Response Surface Methodology Model Research Articles

Combined sand eco-mortar reinforced with polyethylene Terephthalate: Behavior and optimization using RSM method

Today, economic and environmental interests have become imperative in construction works. Therefore, the use of substitute materials for natural aggregates or cement contributes to the preservation of natural resources and the reduction of carbon dioxide diffusion. The large quantity of dune sand available in the Algerian Sahara and the affordable cost of recycled aggregates and plastic waste encourage their use as a replacement for natural aggregates in the production of cementitious materials. The interaction of dune sand (DS) and recycled sand (RS) with the introduction of a polyethylene terephthalate (PET-F) fibre help to design a normative ecological material. The aim of the study was to explore and optimize the proportions of these materials to create an environmentally-friendly cementitious product. The strategy is based on the progressive substitution of dune sand by another originating from demolition waste at different rates: 0%, 25%, 50%, 75% and 100%, and a gradual rate of PET-F fibres, 20 and 30 mm from 0% to 2% with a step of 0.5%. In this article, we study the effect of the new composites on the physico-mechanical performance of the designed eco-mortars through several tests (spreadability, fresh density, porosity, capillary and immersion absorption, compression strength, and flexural strength). In addition, we utilise Response Surface Methodology (RSM) to predict approximate parameters that influence the properties of new cementitious composites. The results obtained show the significant benefit of the following rates: 50% SD, 50% SR and (0.5%, 30 mm) PET-F fibre. Thus, the RSM model correlates well with the experimental data, which has undoubtedly led to an improvement in the physical–mechanical characteristics of the eco-mortar studied. Otherwise, the proposed model reaches a high pertinence, with a good desirability coefficient of 0.917.

Analyzing disinfection by-products yield and mechanisms in UV/Cl2 using response surface methodology and quantitative structure-activity relationship models

The study aimed to investigate the formation of halogenated disinfection byproducts (DBPs) during applying UV/chlorine (UV/Cl2) and unravel the interactive impacts of critical operational parameters and the mechanisms behind DBPs formation. Response surface methodology and quantitative structure-activity relationship models were developed to evaluate the contribution of electrophilic, nucleophilic, and free radical reactions to the formation of DBPs in UV/Cl2. The study found that Cl2 and its interactions dominated the total DBPs and non-Br-DBPs formation, while Br− and the Cl2–Br− interaction played a decisive role in the Br-DBPs formation. The study also observed significant interactions of Br, Cl2, and pH on chloroform, bromodichloromethane, dichloroacetonitrile, 1,1-dichloro-2-propanone, trichloroactic acid, and chlorodibromoacetic acid formations, while no evident interaction on chloral hydrate, dibromochloromethane, trichloroacetone, dibromoacetic acid, and bromodichloroacetic acid formations. The electrophilic substitution of HOBr mainly controlled the formation of trihalomethanes, and the contribution of nucleophilic, electrophilic, and free radical (•OH, Cl•, Cl2•- and ClO•) reactions depended on the molar ratio of Cl2 to Br, and pH-determined hydrolysis rate constants of DBPs and the types of free radicals. Overall, the response surface methodology and quantitative structure-activity relationship models provided a reference for revealing DBPs formation mechanisms in other disinfection processes.

Optimization of colloidal nano-silica based cementitious mortar composites using RSM and ANN approaches

Sustainable development of infrastructure requires the efficient and optimized utilization of nano-silica in concrete applications. Thus, current study aims to explore how the dosages of colloidal nano-silica (CNS) and its surface area affect the mechanical performance of cementitious mortar composites. To assess the compressive strength with aging (7, 28 and 56 days), CNS with different surface areas was used as cement substitutes at three different dosages levels. Additionally, to optimize nano-silica dosages and evaluate the outcomes of the experiment, statistical modelling based on Response Surface Methodology (RSM) and machine learning-based Artificial Neural Network (ANN) were applied. In comparison to the control and all other mixtures, the compressive strength was improved (51.5 MPa, 70.2 MPa, and 73.2 MPa) at 7, 28, and 56 days, respectively, by the CNS with higher surface area (CNS500) at their lower dosages (3%). However, at medium (5%) and high dosages (10%), CNS with a lower surface area (CNS80) resulted in better compressive strength (71.9 MPa, 87 MPa, and 90 MPa) at 7, 28, and 56 days, respectively, comparatively to control as well as all other mixtures. These findings were supported by both the RSM and ANN models, with the ANN model outperforming the RSM model in terms of performance. The ANN model was trained and validated using 70% and 30% of the data, respectively. For the validation data of ANN model, the Coefficient of Determination (R), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE) were revealed as 0.925, 2.263 and 2.974 MPa, respectively. Whereas, for the quadratic RSM models, these values were determined as 0.86, 4.485, and 5.45 MPa, respectively. The results of statistical models validated the experimental results, thus showing that the developed RSM and ANN can be used as reliable prediction model to optimize CNS use in producing stronger and more sustainable concrete.

