Artificial neural network (ANN) approach for modeling Zn(II) adsorption from leachate using a new biosorbent

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

“…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. 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 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). The ANN architecture contains input layer, one or more hidden layers and output layer (Movagharnejad and Nikzad 2007).…”

“…The data and their related statistics are presented in Table 3. Data points of % removal were thereafter interpolated by utilizing a cubic spline curve fitting (CSCF) technique (Turan et al 2011a, b).The major reason of utilizing the CSCF technique is for increasing data points between minimum R e % and maximum R e %. The motivation of employing the CSCF technique is that it is simple for implementing and for producing a curve that looks to be seamless.…”

“…Few investigators tried to develop an assessment of ANN as one valuable approach for predicting and simulating the removal process. Examples for application of AANs in water treatment are removal of arsenic by the modification of solid waste vegetable oil industry with Fenton reagent (FMSWVOI) (Jaafarzadeh et al 2012), removal of Zn(II) ions from leachate by hazelnut shell (Turan et al 2011a), adsorption of Cu(II) from industrial leachate by pumice (Turan et al 2011b) and removal of lead ions by Nigella sativa seeds (black cumin) (Bingöl et al 2012). ANN methods were compared because of their generalization and predictive abilities, sensitivity analysis as well as optimization efficiency in metal ion removal from wastewater (Turan et al 2011a, b;Bingöl et al 2012).…”

Optimization of Quercetin Extraction from Green Tea ( Camellia sinensis ) Using Central Composite Design, and the Pharmacological Activity of the Extract

“…This is important when the effect of one variable differs depending on the level of another variable, and when the knowledge about the process itself is limited. Modeling of extraction 6,8,11,20 , adsorption [25][26][27] , synthesis 28 processes etc., using experimental design were commonly described in the literature. Thus, for instance, Pingret et al 29 optimized production of antioxidant-rich extracts from apple pomace using ultrasound-assisted extractions, while Chua et al 30 optimized the extraction conditions of phospholipids from palmpressed fiber.…”