Designing activated carbon and zeolite amended biosand filters: optimization using response surface methodology

Abstract

a b s t r a c tBiosand filters can be amended with activated carbon and zeolite in order to improve their efficiency at removing micropollutants. The tested pollutants here include ammonia (5 mg/L), lead (210 µg/L), phosphate (12 mg/L), COD (400 mg/L), and iron (0.4 mg/L). The response surface methodology (RSM) is used to evaluate the influence of the adsorbents' column heights (H) and lead ([Pb]) on system efficiency. The results show that (i) increased H values enhanced filter efficiency; (ii) increased [Pb] values only influenced the lead concentration of the effluent; (iii) there was a higher removal of ammonium, COD, and phosphate; and (iv) the removal of iron was below the allowable level. The RSM results suggest that heights of 33 cm and 26 cm could be considered optimal for zeolite and activated carbon, respectively. The optimized filter quantitatively removed biological pollutants and was capable of removing ammonium, lead, COD, and phosphate pollutants at upto 98, 98, 97, and 87%, respectively. Amending biosand filters with efficient adsorbents enables them to be used to remove micropollutants.

A review of the operating parameters on the microbial fuel cell for wastewater treatment and electricity generation

“…To identify and reduce the number of experiments, different DoE methods such as full factorial, fractional factorial, Taguchi, and response surface methodology (RSM) can be applied. (Antony 2006;Raissi & Farsani 2009;Chen et al 2015;Mirbagheri et al 2017). Additionally, for better analysis of the results, statistical methods like ANOVA and non-statistical analysis techniques such as neural networks can be used (Del Vecchio 1998;Jensen 2008;Anderson & Whitcomb 2016;Boudaghpour & Malekmohammadi 2020).…”

Performance evaluation and modelling of an integrated municipal wastewater treatment system using neural networks

“…Mechanical trapping, predation, adsorption, and natural death are the main mechanisms in the sand filtration for contaminants removal (Mirbagheri et al, 2017). The particulate phosphorus is physically removed by filtration on the surface of the sand filter’s bed.…”

Optimization of an artificial neural network topology using response surface methodology for microbial fuel cell power prediction

“…The number of experiments or runs decreases utilizing this method, and also all coefficients of the second‐order regression model and factor interactions can be estimated. Generally, this method has several steps inducing data coding, creating the plan, response surface model fitness, and graphic representation of the response model 60–64 …”

“…Generally, this method has several steps inducing data coding, creating the plan, response surface model fitness, and graphic representation of the response model. [60][61][62][63][64] Therefore, this study uses the response surface model based on the Box-Behnken method using Design Expert Software to optimize NN parameters and find the best structure for predicting the power.…”