Multivariate statistical real-time monitoring of an industrial fed-batch process for the production of specialty chemicals

Abstract

A large number of production processes for the manufacturing of specialty chemicals, pharmaceuticals, foodstuff, and materials for microelectronics are run in batch mode. Batch processes are “simple” in terms of equipment and operation design, but are often quite complicated in terms of product quality monitoring and of production scheduling and organization. In this paper an industrial case study is presented where the challenges related to the real-time estimation of the required time to manufacture a resin and to the instantaneous product quality estimation are addressed using multivariate statistical techniques. The industrial process is poorly automated, subject to several disturbances, and the batches have uneven lengths. It is shown that stage and batch lengths can be estimated in real time with an average error that is not larger than 20% of the inherent batch-to-batch variability, whereas quality estimations can be provided within the accuracy of the hardware instrumentation, but 240 times faster. The industrial benefits deriving from the use of the proposed monitoring system have been a drastic reduction of the number of samples that need to be analyzed by the lab, prompter adjustment of the processing recipe with consequent reduction of the total processing time, and improved capability to plan the production.