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On the database-based strategy of candidate extractant generation for de-phenol process in coking wastewater treatment 被引量:1
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作者 Ran Xu Yuehong Zhao +3 位作者 qingzhen han Xinyu Liu Hongbin Cao Hao Wen 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2018年第7期1570-1580,共11页
A database-based strategy of candidate generation was proposed for molecular design of new de-phenol extractants following the idea of finding new applications of existing commercial compounds. The strategy has the ad... A database-based strategy of candidate generation was proposed for molecular design of new de-phenol extractants following the idea of finding new applications of existing commercial compounds. The strategy has the advantage that the environmental, safety and health risks of candidate compounds are known and controllable. In this work, the Existing Commercial Compounds(ECC) database and special combined search strategy were developed as the base for the proposed CAMD method following such idea, and molecules for phenol extraction used in coking wastewater treatment were selected from the ECC database. The candidate solvents cover the following categories: ketones, esters, ethers, alcohols, anhydrides and benzene compounds, which are consistent with the de-phenol extractants commonly used in the industry or experiment. The compounds with higher partition coefficient and selectivity than widely used methyl isobutyl ketone(MIBK) are mainly ketones. 26 obtained molecules show higher partition coefficient and selectivity than MIBK, which are suggested to be further investigated by experiment. Furthermore, analysis of these potential molecules may present the effective functional groups as the initial group set to generate new molecular structures of de-phenol extractants. The results show that the proposed method enables us to efficiently generate chemicals with benefits of less time, less economical cost, and known environmental impact as well. 展开更多
关键词 Existing commercial compounds database De-phenol extractant Database-based generation strategy Environmental risk
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