Experimental and neural model analysis of styrene removal from polluted air in a biofilter
作者： Eldon R Rene , Maria C Veiga , Christian Kennes
DOI: 10.1002/JCTB.2130
关键词:
摘要: subjectingthebioreactortodifferentflowrates(0.15‐0.9 m 3 h −1 )andconcentrations(0.5‐17.2 g −3 ),thatcorrespondtoinlet loading rates up to 1390 . During the different phases of continuous biofilter operation, greater than 92% styrene removal was achievable for 250 A back propagation neural network algorithm applied model and predict efficiency (%) this process using inlet concentration (g )a nd unit fl ow (h s input variables. The data points were divided into training (115 × 3) testing set (42 3). most reliable condition selected by a trial error approach estimating determination coefficient (R 2 ) value (0.98) achieved during prediction set. CONCLUSION: results showed that simple based with topology 2‐4‐1 able efficiently performance in biofilter. Through sensitivity analysis, influential parameter affecting ascertained be theflow rate. c � 2009 Society Chemical Industry
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Marc A. Deshusses, Joseph S. Devinny, Todd Stephen Webster, Biofiltration for air pollution control ,(1998)
David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams, Learning representations by back-propagating errors Nature. ,vol. 323, pp. 696- 699 ,(1988) , 10.1038/323533A0
J.M. Zurada, A. Malinowski, I. Cloete, Sensitivity analysis for minimization of input data dimension for feedforward neural network international symposium on circuits and systems. ,vol. 6, pp. 447- 450 ,(1994) , 10.1109/ISCAS.1994.409622
J. Glassey, G. A. Montague, A. C. Ward, B. V. Kara, Artificial neural network based experimental design procedure for enhancing fermentation development. Biotechnology and Bioengineering. ,vol. 44, pp. 397- 405 ,(1994) , 10.1002/BIT.260440402
Hasnaa Jorio, Louise Bibeau, Michèle Heitz, Biofiltration of air contaminated by styrene: Effect of nitrogen supply, gas flow rate, and inlet concentration Environmental Science & Technology. ,vol. 34, pp. 1764- 1771 ,(2000) , 10.1021/ES990911C
Hengchang Dai, Colin MacBeth, Effects of learning parameters on learning procedure and performance of a BPNN Neural Networks. ,vol. 10, pp. 1505- 1521 ,(1997) , 10.1016/S0893-6080(97)00014-2
Ian G. McKendry, Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2.5) forecasting. Journal of The Air & Waste Management Association. ,vol. 52, pp. 1096- 1101 ,(2002) , 10.1080/10473289.2002.10470836
Balsam T Mohammad, Maria C Veiga, Christian Kennes, Mesophilic and thermophilic biotreatment of BTEX-polluted air in reactors Biotechnology and Bioengineering. ,vol. 97, pp. 1423- 1438 ,(2007) , 10.1002/BIT.21350
Y. Jin, M. C. Veiga, C. Kennes, Development of a novel monolith-bioreactor for the treatment of VOC-polluted air. Environmental Technology. ,vol. 27, pp. 1271- 1277 ,(2006) , 10.1080/09593332708618744
Brad Warner, Manavendra Misra, Understanding Neural Networks as Statistical Tools The American Statistician. ,vol. 50, pp. 284- 293 ,(1996) , 10.1080/00031305.1996.10473554