Modeling and optimization of the self-embrittle corrosive bifunctional detergent for corrosive deep decontamination of stainless steel surface by RAFT one-pot method based on machine learning and response surface methodology

Nuclear facilities generate lots of contaminated stainless steel metallic material during maintenance and decommissioning. As a new radioactive decontamination method, the self-embrittle decontamination method has the advantage of less secondary contaminants and being able to operate machinery remotely. Adding a certain amount of corrosive components to the self-embrittle compound detergent can achieve the dual functions of self-embrittlement and corrosive decontamination. It was used to optimized the response surface methodology (RSM) and train and evaluate four different machine learning models by the recorded data set. The purpose of the analysis was to quantify the accuracy of the corrosion decontamination effect of RSM model and four types of machine learning model. The results exhibits that the long short-term memory neural network (LSTM) model performs well. The prepared detergent can achieve the average corrosion depth of 5.9454 μm on stainless steel, which can satisfy the corrosion decontamination of radioactively contaminated stainless steel surfaces.

Enhancing eco-concrete performance through synergistic integration of sugarcane, metakaolin, and crumb rubber: Experimental investigation and response surface optimization

Sustainable construction has gained paramount importance due to the consideration of the devastating effects of construction activities on the environment. Researchers are exploring innovative approaches to mitigate the carbon footprint and enhance the durability of concrete. In order to regulate the demand and cost of concrete constituents, such as cement and sand, there is a need to invent alternative materials and utilize various industrial and agricultural wastes instead of concrete ingredients, either partially or completely. The experimental investigation and optimization of eco-concrete composites by integrating sugarcane bagasse ash (SCBA), metakaolin (MK), and crumb rubber (CR) are cutting-edge research areas that aim to develop environmentally friendly and high-performance concrete materials. The present research work has attempted to utilize SCBA up to 15% by weight of cement with an increment of 5%, MK as a fractional exchange of cement up to 15% with 5% intervals, and CR was utilized as fractional volumetric substitution of sand from 0% to 15% in concrete. Different sets of combinations were evaluated to identify effects on density, workability, compressive strength, split tensile strength, flexural strength, and microstructural properties. This study has obtained satisfactory results when compared to the control concrete for 10% substitution of cement with MK and 10% substitution of cement with SCBA, along with a 10% replacement of fine aggregate (i.e., sand) with CR. The results were analyzed and optimized using Response Surface Methodology (RSM), which illuminated a strong correlation between experimental findings and RSM models, with an R squared (R2) value of 0.9580. The experimental findings and RSM models showed a significant correlation. The increment in the substitution of sand with CR resulted in a decline in strength, and it can be controlled by adopting different effective pretreatment techniques for CR.

Optimization of Ultrasonic-Assisted TIG (UA-TIG) Welding Process Parameters for AA7075 Alloy Joints Using RSM-GA Approach

Abstract The current study investigates the optimized process parameters for low hot cracking susceptibility and the enhanced microhardness of ultrasonic-assisted tungsten inert gas (UA-TIG) welded AA7075 joints. The optimization trials were made using response surface methodology (RSM) and RSM coupled with genetic algorithm (RSM-GA) approaches. Welding process parameters, such as welding current, gas flow rate, presence and absence of ultrasonic vibration (UT), and filler metal were varied to study the hot crack sensitivity and microhardness of the AA7075 welded joints. RSM was used to develop the empirical relationships with a higher coefficient of determination (R2 = .9892 for hot cracking sensitivity and R2 = .9919 for microhardness). Welding current (120 A), gas flow rate (13 L/min) with UT and filler material with hot cracking sensitivity (0 %), and microhardness (117.76 HV) are the optimized process parameters. The experimental results and predicted RSM model at optimal conditions were compared to the predicted RSM-GA model for minimal hot cracking sensitivity and maximum microhardness. A good agreement between the experimental results and the predicted RSM model confirms the soundness of the developed RSM-GA model.

