Photocatalytic Degradation of Industrial Dye in Semi-Pilot Scale Prototype Solar Photoreactor: Optimization and Modeling Using ANN and RSM Based on Box–Wilson Approach

Berkani, Mohammed; Bouchareb, Mohammed Kheireddine; Bouhelassa, Mohammed; Kadmi, Yassine

doi:10.1007/s11244-020-01320-0

Cited by 27 publications

(13 citation statements)

References 33 publications

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“…The P -values were also used to verify the significance of each coefficient. If the P -value is <0.05, then the model terms are significant; thus, the coefficient was more significant when the Student’s t -test magnitude was greater and the P -values were smaller . It is obvious that the major significant reaction parameters were ordered in a particular manner (from the most to the least significant): quadratic of the silica content > the effect of the ball milling time > quadratic of the ball milling time > the effect of the silica content.…”

Section: Resultsmentioning

confidence: 99%

“…If the P-value is <0.05, then the model terms are significant; thus, the coefficient was more significant when the Student's t-test magnitude was greater and the Pvalues were smaller. 34 It is obvious that the major significant reaction parameters were ordered in a particular manner (from the most to the least significant): quadratic of the silica content > the effect of the ball milling time > quadratic of the ball milling time > the effect of the silica content. To establish a correlation, a regression analysis was used in tandem with the results of the RSM model to create a quadratic equation containing the actual components silica content (X 1 ) and ball milling time (X 2 ) and their interactions, resulting in an accurate prediction of the silica particle size as a response.…”

Section: Resultsmentioning

confidence: 99%

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Preparation of Natural Rubber Composites with High Silica Contents Using a Wet Mixing Process

2022

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A wet mixing process is proposed for filled rubber composites with a high silica loading to overcome the drawbacks of high energy consumption and workplace contamination of the conventional dry mixing process. Ball milling was adopted for preparing the silica dispersion because it has a simple structure, is easy to operate, and is a low-cost process that can be easily scaled up for industrial production. The response surface methodology was used to optimize the making of the silica dispersion. The optimum conditions for a well-dispersed silica suspension with the smallest silica particle size of 4.9 mm were an about 22% silica content and 62 h of ball milling. The effects of dry and wet mixing methods on the properties of silica-filled rubber composites were investigated in a broad range of silica levels from low to high loadings. The mixing method choice had little impact on the properties of rubber composites with low silica loadings. The silica-filled rubber demonstrated in this study, however, shows superior characteristics over the rubber composite prepared with conventional dry mixing, particularly with high silica loadings. When compared to silica-filled natural rubbers prepared by dry mixing (dry silica rubber, DSR), the wet mixing (for WSR) produced smaller silica aggregates with better dispersion. Due to the shorter heat history, the WSR exhibits superior curing characteristics such as a longer scorch time (2.2–3.3 min for WSR and 1.0–2.1 min for DSR) and curing time (4.1–4.5 min for WSR and 2.2–3.1 min for DSR). Additionally, the WSR has superior mechanical properties (hardness, modulus, tensile strength, and especially the elongation at break (420–680% for WSR and 360–620% DSR)) over the DSR. The rolling resistance of WSR is lower than that of DSR. However, the reversed trend on the wet skid resistance is observed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Preparation of Natural Rubber Composites with High Silica Contents Using a Wet Mixing Process

2022

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“…Photocatalytic water treatment has already reached the stage of the industrial pilot. Although the efficacy of the photocatalytic process has been well established in families of very different organic compounds such as dyes (Guillard et al, 2003;Saggioro et al, 2015;Berkani et al, 2020), pesticides (Herrmann and Guillard, 2000;Kaur and Kaur, 2021), saturated/unsaturated hydrocarbons (Herrmann, 1999;Ul haq et al, 2020), and phenols (Liao et al, 2009;Wang et al, 2009;Mozia et al, 2012;, the application of photocatalysis for the treatment of micro-and nano-plastics has been attempted for just a few years. For instance, Tian et al (2019) conducted a photocatalytic degradation of polystyrene (PS) nanoplastics in water and air mediums under a 254-nm irradiation source utilizing (Hartmann et al, 2019) C radioisotope tracer technology (Tian et al, 2019).…”

Section: Application Of Aops For Degradation Of Microplasticsmentioning

confidence: 99%

Recent Advances in Photocatalytic Removal of Microplastics: Mechanisms, Kinetic Degradation, and Reactor Design

Hamd

Daher

Tofa³

et al. 2022

Front. Mar. Sci.

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Plastic products are used in almost all aspects of our daily life. Due to their low cost, portability, durability, and resistance to degradation, these products are affecting the health of the environment and biota on a global scale. Thus, the removal and mineralization of microplastics is an important challenge in the 21st century. Advanced oxidation processes (AOPs) have recently been identified as a viable treatment technique for tackling recalcitrant organic molecules and polymers. However, information on kinetic degradation mechanisms and photocatalytic reactor design is insufficient. This review discusses the fundamentals of photocatalysis and photo-Fenton processes in addition to the photocatalytic degradation mechanisms. We also introduce different characterization techniques of the major microplastic pollutants such as PE, PP, PVC, PS, PMMA, and PA66. In addition, a detailed overview of the major existing photocatalytic plants and the scaling-up methods of photoreactors are discussed.

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“…Neural network also has a good application in the removal reaction. For example, Berkan et al [36] studied the biological photodegradation reaction of azo fuel in solar photoreactor. The initial dye concentration, pH value and flow rate were used as model input variables to simulate the decolorization removal rate and predict the best decolorization conditions.…”

Section: Reaction Optimizationmentioning

confidence: 99%

Application Progress of Artificial Neural Network in Chemical Industry

Feng

Sun

2022

JERR

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The design and optimization of chemical equipment and devices and the control of chemical processes involve many factors and are very complex. The traditional methods and technologies cannot obtain satisfactory results. Artificial neural network technology has the ability to deal with complex objects and has been widely used in various engineering fields including chemical industry. In this paper, the research progress of artificial neural network in chemical industry is reviewed. The application progress of artificial neural network in signal peak recognition, catalyst optimization, industrial process, reaction process, physical data and other aspects is summarized. The advantages and limitations of artificial neural network in chemical industry are analyzed. Finally, the development trend of its application in chemical industry is prospected.

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Photocatalytic Degradation of Industrial Dye in Semi-Pilot Scale Prototype Solar Photoreactor: Optimization and Modeling Using ANN and RSM Based on Box–Wilson Approach

Cited by 27 publications

References 33 publications

Preparation of Natural Rubber Composites with High Silica Contents Using a Wet Mixing Process

Preparation of Natural Rubber Composites with High Silica Contents Using a Wet Mixing Process

Recent Advances in Photocatalytic Removal of Microplastics: Mechanisms, Kinetic Degradation, and Reactor Design

Application Progress of Artificial Neural Network in Chemical Industry

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