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Chemistry and Industry of Forest Products ›› 2017, Vol. 37 ›› Issue (4): 123-128.doi: 10.3969/j.issn.0253-2417.2017.04.018

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Neural Network Model for Working Capacity Prediction of VOCs Adsorption on Carbon Materials

WANG Guodong1, JIANG Jianchun1,2   

  1. 1. College of Chemical Engineering, Nanjing Forestry University, Nanjing 210037, China;
    2. Institute of Chemical Industry of Forest Products, CAF, Nanjing 210042, China
  • Received:2016-10-27 Online:2017-08-25 Published:2017-09-02

Abstract: The adsorption performance of volatile organic components (VOCs) on adsorbent was strongly affected by its porous structure. The traditional adsorbent screening required not only porous characterization, but also n-butane working capacity (BWC) measurement. In order to improve the screening efficiency, the average deviation of the experimental BWC and the calculated ones obtained from the BP artificial neural network constructed by machine learning on corresponding characterizations after thirty repeats of 61 kinds of activated carbon samples was about 6.64%. It was significant to explore such quantitative relationship between feature properties of activated carbon and their related BWC, which was meaningful for the further reduction of expenditure on adsorbent screening.

Key words: amorphous materials, porous structure, quantitative structure-property relationship, butane adsorption, application of artificial intelligence algorithm

CLC Number: