Response Surface Methodology Technique Research Articles

A tandem approach for precipitant-free highly selective recovery of valuable metals from end-of-life lithium-ion batteries using a green deep eutectic solvent

Recently, recycling lithium-ion batteries (LIBs) waste and the efficient recovery of valuable metals has become crucial for the circular economy. Deep eutectic solvents (DESs) have shown immense potential for metal extraction from end-of-life (EoL) LIBs. However, a complicated subsequent separation process is required due to the non-selectivity of the DES components for metal. Herein, we propose a tandem precipitant-free hydrometallurgical process for the selective recovery of Li and other valuable metals (Ni, Co, Mn) from EoL LIBs using a green DES. The process parameters, viz., reaction temperature (T), time (t), pulp density (S/L), and molar ratio (MR) of ethylene glycol to tartaric acid, were optimized via the statistical optimization technique of response surface methodology. The results revealed maximum extraction efficiencies of 99.2±0.5%, 96.1±1.4%, 95.2±1.2%, and 97.8±1.5% for Li, Ni, Co, and Mn, respectively, under the optimized process conditions of T = 118 °C, t = 17 min, S/L = 42 g/L, and MR = 3:1. The kinetic study showed that interfacial reaction governed the extraction process. A plausible mechanism depicting the role of DES constituents in synergistically facilitating the extraction reaction was elucidated. Moreover, Li and other metals (Ni, Co, and Mn) were selectively separated and recovered with an overall efficiency of ∼99%. The reusability of the recovered metal products and the DES were also assessed for a closed-loop recycling process. At the outset, the proposed process paves the way for a greener alternative for recycling valuable metals from EoL LIBs rather than conventional acid-based leaching processes.

Effect of Al7075 and activated carbon reinforced composite on optimizing WEDM responses

This paper presents wire cut electrical discharge machining (WEDM) response characteristics of Aluminium 7075 (Al7075) reinforced with powdered activated carbon (PAC) composite. In recent days WEDM has become a significant machining process in targeting its benefits of contributing improved material removal rate (MRR) and low surface roughness (SR). This is due the rising need for intricate, accurate, and superior structural components, the WEDM process emerges as a formidable alternative to traditional machine tools. In this work Pulse-on time (Ton), pulse-off time (Toff), discharge current (IA) and servo speed rate (SS) are the variables to be given as input and machining responses such as MRR and SR are studied. From Analysis of Variance (ANOVA) study it is found that discharge current and servo speed is the significant parameters. The optimal desirability condition is obtained with input parameters Ip: 2000 mA; Ton: 8.9 μs; Toff: 25 μs and SS: 150 rpm for the precision machining. The optimum response parameters are found as MRR 10.46 mm3/min and SR 3.32 μm. Results shows that the model designed for the prediction of MRR produces an above 98.27% and the prediction of SR is above 97.17%. The error percentage among the experimental and predicted MRR and SR were estimated. Additionally confirmatory test is performed with optimal results achieved from response surface methodology (RSM) and desirability technique. Metallurgical tests like electron backscatter diffraction analysis (EBSD) and microstructure are conducted to confirm the surface properties and atomic force morphology (AFM) analysis is applied to clarify the structural features of machined composites. The results revealed that the variation of hard deflection is caused by depression of eroded materials on the top layers of machined surface.

Synergistic effect of nano silica and metakaolin on mechanical and microstructural properties of concrete: An approach of response surface methodology

