Abstract
This paper presents a new mathematical model and a two-layer neural network approach to predict the single droplet collection efficiency (SDCE), η d, of countercurrent spray towers. SDCE values were calculated using MATLAB® algorithm for 205 different artificial scenarios given in a large range of operating conditions. Theoretical results were compared with outputs obtained from a two-layer neural network and DataFit® scientific software. The predicted model developed from linear–nonlinear regression analysis and network outputs agreed with the theoretical data, and all predictions proved to be satisfactory with a correlation coefficient of about 0.921 and 0.99, respectively. By using the proposed model, iterations between Reynolds number (Re), drag coefficient (C D) and terminal velocity values (v T) were neglected for a large range of operating conditions. SDCE values were also obtained speedily and practically for five main operating inputs used in the model.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Abdul-Wahab, S. A., & Al-Alawi, S. M. (2002). Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environmental Modelling & Software, 17(3), 219–228.
Ahmadi, G. (2004). Department of Mechanical and Aeronautical Engineering Clarkson University. Particle Transport, Deposition and Removal. ME 437/ME537.
Almasri, M. N., & Kaluarachchi, J. J. (2005). Modular neural networks to predict the nitrate distribution in ground water using the onground nitrogen loading and recharge data. Environmental Modelling & Software, 20(7), 851–871.
Anctil, F., Perin, C., & Andreassian, V. (2004). Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall–runoff forecasting models. Environmental Modelling & Software, 19(4), 357–368.
Avsar, Y., Saral, A., Gonullu, M. T., Arslankaya, E., & Kurt, U. (2004). Neural network modelling of outdoor noise levels in a pilot area. TUBITAK Turkish Journal of Environmental Engineering and Science, 28, 149–155.
Boll, R. H. (1973). Particle collection and pressure drop in venturi scrubbers. Industrial & Engineering Chemistry Fundamentals, 12(1), 40–50.
Burns, M. A., & Lionberger, R. (1999). University of Michigan College of Engineering, Course handouts for ChE 343, Lecture notes 5.
Calvert, S., Lundgren, D., & Mehta, D. S. (1972). Venturi scrubber performance. Journal of the Air Pollution Control Association, 22, 529–532.
Campolo, M., Soldati A., & Andreussi, P. (1999). Forecasting river flow rate during low flow periods using neural networks. Water Resources Research, 35(11), 3547–3552.
Cigizoglu, H. K. (2002). Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves. TUBITAK Turkish Journal of Environmental Engineering and Science, 26, 27–36.
DataFit Help Contents Version 8.1, Description and Capabilities of DataFit.
Djebbar, Y., & Alila, Y. (1998). Neural network estimation of sanitary flows, Hydroinformatics Conference, poster presentation, Copenhagen.
Erturk, F. (2006). Air pollution control lecture notes, Yildiz Technical University. Department of Environmental Engineering.
Fathikalajahi, J., Taheri, M., & Keshavarz, P. (2000). Mathematical modeling of spray towers for gas absorption and particle collection. Journal of Aerosol Science, 31(1), 156–157.
Federal Remedition Technologies Roundtable (FRTR). Screening Matrix and Reference Guide, Version 4.0. Air Emissions/Off-Gas Treatment. 4.58 Scrubbers, Jun. 2004.
Freestudy the Engineering Council. EC Graduate Level. Fluid Mechanics D203. Tutorial 1 – Fluid flow theory.
Goel, K. C., & Hollands, K. G. T. (1977). A general method for predicting particulate collection efficiency of venturi scrubbers. Industrial & Engineering Chemistry Fundamentals, 16(2), 186.
Goktepe, A. B., Agar, E., & Lav, A. H. (2005). Backcalculation of mechanical properties of flexible pavements using neural networks. ITU Dergisi, 2(4), 31–42.
Hall, M. J., & Minns, A. W. (1998). Regional flood frequency analysis using artificial neural networks, Hydroinformatics Conference, Copenhagen.
Hamed, M. M., Khalafallah, M. G., & Hassanien, E. A. (2004). Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling & Software, 19(10), 919–928.
Hsu, K., Gupta, H. V., & Sorooshian, S. (1995). Artificial neural network modelling of the rainfall runoff process. Water Resources Research, 31, 2517–2530.
Jain, S. K., Das, D., & Srivastava, D. K. (1999). Application of ANN for reservoir inflow prediction and operation. Journal of Water Resources Planning and Management ASCE, 125, 263–271.
Jung, C. H., & Lee, K. W. (1998). Filtration of fine particles by multiple liquid droplet and gas bubble systems. Aerosol Science and Technology, 29, 389–401.
Jungblut, J., & Möller, D. P. F. (1999). Modelling and simulation of new biological wastewater treatment plants. UKSim99: Final programme, session I: Simulation in control & dynamical systems. Cambridge, England: St Catharine's College.
Karaca, F., Alagha, O., & Erturk, F. (2005). Statistical characterization of atmospheric PM10 and PM2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey. Chemosphere, 59(8), 1183–1190.
Karaca, F., & Ozkaya, B. (2005). NN-LEAP: a neural network-based model for controlling leachate flow-rate in a municipal solid waste landfill site. Environmental Modelling & Software, in press.
