Pentachlorophenol biodegradation in two-phase bioreactors operated with absorptive polymers: Box-Behnken experimental design and optimization by response surface methodology

2019 ◽  
Vol 131 ◽  
pp. 105-115 ◽  
Author(s):  
Domenica Mosca Angelucci ◽  
Daniela Piscitelli ◽  
M. Concetta Tomei
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Two Phase ◽  
Design And Optimization ◽  
Experimental Design And Optimization

scholarly journals Optimization with Response Surface Methodology of Microwave-Assisted Conversion of Xylose to Furfural

Molecules ◽  
2020 ◽  
Vol 25 (16) ◽  
pp. 3574
Author(s):  
Carmen Padilla-Rascón ◽  
Juan Miguel Romero-García ◽  
Encarnación Ruiz ◽  
Eulogio Castro
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Lignocellulosic Materials ◽  
Operational Conditions ◽  
Design And Optimization ◽  
Microwave Assisted ◽  
Microwave Reactor ◽  
Key Steps ◽  
Experimental Design And Optimization

The production of furfural from renewable sources, such as lignocellulosic biomass, has gained great interest within the concept of biorefineries. In lignocellulosic materials, xylose is the most abundant pentose, which forms the hemicellulosic part. One of the key steps in the production of furfural from biomass is the dehydration reaction of the pentoses. The objective of this work was to assess the conditions under which the concentration of furfural is maximized from a synthetic, monophasic, and homogeneous xylose medium. The experiments were carried out in a microwave reactor. FeCl3 in different proportions and sulfuric acid were used as catalysts. A two-level, three-factor experimental design was developed for this purpose. The results were further analyzed through a second experimental design and optimization was performed by response surface methodology. The best operational conditions for the highest furfural yield (57%) turned out to be 210 °C, 0.5 min, and 0.05 M FeCl3.

Preparation and Optimization of Capsaicin-Loaded Chitosan Nanoparticles as Delivery Systems Using Box-Behnken Design

2020 ◽  
Vol 998 ◽  
pp. 277-282
Author(s):  
Narissara Kulpreechanan ◽  
Feuangthit N. Sorasitthiyanukarn
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Functional Foods ◽  
Water Solubility ◽  
Chitosan Nanoparticles ◽  
Tween 80 ◽  
Limiting Factors ◽  
Design And Optimization ◽  
Chili Peppers

Capsaicin (CAP) is a pungent alkaloid of chili peppers that is obtained from chili peppers that has a variety of pharmacological activities and can be used in various areas, such as functional foods, nutritional supplements and medical nutrition. Capsaicin has important anticancer, antioxidant and anti-inflammatory properties that allow to be applied as treatment for several diseases. However, its lack of water solubility, as well as its poor oral bioavailability in biological systems, show limiting factors for its successful application. Recently, the formulation of capsaicin for food and pharmaceutical use is limited. Therefore, the present study emphasized on preparation of capsaicin-loaded chitosan nanoparticles (CAP-CSNPs) and design and optimization of the formulation using Box-Behnken experimental design (BBD) and response surface methodology (RSM). The capsaicin-loaded chitosan nanoparticles were prepared by o/w emulsification and ionotropic gelification. The optimized formulation of capsaicin-loaded chitosan nanoparticles had a chitosan concentration of 0.11 (%w/v), a Tween 80® concentration of 1.55 (%w/v) and a CAP concentration of 1 mg/mL and that it should be stored at 4°C. Box-Behnken experimental design and response surface methodology was found to be a powerful technique for design and optimization of the preparation of capsaicin-loaded chitosan nanoparticles using limited number of experimental runs. Our study demonstrated that capsaicin-loaded chitosan nanoparticles can be potentially utilized as dietary supplements, nutraceuticals and functional foods.

scholarly journals Design and Optimization of the Antireflective Coating Properties of Silicon Solar Cells by Using Response Surface Methodology

Coatings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 721
Author(s):  
Yahia F. Makableh ◽  
Hani Alzubi ◽  
Ghassan Tashtoush
Keyword(s):  
Response Surface Methodology ◽  
Solar Cells ◽  
Response Surface ◽  
Zno Nanoparticles ◽  
Visible Spectral Range ◽  
Silicon Solar Cells ◽  
Antireflective Coating ◽  
Antireflective Coatings ◽  
Zinc Oxide Nanostructures ◽  
Design And Optimization

The design and optimization of a nanostructured antireflective coatings for Si solar cells were performed by using response surface methodology (RSM). RSM was employed to investigate the effect on the overall optical performance of silicon solar cells coated with three different nanoparticle materials of titanium dioxide, aluminum oxide, and zinc oxide nanostructures. Central composite design was used for the optimization of the reflectance process and to study the main effects and interactions between the three process variables: nanomaterial type, the radius of nanoparticles, and wavelength of visible light. In this theoretical study, COMSOL Multiphysics was utilized to design the structures by using the wave optics module. The optical properties of the solar cell’s substrate and the three different nanomaterial types were studied. The results indicated that ZnO nanoparticles were the best antireflective coating candidate for Si, as the ZnO nanoparticles produced the lowest reflection values among the three nanomaterial types. The study reveals that the optimum conditions to reach minimum surface reflections for silicon solar cell were established by using ZnO nanoparticles with a radius of ~38 nm. On average, the reflectance reached ~5.5% along the visible spectral range, and approximately zero reflectance in the 550–600 nm range.

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

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