Combination effect of hydro-alcoholic extract of spent coffee grounds (HECG) and potassium Iodide (KI) on the C38 steel corrosion inhibition in 1M HCl medium: Experimental design by response surface methodology

2020 ◽  
Vol 29 ◽  
pp. 100499
Author(s):  
Fatima Bouhlal ◽  
Aimad Mazkour ◽  
Houda Labjar ◽  
Mohammed Benmessaoud ◽  
Malika Serghini-Idrissi ◽  
...  
2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Fatima Bouhlal ◽  
Najoua Labjar ◽  
Farah Abdoun ◽  
Aimad Mazkour ◽  
Malika Serghini-Idrissi ◽  
...  

The present work investigates the influence of temperature on C38 steel corrosion in a 1 M HCl medium with and without different concentrations of a hydro-alcoholic extract of used coffee grounds (HECG). The potentiodynamic polarization technique and the electrochemical impedance spectroscopy were performed in temperatures ranging from 293.15 to 323.15 K. It was observed that the inhibition efficiency decreased with increased temperature and inhibitor concentration. The HECG adsorption process on C38 steel surface was found to be spontaneous and obeyed to Langmuir isotherm at all studied temperatures. The associated thermodynamic parameters of adsorption led to suggest the occurrence of physical adsorption of the HECG compounds on the C38 steel surface.


2021 ◽  
Vol 13 (16) ◽  
pp. 8818
Author(s):  
Georgia-Christina Mitraka ◽  
Konstantinos N. Kontogiannopoulos ◽  
Maria Batsioula ◽  
George F. Banias ◽  
Andreana N. Assimopoulou

The amount of spent coffee grounds (SCGs) created, represents an environmental challenge worldwide. In this context, the aim of the present study was to exploit the potential of SCGs as a source of bioactive compounds that can be utilized in high value-added products. Thus, a cost-effective and environmentally friendly extraction technique was developed to ensure extracts with high total phenolic content and antioxidant activity, as well as significant amounts of caffeine and chlorogenic acid. Response surface methodology was implemented to evaluate the effects of the main extraction parameters (i.e., time, temperature, and ethanol-to-water ratio) and their interactions on the defined responses. The ethanol ratio was found to be the most significant variable. Then, a set of optimum values was determined (i.e., 7 min, 75 °C, and ethanol:water ratio 5:95), where the predicted values for responses were found to be 5.65% for the yield (Y1), 152.68 mg gallic acid equivalents per L for total phenolic content (Y2), 0.797 μmol Trolox equivalent per mL for the antioxidant activity (Y3), 30.5 ppm for caffeine concentration (Y4), and 17.4 ppm for chlorogenic acid concentration (Y5). Furthermore, the corresponding high experimental values from the validation experiment fitted well to these predictions, clearly clarifying the high potential of SCG extracts for use in high value-added applications.


Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi

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.


2020 ◽  
Vol 83 (1) ◽  
pp. 27-36
Author(s):  
Mardawani Mohamad ◽  
Rizki Wannahari ◽  
Rosmawani Mohammad ◽  
Noor Fazliani Shoparwe ◽  
Kwan Wei Lun ◽  
...  

Used coffee grounds usually end up as landfill. However, the unique structural properties of its porous surface make coffee grounds can be transformed into biochar and performed as an alternative low cost adsorbent. Malachite green (MG) is a readily water soluble dye which is used extensively in textile and aquaculture industries. The mordant complex structures of MG generate destructive effects to animals and environment. In this study, adsorption of malachite green using spent coffee ground biochar as adsorbent was investigated. The experiments were designed in two methods: classical and optimisation by response surface methodology. Three parameters were studied, which are adsorbent dosage, contact time and pH while the responses in this study are malachite green removal (%) and adsorption capacity (mg/g). Optimisation studies were performed using response surface methodology. Quadratic model was chosen for both response and studied using central composite design. The correlation coefficient, R2 for the quadratic model of malachite green removal (%) and adsorption capacity (mg/g) were 0.95 and 0.99, respectively. The optimum malachite green removal (%) predicted was found at 99.27%, by using 0.12 g of adsorbent dosage, 43.05 minutes of contact time and pH of 9.45 at desirability of 1.0. The optimum adsorption capacity (mg/g) predicted was found at 118.01 mg/g, by using 0.02 g of adsorbent dosage, 60 minutes of contact time and pH of 10.24 at desirability of 0.98. So, it was concluded that the spent coffee ground biochar can be used as an effective adsorbent for malachite green removal from aqueous solution.


2018 ◽  
Vol 138 ◽  
pp. 849-860 ◽  
Author(s):  
Joana M. Pinheiro ◽  
Sérgio Salústio ◽  
Anabela A. Valente ◽  
Carlos M. Silva

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