Response Surface Methodology Model Research Articles

Effective Treatment Methodology for Environmental Safeguard Catalytic Degradation of Fluconazole by Permanganate Ions in Different Acidic Environments: Kinetics, Mechanistics, RSM, and DFT Modeling.

In this paper, the degradation of fluconazole drug (Flz) was explored kinetically utilizing permanganate ion [MnO4-] as an oxidant in different acidic environments, namely sulfuric and perchloric acids at various temperatures. Stoichiometry of the reactions between Flz and [MnO4-] in both acidic environments was attained to be 1.2 ± 0.07 mol. The kinetics of the degradation reactions in both cases were the same, being unit order regarding [MnO4-], fewer than unit orders in [Flz], and fractional second orders in acid concentrations. The rate of oxidative degradation of fluconazole in H2SO4 was higher than that in HClO4 at the same investigational circumstances. The addition of small amounts of Mg2+ and Zn2+ enhanced the degradation rates. The activation quantities were evaluated and debated. The gained oxidation products were characterized using spot tests. A mechanistic approach for the fluconazole degradation was suggested. Finally, the rate law expressions were derived which were agreed with the acquired outcomes. The rates of degradation for various [Flz] were mathematically modeled using the response surface methodology (RSM). The RSM model's conclusions and the experimental findings are in agreement. The oxidative degradation mechanism of Flz using density functional theory (DFT) was performed. The fluconazole drug degrades in acidic settings, protecting both the environment and human health, according to a method that is easy to use, powerful, inexpensive, practical, affordable, and safe.

Modeling and coupling analysis of enhanced coalbed methane recovery by gas injection

AbstractEnhanced coalbed methane (ECBM) recovery by gas injection is regarded as a feasible method for ECBM recovery. To investigate the mechanism of CH4 displacement by N2 injection, a series of physical experiments were conducted in the laboratory under different N2 injection pressures. The experimental results showed a continuous increase in the N2 volume fraction and a decrease in the CH4 volume fraction with the N2 injection pressure. The CH4 displacement efficiency increases rapidly in the initial stage and then gradually stabilizes. Given the effect of matrix shrinkage and effective stress on coal permeability, a dynamic multi‐field coupling model of ECBM recovery by N2 injection (N2‐ECBM) was proposed in this work. Then, numerical simulations were implemented by the coupling model to analyze the key factors affecting coal permeability, N2 injection pressure and CH4 pressure during the N2–ECBM process. Furthermore, the Response Surface Methodology (RSM) was used to investigate the interactions of multiple factors, the results reveal that an increase in one factor would weaken the influence of another factor. In addition, a RSM regression model was obtained and verified by the experimental data, with a well‐fitting outcomes. Therefore, the RSM model can be used in practical engineering applications to optimize specific extraction parameters for ECBM recovery at minimum cost in a limited extraction period.

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.

Parametric and sensitive analysis of Pd-Ag membrane reactor performance in biogas reforming to generate decarbonized hydrogen by Computational Fluid Dynamic-Response Surface Methodology

The efficient production of hydrogen from renewable sources is pivotal for the development of sustainable energy systems. Biogas steam reforming (BSR) conducted in palladium-based membrane reactors (MRs) offers a pathway for high-purity H2 generation at lower temperatures compared to traditional processes. In this study, a computational fluid dynamics (CFD) model was developed to simulate the BSR process within a Pd-Ag MR. The CFD model has integrated comprehensive kinetics, mass transport, and hydrodynamics, and its accuracy was confirmed through validation against experimental data, demonstrating substantial agreement. As a novel approach, to optimize the BSR reaction conditions to get the best MR performance, the CFD model was coupled with response surface methodology (RSM), which has been employed to enable the identification of optimal values for key parameters such as reaction temperature, pressure, gas hour space velocity, feed molar ratio, and sweep gas ratio, where the temperature was found to have the most significant impact on the MR’s performance within the examined intervals of input parameters. The objective of RSM method applied to CFD modeling was to maximize with high accuracy CH4 conversion, H2 recovery, and reduce CO2 emissions. RSM models effectively established correlations between input parameters and responses. The determined optimal conditions for the BSR process were a temperature of 683 K, a reaction pressure of 6 bar, a GHSV of 3000 h−1, a H2O/CH4 ratio of 1.34, and a sweep ratio of 9. Under these conditions, the system achieved remarkable results with 100 % CH4 conversion, 43 % H2 recovery, and an 81 % reduction in CO2 emissions. This study underscores the effectiveness of integrating CFD with RSM for the precise modeling and optimization of MRs in BSR reactions. The synergy between these approaches provides a robust framework for advancing the efficiency and sustainability of hydrogen production processes from renewable sources.

Assessment of metal extraction from e-waste using supported IL membrane with reliable comparison between RSM regression and ANN framework

Recently, efficient techniques to remove indium ions from e-waste have been described due to their critical application. This paper illustrates the recovery of indium ions from an aqueous solution using a liquid membrane. CyphosIL 104 described the excellent potential for the extraction of indium ions. Evaluation of the five process parameters, such as indium concentration (10–100 mg/L), carrier concentration (0.05–0.2 mol/L), feed phase acidity (0.01–3 mol/L), chloride ion concentration (0.5–4 mol/L) and the stripping agent concentration (0.1–5 mol/L) were conducted. The interactive impacts of the various parameters on the extraction efficiency were investigated. The response surface methodology (RSM) and artificial neural network (ANN) were employed to model and compare the FS-SLM process results. RSM model with a quadratic equation (R2 = 0.9589) was the most suitable model for describing the efficiency. ANN model with six neurons showed a prediction of extraction efficiency with R2 = 0.9860. The best-optimized data were: 73.92 mg/L, 0.157 mol/L, 1.386 mol/L, 2.99 mol/L, and 3.06 mol/L for indium concentration, carrier concentration, feed phase acidity, chloride ion concentration, and stripping agent concentration. The results achieved by RSM and ANN led to an experimentally determined extraction efficiency of 93.91%, and 94.85%, respectively. It was close to the experimental data in the optimization condition (95.77%). Also, the evaluation shows that the ANN model has a better prediction and fitting ability to reach outcomes than the RSM model.

Experimental evaluation and prediction on drilling of Corchorus olitorius filler-based composite

ABSTRACT Naturally derived fibers/fillers-based polymers have gained much attention in many industrial applications due to the requirement for renewable and biodegradable components. Machining is sometimes needed to simplify the assembly of pieces in a finished product. Here, this article reports how drill machining affects polymer composites made with 5 wt.% jute filler that has undergone alkaline treatment. The experimental tests have been conducted to explore the impact of tool bit diameter, cutting speed and feed on the various generated responses (thrust force, surface roughness, delamination peel-up and delamination push-out). The results reveal that the appropriate responses are achieved when the cutting speed of the tool is at its maximum while the feed and tool diameter are at their minimal amounts. The response surface methodology (RSM) and support vector machine (SVM) algorithms have been devised to anticipate the drilling performance of produced composites, and both strategies have shown high agreement with experimentally acquired findings. However, the ML tool algorithm proven more reliable than RSM models because of the superior correlation of determination coefficients.

Performance assessment and optimization of the PEM water electrolyzer by coupled response surface methodology and finite element modeling

Proton exchange membrane water electrolyzer (PEMWE), integrated with renewable energy sources, is promising in green hydrogen production with its compact system design, high efficiency, and high-purity hydrogen production capacity. Renewable hydrogen production from PEMWE depends significantly on operational factors. This study optimized the process variables affecting the PEM electrolysis cell performance using the response surface methodology (RSM) of the Design-Expert 13 software (trial version) at a fixed temperature of 80 °C. Water flow rate, cell voltage, and bolt torque were defined as the input factors of the model, while hydrogen flow rate and current were considered as the responses of the model. The statistical analysis showed that cell voltage, torque, and cell voltage-torque interaction significantly affected the current. Besides, it has been observed that the water flow rate plays a vital role in the hydrogen flow rate. The optimum conditions for green hydrogen production were a water flow rate of 2.45 mL/min, a cell voltage of 2.2 V, and a bolt torque of 10 Nm. In addition, a good match between the predicted and measured responses of the model was observed. This paper also includes finite element (FE) modeling and simulation of an experimental PEMWE. Based on the FE method, a three-dimensional simulation model of the PEMWE was established to investigate the effects of different bolt torques on the mechanical responses of the cell components. Numerical simulations were performed with a commercial code (ANSYS Mechanical). A pressure-sensitive film was placed instead of the catalyst-coated membrane (CCM) component of the single-cell PEMWE. The experimental results obtained from pressure-sensitive films were compared with the numerical results, and it was observed that the results were in agreement with each other. The maximum equivalent stress on the CCM for a bolt torque of 10 Nm is around 116.3 MPa.

Iodine–β-Cyclodextrin: An Effective Corrosion Inhibitor for Carbon Steel in Sulfuric Acid Solution - Experimental Design and Investigating Thermodynamic Parameters

Widely used across industries, carbon steel is vulnerable to corrosion in aggressive environments, especially acidic ones. Thus, effective methods to mitigate metal corrosion from acids are crucial. Inhibitors are extensively used to prevent corrosion in industries, with the potential for improved protective performance. The design of experiments was employed to determine the optimal conditions for enhancing the inhibitor efficiency of Iodine–β-Cyclodextrin (Iodine/β-CD) in a sulfuric acid solution at temperatures ranging from 20°C to 50°C. The relationship between the factors and responses was established using response surface methodology (RSM), employing regression statistical analysis and probabilistic analysis. A single response was recorded: inhibitor efficiency was determined by measuring weight loss before and after immersion in the inhibitor solution. Thermodynamic parameters were also computed to determine adsorption and activation processes. The statistical analysis revealed that the quadratic models for inhibition efficiencies (IE) were highly significant with a coefficient of multiple regressions R2= 0.997. Further validation of the model indicated a good fit (R2 Adj= 0.994), and the experimentally observed values aligned well with predicted ones, demonstrating a highly significant model with Q2= 0.978. The theoretical efficiency predicted by the RSM model was 88.41%, whereas the efficiency observed during the experimental test procedure with the best-evaluated variables was 82.45%. In conclusion, this paper aims to identify the optimal conditions for employing Iodine–β-Cyclodextrin as a new corrosion inhibitor for carbon steel, utilizing experimental design methods. The results indicate that iodine/β-CD exhibits remarkable corrosion inhibitory properties for carbon steel under specific conditions.

Optimizing nanoparticle attributes for enhanced anti-wear performance in nano-lubricants

AbstractThis study delves into optimizing nanoparticle attributes to enhance the anti-wear performance of nano-lubricants, specifically exploring the influence of nanoparticle material hardness and concentration. Investigating the impact of contamination-induced abrasive wear in lubricants and the subsequent enhancement of anti-wear properties through nanoparticle integration into base oil, the research focuses on, CaCO3, TiO2, and Al2O3 materials representing varied hardness levels. Using ASTM D4172 standards, the study examines the wear resistance of base oil infused with these nanoparticles. Employing a response surface methodology model based on experimental data, the criticality of the interaction between nanoparticle material hardness and concentration in determining wear effects is revealed. Analysis through atomic force microscopy and energy dispersive spectrometry aids in comprehending alterations in wear mechanisms. The research highlights the nuanced relationship between nanoparticle material hardness and concentration in shaping wear behavior within lubricants. Softer materials, like CaCO3, demand higher concentrations for comparable wear reduction as observed with lower concentrations of harder materials, such as Al2O3. Conversely, higher concentrations of harder materials can exacerbate wear, as confirmed by EDS analysis and surface topography studies. This study underscores the importance of nanoparticle material hardness and concentration interaction in determining the efficacy of nanoparticles as anti-wear agents in lubricants. It emphasizes the need to optimize both factors for enhanced anti-wear properties in nanoparticle-based nano-lubricants, offering insights crucial for their application in practical scenarios.

WITHDRAWN: Effects of hydraulic retention time and cultivation on nutrients removal and biomass production in wastewater by membrane photobioreactor: modeling and optimization by machine learning and response surface methodology

