Predicting the cradle-to-gate environmental impact of chemicals from molecular descriptors and thermodynamic properties via mixed-integer programming
Predicting the cradle-to-gate environmental impact of chemicals from molecular descriptors and thermodynamic properties via mixed-integer programming
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DOI：
10.1016/j.compchemeng.2017.09.010
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发表时间：
2018-01-04
影响因子：
4.3
通讯作者：
Guillen-Gosalbez, Gonzalo
中科院分区：
文献类型：
--
作者：
Calvo-Serrano, Raul;Gonzalez-Miquel, Maria;Guillen-Gosalbez, Gonzalo
近年来，生命周期评价在化学品环境影响评价中得到了广泛的认可。不幸的是，LCA研究需要大量的数据，而这些数据在实践中很难收集，这是评估化学工业中存在的流程和价值链时的一个关键限制。我们在这里开发了一种方法，根据与分子结构和热力学性质相关的属性来预测有机化学品从摇篮到门的生命周期生产影响。该方法基于一个混合整数规划(MIP)优化框架，该框架系统地构建了生命周期影响的最短预测模型。在将我们的方法应用于包含88种化学物质、17个分子描述符和15个热力学性质的数据集时，我们以足够的精度(对于标准LCA的目的)估计了在LCA研究中广泛应用的几个影响类别，包括累积能源需求、全球变暖潜力和生态指标99。我们的框架最终产生的线性模型可以很容易地集成到现有的建模和优化软件中，从而促进更可持续的流程设计。(C)2017爱思唯尔有限公司。保留所有权利。
Life Cycle Assessment (LCA) has recently gained wide acceptance in the environmental impact evaluation of chemicals. Unfortunately, LCA studies require large amounts of data that are hard to gather in practice, a critical limitation when assessing the processes and value chains present in the chemical industry. We here develop an approach that predicts the cradle-to-gate life cycle production impact of organic chemicals from attributes related to their molecular structure and thermodynamic properties. This method is based on a mixed-integer programming (MIP) optimisation framework that systematically constructs short-cut predictive models of life cycle impact. On applying our approach to a data set containing 88 chemicals, 17 molecular descriptors and 15 thermodynamic properties, we estimate with enough accuracy (for the purposes of a standard LCA) several impact categories widely applied in LCA studies, including the cumulative energy demand, global warming potential and Eco-indicator 99. Our framework ultimately leads to linear models that can be easily integrated into existing modelling and optimisation software, thereby facilitating the design of more sustainable processes. (C) 2017 Elsevier Ltd. All rights reserved.