Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis

This work presents a way to predict the biochemical oxygen demand (BOD) of the output stream of the biological wastewater treatment plant at RIPASA S/A Celulose e Papel, one of the major pulp and paper plants in Brazil. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a backpropagated neural network. The influence of input variables is analyzed and satisfactory prediction results are obtained for an optimized situation.365370Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis

This work presents a way to predict the biochemical oxygen demand (BOD) of the output stream of the biological wastewater treatment plant at RIPASA S/A Celulose e Papel, one of the major pulp and paper plants in Brazil. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a backpropagated neural network. The influence of input variables is analyzed and satisfactory prediction results are obtained for an optimized situation

Application Of Steady-state And Dynamic Modeling For The Prediction Of The Bod Of An Aerated Lagoon At A Pulp And Paper Mill Part I. Linear Approaches

Accurate well-timed measurement of quality variables is essential to the successful monitoring and controlling of wastewater treatment systems. Because the measurements of these variables are difficult and often involve large time delays, predictive models for target quality variables have been widely considered. However, many microbial reactions and their interactions with the environment result in time dependent processes, making the development of bioprocess models difficult and time-consuming. In this paper, steady-state and dynamic predictive models based on multiple linear regression (MLR) and partial least squares (PLS) regression are presented. Water quality measurements and process information are used to develop models to predict biochemical oxygen demand (BOD) at the inlet and outlet of an aerated lagoon of a pulp and paper mill operated by International Paper of Brazil (IPB). The results show that linear steady-state and dynamic models are able to predict inlet and outlet BOD even for a complex process that has operational data limitations (imprecise measurements, a large number of missing values, etc.). A companion paper [Chem. Eng. J., submitted for publication] reports static and dynamic nonlinear models that were developed from the same 4 years of data using a neural network approach. Together, the two papers provide a well-documented application of linear and nonlinear empirical modeling techniques to an industrial case study. The modeling techniques are also valid for other types of industrial applications. © 2004 Elsevier B.V. All rights reserved.1041-37381Oliveira-Esquerre, K.P., Seborg, D.E., Bruns, R.E., Mori, M., Application of steady-state and dynamic modeling for the prediction of BOD for an aerated lagoon at a pulp and paper mill. Part II. Nonlinear approaches Chem. Eng. J., , submitted for publicationHarremoës, P., Capodaglio, A.G., Hellstrom, B.G., Henze, M., Jensen, K.N., Lynggaard-Jensen, A., Otterpohl, R., Soeborg, H., Wastewater treatment plants under transient loading-performance, modeling and control (1993) Water Sci. Technol., 27, pp. 71-115Lee, D.S., Park, J.M., Neural network modeling for on-line estimation of nutrient dynamics in a sequentially operated batch reactor (1999) J. Biotechnol., 75, pp. 229-239Hamoda, M.F., Al-Ghusain, I.A., Hassan, A.H., Integrated wastewater treatment plant performance evaluation using artificial neural networks (1999) Water Sci. Technol., 40, pp. 55-65Cote, M., Grandijean, B.P.A., Lessard, P., Yhibault, J., Dynamic modeling of the activated sludge process: Improving prediction using neural networks (1995) Water Res., 29, pp. 995-1004Steyer, J.P., Rolland, D., Bouvier, J.C., Moletta, R., Hybrid fuzzy neural network for diagnosis-application to the anaerobic treatment of wine distillery wastewater in a fluidized bed reactor (1997) Water Sci. Technol., 36, pp. 209-217Park, S., Han, C., A nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns (2000) Comp. Chem. Eng., 24, pp. 871-877Baffi, G., Martin, E.B., Morris, A.J., Non-linear projection to latent structures revisited (the neural network PLS algorithm) (1999) Comp. Chem. Eng., 23, pp. 1293-1307Atkinson, A.C., Cheng, T.-C., On robust linear regression with incomplete data (2000) Comput. Stat. Data Anal., 33, pp. 361-380Draper, N.R., Smith, H., (1998) Applied Regression Analysis, 3rd Ed., , Wiley, New YorkMontgomery, D.C., Peck, E.A., (1992) Introduction to Linear Regression Analysis, , Wiley Press, New YorkSjostrom, M., Wold, S., A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables (1983) Anal. Chim. Acta, 150, pp. 61-70Martens, H., Naes, T., (1979) Multivariate Calibration, , Wiley, New YorkStefanov, Z.I., Hoo, K.A., Hierarchical multivariate analysis of cockle phenomena (2003) J. Chemometr., 17, pp. 550-568Hoo, K.A., Tvarlapati, K.J., Piovoso, M.J., Hajare, R., A method of robust multivariate outlier replacement (2002) Comp. Chem. Eng., 26, pp. 17-39Ortiz-Estarelles, O., Martín-Biosca, Y., Medina-Hernández, M.J., Sagrado, S., Bonet-Domingo, E., On the internal multivariate quality control of analytical laboratories. A case study: The quality of drinking water (2001) Chem. Intell. Lab. Syst., 56, pp. 93-103(1998) MATLAB PLS_Toolbox 2.0, , Eigenvector Research Inc., Manson, W

