Dynamic numerical reconstruction of a fungal biofiltration system using differential neural network

Chairez, Isaac; García-Peña, E.I.; Cabrera, A.

doi:10.1016/j.jprocont.2008.12.009

Cited by 39 publications

(12 citation statements)

References 20 publications

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“…ANN is a numerical estimation method that can be applied to simulate the experimental input variables and determine the governing rules among the corresponding factors [17]. It is a potential preeminent tool to model nonlinear processes such as electrostatic separation with its ability to learn the relationship among independent variables and predict accurate output [18].…”

Section: Methodsmentioning

confidence: 99%

An Artificial Neural Network Approach to Predicting Electrostatic Separation Performance for Food Waste Recovery

Lai

Lim

Teh

et al. 2017

Pol. J. Environ. Stud.

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This study presents the empirical exploration of food waste recovery throughout the electrostatic separation process. In addition, the paper discusses the potential of artificial neural network (ANN) in predicting the responses. A five-level three-factor Taguchi orthogonal array (OA) design of experiment was employed as an initiative to optimize the prediction process. The electrostatic separation process was modelled using ANN by considering the recovered food waste and misclassified middling product during separation. A multi-layer feed-forward network developed in MATLAB was constructed. It was found that the results from the experiment and predicted model were in very good agreement. To our best knowledge, this is the first report for prediction of food waste separation performance employing ANN and Taguchi design.

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Section: Methodsmentioning

confidence: 99%

An Artificial Neural Network Approach to Predicting Electrostatic Separation Performance for Food Waste Recovery

Lai

Lim

Teh

et al. 2017

Pol. J. Environ. Stud.

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“…ANNs were previously established from the primary conception of artificial intelligence that work for simulating the process of nervous system and human brain (Chairez et al 2009;Rene et al 2009;Ekici and Aksoy 2010). Actually an artificial neural network (ANN) is an enormously interconnected network structure comprising of several simple processing elements proficient of executing parallel computation for data processing.…”

Section: Modelling Technique Artificial Neural Network Modellingmentioning

confidence: 99%

“…This technique is valuable where the complication of the mechanisms indicating performance of process is very high (Turan et al 2011a(Turan et al , 2011b.They comprise a chain of mathematical correlation which are utilized for simulating the learning and memorizing operation. ANNs learn by example in which an actual measured input variables set and analogous outputs are offered for determining the guidelines that manage the relationship between the variables (Chairez et al 2009). ANNs are taken into account to be commanding for apprehending the non-linear effect and are (Turan et al 2011a, b;Jaafarzadeh et al 2012).…”

Section: Modelling Technique Artificial Neural Network Modellingmentioning

confidence: 99%

Prediction of phycoremediation of As(III) and As(V) from synthetic wastewater by Chlorella pyrenoidosa using artificial neural network

Podder

Majumder

2017

Appl Water Sci

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An artificial neural network (ANN) model was developed to predict the phycoremediation efficiency of Chlorella pyrenoidosa for the removal of both As(III) and As(V) from synthetic wastewater based on 49 data-sets obtained from experimental study and increased the data using CSCF technique. The data were divided into training (60%) validation (20%) and testing (20%) sets. The data collected was used for training a three-layer feed-forward back propagation (BP) learning algorithm having 4-5-1 architecture. The model used tangent sigmoid transfer function at input to hidden layer (tansing) while a linear transfer function (purelin) was used at output layer. Comparison between experimental results and model results gave a high correlation coefficient (R allANN 2 equal to 0.99987 for both ions and exhibited that the model was able to predict the phycoremediation of As(III) and As(V) from wastewater. Experimental parameters influencing phycoremediation process like pH, inoculum size, contact time and initial arsenic concentration [either As(III) or As(V)] were investigated. A contact time of 168 h was mainly required for achieving equilibrium at pH 9.0 with an inoculum size of 10% (v/v). At optimum conditions, metal ion uptake enhanced with increasing initial metal ion concentration.

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“…The same authors [14] adopted the method for predicting and modelling RE in immobilized-cell biofilteration treating ammonia. Chairez et al [15] applied the differential neural network (carbon dioxide production and pressure drop as input data) and designed an observer to predict EC of toluene vapors in a fungal biofilter. This observer was successfully applied for the variations in reaction and was considered as a practical tool for on-line EC.…”

Section: Introductionmentioning

confidence: 99%

Applying an ANFIS-based Algorithm in Comparison with Mechanistic Modelling in a Biofilter Treating Hexane

Danaee

Darakeh

Khani

2018

JGE

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Predicting the dynamic performance of a waste air biofilter is sought here. This predicting method in biological purification of waste air is adopted where the microbial biofilm formed on bed particles degrade the pollutants. The model can be adopted in scaling-up and predicting the biofilteration performance. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied as a new approach which is compared to a mechanistic model where moisture content balance is of concern. To get a better view of each modelling procedures, they are compared for the prediction of a set of experimental data from the literature. Both of methods could effectively predict outlet concentrations.

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Dynamic numerical reconstruction of a fungal biofiltration system using differential neural network

Cited by 39 publications

References 20 publications

An Artificial Neural Network Approach to Predicting Electrostatic Separation Performance for Food Waste Recovery

An Artificial Neural Network Approach to Predicting Electrostatic Separation Performance for Food Waste Recovery

Prediction of phycoremediation of As(III) and As(V) from synthetic wastewater by Chlorella pyrenoidosa using artificial neural network

Applying an ANFIS-based Algorithm in Comparison with Mechanistic Modelling in a Biofilter Treating Hexane

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