Nano and micro materials are both effective individually in enhancing the mechanical and microstructural properties of concrete. The combination illustrates an iconic shift in material engineering to produce concrete with distinctive characteristics. In this work, the combined effect of Nano Silica (NS) and Metakaolin (MK) on the mechanical and microstructure properties were examined by conducting compressive strength, split tensile strength, and Field Emission Scanning Election Microscope (FE-SEM) with Energy-dispersive X-ray (EDX) analysis, X-ray Diffraction (XRD) spectroscopy, Fourier Transform Infra-Red (FTIR) spectroscopy, respectively. The concrete matrix was investigated by varying the concentration of NS and MK with different Water-Binder Ratio (WBR). Further, the Response Surface Methodology (RSM) technique was used to identify the complex interaction between NS (1–3 %) and MK (5–20 %) at different WBR (0.35, 0.40 and 0.45). The synergistic effect between NS and MK resulted in enhanced compressive and split tensile strengths of the concrete. The optimum mix proportion of the concrete was found to be 2 % NS and 12.5 % MK at a WBR of 0.4. The compressive strength enhancements for the optimum mix proportion were 35.75 %, 34.4 %, 33.1 %, and 32.5 %, while the split tensile strength enhancements were 26.56 %, 25.92 %, 20.95 %, and 17.8 %, respectively on the 7th, 28th, 56th, and 90th day. The RSM technique developed for all the mixes has been statistically validated to have less than 5 % of mean error. Moreover, the microstructural analysis revealed early hydration, formation of additional Calcium Silicate Hydrate (C-S-H) products, and presence of dense microstructure resulting in improved microstructural properties. The incorporation of NS and MK in the concrete changes the concrete’s configuration at the nano and micro levels, thus paving the way for the development of novel and sustainable construction materials.

Managing energy transition alongside environmental protection by making use of AI-led butanol powered SI engine optimization in compliance with SDGs

Enormous consumption of fossil fuel resources has risked energy accessibility in the upcoming years. The price fluctuation and depletion rate of fossil fuels instigate the urgent need for searching their reliable substitute. The current study tries to address these issues by presenting butanol as a replacement for gasoline in SI engines at various speeds and loading conditions. The emission and performance parameters were ascertained for eight distinct butanol-gasoline fuel blends. The oxygenated butanol substantially increases engine efficiency and boosts power with lower fuel consumption. The carbon emissions were also observed to be lower in comparison with gasoline. Furthermore, the Artificial Intelligence (AI) approach was used in predicting engine performance running on the butanol blends. The correlation coefficients for the data training, validation, and testing were found to be 0.99986, 0.99942, and 0.99872, respectively. It was confirmed that the ANN predicted results were in accordance with the established statistical criteria. ANN was paired with Response Surface Methodology (RSM) technique to comprehend the influence of the sole design parameters along with their statistical interactions controlling the responses. Similarly, the R2 value of responses in case of RSM were close to unity and mean relative errors (MRE) were confined under specified range. A comparative study between ANN and RSM models unveiled that the ANN model should be preferred. Therefore, a joint utilization of the RSM and ANN can be more effective for reliable statistical interactions and predictions.

A metaheuristic particle swarm optimization for enhancing energetic and exergetic performances of hydrogen energy production from plastic waste gasification

In recent decades, there has been a surge in demand for the development of renewable energies, leading to extensive research efforts focused on the gasification process. Consequently, a wide range of studies has been conducted to explore different feedstocks for gasification, with particular emphasis on plastic waste. Although a wide range of studies conducted various optimization methods for the gasification process, there was no specific study to employ metaheuristic methods in the field of plastic waste gasification. This study aimed to perform a multi-objective particle swarm optimization (MOPSO) method to solve a multi-objective plastic waste gasification optimization problem for enhancing energetic and exergetic viewpoints. Moreover, exact methods including non-linear optimization and response surface methodology (RSM) were conducted to be compared with the results derived from MOPSO. Pareto-front solutions obtained from MOPSO were ranked using grey relational analysis (GRA) and the optimum responses were 517 kJ/kg, 84 %, and 57 % for lower-heating value, cold gas efficiency, and exergy efficiency, respectively, which were compared to optimum options obtained from multi-objective non-linear optimization and RSM techniques. While the MOPSO model may not have performed as well as other methods in this particular problem, it is important to acknowledge its capabilities. Future research can concentrate on improving the behavior and performance of the MOPSO method when dealing with a larger number of points or when applied to different problem domains.