Application Of Steady-state And Dynamic Modeling For The Prediction Of The Bod Of An Aerated Lagoon At A Pulp And Paper Mill Part Ii. Nonlinear Approaches

Neural networks can provide effective predictive models for complex processes that are poorly described by first principle models, such as wastewater biological treatment systems. In this paper multilayer perception (MLP) and functional-link neural networks (FLN) are developed to predict inlet and outlet biochemical oxygen demand (BOD) of an aerated lagoon operated by International Paper of Brazil. In Part I, predictive models for both inlet and outlet BOD for the aerated lagoon were developed using linear multivariate regression techniques. For the current case study, MLP networks are the best choice for the prediction models. When only a relatively small number of samples is available, substantial improvement in inlet and outlet BOD prediction is shown for both FLN and MLP modeling using a reduced input variable set that was generated using partial least squares (PLS). Thus, this paper provides a novel approach for developing PLS-FLN model structures. © 2004 Elsevier B.V. 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Simulation Of Aerated Lagoon Using Artificial Neural Networks And Multivariate Regression Techniques

The aim of this study was to develop an empirical model that provides accurate predictions of the biochemical oxygen demand of the output stream from the aerated lagoon at International Paper of Brazil, one of the major pulp and paper plants in Brazil. Predictive models were calculated from functional link neural networks (FLNNs), multiple linear regression, principal components regression, and partial least-squares regression (PLSR). Improvement in FLNN modeling capability was observed when the data were preprocessed using the PLSR technique. PLSR also proved to be a powerful linear regression technique for this problem, which presents operational data limitations.10601/03/15437450Hamoda, M.F., Al-Ghusain, I.A., Hassan, A.H., (1999) Water Sci. Technol., 40, pp. 55-65Harremoës, P., Capodaglio, A.G., Hellstrom, B.G., Henze, M., Jensen, K.N., Lynggaaard-Jensen, A., Otterpohl, R., Soeborg, H., (1993) Water Sci. Technol., 27, pp. 71-115Lee, D.S., Park, J.M., (1999) J. Biotechnol., 75, pp. 229-239Cote, M., Grandijean, B.P.A., Lessard, P., Yhibault, J., (1995) Water Res., 29, pp. 995-1004Steyer, J.P., Rolland, D., Bouvier, J.C., Moletta, R., (1997) Water Sci. Technol., 36, pp. 209-217Baffi, G., Martin, E.B., Morris, A.J., (1999) Comp. Chem. Eng., 23, pp. 1293-1307Gontarski, C.A., Rodrigues, P.R., Mori, M., Prenem, L.F., (2000) Comp. Chem. Eng., 24, pp. 1719-1723Häck, M., Köhne, M., (1996) Water Sci. Technol., 33, pp. 101-115Oliveira-Esquerre, K.P., Mori, M., Bruns, R.E., (2002) Braz. J. Chem. Eng., 