Search Results (18)

The release of synthetic dyes like Basic Red 46 (BR46) from industrial wastewater has raised growing concerns due to their toxicity, long-term persistence, and resistance to standard biological treatment methods. In this work, we developed and tested a pilot-scale electrocoagulation–ozonation (EC–O) hybrid system aimed at removing BR46 from aqueous solutions. The system integrates electrocoagulation, using iron electrodes, with ozone-based advanced oxidation processes, facilitating a combination of coagulation, adsorption, and oxidative breakdown of dye molecules. The response surface methodology (RSM) with a central composite design (CCD) was applied to optimize the treatment process, focusing on five variables: current density, flow rate, ozone dosage, ozonation time, and initial dye concentration. The quadratic model exhibited strong predictive power, with an adjusted R² of 0.9897 and a predicted R² of 0.9812. The optimal conditions identified included a current density of 70 A/m², flow rate of 1.6 L/min, ozone dose of 2.0 g/h, and an ozonation time of 20 min, achieving a predicted removal efficiency of 91.67% for a solution with BR46 at an initial concentration of 300 mg/L. Experiments conducted under these conditions confirmed the model’s reliability, with observed removal rates exceeding 90% and deviations under 2%. The EC–O system had a treatment capability of 26.19 L/h and an energy consumption of 3.04 kWh/m³. These findings suggest that the EC–O system is an effective and scalable option for treating dye-contaminated wastewater, offering faster and more efficient results than conventional techniques. Full article

In the present work, hybrid Cellulose Nanocrystal–MXene (CNC–MXene) nanolubricants were prepared via a two-step method and investigated as potential heat-transfer hybrid nanofluids for the first time. CNC–MXene nanolubricants were synthesized via a two-step method by varying the weight percentage of CNC–MXene nanoparticles (ranging from 0.01 to 0.05 wt%) and characterized using Fourier-Transform Infrared Spectroscopy and TGA (Thermogravimetric Analysis). Response surface methodology (RSM) was used in conjunction with the miscellaneous design model to identify prediction models for the thermophysical properties of the hybrid CNC–MXene nanolubricant. Minitab 18 statistical analysis software and Response Surface Methodology (RSM) based on Central Composite Design (CCD) were utilized to generate an empirical mathematical model investigating the effect of concentration and temperature. The analysis of variance (ANOVA) results indicated significant contributions from the type of nanolubricant (p < 0.001) and the quadratic effect of temperature (p < 0.001), highlighting non-linear interactions that affect viscosity and thermal conductivity. The findings showed that the predicted values closely matched the experimental results, with a percentage of absolute error below 9%, confirming the reliability of the optimization models. Additionally, the models could predict more than 85% of the nanolubricant output variations, indicating high model accuracy. The optimization analysis identified optimal conditions for maximizing both dynamic viscosity and thermal conductivity. The predicted optimal values (17.0685 for dynamic viscosity and 0.3317 for thermal conductivity) were achieved at 30 °C and a 0.01% concentration, with a composite desirability of 1. The findings of the percentage of absolute error (POAE) reveal that the model can precisely predict the optimum experimental parameters. This study contributes to the growing field of advanced nanolubricants by providing insights into the synergistic effects of CNC and MXene in enhancing thermophysical properties. The developed models and optimization techniques offer valuable tools for tailoring nanolubricant formulations to specific tribological applications, potentially leading to improved efficiency and durability in various industrial settings. Full article

One interesting method for environmental remediation is the use of ZnO/ZrO₂ composites in the photocatalytic degradation of lead (Pb) in wastewater. Several studies have investigated different types of composites for the removal of heavy metals from wastewater. However, the efficiency of these composites in removing the heavy metals remains debatable. Hence, this study investigated the potential of using a ZnO/ZrO₂ composite for the removal of Pb from wastewater. Response surface methodology (RSM) was utilized in this work to maximize the Pb photocatalytic removal over ZnO/ZrO₂ in simulated wastewater. Based on a central composite design (CCD), the experimental design included adjusting critical process parameters such as catalyst dosage, initial Pb concentration, and pH. The ZnO/ZrO₂ composite was synthesized using a physical mixing technique, and its physicochemical properties were studied by field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDS), Fourier transform infra-red (FTIR), and X-ray diffraction (XRD). Under visible light irradiation, photocatalytic Pb removal tests were carried out in a batch reactor. The findings showed that a ZnO/ZrO₂ dose of 100 mg/L, a pH of 10, and an initial Pb content of 15 ppm were the optimal conditions for maximal Pb removal (above 91.2%). The actual Pb removal obtained from the experimental runs was highly correlated with that predicted using the RSM quadratic model. The usefulness of ZnO/ZrO₂ composites for photocatalytic Pb removal is demonstrated in this work, which also emphasizes the significance of RSM in process parameter optimization for improved pollutant degradation. The models that have been proposed offer significant perspectives for the development and scalability of effective photocatalytic systems intended to remove heavy metals from wastewater. Full article

This study aims to assess the impact of the water ratio and nanoparticle concentration of neat diesel fuel on the performance characteristics of and exhaust gas emissions from diesel engines. The experimental tests were conducted in two stages. In the first stage, the effects of adding water to neat diesel fuel in ratios of 2.5% and 5% on engine performance and emissions characteristics were examined and compared to those of neat diesel at a constant engine speed of 3000 rpm under three different engine loads. A response surface methodology (RSM) based on a central composite design (CCD) was utilized to simulate the design of the experiment. According to the test results, adding water to neat diesel fuel increased the brake-specific fuel consumption and reduced the brake thermal efficiency compared to neat diesel fuel. In the examination of exhaust emissions, hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx) in the tested fuel containing 2.5% of water were decreased in comparison to pure diesel fuel by 16.62%, 21.56%, and 60.18%, respectively, on average, through engine loading. In the second stage, due to the trade-off between emissions and performance, the emulsion fuel containing 2.5% of water is chosen as the best emulsion from the previous stage and mixed with aluminum oxide nanoparticles at two dose levels (50 and 100 ppm). With the same engine conditions, the emulsion fuel mixed with 50 ppm of aluminum oxide nanoparticles exhibited the best performance and the lowest emissions compared to the other evaluated fuels. The outcomes of the investigations showed that a low concentration of 50 ppm with a small amount of 11 nm of aluminum oxide nanoparticles combined with a water diesel emulsion is a successful method for improving diesel engine performance while lowering emissions. Additionally, it was found that the mathematical model could accurately predict engine performance parameters and pollution characteristics. Full article

Several dynamic projects and fault diagnosis of mechanical structures require the knowledge of the acting external forces. However, the measurement of such forces is often difficult or even impossible; in such cases, an inverse problem must be solved. This paper proposes a force identification method that uses the response surface methodology (RSM) based on central composite design (CCD) in conjunction with a random forest regression algorithm. The procedure initially required the finite element modal model of the forced structure. Harmonic analyses were then performed with varied parameters of forces, and RSM generated a dataset containing the values of amplitude, frequency, location of forces, and vibration acceleration at several points of the structure. The dataset was used for training and testing a random forest regression model for the prediction of any location, amplitude, and frequency of the force to be identified with information on only the vibration acquisition at certain points of the structure. Numerical results showed excellent accuracy in identifying the force applied to the structure. Full article

One of the most serious effects of micropollutants in the environment is biological magnification, which causes adverse effects on humans and the ecosystem. Among all of the micro-pollutants, antibiotics are commonly present in the aquatic environment due to their wide use in treating or preventing various diseases and infections for humans, plants, and animals. Therefore, an aluminum-based electrocoagulation unit has been used in this study to remove cephalexin antibiotics, as a model of the antibiotics, from water. Computational and statistical models were used to optimize the effects of key parameters on the electrochemical removal of cephalexin, including the initial cephalexin concentration (15–55 mg/L), initial pH (3–11), electrolysis time (20–40 min), and electrode type (insulated and non-insulated). The response surface methodology-central composite design (RSM-CCD) was used to investigate the dependency of the studied variables, while the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods were applied for predicting the experimental training data. The results showed that the best experimental and predicted removals of cephalexin (CEX) were 88.21% and 93.87%, respectively, which were obtained at a pH of 6.14 and electrolysis time of 34.26 min. The results also showed that the ANFIS model predicts and interprets the experimental results better than the ANN and RSM-CCD models. Sensitivity analysis using the Garson method showed the comparative significance of the variables as follows: pH (30%) > electrode type (27%) > initial CEX concentration (24%) > electrolysis time (19%). Full article

Cosmos caudatus has been proclaimed as a high source of bioactive compounds, such as chlorogenic acid, quercetin, and ascorbic acid. Reported evidence indicates that C. caudatus exhibits high antioxidant activities. This study is undertaken to study the effect of extraction time and ethanol concentration on yield and DPPH scavenging activity and to optimize the maceration extraction conditions for C. caudatus. Response surface methodology (RSM) based on central composite design (CCD) was applied to obtain significant variables and the optimum conditions. The independent variables were extraction time (h) and ethanol concentration (%, v/v). The range of the extraction time was between 6 and 24 h and ethanol concentration was 30 to 100% (v/v). The dependent variables were yield (%) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity (%). Results show that extraction yields ranged between 11% and 25%, and DPPH scavenging activity ranged between 29% and 99%. Yield was only influenced by ethanol concentration. However, both extraction time and ethanol concentration significantly affected DPPH scavenging activity. Maximum yield and DPPH scavenging activity are the optimal extract qualities. Thus, the optimal conditions were 24 h extraction time and 84% (v/v) ethanol concentration. The experimental values agreed with those expected by RSM models, showing the eligibility of the method used and RSM’s performance in optimizing the conditions of extraction. Full article

Commercial poly methyl methacrylate (PMMA)-based cement is currently used in the field of orthopedics. However, it suffers from lack of bioactivity, mechanical weakness, and monomer toxicity. In this study, a PMMA-based cement nanocomposite reinforced with hydroxyapatite (HA) nanofibers and two-dimensional (2D) magnesium phosphate MgP nanosheets was synthesized and optimized in terms of mechanical property and cytocompatibility. The HA nanofibers and the MgP nanosheets were synthesized using a hydrothermal homogeneous precipitation method and tuning the crystallization of the sodium-magnesium-phosphate ternary system, respectively. Compressive strength and MTT assay tests were conducted to evaluate the mechanical property and the cytocompatibility of the PMMA-HA-MgP nanocomposites prepared at different ratios of HA and MgP. To optimize the developed nanocomposites, the standard response surface methodology (RSM) design known as the central composite design (CCD) was employed. Two regression models generated by CCD were analyzed and compared with the experimental results, and good agreement was observed. Statistical analysis revealed the significance of both factors, namely, the HA nanofibers and the MgP nanosheets, in improving the compressive strength and cell viability of the PMMA-MgP-HA nanocomposite. Finally, it was demonstrated that the HA nanofibers of 7.5% wt and the MgP nanosheets of 6.12% wt result in the PMMA-HA-MgP nanocomposite with the optimum compressive strength and cell viability. Full article

The present research aimed to investigate the effect of working parameters on the electrospinning of niobium–tungsten oxide nanofibers and optimize the process using central composite design (CCD) based on the response surface methodology (RSM). An experiment was designed to assess the effects of five variables including the applied voltage (V), spinning distance (D), polymer concentration (P), flow rate (F), and addition of NaCl (N) on the resulting diameter of the nanofibers. Meanwhile, a second-order prediction model of nanofibers diameter was fitted and verified using analysis of variance (ANOVA). The results show that the diameter of the nanofibers was significantly influenced by all the variables except the flow rate. Some second-order and cross factor interactions such as VD, DP, PF, PN, and P² also have significant effects on the diameter of the nanofibers. The results of the ANOVA yielded R² and adjusted R² values of 0.96 and 0.93 respectively, this affirmed that the predictive model fitted well with the experimental data. Furthermore, the process parameters were optimized using the CCD method and a maximum desirability function of 226 nm was achieved for the diameter of the nanofibers. This is very close to the 233 nm diameter obtained from a confirmatory experiment using the optimum conditions. Therefore, the model is representative of the process, and it could be used for future studies for the reduction of the diameter of electrospun nanofibers. Full article

This study presents an optimal double-pole magnetization brushless DC (BLDC) motor design, compared to a single-pole magnetization BLDC motor in terms of electromagnetic performance. Initially, a double-pole model is selected based on the permanent magnet (PM) of the single-pole model. The pole separation space, which is generated in the magnetization process of the double-pole PM, is selected based on the pole space of the single-pole model. Moreover, the PM offset is selected considering the PM volume of the single-pole model. Further, an optimal model is selected using the multiple response optimal method, which is a type of response surface methodology (RSM). The objective of the optimal design is to maintain the back EMF and decrease the cogging torque; the design variables include the pole separation space and PM offset. The experimental points of the initial model are designed using the central composite method (CCD). Finally, the optimization is verified by comparing the experimental and analysis results of the single-pole model with the analysis results of the optimal model. Full article

Recently, anti-glare (AG) surface treatment technology has been considered as a standard process to enhance the visibility of electronic display devices. For AG, the hydrofluoric acid (HF)-based chemical etch method is the most common approach for the current display glass industry. However, in order to overcome the environmental and durability degradation problems of the HF-based chemical etch method, this paper proposes an eco-friendly physical surface treatment technology using the sandblasting method. Based on the preliminary analysis results using the central composite design (CCD) method-based response surface modeling methodology (RSM), additional experiments and analyses were performed for process modeling and optimal process recipe generation. To characterize the sandblasting process, the mean value of haze was considered as the process output, and the pressure of the nozzle, the distance of the nozzle from the surface of glass, the glass feed rate, and the grit size of the abrasives were considered as process inputs. Based on the process model using the statistical response surface regression method and machine learning-based approaches, the proposed method can generate optimized process recipes for various haze targets of 10%, 20%, and 30%, with an average haze difference of 0.84%, 0.02%, and 0.86%, and maximum deviations of 1.26%, 1.14%, and 1.4%, respectively. Through the successful completion of this work, it is expected that the proposed surface treatment method can be applied to various products including mobile phones, tablet PCs, and windshields of vehicles. Full article

