ECGiT Private Limited’s Post

Multivariate Batch Evolution Models (BEMs) use control limits based on reducing many process variables into a smaller set of key factors. These key factors are chosen based on their strong correlation with the batch process over time. This works well in batch processing, where most variables are highly related and can be combined into a single representation. BEMs can spot out-of-control or drifting processes by comparing these key factors with averages and standard deviations from previous batches. What's notable is that BEMs are effective even with a small number of batches (~10-15), unlike other machine learning methods that need larger samples. Operators can trace back BEM control limits to specific process variables using a Variable Importance Plot, which helps identify which variables are causing deviations. To successfully deploy BEMs, a lot of metadata is needed—like process parameters, sampling rates, data aggregation methods, and historical tests. For scalable use, this metadata must be standardized across a site or the entire organization. Real-time analytics with BEMs require interactive dashboards that let operators not only monitor the process but also investigate issues quickly. These dashboards aren’t static—they dynamically link data, helping identify root causes. In summary, BEMs don’t just show what is happening in a process, but also explain why, allowing operators to take fast, informed action. #questdb #iiot #batch_evolution_model #golden_batch #univariate #multivariate #pls #grafana #control_limit #lolo #hihi #ml #machine_learning