Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain.
Faculty of Science and Technology, University of Basque Country UPV/EHU, 48940, Leioa, Spain.
Curr Top Med Chem. 2020;20(9):720-730. doi: 10.2174/1568026620666200211110043.
Computational modelling may help us to detect the more important factors governing this process in order to optimize it.
The generation of hazardous organic waste in teaching and research laboratories poses a big problem that universities have to manage.
In this work, we report on the experimental measurement of waste generation on the chemical education laboratories within our department. We measured the waste generated in the teaching laboratories of the Organic Chemistry Department II (UPV/EHU), in the second semester of the 2017/2018 academic year. Likewise, to know the anthropogenic and social factors related to the generation of waste, a questionnaire has been utilized. We focused on all students of Experimentation in Organic Chemistry (EOC) and Organic Chemistry II (OC2) subjects. It helped us to know their prior knowledge about waste, awareness of the problem of separate organic waste and the correct use of the containers. These results, together with the volumetric data, have been analyzed with statistical analysis software. We obtained two Perturbation-Theory Machine Learning (PTML) models including chemical, operational, and academic factors. The dataset analyzed included 6050 cases of laboratory practices vs. practices of reference.
These models predict the values of acetone waste with R2 = 0.88 and non-halogenated waste with R2 = 0.91.
This work opens a new gate to the implementation of more sustainable techniques and a circular economy with the aim of improving the quality of university education processes.
计算模型可以帮助我们发现控制这一过程的重要因素，从而对其进行优化。
教学和研究实验室产生的有害有机废物是一个大问题，大学必须加以管理。
在这项工作中，我们报告了对我们系化学教育实验室废物生成的实验测量。我们测量了 2017/2018 学年第二学期有机化学系 II （UPV/EHU）教学实验室产生的废物。同样，为了了解与废物生成有关的人为和社会因素，我们利用了一份问卷。我们将重点放在所有实验有机化学（EOC）和有机化学 II（OC2）科目的学生身上。这有助于我们了解他们对废物的预先知识、对单独有机废物问题的认识以及对容器的正确使用。这些结果与体积数据一起用统计分析软件进行了分析。我们得到了两个包含化学、操作和学术因素的摄动理论机器学习（PTML）模型。分析的数据集包括 6050 例实验室实践与参考实践的对比。
这些模型对丙酮废物的预测值为 R2 = 0.88，对非卤代废物的预测值为 R2 = 0.91。
这项工作为实施更可持续的技术和循环经济开辟了新的途径，旨在提高大学教育过程的质量。