Prediction of concrete residual compressive strength under elevated temperatures: Response surface methodology (RSM) approach

Exposure of concrete to elevated temperatures causes irreversible damage to the concrete structure and poses a serious threat to its service life. Due to the importance of concrete fire performance, extensive research has been conducted to investigate its behavior under different conditions of elevated temperatures. The properties of concrete are significantly affected by various factors, including heating temperatures, heating durations, and cooling methods. Among these factors, the residual compressive strength of concrete is considered the most crucial characteristic after exposure to elevated temperatures. This paper aims to develop mathematical models for analyzing and predicting the relative residual compressive strength of concrete at high temperatures. Three independent factors were identified in this study: heating temperatures, heating duration, and cooling method. Two groups of datasets on the relative residual compressive strength of concrete under elevated temperatures were reviewed and collected from previous studies, serving as the benchmark dataset and validation dataset, respectively. Response Surface Methodology (RSM) was employed to analyze the datasets. The results of various statistical parameters, such as the coefficient of determination, sum of squares, F-value, and P-value, indicate the significance of the predicted model for estimating concrete's relative residual compressive strength under elevated temperatures. The RSM analysis reveals that heating temperatures have the most significant effect on the relative residual compressive strength of concrete. In summary, the RSM model shows a strong correlation with the validation datasets, with a coefficient of determination (R2) of 0.7869.

Removal of the antibiotic ciprofloxacin by using a polyvinyl alcohol/agar/maltodextrin composite membrane: Optimization by response surface methodology

Polyvinyl alcohol/agar/maltodextrin composite membranes at different ratios are investigated for the removal of the antibiotic ciprofloxacin from water. The factors affecting the adsorption capacity of the as-synthesized membranes include time, pH, temperature, concentration, and dosage of film. The PAM244 and PAM433 membranes are subjected to optimization using response surface methodology model employing the Box–Behnken experimental design model. The optimal model has shown compatibility between the experimental value and the predicted value of the model. The correlation coefficients of PAM244 and PAM433 are recorded as R2 = 0.999 and R2 = 0.991, respectively. The model also proposes the optimal parameters. For PAM244, the adsorption capacity predicted from the model is 11.52 mg g–1 (42% of efficiency) at pH 5.89, a concentration of 41.28 mg L–1, over a time period of 19.87 min, and dosage of film 2.08 g L–1. For PAM433, the adsorption capacity predicted from the model is 10.46 mg g–1 (40% of efficiency) at pH 6.07, a concentration of 41.24 mg L–1, over a time period of 19.59 min, and dosage of film 2.16 g L–1. To enhance the potential of the material, the membrane reusability is also examined with the number of reuses being three times with the PAM244 membrane and twice with the PAM433 membrane.

Nanoplastics removal from spiked laundry wastewater using electro-peroxidation process

Microplastics and nanoplastics (NPs) in laundry wastewater (LWW) are major sources of plastic particles in wastewater treatment plants. Unlike microplastics, almost no information exists in the literature on the degradation of NPs in LWW. In this work, the degradation of NPs in commercial LWW by the electro-peroxidation process is investigated. The obtained results demonstrated that already existing ions in LWW such as Cl− contribute to faster degradation of NPs and a complete removal could be obtained as fast as 40 min. In addition, three-dimensional excitation and emission matrix fluorescence analysis was performed, which revealed humic acid-like, aromatic proteins-like, and fulvic acid-like compounds could be oxidized after 20, 40, and 60 min of treatment respectively. The effects of operating parameters on the process performance were then examined by response surface methodology (RSM) models. The results showed that initial TOC concentration was the most important parameter influencing negatively the percentage of NP degradation. Afterward, optimization of the process revealed that the energy consumption could be minimized at 31.2 mA/cm2, 0.025 mol/L [Na2SO4], and 52 min treatment time for 52.2 mg/L initial TOC. Finally, analysis of treated LWW showed no toxicity on Daphnia magna. This study showed that the electro-peroxidation process can completely degrade NPs in LWW without any remaining toxic compounds.

