Neural network modelling of adsorption isotherms
Tóm tắt
This paper examines the possibility to use a single neural network to model and predict a wide array of standard adsorption isotherm behaviour. Series of isotherm data were generated from the four most common isotherm equations (Langmuir, Freundlich, Sips and Toth) and the data were fitted with a unique neural network structure. Results showed that a single neural network with a hidden layer having three neurons, including the bias neuron, was able to represent very accurately the adsorption isotherm data in all cases. Similarly, a neural network with four hidden neurons, including the bias, was able to predict very accurately the temperature dependency of adsorption data.
Tài liệu tham khảo
Basu, S., Henshaw, P.F., Biswas, N., Kwan, H.K.: Prediction of gas-phase adsorption isotherms using neural nets. Can. J. Chem. Eng. 80, 1–7 (2002)
Bhat, N., McAvoy, T.: Use of neural nets for dynamic modeling and control of chemical process systems. In: American Control Conference, Pittsburgh, pp. 1336–1341 (1989)
Brunauer, S., Deming, L.S., Deming, W.E., Teller, E.: On a theory of the van der Waals adsorption of gases. J. Am. Chem. Soc. 62, 1723–1732 (1940)
Bulsari, A.B., Palosaafi, A.: Application of neural networks for system identification of an adsorption column. Neural Comput. Appl. 1, 160–165 (1993)
Carsky, M., Do, D.D.: Neural network modeling of adsorption of binary vapour mixtures. Adsorption 5, 183–192 (1999)
Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Syst. 2, 303–314 (1989)
Freundlich, H.: Ueber die adsorption in loesungen. Z. Phys. Chem. 57, 385–470 (1907)
Gao, W., Engell, S.: Neural-network based identification of nonlinear adsorption isotherms. In: IFAC Dynamics and Control of Process Systems, Cambridge, MA, USA, pp. 721–724 (2004)
Giraudet, S., Pré, P., Tezel, H., Le Cloirec, P.: Estimation of adsorption energies using physical characteristics of activated carbons and VOCs’ molecular properties. Carbon 44, 1873–1883 (2006)
Hoskins, J.C., Himmelblau, D.M.: Artificial neural network models of knowledge representation in chemical engineering. Comput. Chem. Eng. 12(9/10), 881–890 (1988)
Kumar, K.V., Monteiro de Castro, M., Martinez-Escandell, M., Molina-Sabio, M., Rodriguez-Reinoso, F.: Neural network and principal component analysis for modeling of hydrogen adsorption isotherms on KOH activated pitch-based carbons containing different heteroatoms. Chem. Eng. J. 159, 272–279 (2010)
Langmuir, I.: The Adsorption of Gases on plane surfaces of glass, mica and platinum. J. Am. Chem. Soc. 40, 1361 (1918)
Lewandowski, J., Lemcoff, N.O., Palosaari, S.: Use of neural networks in the simulation and optimization of pressure swing adsorption processes. Chem. Eng. Technol. 21(7), 593–597 (1998)
Mjalli, F., Al-Asheh, S., Banat, F., Al-Lagtah, F.: Representation of adsorption data for isopropanol-water system using neural network techniques. Chem. Eng. Technol. 28(12), 1529–1539 (2005)
Padmesh, T.V.N., Vijayaraghavan, K., Sekaran, G., Velan, M.: Application of two-and three-parameter isotherm models: Biosorption of acid Red 88 onto Azolla microphylla. Bioremediation Journal 10(1), 37–44 (2006)
Powell, M.J.D.: Some global convergence properties of a variable metric algorithm for minimization without exact line search. In: ASM/SIAM Symp. on Nonlinear Programming, New York (1975)
Sing, K.S.W., Everette, D.H., Haul, R.A.W., Moscou, L., Pierotti, R.A., Rouquérol, J., Siemieniewska, T.: Reporting physisorption data for gas/solid systems with special reference to the determination of surface area and porosity. Pure Appl. Chem. 57, 603–619 (1985)
Sips, R.J.: On the structure of a catalyst surface. J. Chem. Phys. 16, 490–495 (1948)
Sundaram, N.: Training neural networks for pressure swing adsorption processes. Ind. Eng. Chem. Res. 38, 4449–4457 (1999)
Toth, J.: State equations of the solid gas interface layer. Acta Chem. Acad. Hung 69, 311–317 (1971)
Vasina, E.N., Paszek, E., Nicolau, Jr., D.V., Nicolau, D.V.: The BAD project: data mining, database and prediction of protein adsorption on surfaces. Lab Chip 9, 891–900 (2009)
Yang, M., Hubble, J., Fang, M., Locke, A.D., Rathbone, R.R.: A neural network for breakthrough prediction in packed bed adsorption. Biotech. Tech. 7(2), 155–158 (1993)