Statistical analysis by using soft computing methods for seawater biodegradability using ZnO photocatalyst.

Water quality plays a significant role as a key factor in water resource management. The photocatalytic method is widely used for the removal of recalcitrant pollutants present in seawater. Photocatalysis is a cost-effective technology, sustainable, and environmentally friendly treatment process. In the current approach, a batch reactor was utilized experimentally to study the degradation of contaminants present in seawater by utilizing ZnO as a photocatalyst under natural sunlight. The performance of the process was studied by measuring the percentage removal efficiencies of total organic carbon (TOC), chemical oxygen demand (COD), biological oxygen demand (BOD), and biodegradability with respect to photocatalyst dosage, reaction time and pH of the solution. Biodegradability is defined as the ratio of BOD to COD and this parameter significantly removes pollutants from seawater. The higher the biodegradability, the better the performance of the treatment technology. It also significantly reduces the fouling characteristics of seawater during the desalination process. According to experimental values, the maximum percentage removal efficiencies were found to be TOC = 45.6, COD = 65.4, BOD = 20.01% and biodegradability = 0.038 with respect to the initial values of the seawater sample. The response surface methodology based on Box Behnken design (RSM-BBD) and a predictive model based on the MATLAB adaptive neuro-fuzzy inference system (ANFIS) tool were employed for modeling, optimizing, and evaluating the effects of parameters. According to the RSM-BBD and ANFIS models, the determination coefficients were R2 = 0.959 and R2 = 0.99, respectively, which was very close to 1. The maximum percentage removal efficiencies according to the RSM-BBD design were found to be TOC = 40.3; COD = 61.9; BOD = 18.8% and BOD/COD = 0.0390, whereas for the ANFIS model, the maximum reduction were found to be TOC = 46.5; COD = 65.4; BOD = 20.4% and BOD/COD = 0.040. In process optimization, the ANFIS model was shown better prediction than RSM-BBD in the process's optimization.

Comparative studies of RSM Box-Behnken and ANN-Anfis fuzzy statistical analysis for seawater biodegradability using TiO2 photocatalyst.

In this approach, a batch reactor was employed to study the degradation of pollutants under natural sunlight using TiO2 as a photocatalyst. The effects of photocatalyst dosage, reaction time and pH were investigated by evaluating the percentage removal efficiencies of total organic carbon (TOC), chemical oxygen demand (COD), biological oxygen demand (BOD) and biodegradability (BOD/COD). Design Expert-Response Surface Methodology Box Behnken Design (BBD) and MATLAB Artificial Neural Network - Adaptive Neuro Fuzzy Inference system (ANN-ANFIS) methods were employed to perform the statistical modelling. The experimental values of maximum percentage removal efficiencies were found to be TOC = 82.4, COD = 85.9, BOD = 30.9% and biodegradability was 0.070. According to RSM-BBD and ANFIS analysis, the maximum percentage removal efficiencies were found to be TOC = 90.3, 82.4; COD = 85.4, 85.9; BOD = 28.9, 30.9% and the biodegradability = 0.074, 0.080 respectively at the pH 7.5, reaction time 300 min and photocatalyst dosage of 4 g L-1. The study reveals both models found to be well predicted as compared with experimental values. The values of R2 for RSM-BBD (0.920) and for ANFIS (0.990) models were almost close to 1. The ANFIS model was found to be marginally better than that of RSM-BBD.