Photo-catalytic degradation of formaldehyde using nitrogen-doped TiO2 nano-photocatalyst: Statistical design with response surface methodology (RSM)

2018 ◽  
Vol 96 (12) ◽  
pp. 2544-2552 ◽  
Author(s):  
Maryam Mirzaei ◽  
Samad Sabbaghi ◽  
Mohammad Mahdi Zerafat
2018 ◽  
Vol 8 (1) ◽  
pp. 31-42
Author(s):  
M. Amimour ◽  
T. Idoui ◽  
A. Cheriguene

The Aim of this study was to develop an optimized method for manufacturing process of traditional Algerian Jben cheese, using response surface methodology (RSM). In order to develop the objective method of making this traditional cheese, several factors have been studied and a Plackett-Burman statistical design was applied. The effects of the four screened factors (enrichment with milk powder, 10 - 20 g/l; pH of milk, 5.75 - 6.75, enzymatic extract dose, 0.5 - 1.5 ml and coagulation temperature 40 - 60 °C) on the response were investigated, using a Box-Behnken statistical design. Multiple regression analysis was used so that experimental data fits to a second-order polynomial equation. This multiple analysis showed that the model explains about 90.73% of the variation. Based on statistical results, it can be noticed that enrichment with milk powder and pH of milk (Ë‚0.0001***) were highly significant factor influincing cheese yield. The optimal production parame-ters that maximized cheese product (20 g/l enrichment with milk powder, 5.75 pH of milk, 1.29 ml enzymatic extract dose and 60°C coagulation temperature) and the maximal predicted cheese yield (52.68 % ) were found out through response surface methodology. Under these conditions, a verification experiment was carried out and cheese yield was found to be 49.46 %. The overall percentage of agreement for the experimental results (more than 93 % validity) with the predicted values indicates the validation of the statistical model and the success of the optimization process.


2019 ◽  
Vol 6 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Abdul Aziz Hamidi ◽  
Syed Zainal Sharifah Farah Fariza ◽  
Alazaiza Motasem Y.D

Landfill leachate is highly polluted and generated as a result of water infiltration through solid waste produced domestically and industrially. This study investigated the applicability of the response surface methodology (RSM) to optimize the removal performances of chemical oxygen demand (COD), color, and suspended solids (SS) from landfill leachate by coagulation process using Tin tetrachloride pentahydrate. The leachate samples were collected from Alor Pongsu Landfill (APLS) in Perak, Malaysia. Before starting the experiments, general characterization was carried out for raw leachate samples to investigate their physical and chemical properties. The effects of the dosage and pH of SnCl4 on the removal performances were evaluated as well. An ideal experimental design was performed based on the central composite design (CCD) by RSM. In addition, this RSM was used to evaluate the effects of process variables and their interaction toward the attainment of their optimum conditions. The statistical design of the experiments and data analysis was resolved using the Design-Expert software. Further, the range of coagulant dosage and pH was selected based on a batch study which was conducted at 13000 mg/L to 17000 mg/L of SnCl4 and pH ranged from 6 to 10. The results showed that the optimum pH and dosage of SnCl4 were 7.17 and 15 g/L, respectively, where the maximum removal efficiency was 67.7% for COD and 100% for color and SS. The results were in agreement with the experimental data with a maximum removal efficiency of 67.84 %, 98.6 %, and 99.3%, for COD, color, and SS, respectively. Overall, this study verified that the RSM method was viable for optimizing the operational condition of the coagulation-flocculation process.


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