A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Application of ultrasound-assisted and subcritical water oxidation methods in the mineralisation of Procion Crimson H-EXL using response surface methodology and artificial neural network
Eco-friendly methods, the ultrasound-assisted oxidation (UAO) and the subcritical water oxidation (SWO) methods, were applied to mineralise the widely used commercial reactive azo dye, Procion Crimson H-EXL in the presence of H2O2. 72.20% and 72.86% of total organic carbon removal were achieved in the UAO and SWO methods, respectively. The Box-Behnken design (BBD) was applied to design the experimental processes and optimise both methods. ANOVA and validation tests were performed to assess the employed models. F and P values were obtained as 36.72 and <0.0001 in the UAO method, respectively, and 605.97 and <0.0001 in the SWO method, respectively. The artificial neural network (ANN) was applied in both the UAO and the SWO methods. The predictive performance of the BBD and ANN models were evaluated and compared to each other over R2, root mean square error and absolute average deviation values.
<title>Graphical Abstract</title><fig><graphic></graphic></fig>
Application of ultrasound-assisted and subcritical water oxidation methods in the mineralisation of Procion Crimson H-EXL using response surface methodology and artificial neural network
Eco-friendly methods, the ultrasound-assisted oxidation (UAO) and the subcritical water oxidation (SWO) methods, were applied to mineralise the widely used commercial reactive azo dye, Procion Crimson H-EXL in the presence of H2O2. 72.20% and 72.86% of total organic carbon removal were achieved in the UAO and SWO methods, respectively. The Box-Behnken design (BBD) was applied to design the experimental processes and optimise both methods. ANOVA and validation tests were performed to assess the employed models. F and P values were obtained as 36.72 and <0.0001 in the UAO method, respectively, and 605.97 and <0.0001 in the SWO method, respectively. The artificial neural network (ANN) was applied in both the UAO and the SWO methods. The predictive performance of the BBD and ANN models were evaluated and compared to each other over R2, root mean square error and absolute average deviation values.
<title>Graphical Abstract</title><fig><graphic></graphic></fig>
Application of ultrasound-assisted and subcritical water oxidation methods in the mineralisation of Procion Crimson H-EXL using response surface methodology and artificial neural network
Yabalak, Erdal (author) / Külekçi, Büşra (author) / Gizir, A. Murat (author)
Journal of Environmental Science and Health, Part A ; 54 ; 1412-1422
2019-12-06
11 pages
Article (Journal)
Electronic Resource
English
ultrasound , subcritical water , mineralisation , RSM , ANN , Procion Crimson , oxidation
| DOAJ | 2024
Subcritical water treatment of landfill leachate: Application of response surface methodology
| Online Contents | 2014
Optimizing Wood Composite Drilling with Artificial Neural Network and Response Surface Methodology
| DOAJ | 2024
| American Institute of Physics | 2013