Artificial neural network to predict the growth of the indigenous Acidthiobacillus thiooxidans

Hsuan Liang Liu, Fu Chiang Yang, Hsin Yi Lin, Chih Hung Huang, Hsu Wei Fang, Wei Bor Tsai, Yung Chu Cheng

研究成果: 雜誌貢獻文章同行評審

13 引文 斯高帕斯(Scopus)

摘要

In this study, the growth of the indigenous Acidithiobacillus thiooxidans was predicted using artificial neural network (ANN). Four important variables of the growth medium: KH 2 PO 4 , (NH 4 ) 2 SO 4 , MgSO 4 , and elemental sulfur (S 0 ) were fed as input into the ANN model, while the dry cell weight (DCW) was the output. The ANN model adopted in this study, consisting of an input layer, a hidden layer, and an output layer, was found to give satisfactory results. Among different combinations of 10 mostly used transfer functions, Gaussian and Sigmoid transfer functions were selected for the hidden and the output layers, respectively, to minimize the error between the experimental results and the estimated outputs. Experimental data were randomly separated into a training set and a test set with 22 and 8 experimental runs, respectively. The resulting ANN shows satisfactory prediction of the DCW with R 2 = 0.991 and mean relative deviation (RD) = 0.026. The optimal medium composition of the indigenous A. thiooxidans was further predicted to be KH 2 PO 4 = 1.0 g/l, (NH 4 ) 2 SO 4 = 3.5 g/l, MgSO 4 = 0.65 g/l, and S 0 = 23 g/l with the optimal DCW being 0.722 g/l. The results of this study suggest that ANN provides a powerful tool in studying the nonlinear and time-variant biological processes.
原文英語
頁(從 - 到)231-237
頁數7
期刊Chemical Engineering Journal
137
發行號2
DOIs
出版狀態已發佈 - 四月 1 2008
對外發佈Yes

ASJC Scopus subject areas

  • Chemistry(all)
  • Environmental Chemistry
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

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