Investigating the Emissions and Performance of Ethanol and Biodiesel Blends on Al2O3 Thermal Barrier Coated Piston Engine Using Response Surface Methodology Design - Multiparametric Optimization

The Response Surface Methodology (RSM) optimization technique to examine the effect of load, Tomato Methyl Ester (TOME), and Ethanol injection enhanced diesel on engine performance and exhaust gas emissions with normal piston and Al2O3 coated piston. TOME biodiesel (10, 20, and 30%) and Ethanol (10, 20, and 30%) were chosen to increase BTE while minimizing BSFC, NOx, CO, smoke, and HC. The RSM technique was used to operate the engine by load (0-100%). The results revealed that engine load, TOME, and ethanol concentration all exhibited a considerable effect on the response variables. The (ANOVA) results for the established quadratic models specified that each model, furthermore, an ideal was discovered by optimizing an experiment's user-defined historical design. The present research efforts to improve the performance of a diesel engine by using a thermal barrier-coated piston that runs on biodiesel blends. Al2O3 is the chosen material for TBC due to its excellent thermal insulation properties. B20E30 has a 4% higher brake thermal efficiency than diesel, but B10E20 and B30E20 mixes have a 3.6% and 12% reduction in (BSFC). The B20 blends lowered CO and HC emissions by 6% to 8% respectively. In terms of performance and emissions, biodiesel blends performed similarly to pure diesel, and the combination was optimized through a design of experiment tool.

Optimizing hemicelluloses pre-extraction in eucalyptus kraft pulping: A pathway towards enhancing pulp mill biorefineries

A critical pathway towards enhancing pulp mill biorefineries is to integrate the extraction and utilization of hemicelluloses into the pulping processes. Hence, an industrial pre-extraction strategy for hemicelluloses targeting eucalyptus kraft pulping process was developed. Alkaline solution or pulping white liquor was used to pre-extract hemicelluloses before the actual pulping process. The Response Surface Methodology (RSM) technique was applied to investigate the most suitable conditions to maximize the yield of these hemicelluloses while simultaneously minimizing the damage to pulp yields and properties. Temperature (°C) [105, 155], alkali concentration (%) [3, 8], sulfidity (%) [20, 30] and retention time (min) [19, 221] were combined to evaluate their effects on hemicellulose yields and chemical structures. The optimal pre-extraction conditions identified in this work (5.75% NaOH concentration, 25% sulfidity at 135 °C for 60 min) successfully allowed recovering 4.8% of hemicelluloses (based on the wood dry weight) and limited damages to pulp yields and properties. The cellulose content in pulp can even be increased by about 10%. Hemicellulose emulsification properties were also evaluated, which were comparable to synthetic emulsifiers. This study provides an industrial pathway to effectively separate and utilize wood hemicelluloses from the pulping process, which has the potential to improve the economy and material utilization of pulp and paper mills.

Optimization of biodiesel production parameters for hybrid oil using RSM and ANN technique and its effect on engine performance, combustion, and emission characteristics

The increased need for renewable energy sources and growing concerns about environmental pollution has driven the need for alternative fuels (particularly biodiesel) in the transportation industry. The most used technique for producing biodiesel is transesterification, which involves the oil reacting with alcohol in the presence of a catalyst. This study looks at the usage of artificial neural network (ANN) and response surface methodology (RSM) to optimize biodiesel synthesis factors for a hybrid oil feedstock (30% cottonseed and 70% spirulina microalgae oil). The parameters that were optimized were the methanol/oil ratio, reaction time, and catalyst concentration. Cottonseed oil was combined with spirulina microalgae oil to minimize its higher free fatty acid content to less than 1% and prevent soap formation during the transesterification process. The optimal biodiesel production was 94.37%, with 106.4 minutes of reaction time, 1.612% catalyst loading, and a methanol/oil ratio of 20:1. The optimized findings, when confirmed with experiments, gave a biodiesel yield of 91.03% with an error of 3.6%. To provide vital insights into the potential environmental benefits of using this alternative fuel, the study further investigates how the optimized biodiesel blend influences engine performance and emission characteristics. Testing was done at 1500 RPM and 17.5 CR for maximum loading conditions, diesel, HBD20, and HBD fuel produced 0.2746, 0.2805, and 0.288 kg/kWh of specific fuel consumption (SFC), respectively. The CO2 emissions of HBD and HBD20 were 2.49% and 8.30% lower, respectively, than pure diesel. NOx emissions increased by 14% and 18.3%. The highest cylinder pressure was 66.87 at 363 °C.