Modeling and optimization of integrated earth air heat exchanger and direct evaporative cooling system using ANN and RSM approaches for preserving fruits and vegetables

AbstractConventional air‐conditioned storage systems are more energy intensive which requires a consistent supply of electricity. Apart from this, it is costly and not suitable in remote areas where electricity is almost inaccessible. Therefore, the present study aimed to test the performance and feasibility of an integrated earth air heat exchanger and direct evaporative cooling system for storage of fruits and vegetables. The effects of air velocity (4–6 m/s), water flow rate (0.032–0.096 kg/s), and thickness of the cooling pad (50–150 mm) with corresponding responses were investigated in order to develop response surface methodology (RSM) and artificial neural network (ANN) models and compared them to predict temperature drop and humidity rise. Using RSM model, the optimum values for maximum temperature drop (16.80°C) and humidity rise (70.85%) within a range of input variables were found to be 4 m/s air velocity, 0.065 kg/s water flow rate, and 150 mm thickness of the cooling pad. For predicting the temperature drop and humidity rise, the higher (0.992 and 0.998) lower MAE (0.045 and 0.064), RMSE (0.187 and 0.266), and values (0.028 and 0.001) were observed in the ANN model. The developed ANN model was better than RSM models, and the model prediction was in good agreement with experiment values within the range of ±5% uncertainty. The results of this study indicate that the proposed cooling system can be successfully applied to store horticultural commodities.Practical ApplicationIntroduced hybrid cooling technique is low‐cost and eco‐friendly, and can be commercially used for on‐farm storage of fruits and vegetables to enhance their shelf life. The system is helpful for smallholder farmers/retailers to maintain the quality of the commodity during storage, minimize post‐harvest losses, and fetch good market value for the commodity.

Ultrasonication/dynamic maceration-assisted extraction method as a novel combined approach for recovery of phenolic compounds from pomegranate peel.

According to recent studies, pomegranate peel (PP) has the potential to be inverted from environmental pollutant waste to wealth due to possessing valuable phenolic compounds at a higher amount compared to edible parts. So far, different types of biological activities such as antimutagenic, antiproliferative, anti-inflammatory, and chemo-preventive properties were stated for pomegranate peel extract (PPE) according to chemical composition. In the present research, the probable intensifying effects of two extraction methods and optimum conditions for novel combined method of ultrasonication and dynamic maceration-assisted extraction of PPE using response surface methodology (RSM) were determined. A Box-Behnken Design (BBD) was employed to optimize three extraction variables, including sonication time (X1), sonication temperature (X2), and stirring speed (X3) for the achievement of high extraction yield of the phenolic compounds and antioxidant activity. The optimized conditions to obtain maximum extraction efficiency were determined as X1 = 70 min, X2 = 61.8°C, and X3 = 1000 rpm. The experimental values were in line with the values anticipated by RSM models, which indicates the appropriateness of the applied quadratic model and the accomplishment of RSM in optimizing the extraction conditions. The results suggest that the extraction of PPE by mix of ultrasonication as a modern method and dynamic maceration as a conventional method could improve its bioactive extractability and the obtained values were higher than any of the methods used. In other words, these two methods together have intensifying effects in increasing extraction efficiency which could further be utilized in food and agricultural industry.

