(642g) A Big Data Analytics Workflow for Pharmaceutical Manufacturing Industry
AIChE Annual Meeting
Thursday, November 14, 2019 - 9:42am to 9:58am
In this study, we proposed a big data analytics workflow for pharmaceutical industry manufacturing to mine process knowledge, which helped to improve drug quality and production efficiency. The workflow contains five steps: problem definition, data acquisition, data preprocessing, data modelling and model application. The workflow provides an idea for pharmaceutical enterprises to collect, organize and analyze manufacturing data systematically. Meanwhile, a data analytics case study of a double effect evaporation process for herbal medicines was used for further illustration. In the case, the production efficiency of a double effect evaporator was observed to decrease during a long time. To handle this problem, the data of sensors, valves and instructions from 172 batches were collected. After data preprocessing, data modelling task was carried out. Correlation analysis was first applied to study the relationships among each single process variables. Next, multivariate data analysis methods were used to evaluate the profiles of all the batches: different process phases were identified using hierarchical clustering analysis according to the dissimilarities among the batches; the root causes of the phase transformations were also investigated through principal component analysis. The models above could be applied for on-line process monitoring, thus helping to enhance process understanding and to support manufacturing decision.