Removal of Ni(II) ions from wastewater by ion exchange resin: Process optimization using response surface methodology and ensemble machine learning techniques

This study investigates the removal of nickel ions from aqueous solutions using Amberlite IR120 Na resin. Response surface methodology (RSM) and Ensemble machine learning models were employed to predict and evaluate the adsorption capacity of the resin. The effects of the initial concentration of nickel ions (10–90 mg/L), resin dose (0.1–0.7 g/L), initial pH (3−9), and temperature (10–40 ºC) were analyzed as independent variables. In order to assess the efficiency of Ensemble models, five methods, including random forest, extra tree, AdaBoost, gradient boosting, and XGboost models, were employed. The results demonstrated that the XGboost model predicts the adsorption capacity of the resin for Ni(II) with very high accuracy in the training phase (R2 = 1) and the test phase (R2 =0.96). The maximum adsorption capacity of the resin in the optimization step was achieved for CCD (initial Ni(II) concentration of 45.69 mg/L, resin dose of 0.1 g/L, pH of 6.64, and temperature of 39.68 ºC) and Bayesian optimization (initial Ni(II) concentration of 54.78 mg/L, resin dose of 0.11 g/L, pH of 4.72, and temperature of 39.02 ºC) techniques by 142.67 mg/g and 139.60 mg/g, respectively. Moreover, the Bayesian optimization method resulted in a lower error (0.63 mg/g) than the CCD model (1.53 mg/g). Based on statistical measures, it can be concluded that XGBoost outperforms CCD.

A combined approach-based techno-economic-environmental multi-optimization of a hydrogen generation system through waste biomass air-steam gasification

In this study, a biomass air-steam gasification system for hydrogen production was simulated in Aspen Plus and validated using experimental studies. Sewage sludge was considered as the biomass feed. A combined approach including the combination of the NSGA-II algorithm, the response surface methodology (RSM) and TOPSIS decision-making method, was used to multi-optimize this hydrogen production system. To optimize the performance of the system, first, mathematical equations between equivalence ratio (ER), steam to biomass ratio (SBR), and gasifier temperature as the decision variables and the mole flow of H2 and CO2 and the levelized cost of hydrogen (LCOH) as the objective functions were obtained by the RSM technique. To maximize the mole flow of H2 and minimize CO2 emission and LCOH, a multi-objective optimization using NSGA-II was conducted. The ER, SBR and gasifier temperature were analyzed in the range of 0.1–0.4, 0.2 to 2 and 600 °C–1000 °C, respectively. By employing the TOPSIS decision-making method, several optimum points were obtained by giving each of the decision variables a specific weight. The lowest LCOH was found to be 1.5 €/kg H2 at the ER of 0.103, SBR of 0.83, and gasification temperature of 956 °C.

Response surface method and finite element-based investigation of uniform/nonuniform hydromagnetic convection of nanofluids inside a quarter-circular enclosure

Under the influence of magnetohydrodynamics (uniform, periodic, and inclined), the current research evaluates the two-dimensional unsteady dynamics of natural convection flow and heat transportation in the quarter-circular domain considering the Brownian motion of nanoparticles. The nonlinear partial differential equations (PDEs) solver finite element method (FEM) of the Galerkin type is used for the numerical simulation. In addition, the central composite design-based response surface methodology (RSM) is applied to analyze the optimization of the outcomes. To assess the models’ significance in learning the optimum thermal transport efficiency of nanofluids, an analysis of variance is performed. The impacts of several physical characteristics such as Rayleigh number, nanoparticles volume fraction, mean Nusselt number, Brownian effects, size, and shape of the nanoparticles, and magnetic effects (uniform, periodic, and inclined) are examined. The heat transfer rate is significantly influenced by the magnetic field’s period and inclined angle. As an illustration, the optimal heat transmission is noted at λ = 0.75 and γ = 0°. When Brownian effects are included, the heat transfer rate increases by 23.44% and it rises by 39.34% as nanoparticle size drops from 100 nm to 10 nm. Moreover, compared to spherical nano-sized particles, blade-shaped nano-sized particles can improve the transportation of heat by 9.56%. Furthermore, the optimization of the independent factors (Ra, Ha, ϕ, and d p) on the response function Nu (average) is demonstrated by the statistical RSM technique, and a novel correlation is proposed based on FEM numerical data. The optimal thermal transport (Nu av = 24.045) is found when Ra = 10, Ha = 0, ϕ = 0.05, and d p = 10 nm. The model’s average Nusselt number’s R 2-value of 0.9927 indicates that the results are effective.