Central composite design‐based response surface methodology for optimization of tetrodotoxin extraction from the horseshoe crab Carcinoscorpius rotundicauda

AbstractTetrodotoxin analogues (TTXs) used in research and development were originated from biological sources for the last decades. Among TTXs‐bearing aquatic animals, horseshoe crab Carcinoscorpius rotundicauda from Vietnam's Coast was reported to contain a high level of TTXs, indicating a promising source for extraction and purification of TTXs. In this work, horseshoe crab C. rotundicauda collected from Can Gio Coast, Vietnam was used for TTX extraction using homogenization‐assisted method with acetic acid solvent. The extraction process was analysed and optimized by single‐factor investigation and response surface methodology (RSM). Single‐factor investigation pointed out that the optimal conditions for TTX recovery yield from C. rotundicauda's soft tissues were homogenization time of 20 min, acetic acid concentration of 1.0%, solvent/matrix ratio of 4 mL/g, extraction temperature of 70°C, extraction time of 30 min and number of extraction of two times. The most significant factors including acetic acid concentration, solvent/matrix ratio and extraction temperature were utilized for constructing the RSM model to predict TTX recovery yield. The maximal TTX recovery yield predicted from RSM model was 97.3% under solvent/matrix ratio of 4 mL/g, extraction temperature of 70°C and acetic acid concentration of 1% (v/v). The RSM model displayed a high stability and precision for prediction of TTX recovery yield from experiments of 1–10 kg C. rotundicauda's soft tissues/batch extractions producing low errors (−1.95% to 1.85%). TTXs in C. rotundicauda's soft tissues were elucidated by HPLC‐MS/MS, revealing the presence of three analogues TTX, 4epi‐TTX and Anh‐TTX with their contents of 43.02, 22.54 and 45.07 μg/g, respectively.

Development of knowledge management for viscosity of nanolubricant in hot and cold lubrication conditions

The optimization and prediction of SAE40 based nanofluid (NF) viscosity with a knowledge management approach have been discussed using Response surface methodology (RSM). The used data are independent variables (SR,T,SVF) and viscosity as the response or the dependent variable. For this purpose, in the first step, six RSM models were analyzed compared to select a model accurately in ANOVA terms and statistical plots and Fifth model with values of R2 = 0.9832, R2-adjusted= 0.9768 and Margin of Deviation −17 <MOD< +15 has the most accurate among the selected models. The findings have shown that, according to the correlation relationship presented based on the selected model, behavior of NF is non-Newtonian. Also, in interaction between the independent variables, NF viscosity gradually decreases with increase in T and gradually increases with the increase in SVF. Thus, the highest viscosity (in SVF= 0.0625 % and T = 25 °C) is 497 mPa.sec and lowest value (in T = 50 °C and SVF=0.125 %) is 69.7 mPa.sec. In optimization section, to find the most optimal viscosity in hot and cold temperature conditions with the lowest amount of ZnO nanoparticles (NPs) use, the viscosity value for two cases was obtained as 202.07 mPa.sec and 190.36 mPa.sec, respectively. Also, the implementation of knowledge management steps in NFs, in addition to collecting information, storing and accessing various resources, brings a wide range of benefits to researchers and industrialists.

A Comparative Study of a Machine Learning Approach and Response Surface Methodology for Optimizing the HPT Processing Parameters of AA6061/SiCp Composites

This work investigates the efficacy of high-pressure torsion (HPT), as a severe plastic deformation mechanism for processing plain and silicon-carbide-reinforced AA6061, with the broader objective of using the technique for improving the properties of lightweight materials for a range of objectives. The interactions between input variables, such as the pressure and equivalent strain (εeq) applied during HPT processing, and the presence of SiCp and response variables, like the relative density, grain refinement, homogeneity of the structure, and the mechanical properties of the AA6061 aluminum matrix, were investigated. Hot compaction (HC) of the mixed powders followed by HPT were employed to produce AA6061 discs with and without 15% SiCp. The experimental findings were then analyzed statistically using the response surface methodology (RSM) and a machine learning (ML) approach to predict the output variables and to optimize the input parameters. The optimum combination of HPT process parameters was confirmed by the genetic algorithm (GA) and ML approaches. Furthermore, the constructed ML and RSM models were validated experimentally by HPT processing the same material under new conditions not fed into the models and comparing the experimental results to those predicted by the model. From the ML and RSM models, it was found that processing the AA6061/SiCp composite HPT via four revolutions at 3 GPa produced the highest mechanical properties coupled with significant grain refinement compared to the HC condition. ML analysis revealed that the equivalent strain induced by the number of revolutions was the most effective parameter for grain refinement, whereas the presence of SiCp played the highest role in improving both the hardness values and the compressive strength of the AA6061 matrices.

