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Simulation of aerated lagoon using artificial neural networks and multivariate regression techniques

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Abstract

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.

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Abbreviations

BOD:

outlet wastewater BOD (mg/L)

COD:

inlet wastewater COD (mg/L)

COLOR:

color (ppm or mg/L)

COND:

conductivity (µS/cm at 20°C)

f :

transfer function proposed by Henrique (28)

FLOW:

inlet flow rate (m3/d)

h :

polynomial expansion

N :

number of monomials

N.AM.:

inlet ammonia concentration (mg/L)

N.N.:

inlet nitrate concentration (mg/L)

PAPER:

paper production (t/d)

PULP:

pulp production (t/d)

RAIN:

rainfall (mL/d)

S.S.:

inlet suspended solids (mg/L)

T.:

temperature (°C)

x :

input matrix

y :

predicted output

w ij :

neural network weights of i input and j monomial

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Authors and Affiliations

  1. DPQ/FEQ/UNICAMP, PO Box 6066, 13081-970, Campinas, SP, Brazil

    Karla Patricia Oliveira-Esquerre, Aline C. da Costa & Milton Mori

  2. IQ/UNICAMP, PO Box 6154, 13083-970, Campinas, SP, Brazil

    Roy Edward Bruns

Authors
  1. Karla Patricia Oliveira-Esquerre
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  2. Aline C. da Costa
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  3. Roy Edward Bruns
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  4. Milton Mori
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Correspondence to Karla Patricia Oliveira-Esquerre.

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Oliveira-Esquerre, K.P., da Costa, A.C., Bruns, R.E. et al. Simulation of aerated lagoon using artificial neural networks and multivariate regression techniques. Appl Biochem Biotechnol 106, 437–449 (2003). https://doi.org/10.1385/ABAB:106:1-3:437

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  • DOI: https://doi.org/10.1385/ABAB:106:1-3:437

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