Characterization of wear rate of Al-12 wt%Si alloy based MMC reinforced with ZrO2 particulates

The primary objective of this study is to fabricate an Al-12 wt% Si Alloy/ZrO2 composite using the Stir Casting technique, with a specific focus on assessing its performance, particularly in terms of wear characteristics. This research presents a unique approach by utilizing Al-12 wt% Si Alloy as the matrix material, aiming to develop tailored Al Alloy matrix composites suitable for applications requiring enhanced tribological properties. The composites are systematically manufactured with varying percentages of micro-sized ZrO2 reinforcements, specifically 0.5, 1, and 3 wt%. The incorporation of ZrO2 results in significant improvements in wear resistance, a critical attribute for Al-12 wt% Si Alloy-based composites. These composites find extensive utility across industries such as marine, aerospace, automotive, and the power sector, where they are indispensable for producing vital components like electrical sliding contacts, gears, bearings, bushes, pistons, piston rings, and clutches. Despite the availability of various promising reinforcement materials, researchers persistently explore novel combinations of matrices and reinforcements to tailor properties and enhance cost-effectiveness. ZrO2 has emerged as a notable reinforcement material in metal matrix composites, as evidenced by numerous research endeavours. The composites fabricated with ceramic reinforcement’s exhibit enhanced tribological characteristics. The study observes that the wear rate decreases up to 3 wt% of reinforcements, beyond which it increases due to reinforcement agglomeration. The optimal wear-resistant combination is found at 3 wt% of ZrO2, attributed to robust micro-coring and interstitial metal-oxygen bonding facilitated by the Si content in the Al-12%Si matrix. The results are further optimized using Response Surface Methodology (RSM) techniques and validated using the ANOVA table to elucidate the behaviour of the composites under different operational conditions. The hardness results further ascertain the decrease in the wear rate due to the inclusion of ZrO2 reinforcements owing to micro coring and strengthening.

Enhancing the efficiency of metals extraction from waste lithium-ion batteries through glycine leaching using response surface methodology for economic feasibility

Recycling of lithium-ion batteries (LiBs) has captured remarkable interest due to its potential to mitigate pollution and conserve natural resources and energy. This study is establishing an environment friendly method for metals leaching from LiBs utilizing glycine (NH2CH2COOH) as a leaching agent. Li dissolution is being investigated as a function of time, pulp density, glycine concentration, and temperature. The results are evincing the leaching of Li (around 94%) using 2 M glycine at 75 °C temperature, 50 g/L pulp density and 120 min mixing time. Response surface methodology (RSM) techniques are being employed to enhance the standard hydrometallurgical process. The kinetics of Li leaching are well fitted with the “chemical reaction-controlled process” equation of the shrinking core model (1 - (1 - x) 1/3 = kt) and the energy of activation is found to be 23.69 kJ/mol. Moreover, the economic evaluation of the leaching process reveals that at optimum conditions, the average profit response for treated metals is 2394.56 USD per tonne of black powder. Therefore, the use of glycine as a green extractant not only augmenting the leaching efficiency but also minimizing the usage of inorganic acids, proffering a better alternative for green recovery of waste LiBs in future.

Revealing the interface chemistry of polyaniline grafted biomass via statistical modeling of multi-component dye systems: optimization, kinetics, thermodynamics, and adsorption mechanism.

