基于机器学习的典型制药企业工艺过程VOCs排放特征因子识别

The identification of VOCs emission characteristic factors in production process of a typical pharmaceutical factory based on machine learning

制药行业生产工艺复杂,VOCs排放显著,是实施工业VOCs减排的重点行业。为落实制药行业VOCs减排策略,需准确识别重点排放企业和工艺过程。基于精细化工园区典型化学合成制药企业VOCs污染源成分谱,结合特征选择、分类分析、聚类分析等机器学习手段,进行了VOCs特征因子识别。结果表明:该企业VOCs排放的特征因子为甲苯、丙酮、乙醛、苯甲醛和正己烷;机器学习手段所识别的特征因子数量精简,在各个生产过程具有相似的浓度分布,体现了污染源VOCs排放物种组成上的差异。

Pharmaceutical industry,which has complex production processes and a serious VOCs emission problem,is a key industry for implementing VOCs emission reduction.To implement VOCs emission reduction,it is necessary to accurately identify key companies and production processes.Based on the VOCs source profiles of a typical chemical synthetic pharmaceutical factory in a fine chemical industrial park,the VOCs characteristic factors identification were carried out based on machine learning methods such as feature selection,classification analysis,and cluster analysis.The results showed that the VOCs emission characteristic factors were identified as toluene,acetone,acetaldehyde,benzaldehyde and n-hexane.The identified characteristic factors were obviously simplified by machine learning methods,and had similar concentration distribution in each production process.They could reflect the differences in the species composition of VOCs emission from pollution sources.