Prediction of infinite-dilution activity coefficients with neural collaborative filtering was written by Tan, Tian;Cheng, Hongye;Chen, Guzhong;Song, Zhen;Qi, Zhiwen. And the article was included in AIChE Journal in 2022.Recommanded Product: 1,2-Propanediol This article mentions the following:
Accurate prediction of infinite dilution activity coefficient (γ∞) for phase equilibrium and process design is crucial. In this work, an exptl. γ∞ dataset containing 295 solutes and 407 solvents (21,048 points) is obtained through data integrating, cleaning, and filtering. The dataset is arranged as a sparse matrix with solutes and solvents as columns and rows, resp. Neural collaborative filtering (NCF), a modern matrix completion technique based on deep learning, is proposed to fully fill in the γ∞ matrix. Ten-fold cross-validation is performed on the collected dataset to test the effectiveness of the proposed NCF, proving that NCF outperforms the state-of-the-art phys. model and previous machine learning model. The completed γ∞ matrix makes solvent screening and extension of UNIFAC parameters possible. Taking two typical hard-to-sep. systems (benzene/cyclohexane and Me cyclopentane/n-hexane mixtures) as examples, the NCF-developed database provides high-throughput screening for separation systems in terms of solvent selectivity and capacity. In the experiment, the researchers used many compounds, for example, 1,2-Propanediol (cas: 57-55-6Recommanded Product: 1,2-Propanediol).
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Referemce:
Alcohol – Wikipedia,
Alcohols – Chemistry LibreTexts