically adjusting the recipe via operator or automated intervention. The question is how do YOU get started? We’re willing to analyze 15 of your batches for free to show you that your data could provide real-time monitoring, prediction, optimization, and control of your batch process. Contact us for a data template to get started.