19, pp. 365-370Pu, H., Hung, Y., (1995) Environ. Manage. Health, 6, pp. 16-27Wilcox, S.J., Hawkes, D.L., Hawkes, F.R., Guwy, A.J., (1995) Water Res., 29, pp. 1465-1470Zhao, H., Hao, O.I., Fellow, A.S.C.E., McAvoy, T.J., Chang, C.H., (1997) J. Environ. Eng., 123, pp. 311-319Chen, S., Billings, S.A., (1992) Int. J. Control, 56, pp. 319-346Alkulaibi, A., Soraghan, J.J., (1997) Signal Processing, 62, pp. 101-109Maier, H.R., Dandy, G.C., (2000) Environ. Modelling Software, 15, pp. 101-124Kanjilal, P.P., (1995) IEEE Trans. Neural Networks, 6, pp. 1061-1070Kompany-Zared, M., (1999) Talanta, 48, pp. 283-292Cancilla, D.A., Fang, X., (1996) J. Great Lagoons Res., 22, pp. 241-253Holcomb, T.R., Morari, M., (1992) Comp. Chem. Eng., 16, pp. 393-411Despagne, F., Massart, D.L., (1998) Analyst, 123, pp. 157-178Geladi, P., Kowalski, B.R., (1986) Anal. Chim. Acta, 185, pp. 1-17Mardia, K.V., Kent, J.T., Bibby, J.M., (1979) Multivariate Analysis, , Academic, London, UKDraper, N.R., Smith, H., (1981) Applied Regression Analysis, 2nd Ed., , Wiley, New York, NYWold, S., Martens, H., Russwurm, H., (1983) Food Research and Data Analysis, , Applied Science Publishers, London, UKWold, S., Kowalski, B., (1984) Chemometrics: Mathematics and Statistics in Chemistry, , Reidel, Dordrecht, The NetherlandsHenriques, A.W.S., Costa, A.C., Alves, T.L.M., Lima, E.L., (1999) Braz. J. Chem. Eng., 16, pp. 171-177Cass, R., Radl, B., (1996) Control Eng. Pract., 4, pp. 1579-1584Henrique, H.M., (1999), PhD thesis, PEQ/COPPE/UFRJ, Rio de Janeiro, RJ, BrazilBillings, S.A., Chen, S., Korenberg, M.J., (1989) Int. J. Control, 49, pp. 2157-2189Costa, A.C., Henriques, A.S.W., Alves, T.L.M., Maciel Filho, R., Lima, E.L., (1999) Braz. J. Chem. Eng., 16, pp. 53-63Hornik, K., Stinchcombe, M., White, H., (1989) Neural Networks, 2, pp. 359-366Pao, Y.H., (1989) Adaptative Pattern Recognition and Neural Networks, , Addison-Wesley, Reading, MACosta, A.C., Alves, T.L.M., Henriques, A.W.S., Maciel Filho, R., Lima, E.L., (1998) Comp. Chem. Eng., 22 (SUPPL.), pp. S859-S862Harada, L.H., Da Costa, A.C., Maciel Filho, R., (2002) Appl. Biochem. Biotechnol., 98-100, pp. 1009-1023Montgomery, D.C., Peck, E.A., (1992) Introduction to Linear Regression Analysis, , Wiley, New York, NYSjöstrom, M., Wold, S., (1983) Anal. Chim. Acta, 150, pp. 61-70Ortiz-Estarelles, O., Martín-Biosca, Y., Medina-Hernández, M.J., Sagrado, S., Bonet-Domingo, E., (2001) Chem. Intel. Lab. Syst., 56, pp. 93-103Todeschine, R., (1997) Anal. Chim. Acta, 348, pp. 419-430(1998) MATLAB PLS Toolbox Help, , Eigenvector Research Inc., Manson, WABarros, B.N., Scarminio, I.S., Bruns, R.E., (1995) Planejamento e Otimização de Experimentos, , UNICAMP Press, Campinas, SP, BrazilMorales, M.M., Martí, P., Llopis, A., Campos, L., Sagrado, S., (1999) Anal. Chim. Acta, 394, pp. 109-11