The growing need to examine the adsorption capabilities of innovative materials in real-world water samples has encouraged a shift from single to multicomponent adsorption systems. In this study, a novel composite, PANI-g-SM was synthesized by covalently grafting a lignocellulosic biomass, Saccharum munja (SM) with polyaniline (PANI). The as-synthesized composite was investigated for the simultaneous adsorption of cationic (Methylene Blue (MB); Crystal Violet (CV)) and anionic dyes (Reactive Red 35 (RR); Fast Green FCF (FG)) from four single components and two binary systems, MB + RR and CV + FG. Further, the effect and interaction of pH (2-11), dosage (0.01-0.04g/10mL), and initial concentration (0.0313 to 0.1563mmol/L) on the elimination of dyes by PANI-g-SM were studied through a novel design of Box-Behnken of Response Surface Methodology (RSM) technique which was found to be highly useful for revealing the chemistry of interfaces in multi-component systems. The extended Langmuir model for the binary system indicated the presence of synergism, as result the maximum monolayer adsorption capacity increased by 44.44%, 645.83%, 67.88%, and 441.07% for MB, RR, CV, and FG dye, respectively. Further, the adsorption process mainly followed a pseudo-second-order kinetic model, and the thermodynamic studies revealed the exothermic nature of adsorption for RR and FG dye while endothermic for MB and CV dye, respectively with varying from - 1.68 to - 6.12kJ/mol indicating the spontaneity of the process. Importantly, the efficacy of the composite was evaluated for the treatment of textile industry effluent highlighting its potential as an adsorbent for wastewater treatment.

Study on Adhesive and Microstructure Properties of Rice Husk Ash-Based Geopolymer Hybrid Alkyd Coating

ABSTRACT Ensuring strong adhesion, especially under varying environmental conditions, is a significant challenge in the field of coating technology. This research aims to enhance alkyd paint adhesive strength by incorporating rice husk ash-based geopolymer (GP-RHA) as a filler. Response Surface Methodology (RSM) technique was employed to systematically study the impact of GP-RHA levels on the adhesive strength of alkyd coatings under dry and wet conditions. The morphology of the coatings was analyzed using SEM-EDX. In dry pull-off test, the incorporation of GP-RHA in alkyd coatings led to an improvement in adhesive strength with the highest recorded adhesive strength of 3.9 MPa at a ratio of 41.67% wt. GP-RHA and 58.33% wt. alkyd paint. However, this higher proportion of GP-RHA resulted in the formation of a larger pore sizes hybrid coating, further causing water infiltration into the coating’s pore structure in wet pull-off test. Outcomes of this research showed the promising potential of GP-RHA filler in alkyd paint formulations to enhance adhesion. These findings offer valuable insights into the development of coatings utilizing agro-waste as a filler to improve its adhesive properties.

An integrated evaluation of graphene-based concrete mixture with copper slag and quarry dust using response surface methodology

The study aims to suggest an optimization technique to determine the proportion of Graphene Nanoplatelets, copper slag, and quarry dust for concrete manufacturing. The individual and combined effects of the aforementioned materials on slump, compressive, splitting tensile and flexural strength are studied through Response Surface Methodology (RSM). The central composite design (CCD) is applied in the RSM technique to optimize the mix among the 28 concrete groupings. The optimum performance of the concrete mixes with different materials is determined using variables in the model, which are then verified using experimental results. RSM is a technique with multiple response optimization (MRO) conflicts: the attainment of the slump must be within the target, whereas other responses require maximization. The MRO problems are addressed by developing a desirability function, which is a statistical method. The desirability of each model attained greater than 95%. The accuracy of the RSM model is evaluated using analysis of variance (ANOVA), absolute relative deviation, and desirability function. Hence, it is established that the RSM model is reliable and effective for analyzing and optimizing concrete mixes based on their fresh and mechanical properties. The incorporation of 0.25% of Graphene Nanoplatelets into concrete mixtures manufactured with 60% of copper slag and 40% of quarry dust enhanced the mechanical properties of concrete.

Preparation and utilization of Zn-La oxide nanocatalyst as a binary composite for photocatalytic degradation of methylene blue dye: Optimization through RSM-BBD

