A data-driven workflow for evaporation performance degradation analysis: a full-scale case study in the herbal medicine manufacturing industry

Sheng Zhang, Xinyuan Xie and Haibin Qu ()
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Sheng Zhang: Zhejiang University
Xinyuan Xie: Zhejiang University
Haibin Qu: Zhejiang University

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 2, No 14, 668 pages

Abstract: Abstract The evaporation process is a common step in herbal medicine manufacturing and often lasts for a long time. The degradation of evaporation performance is inevitable, leading to more consumption of steam and electricity, and it may also have an impact on the content of thermosensitive components. Recently, a vast amount of evaporation process data is collected with the aid of industrial information systems, and process knowledge is hidden behind the data. But currently, these data are seldom deeply analyzed. In this work, an exploratory data analysis workflow is proposed to evaluate the evaporation performance and to identify the root causes of the performance degradation. The workflow consists of 6 steps: data collecting, preprocessing, characteristic stage identification, feature extraction, model development and interpretation, and decision making. In the model development and interpretation step, the workflow employs the HDBSCAN clustering algorithm for data annotation and then uses the ccPCA method to compare the differences between clusters for root cause analysis. A full-scale case is presented to verify the effectiveness of the workflow. The evaporation process data of 192 batches in 2018 were collected in the case. Through the steps of the workflow, the features of each batch were extracted, and the batches were clustered into 6 groups. The root causes of the performance degradation were determined as the high Pv,II and high LI by ccPCA. Recommended suggestions for future manufacturing were given according to the results. The proposed workflow can determine the root causes of the evaporation performance degradation.

Keywords: Evaporation; Herbal medicine; Exploratory data analysis; Root cause analysis; Contrastive PCA (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-021-01816-w

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