Optimized conditions for extraction, quantification and detection of bioactive compound from Nettle (Urtica dioica L.) using the deep eutectic solvents, ultra-sonication and liquid chromatography-mass spectrometry (LC-DAD-ESI-MS/MS)

ABSTRACT The bioactive constituents of Nettle (Urtica dioica L.) plants were extracted by green and environmentally friendly methods using several deep eutectic solvents (DESs) followed by liquid chromatography-mass spectrometry separation and identification. To optimize the extraction process, bio-solvents composed of organic acids such as hydrogen bond donors (HBD) and mannitol/menthol/sugars as HBA were selected and coupled with ultrasound-assisted extraction. The maximum bioactive compound contents were TPC 2423 mg GAE/100 g, TFC 134.71 mg CE/100 g, and DPPH 1071.05 mol TE/mL. The optimum extraction parameters to achieve the highest values of phenolic compounds from Nettle (Urtica dioica L.) via UAE-DES were obtained at an extraction temperature 70°C, extraction time of 30 min, and water addition on DES 20%. The ideal experimental conditions were confirmed using the Central Composite Design (CCD) of the Response Surface Methodology (RSM) model to analyze the significance of extraction parameters demonstrating high prediction levels of the bioactive compounds. Liquid chromatography coupled with diode array detection and electrospray ionization tandem mass spectrometry (LC-DAD-ESI-MS/MS) was used to identify the extracts. The results showed that quercetin-3-O-rutinoside, 5-ocaffeoylquinic acid, and caffeoyl malic acid were the three compounds with the highest abundance. For the first time, the extraction of bioactive compounds from nettle using a food-grade DES that contains GRAS components is reported. According to these findings, the best solvent for extracting bioactive substances for food-grade applications is DESCAMAL (citric acid/maltose).

Treatment of carpet and textile industry effluents using Diplosphaera mucosa VSPA: A multiple input optimisation study using artificial neural network-genetic algorithms

The wastewater treatment efficiency of Diplosphaera mucosa VSPA was enhanced by optimising five input parameters and increasing the biomass yield. pH, temperature, light intensity, wastewater percentage (pollutant concentration), and N/P ratio were optimised, and their effects were studied. Two competitive techniques, response surface methodology (RSM) and artificial neural network (ANN), were applied for constructing predictive models using experimental data generated according to central composite design. Both MATLAB and Python were used for constructing ANN models. ANN models predicted the experimental data with high accuracy and less error than RSM models. Generated models were hybridised with a genetic algorithm (GA) to determine the optimised values of input parameters leading to high biomass productivity. ANN-GA hybridisation approach performed in Python presented optimisation results with less error (0.45%), which were 7.8 pH, 28.8 °C temperature, 105.20 μmol m−2 s−1 light intensity, 93.10 wastewater % (COD) and 23.5 N/P ratio.

Effect of graphene oxide as a nanomaterial on the bond behaviour of engineered cementitious composites by applying RSM modelling and optimization

A crucial aspect of the design approach for bar-reinforced concrete buildings is the proper transfer of loads between the concrete and steel reinforcement via bonding. Bar pull-out tests were done to see how graphene oxide (GO) affected the bond strength, bond energy, and bond stress–slip response of deformed reinforcing bars implanted in engineered cementitious composites (ECC). This was done to improve the bond strength of steel/concrete composites. This article examines the bonding behaviour of steel reinforcing bars in a mixture of GO-modified ECC. The findings of pull-out assessments conducted on two distinct diameters (12 and 16 mm) of reinforcement steel bar inserted in ECC are initially provided. The investigation's findings are then utilised to determine the bond-slip interactions that describes the relations between the ECC mixtures and steel reinforcement bars. According to the experiment results, the introduction of GO as a nano-reinforcement in the ECC mixture had a considerable positive effect on the bar-matrix interactions owing to its bridging function. After 28 days, the bond strength of steel bars with widths of 12 and 16 mm was increased by 54.80% and 26.70%, respectively, when 0.05 wt% of GO was added to 1% of PVA fibre. The response surface methodology (RSM) is employed for developing predictive algorithms, which are then utilised in performing multi-variate optimization on bond-slip parameters, including bond strength, bond slip and bond energy. The acquired actual and predicted findings suggest that the created models are adequate for interpreting the bond performance of reinforcement steel bars in the GO-ECC.

