Abstract
Stirred tank reactors are widely used as the major processing unit in environmental and waste management engineering. It also finds its applicability in many chemical, pharmaceutical and petroleum industries. Due to the dynamic nature of the chemical reactions involved and the non-linear functional relationship between the input and output variables, it is difficult to correctly predict a universal empirical correlation for the process variables, i.e. mass transfer coefficient and gas hold-up rate. As such, intelligent modelling using neural networks was adopted in the present experimental work. Experiments were conducted with two types of impeller such as Rushton and curved blade to observe the mass transfer rate and gas hold-up characteristics. The Levenberg–Marquardt optimization algorithm was used to train the neural network so that the error between the desired output and actual output is reduced. The predictive capability of the model has been found satisfactorily and also independent of the impeller type.
This is a preview of subscription content, log in via an institution to check access.
References
Arjunwadkar SJ, Sarvanan K, Kulkarni PR, Pandit AB (1998) Gas–liquid mass transfer in dual impeller bioreactor. Biochem Eng J 1(2):99–106
Bose NK, Liang P (1996) Neural network fundamentals with graphs, algorithms, and applications. Tata McGraw-Hill, NewDelhi
Cruz AJG, Silva AS, Araujo MLGC, Giordano RC, Hokka CO (1999) Estimation of the volumetric oxygen transfer coefficient (KLa) from the gas balance and using a neural network technique. Brazilian J Chem Eng 16
Fadavi A, Chisti Y (2007) Gas hold-up and mixing characteristics of a novel forced circulation loop reactor. Chem Eng J 131:105–111
Garcı́a-Ochoaa F, Castro EG (2001) Estimation of oxygen mass transfer coefficient in stirred tank reactors using artificial neural networks. Enzyme Microb Technol 28(6):560–569
Garcia-Ochoa F, Gomez E (1998) Mass transfer coefficient in stirrer tank reactors for xanthan solutions. Biochem Eng J 1:1–10
Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 6:989–993
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximaters. Neural Netw 2:359–366
Irie B, Miyanki S (1988) Capabilities of three layer perceptrons. In: IEEE second international conference on neural networks, San Diego, vol 1, pp 641–648
Linek V, Vacek V, Benes P (1987) A critical review and experimental verification of correct use of dynamic method for the determination of oxygen transfer in aerated agitated vessels to water, electrolytic solutions and viscous liquids. Chem Eng J 34:11–34
Moucha T, Linek V, Sinkule J (1995) Measurement of kLa in multiple impeller vessels with significant axial dispersion in both phases. Trans IChemE 73, part A, 99 286–290
Moucha T, Linek V, Prokopová E (2003) Gas hold-up, mixing time and gas–liquid volumetric mass transfer coefficient of various multiple-impeller configurations: Rushton turbine, pitched blade and techmix impeller and their combinations. Chem Eng Sci 58:1839–1846
Nagy ZK (2006) Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks. Chem Eng J 127(1):95–109
Nocentini M, Fajner D, Pasquali G, Magelli F (1993) Gas–liquid mass transfer and hold-up in vessels stirred with multiple Rushton turbine: water and water-glycerol solutions. Ind Eng Chem Res 32:19–26
Pignon D, Parmiter PJM, Slack J, Hall TJ, van Daalen M, Shawe-Taylor J (1996) Sigmoid neural transfer function realised by percolation. Opt Lett 21(3):222–224
Rewatkar VB, Deshpande AJ, Pandit AB, Joshi JB (1993) Gas hold-up behavior of mechanically agitated gas–liquid reactors using pitched blade downflow turbines. Can J Chem Eng 71:226–237
Shewale SD, Pandit AB (2006) Studies in multiple impeller agitated gas–liquid contactors. Chem Eng Sci 61:489–504
Smith JM (1991) Simple performance correlations for agitated vessels. In: Proceedings of 7th European conference on mixing. Brugge, Belgium
Zhengming G, Yinchen W, Litian S, Jufu F (1996) Theoretical and experimental studies on bubble diameter and gas hold-up in aerated stirred tanks. Chinese J Chem Eng 4(4):283–289
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Phukon, N., Sarmah, M., Kumar, B. (2018). Process Modelling of Gas–Liquid Stirred Tank with Neural Networks. In: Singh, V., Yadav, S., Yadava, R. (eds) Environmental Pollution. Water Science and Technology Library, vol 77. Springer, Singapore. https://doi.org/10.1007/978-981-10-5792-2_40
Download citation
DOI: https://doi.org/10.1007/978-981-10-5792-2_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5791-5
Online ISBN: 978-981-10-5792-2
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)