Kim, H.T., Jung, C.H., Oh, S.N., & Lee, K.W. (2001). Particle removal efficiency of gravitational wet scrubber considering diffusion, interception, and impaction. Environmental Engineering Science, 18(2), 125–136.
Kisi, O. (2004). River Flow Modeling using artificial neural networks. ASCE Journal of Hydrologic Enginnering, 9(1) 60–63.
Kisi, O. (2005). Daily river flow forecasting using artificial neural networks and auto-regressive models. TUBITAK Turkish Journal of Environmental Engineering and Science, 29, 9–20.
Kohonen, T. (1993). State of the art in neural computing. IEEE First International Conference on Neural Networks, 1, 77–91, San Diego.
Lanzerstorfer, C. (2003). Solid/liquid–gas separation with wet scrubbers and wet electrostatic precipitators: a review. Filtration & Separation, 37(5), 30–34.
Leith, D. (2005). The University of North Carolina at Chapel Hill, ENVR 251-Air Pollution Control, Scrubbers Design.
Licht, W. (1988). Air pollution control engineering. Basic calculations for particulate collection, 2nd edition. New York: Marcel Dekker.
Maier, H. R., & Dandy, G. C. (1998). Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study. Environmental Modelling & Software, 13(2), 179–191.
Massong, E., Wang, J. (1990). Introduction to computation and learning in artificial neural networks. European Journal of Operational Research, 47, 1–28.
MATLAB® Help Contents V7.0, Neural Network Toolbox, What's New in Version 4.0.
McCann, D. W. (2005). NNICE – a neural network aircraft icing algorithm. Environmental Modelling & Software, 20(10), 1335–1342.
Milosavljevic, N., & Heikkila, P. (2001). A comprehensive approach to cooling tower design. Applied Thermal Engineering, 21(9), 899–915.
Nunnari, G., Dorling, S., Schlink, U., Cawley, G., Foxall, R., & Chatterton, T. (2004). Modelling SO2 concentration at a point with statistical approaches. Environmental Modelling & Software, 19(10), 887–905.
Onkal-Engin, G., Demir, I., & Engin, S. N. (2005). Determination of the relationship between sewage odour and BOD by neural networks. Environmental Modelling & Software, 20(7), 843–850.
Rath, M. W. (1988). Neural network technology and its applications. John Hopkins Appl Tech Dig, 242–253.
Rodriguez, M. J., & Serodes, J. B. (1999). Assessing empirical linear and non-linear modelling of residual chlorine in urban drinking water systems. Environmental Modelling & Software, 14(1), 93–102.
Rogers, L. L., & Dowla, F. U. (1994). Optimization of groundwater remediation using artificial neural networks with parallel solute transport modelling. Water Resources Research, 30(2), 457–481.
Rudnick, S. N., Koehler, J. L. M., Martin, K. P., Leith, D., & Cooper, D. W. (1986). Particle collection efficiency in a venturi scrubber: comparison of experiments with theory. Environmental Science & Technology, 20(3), 237–242.
Saral, A., & Erturk, F. (2003). Prediction of ground level SO2 concentration using artificial neural networks. Water, Air Soil Pollution: Focus, 3, 297–306.
Sengorur, B., & Oz, C. (2002). Determination of the effects of water pollution of aquacultures using neural networks. TUBITAK Turkish Journal of Environmental Engineering and Science, 26, 95–105.
Stanley, J. (1988). Introduction of Neural Networks. California Scientific Software, p. 255.
Stevenson, W. J. (1991). The use of artificial neural nets in mechanical engineering. Council for Scientific and Industrial Research Tech. Rep. AERO 91/198.
Sutherland, J. W. (2002). Forces on airborne particles. The John W. Sutherland Research Page. CYBERMAN, Environment Related, Aerosols & PM.
U.S. Environmental Protection Agency (EPA). Basic Concepts in Environmental Sciences. Module 3: Characteristics of Particles. Collection Mechanisms, Sept. 2002.
U.S. Environmental Protection Agency (EPA). Basic Concepts in Environmental Sciences. Module 6: Air Pollutants and Control Techniques. Particulate Matter Control Techniques. Particulate Wet Scrubbers, Spray Tower Scrubbers, Sept. 2002.
Wark, K., Warner, C. F., & Davis, W. T. (1998). Air pollution its origin and control, 3rd edition (pp. 286–287). Addison Wesley Longman.
Wiencke, B. (2001). Gravity liquid separators for industrial refrigeration. Seminar 32-Refrigeration: Back to Basics. ASHRAE Annual Meeting in Cincinnati.
Yung, S., Calvert, S., Barbarika, H. F., & Sparks, L. E. (1978). Venturi scrubber performance model. Environmental Science & Technology, 12(4), 456–459.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yetilmezsoy, K., Saral, A. Stochastic modeling approaches based on neural network and linear–nonlinear regression techniques for the determination of single droplet collection efficiency of countercurrent spray towers. Environ Model Assess 12, 13–26 (2007). https://doi.org/10.1007/s10666-006-9048-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10666-006-9048-4