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

Oliveira-Esquerre, Karla Patrícia; Mori, Milton; Bruns, Roy E.

doi:10.1590/s0104-66322002000400002

Cited by 73 publications

(32 citation statements)

References 20 publications

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“…The study also includes Principal Component Analysis, which is a statistical method used to identify the patterns in large data matrices [17] [18] [19]. In this analysis various parameters are considered as vectors in a multi-dimensional space and the vector (direction) in which the largest variance is identified as principal component vector and the vectors are numbered as PC1, PC2 and so on in the order of significance.…”

Section: Discussionmentioning

confidence: 99%

Study on the Effect of Variation of Flow in Sequencing Batch Reactor Using PCA and ANOVA

Vijayan¹,

Saravanane²,

Sundararajan³

2017

GEP

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Wastewater treatment using Sequencing Batch Reactor (SBR) technology is one of the state-of-the art wastewater management systems. In this technology equalization, biological treatment and secondary clarification are performed in a single reactor in a time control sequence. SBR system is idler for the areas where the available land is limited, since it operates in less space and very cost effective even on small scales. The control of the operational parameters during the process of biological wastewater treatment is often complicated due to the dynamic change in the composition and characteristics of the raw wastewater, flow rates and other parameters influencing the complex nature of the treatment process and the process in SBR has a unique cyclic batch operation.

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Section: Discussionmentioning

confidence: 99%

Study on the Effect of Variation of Flow in Sequencing Batch Reactor Using PCA and ANOVA

Vijayan¹,

Saravanane²,

Sundararajan³

2017

GEP

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“…In fact, most of the time networks require at least one hidden layer to solve non-linearly separable problems (Oliveira-Esquerre et al, 2002). The number of neurons in this layer was optimized by the Intelligent Problem Solver (IPS) tool of Statistica 7.0 software (StaSoft Inc., 2005), using a sigmoidal activation function.…”

Section: Ann Adjustmentsmentioning

confidence: 99%

Artificial neural networks in predicting energy density of Bambusa vulgaris in Brazil

Vale¹,

Miguel²,

Moreira³

et al. 2017

Afr. J. Agric. Res.

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In this study, the physical and chemical characteristics of Bambusa vulgaris ex J.C. Wendl. var.vulgaris (Bambusa vulgaris) aged 1, 2 and 3 years were evaluated. The objective was to train, validate and evaluate the efficiency of artificial neural networks (ANNs) as predictive tools to estimate bamboo stem energy density grown commercially in northeastern Brazil. For that, samples were collected in a commercial plantation and managed for energy production, determining the energy properties. Among all the characteristics analyzed, basic apparent density was the one with major correlation with bamboo stem energy density. This factor has a great advantage because it is easy to estimate, determined both by dry mass at 0% moisture, and at saturated mass. Also, the precision of ANNs was verified when associated with basic density, as a predictor of bamboo stem energy density, showing low standard error (Syx%, 1.52) and high coefficient of determination (R² = 0.98). ANN-estimated values had no statistical difference (t cal 0.58 ≤ t tab 2.08) with energy density estimated in the laboratory. Therefore, this tool was efficient, being recommended to predict the energetic density of the species under study, with basic density as the only predictive variable.

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“…Theoretical tasks performed in the field have shown that a hidden layer for these models can approximate virtually any complicated and non-linear function (Maier and Dandy, 2005;Cybenko, 1989;Hornik et al, 1989), as proven by experimental and practical results (Homada and Al-Ghusian, 1999;Oliveira-Esquerre et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Optimum coagulant forecasting by modeling jar test experiments using ANNs

Haghiri

Daghighi

Moharramzadeh

2018

Drink. Water Eng. Sci.

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Abstract. Currently, the proper utilization of water treatment plants and optimizing their use is of particular importance. Coagulation and flocculation in water treatment are the common ways through which the use of coagulants leads to instability of particles and the formation of larger and heavier particles, resulting in improvement of sedimentation and filtration processes. Determination of the optimum dose of such a coagulant is of particular significance. A high dose, in addition to adding costs, can cause the sediment to remain in the filtrate, a dangerous condition according to the standards, while a sub-adequate dose of coagulants can result in the reducing the required quality and acceptable performance of the coagulation process. Although jar tests are used for testing coagulants, such experiments face many constraints with respect to evaluating the results produced by sudden changes in input water because of their significant costs, long time requirements, and complex relationships among the many factors (turbidity, temperature, pH, alkalinity, etc.) that can influence the efficiency of coagulant and test results. Modeling can be used to overcome these limitations; in this research study, an artificial neural network (ANN) multi-layer perceptron (MLP) with one hidden layer has been used for modeling the jar test to determine the dosage level of used coagulant in water treatment processes. The data contained in this research have been obtained from the drinking water treatment plant located in Ardabil province in Iran. To evaluate the performance of the model, the mean squared error (MSE) and correlation coefficient (R 2 ) parameters have been used. The obtained values are within an acceptable range that demonstrates the high accuracy of the models with respect to the estimation of water-quality characteristics and the optimal dosages of coagulants; so using these models will allow operators to not only reduce costs and time taken to perform experimental jar tests but also to predict a proper dosage for coagulant amounts and to project the quality of the output water under real conditions.

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

Cited by 73 publications

References 20 publications

Study on the Effect of Variation of Flow in Sequencing Batch Reactor Using PCA and ANOVA

Study on the Effect of Variation of Flow in Sequencing Batch Reactor Using PCA and ANOVA

Artificial neural networks in predicting energy density of Bambusa vulgaris in Brazil

Optimum coagulant forecasting by modeling jar test experiments using ANNs

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