Optimization of silver cementation yield in fixed bed reactor using factorial design and central composite design

Abstract

This work deals with cementation of silver onto iron grid in fixed bed reactor. The influence of several parameters is studied namely: initial concentration of silver [Ag + ] 0 , flow rate, solution pH, and mass of iron. Moreover, their influence on the yield of the reaction of cementation is investigated statistically by the experimental design in view of industrial application. The estimation and the comparison of the parameter's effects are realised by using two-level factorial design. The analysis of these effects permits to state that the most influential factor is the mass of iron with an effect of (+5.642), the second in the order is the initial concentration of silver ions (Ag + ) with an effect of (+4.005), the third is the flow rate of the electrolytic solution with an effect of (+3.824). A central composite design methodology is employed to determine the optimum conditions for a silver cementation yield onto iron grid. For this end, the experimental results were approximated by a second-order model as well as the surface contour plots and surface responses are drowned. The optimal conditions found for initial silver concentration, such as a flow rate, pH of the solution and mass of iron, are respectively: 21.25 mg/L, 4.43 L/min, 3.6 and 50 g. Under these conditions, the obtained silver cementation yield is 96.851%.Ce travail concerne la cémentation de l'argent sur une grille de fer dans un réacteurà lit fixe. L'influence de plusieurs paramètres estétudiée, a savoir: la concentration initiale d'argent [Ag + ] 0 , le débit, le pH de la solution et la masse du fer. De plus, leur influence sur le rendement de la réaction de la cémentation est analysée de façon statistique par le plan expérimental enégardà l'application industrielle. L'estimation et la comparaison des effets des paramètres sont réalisées en utilisant un plan factorielà deux niveaux. L'analyse de ces effets permet de déclarer que le facteur présentant le plus d'influence est la masse du fer avec un effet de (+5.642), le deuxième dans l'ordre est la concentration initiale d'ions argent (Ag + ) avec un effet de (+4.005), le troisième est le débit de la solutionélectrolytique avec un effet de (+3.824). Une méthodologie de plan composite centrale est utilisée pour déterminer les conditions optimales pour un rendement de cémentation de l'argent dans la grille en fer. A cette fin, les résultats expérimentaux ontété approximés par un modèle de deuxième ordre ainsi que les tracés de lignes hypsométriques et les réponses de surface sont inondées. Les conditions optimales que l'on trouve pour la concentration d'argent initiale, comme le débit, le pH de la solution et la masse de fer sont respectivement: 21,25 mg/L, 4,43 L/min, 3,6 et 50 g. Dans ces conditions, le rendement de cémentation d'argent obtenu est de 96,851%.

Combination of an electrochemical pretreatment with a biological oxidation for the mineralization of nonbiodegradable organic dyes: Basic yellow 28 dye

“…In this study, to respect the rotatability of the design α = (2 k ) 1/4 ( k was the number of studied parameters). This characteristic provides good predictions at points equally distant from the centre . The response is expressed as the percent of BY28 degradation as follow: where [ BY 28 ] 0 and [ BY 28 ] t were the initial dye concentration and its concentration at a given time t (mg L −1 ), respectively.…”

“…When the response data are obtained from the test work, a regression analysis is performed to determine the coefficients of the response model ( b 1 , b 2 , …, b n ), as well as their standard errors and their significance. In addition to the constant b 0 , the response model incorporates : predicted yield ( ŷ) linear terms, corresponding to the variables ( x 1 , x 2 , …, x n ) squared terms, corresponding to the variables ( x 2 1 , x 2 2 , …, x 2 n ) first‐order interaction terms for each paired combination ( x 1 x 2 , x 1 x 3 , …, x n‐ 1 x n ) …”

“…This technique can be used for process characterization, optimization, and modeling. It is widely accepted in manufacturing industry for improving the product performance and reliability, the process efficiency and yield . In the statistical design of experiments, the factors involved can be simultaneously varied.…”

Preparation and Characterization of Sulfonic Acid Functionalized Silica and Its Application for the Esterification of Ethanol and Maleic Acid

“…Catalytic activity of SAFS was evaluated for the esterification of maleic acid with ethanol using BBD-RSM [30][31][32][33]. An effect of various reaction parameters viz.…”

Experimental Design for Modeling and Multi‐response Optimization of Catalytic Cyclohexene Epoxidation over Polyoxometalates

“…Therefore, factorial design is economical in characterizing a complicated process. [25][26][27][28][29][30][31] So, the purpose of this work is to study the influence of several operating parameters on cyclohexene epoxidation and to find the optimal activity and selectivity. The desired responses of the support are the conversion of cyclohexene and selectivity to cyclohexane-1,2 diol.…”