RESUMEN
An experimental design methodology was applied to study the effects of temperature, pH, biomass dose, and stirring speed on copper removal from aqueous solutions by Aspergillus terreus in a biosorption batch system. To identify the effects of the main factors and their interactions on copper removal efficiency and to optimize the process, a full 2(4) factorial design with central points was performed. Four factors were studied at two levels, including stirring speed (50-150 min(-1)), temperature (30-50°C), pH (4-6) and biosorbent dose (0.01-0.175 g). The main factors observed were pH and biomass dose, along with the interactions between pH and biomass, and stirring speed. The optimal operational conditions were obtained using a response surface methodology. The adequacy of the proposed model at 99% confidence level was confirmed by its high adjusted linear coefficient of determination (R(Adj)(2)=0.9452). The best conditions for copper biosorption in the present study were: pH 6, biosorbent dose of 0.175 g, stirring speed of 50 min(-1) and temperature of 50°C. Under these conditions, the maximum predicted copper removal efficiency was 68.52% (adsorption capacity of 15.24 mg/g). The difference between the experimental and predicted copper removal efficiency at the optimal conditions was 4.8%, which implies that the model represented very well the experimental data.
Asunto(s)
Aspergillus/metabolismo , Cobre/aislamiento & purificación , Contaminantes Químicos del Agua/aislamiento & purificación , Biomasa , Concentración de Iones de Hidrógeno , Microbiología Industrial/métodos , Modelos Teóricos , Soluciones/química , TemperaturaRESUMEN
An artificial neural network (ANN) was used to predict the biosorption of methylene blue on Spirulina sp. biomass. Genetic and anneal algorithms were tested with different quantity of neurons at the hidden layers to determine the optimal neurons in the ANN architecture. In addition, sensitivity analyses were conducted with the optimised ANN architecture for establishing which input variables (temperature, pH, and biomass dose) significantly affect the predicted data (removal efficiency or biosorption capacity). A number of isotherm models were also compared with the optimised ANN architecture. The removal efficiency or the biosorption capacity of MB on Spirulina sp. biomass was adequately predicted with the optimised ANN architecture by using the genetic algorithm with three input neurons, and 20 neurons in each one of the two hidden layers. Sensitivity analyses demonstrated that initial pH and biomass dose show a strong influence on the predicted removal efficiency or biosorption capacity, respectively. When supplying two variables to the genetic algorithm, initial pH and biomass dose improved the prediction of the output neuron (biosorption capacity or removal efficiency). The optimised ANN architecture predicted the equilibrium data 5,000 times better than the best isotherm model. These results demonstrate that ANN can be an effective way of predicting the experimental biosorption data of MB on Spirulina sp. biomass.