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 P_v,II and high L_I 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.

蒸发过程是草药制造中的常见步骤，通常持续很长时间。蒸发性能的下降是不可避免的，导致蒸汽和电力的消耗更多，也可能对热敏成分的含量产生影响。近来，借助工业信息系统收集了大量的蒸发过程数据，数据背后隐藏着过程知识。但目前，很少对这些数据进行深入分析。在这项工作中，提出了一种探索性数据分析工作流程来评估蒸发性能并确定性能下降的根本原因。工作流程包括6个步骤：数据收集、预处理、特征阶段识别、特征提取、模型开发和解释、和决策。在模型开发和解释步骤中，工作流采用 HDBSCAN 聚类算法进行数据标注，然后使用 ccPCA 方法比较聚类之间的差异进行根本原因分析。提供了一个全面的案例来验证工作流程的有效性。本案收集了2018年192批次的蒸发过程数据。通过工作流的步骤，提取出每个批次的特征，并将批次聚类为6组。性能下降的根本原因被确定为高 提供了一个全面的案例来验证工作流程的有效性。本案收集了2018年192批次的蒸发过程数据。通过工作流的步骤，提取出每个批次的特征，并将批次聚类为6组。性能下降的根本原因被确定为高 提供了一个全面的案例来验证工作流程的有效性。本案收集了2018年192批次的蒸发过程数据。通过工作流的步骤，提取出每个批次的特征，并将批次聚类为6组。性能下降的根本原因被确定为高P _V，II和高大号_我的ccPCA。根据结果​​给出了对未来制造的建议。建议的工作流程可以确定蒸发性能下降的根本原因。