Data Analytics Can Help Reduce Energy Consumption in the Industrial Manufacturing of Specialty Chemicals

Batch processes are operated following recipes that consist of a sequence of steps of given time lengths carried out in different pieces of equipment. Large variability in the length of a processing step can cause that step to become a bottleneck for the entire process, thus leading to an increase of the energy consumption per unit of product manufactured. Debottlenecking the process can therefore lead to reduction of the energy requirements. We consider the case of a batch reaction that is a key step in the industrial manufacturing of a polymer additive. The available data historians revealed that, over a period of 12 months of operation, the length of the reaction step ranged between 0.9 and 2.8 h, with an average value of 1.3 h. This acted as a limit to the performance of the overall manufacturing system, but no cause was initially identified to explain this behavior. Advanced analytics on the process data historians by means of multivariate statistical techniques revealed that over 40% of the batches had been affected by intervention of a safety interlock in the reactor, whose occurrence strongly correlated to an increase of the batch length. Reconfiguration of the interlock system resulted in a reduction of both average batch length and batch length variability. Namely, over the 6-month assessment that followed this study, a 29% reduction in the average batch length for the reactor under investigation was observed, which resulted in an 8% reduction of the overall process cycle duration, thus entailing significant energy savings. Furthermore, an 11% reduction on nitrogen consumption was achieved.