Polycyclic aromatic hydrocarbons (PAHs) are mutagenic and carcinogenic contaminants made up of fused benzene rings. Their presence has been reported in several wastewater streams, including produced water (PW), which is the wastewater obtained during oil and gas extraction from onshore or offshore installations. In this study, ferrate (VI) oxidation was used for the first time for the treatment of 15 PAHs, with the total concentration of 1249.11 μg/L in the produced water sample. The operating parameters viz., ferrate (VI) dosage, pH, and contact time were optimized for maximum removal of PAHs and chemical oxygen demand (COD). Central composite design (CCD) based on response surface methodology (RSM) was used for optimization and modeling to evaluate the optimal values of operating parameters. PAH and COD removal percentages were selected as the dependent variables. The study showed that 89.73% of PAHs and 73.41% of COD were removed from PW at the optimal conditions of independent variables, i.e., ferrate (VI) concentration (19.35 mg/L), pH (7.1), and contact time (68.34 min). The high values of the coefficient of determination (R²) for PAH (96.50%) and COD (98.05%) removals show the accuracy and the suitability of the models. The results showed that ferrate (VI) oxidation was an efficient treatment method for the successful removal of PAHs and COD from PW. The study also revealed that RSM is an effective tool for the optimization of operating variables, which could significantly help to reduce the time and cost of experimentation. Full article

Circular economy principles are based on the use of by-products from one operation as the raw materials in another. The aim of this work is to obtain extracts with high antioxidant capacity and resveratrol content for the superior capitalization of the biomass of Feteasca regala leaves obtained during vineyard horticultural operations in spring. In order to obtain a high-quality extract at an industrial level, an optimal extraction process is needed. Central composite design (CCD) was used for the experiment design, which contained three independent variables: the ratio of extraction solvent to solid matter, temperature (°C) and time (minutes). The evaluation of extracts was done by measuring the total antioxidant capacity of the extracts using photo-chemiluminescent techniques, and the resveratrol content using liquid chromatography. Process optimization was done using response surface methodology (RSM). Minitab software version 17.0 was used for the design of experiments and data analysis. Regression analysis showed that the model predicts 87.5% of the variation for resveratrol and 96% for total antioxidant capacity (TAC). The temperature had the biggest influence on the extraction yield. The optimal operational conditions for the extraction method applied had the following conditions: ratio e/m 2.92; 43.23 °C and 55.4 min. A maximum value of 34,623 µg ascorbic acid equivalent (AAE) /mL total antioxidant capacity and 182.4 µg/mL resveratrol content were obtained when the optimal extraction parameters where used. The values obtained in experiments proved that by using RSM an accurate model can be obtained for extraction of Feteasca regala leaves. Full article

Monel K-500, a nickel–copper based alloy, is a very hard and tough material. Machining of such hard and tough materials always becomes a challenge for industry and this has been resolved by wire electric discharge machining (WEDM), a popular non-conventional machining method used for machining tough and hard materials having complex shapes. For the first time reported in this present research work is an experimental investigation executed on Ni-27Cu-3.15Al-2Fe-1.5Mn based superalloy using WEDM to model cutting rate (CR) and surface roughness (SR) using response surface methodology (RSM). The process parameters have been selected as pulse-on time, pulse-off time, spark-gap voltage and wire-feed rate. Experiments have been planned according to the central composite design (CCD). The results show that pulse-on time has a direct effect on CR while the pulse-off time has a reverse effect. The CR increases as pulse-on time increases, and decreases as pulse-off time increases. SR increases as pulse-on time increases, and decreases as pulse-off time increases. Furthermore, increase in spark-gap voltage decreases CR and SR both. The wire feed-rate has a negligible effect for both the response parameters. The optimized values of CR and SR achieved through multi-response optimization are 2.48 mm/min and 2.12 µm, respectively. Full article

This work compared the effect of soaking and roasting Phoenix dactylifera L. seeds pretreatment methods on oil yield. The conversion of the Phoenix dactylifera L. seed oil to fatty acid methyl ester (FAME) was conducted via transesterification reaction using Ce-Zr/Al-MCM-41 monometallic and bimetallic catalysts. The reaction conditions were optimized using response surface methodology based on the central composite design (RSM-CCD). The result shows a quadratic model fitting with an R² value of ~0.98% from the analysis of variance. In addition, the optimum FAME yield of 93.83% was obtained at a reaction temperature of 60.5 °C, a reaction time of 3.8 h, a catalyst concentration of 4 wt.%, and a methanol to oil molar ratio of 6.2:1 mol/mol. The effect of the regenerated catalyst was significantly maintained for five cycles. The fuel properties of the produced FAME lie within the values reported in studies, ASTM D6751, and EN14214 standards. Full article