In this research, the photocatalytic degradation of methylene blue (MB) dye was explored by employing Zn-La (zinc-lanthanum) oxide nanocatalysts under UV irradiation. Preparation of Zn-La oxide nanocatalysts was executed utilizing the co-precipitation technique, and the surface and morphology of the fabricated nanocatalyst were examined via X-Ray diffraction analysis (XRD), Fourier-transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET), Barrett-Joyner-Halenda (BJH), Field emission scanning electron microscopy (FESEM), Transmission electron microscopy (TEM), and energy dispersive X-ray (EDX) methods. BET-BJH analysis demonstrated that the specific surface area of the Zn-La oxide nanocatalyst was 20.43 m2/g. Moreover, the response surface methodology (RSM) procedure was utilized to influence the operational factors i.e., catalyst amount, pH, H2O2 dose, and UV power. The quadratic model proposed via the RSM technique with a high R2 value of 0.89, demonstrates its reliability. Besides, the maximum percentage of MB dye removal reached 75.18% under optimal conditions, which include a catalyst amount of 12.05 mg, pH of 9, H2O2 dose of 0.44 mL, UV power of 23 W, and a reaction time of 45 min. Further, under the mentioned conditions, after 120 min, the MB degradation efficiency reached 98.8%. The kinetic study exhibited that the reaction rate constant was 0.0363 min−1. According to the outcomes, Zn-La oxides have a higher ability to remove MB. After 5 consecutive reuse rounds, the reduction in the yield of MB removal using Zn-La oxide nanocatalysts was 5.4%. This study demonstrates that the Zn-La oxide nanocatalyst can be promisingly utilized for the degradation of MB dye suspension solution.

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio.

Optimize the process for encapsulated spray dried Lemon juice powder to enhance the Vitamin C

In this study, the parameters were optimized for spray-dried method of fruit juice powder that is encapsulated to increase the bioavailability of vitamin C content. Fruits like lemon were selected to produce the fruit juice powder which provides a high content of vitamin C. Maltodextrin is a carrier material that is used in spray drying techniques at different levels of concentration like 15%, 20% and 25% to coat the fruit juices, reduce stickiness, and produce large quantities of fruit juice powders. In this method, the inlet temperatures ranged from 160 ºC to 190 ºC while outlet temperatures between 90 ºC to 110 ºC. Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used to standardize the method for encapsulating the spray dried fruits juice powder. The physicochemical properties of spray-dried fruit juice powders were analyzed to enhance the nutritional content as well as shelf life. The yield of spray-dried lemon juice powder ranged from 121.56 g to 221.72 g, depending on the maltodextrin concentration. Spray-dried lemon juice powder contains 4.7 % to 5.7 % of moisture, 0.2141 aw to 0.2795 aw of water activity, 14.28% to 17.64 % of hygroscopicity, 91.23 g/100g to 93.26 g/100g of carbohydrates, 138.32 mg/100g to 192.43 mg/100g of vitamin C, 96.54 (%RSA) to 128.62(%RSA) of antioxidant activity respectively. The spray dried fruits juice powders were standardized by using RSM and ANN.

Carboxylic acid functionalized para-xylene based hypercrosslinked polymer as a novel and high performance adsorbent for heavy metal removal

In this work, hypercrosslinked polymeric adsorbent (HCP) was synthesized from para-Xylene precursor through Friedel-Crafts reaction. Additionally, heavy metal adsorption experiments from a multi component system including lead, cadmium, nickel, and copper ions were conducted. To enhance lead uptake selectivity over other metallic ions, the resulting HCP sample was functionalized through carboxylic acid incorporation into the HCP adsorbent skeleton. The adsorption process modeling was carried out by using Central Composite Design- Response Surface Methodology technique. In addition, the adsorption capability dependency of the resulting samples to the operational condition including temperature from 25 °C to 65 °C, solution pH value from 4 to 10, and solution initial concentration from 20 mg/l to 100 mg/l was investigated. Additionally, process optimization was applied aim to maximize the lead uptake selectivity, the optimal condition including a temperature of 31.8 °C, pH of 5.0, and initial concentration of 82.50 mg/l were obtained for HCP sample, also the optimal condition namely a temperature of 30 °C, pH of 5.5, and initial concentration of 100 mg/l were obtained for modified HCP sample. As a result of isotherm modeling, the Hill model was obtained as the best model, additionally, thermodynamically investigation of the process resulted in spontaneously and physically adsorption of the metallic ions by HCP samples, with respect to Gibbs free energy (ΔG0 < 0) and adsorption process enthalpy (ΔH0 < 20 kJ/mol). Finally, adsorbents regeneration were evaluated and minor losses in the adsorption capacity of samples were yielded after ten cycles.