Bioremediation of Malachite Green from Contaminated Water by Three Microalgae: Neural Network Modeling

Abstract

Biological decolorization of the triphenylmethane dye, Malachite Green (MG), by three microalgae, Chlorella, Cosmarium and Euglena species was investigated. Results indicated that the decolorization was dependent on reaction time, initial dye concentration, algal concentration, pH and temperature. The reusability and efficiency of the algae in long term repetitive operations were also examined. Since all of the algae had reasonable reusability in repetitive decolorization operations, the process seems to be biodegradation. An artificial neural network (ANN) model was developed to predict the biological decolorization of MG solution. The findings indicated that the ANN provided reasonable predictive performance (R 2 = 0.98). The influence of each parameter on the variable studied was assessed, and reaction time and initial pH were found to be the most significant factors, followed by temperature, initial dye concentration and amount of algae. Simulations based on the developed ANN model can estimate the behavior of the biological decolorization process under different conditions.

Removal of Reactive Red 24 Dye by Clean Electrocoagulation Process Using Iron and Aluminum Electrodes

“…The polymeric hydroxides, which are highly charged cations, destabilize the negatively charged colloidal particles allowing the formation of flocs. When the amount of iron in the solution exceeds the solubility of the metal hydroxide, the amorphous metal hydroxide precipitates is formed, which causes sweep-floc coagulation [32].…”

Intelligent Multivariate Model for the Optical Detection of Total Organic Carbon

“…Moreover, the application of artificial neural network to spectrophotometric determination of challenging chemical substances is known to be very efficient [7]. The ANN model develops a mapping of the input and output variables, which can subsequently be used to predict as a function of suitable inputs making it very popular in handling various water quality problems [8][9][10][11][12][13][14][15][16][17].…”

“…Successful application of ANN based models for environmental problems in past decades stands for its reliability, robustness and adjustability [16][17][18]. These properties stem from the ability of learning complex nonlinear relationships within multiple variables particularly in situations where the explicit form of the relations is unknown [19].…”

New treatment at source approach using combination of microfiltration and nanofiltration for dyeing effluents reuse

“…Direct discharge of the generated effluents into the environment can cause irreversible ecological problems such as eutrophication and anarchic algae proliferation in the aquatic systems (Khataee et al 2010) and can have disastrous effects on potable water even in the deepest aquifers. So, there is a stringent need to suitably treat the dyeing effluents before discharge.…”