Optimized Adsorption of Pb(II) ION from Aqueous Solution Using Sharp Sand: Ann and RSM Modelling

Aim: This study was aimed at using sharp sand as an adsorbent to optimize the removal of Pb ionsfrom simulated water and water obtained from Enyigba River.&#x0D; Study Design: Adsorption experiment was designed using Response Surface Methodology (RSM). Artificial Neural Network (ANN) was also used to predict % Pb(II)removal.&#x0D; Place and Duration of Study: Department Of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, June 2012 to June 2023.&#x0D; Methodology: Sharp sand was modified with salicylic acid, noted as SSM and then characterized. Experiments were carried out by varying four adsorption factors: concentration, time, pH and temperature. Adsorption experiments were also done for equilibrium isotherm, kinetics and thermodynamic studies.&#x0D; Results: The modified sharp sand showed a high percentage of silicon. RSM and ANN predicted the sorption of Pb(II) well with R2 values &gt; 0.9. The optimal values for concentration, time, pH and temperature are 0.0125mg/l, 100sec, 2, 20oC respectively. Langmuir isotherm gave the better fit and adsorption was endothermic with positive ∆H. Adsorption followed second-order kinetics. FTIR analysis showed the presence of functional groups that were responsible for adsorption. Enyigba River water is slightly polluted with lead ions with concentrations higher than WHO standard.&#x0D; Conclusion: The adsorbent was able to remove over 70% of the metal ions from both the simulated and Enyigba River water.

Simultaneous leaching and separating of palladium from spent palladium catalyst by tribromide ionic liquids as non-volatile oxidizing solvents

The recovery of palladium (Pd) from secondary resources has attracted much attention because of its unique environmental effects and economic value. A novel technique for the oxidation, dissolution, and separation of precious metals is presented in this paper. Tribromide ionic liquids (ILs) with low viscosity, high stability, and strong oxidation were synthesized and applied for the one-step leaching and separation of Pd from spent Pd-Al2O3 catalysts. Through the preliminary performance test, [C1C12PYR]Br3 was selected as the optimal leaching agent from three kinds of tribromide ILs, and the reasons for the obvious difference in leaching ability were analyzed from the perspective of quantum chemistry.The optimum leaching conditions were determined as follows: leaching time was 12 h, leaching temperature was 45 °C, and the dosage of [C1C12PYR]Br3 was 0.8 g (the mass of fixed Pd was 20 mg). Under these conditions, the leaching efficiency (η%) can reach 94.82%. By optimizing the response surface methodology (RSM) model, the leaching temperature in the actual system was determined to be 50.28 °C, and the dosage of [C1C12PYR]Br3 was 0.74 g, which achieved the best economic value of the system. Using XPS, HPLC, and EDS, the leaching mechanism of Pd was demonstrated to be a process that combined redox with coordination and complexation. The kinetic study shows that the rate control step of the leaching process was a chemical reaction process. The 20% hydrazine hydrate can achieve efficient (99%) stripping of Pd in the IL phase. As a result of the leaching agent's good selectivity and reusability when used to recover spent Pd-Al2O3 catalyst, [C1C12PYR]Br3 is regarded as a promising precious metal leaching agent.

Response surface methodology, multi-objective optimization of biogas, and methane yield of pretreated Arachis hypogea shells

This study investigated the biogas and methane yield optimization of Arachis hypogea shells with the pretreatment method and the influence of three process parameters using the Response Surface Methodology (RSM) model. Arachis hypogea shells of different particle sizes were combined with 20 mg/L Fe3O4 nanoparticles and digested anaerobically. The RSM model was developed using three input variables to optimize and predict biogas and methane yield. The result showed that particle size reduction and Fe3O4 nanoparticle additives significantly influence the biogas and methane yield. The optimum yields were 8.03 and 6.18 ml/gVSadded, with R2 values of 0.9889 and 0.8736 for biogas and methane.