Discharge of excess nutrients in wastewater can potentially cause eutrophication and poor water quality in the aquatic environment. A new approach of nutrients removal in wastewater is by utilizing microalgae which grow by absorbing CO2 from air. Furthermore, the use of membrane photo-bioreactor (MPBR) that combines membranes and photo-bioreactor has emerged as a novel wastewater treatment method. This research sought to model, forecast, and optimize the behavior of dry biomass, dissolved inorganic nitrogen (DIN), and dissolved inorganic phosphorus (DIP) in MPBR by response surface methodology (RSM) and artificial neural network (ANN) algorithms, which saved time and resources of experimental work. The independent variables that have been used for modeling were hydraulic retention time (HRT) and cultivation. For this purpose, the dry biomass of algal production, DIN and DIP behavior were modeled by RSM and ANN algorithms, to identify the optimum mode of processing. RSM modeling has shown good agreement with experimental data. According to RSM optimization, the optimum mode for DIN and DIP occurred at 1.15 days of HRT and 1.92 days of cultivation. The ANN showed better performance than the RSM model, with the margin of deviation being less than 10%. Furthermore, the ANN algorithm showed higher accuracy than RSM method in predicting the dry biomass, DIN and DIP behavior in MPBR.

Integrating response surface methodology and machine learning for analyzing the unconventional machining properties of hybrid fiber‐reinforced composites

AbstractThe aim of this investigation was to delve into the impact of abrasive water jet machining (AWJM) process variables on the surface roughness (Ra) and kerf angle (Ka) of hybrid fiber‐reinforced polyester composites. Utilizing both response surface methodology (RSM) and artificial neural network (ANN) prediction models, the study sought to optimize the input parameters for abrasive water jet machining, specifically in the context of paddy straw and PALF‐reinforced polyester hybrid composites. The process parameters targeted for optimization included the abrasive flow rate, traverse rate, and standoff distance during AWJM. The investigation identified an optimal combination of AWJM parameters that effectively meets the practical requirements for machining hybrid fiber‐reinforced polyester composites. According to the RSM, the suggested optimal values for the process parameters are an abrasive flow rate set at 300 g/min, traverse speed at 110 mm/min, and standoff distance at 1 mm. The ANN exhibited robust predictive capabilities, achieving high R2 scores of 0.932 and 0.962 for surface roughness and kerf angle, respectively. To enhance the performance of abrasive water jet machining and minimize surface roughness and kerf angle, the researchers conducted an optimization of the process parameters. Subsequently, confirmation experiments were executed to validate the predictive model and fine‐tune the set of process parameters for practical application.Highlights Investigated AWJM impact on Ra value and kerf angle of hybrid composites. Used RSM and ANN models for parameter optimization in biocomposite. Optimal AWJM parameters: AFR (300 g/min), TS (110 mm/min), and SOD (1 mm). ANN showed strong predictions: R2 scores 0.932 (Ra) and 0.962 (Ka). Confirmation experiments validated the predictive model for applications.

Chemometrics Assisted Formulation of Glimepiride Nanosuspension for Solubility Enhancement in Diabetic Therapy—A Systematic Approach

AbstractGlimepiride (GLM), a third‐generation sulfonylurea oral hypoglycemic medication, is used to treat type II diabetes. Admittedly, it has a low bioavailability, a relatively short half‐life (t1/2), and high toxicity. To address the issue, a nanosuspension of poorly soluble GLM is designed using a Quality‐by‐Design (QbD) method, to enhance its solubility. Prospective risk factors are identified and assessed using Critical Material Attribute (CMAs) and Critical Processing Parameter (CPPs) to evaluate the targets by Taguchi orthogonal array (OA) design and to study the effects of formulation and process variables on dependent variables. The amount of Polyvinylpyrrolidone (PVP) (X2), agitation time (X5), and concentration of Poloxamer (X7) are found as significant parameters (p < 0.05), and are further optimized using the Box Behnken Design as response surface methodology model. The optimized GLM nanosuspension 1) has mean particle size (PS), zeta potential, viscosity (VS), and percentage cumulative drug release (CDR%) of 258.17 nm, −25.2 mV, 1.087cPs, and 98.52%, respectively. The pattern of drug release is fitted to Non‐Fickian kinetics. The study results in an economic and efficient nano formulation with enhanced solubility.

Degradation of direct red 23 dye in a plug flow reactor with adjustable angle baffles using nano-Fe3O4: modelling and optimisation

ABSTRACT Photocatalytic degradation is a key technique in wastewater treatment, particularly for toxic dye removal, yet challenges related to poor hydrodynamics and mass transfer limitations persist. This study addresses these challenges by innovatively employing an adjustable-angle baffle in a plug flow reactor (PFR) to enhance dye removal efficiency. A lab-scale PFR with an adjustable baffle was utilised to assess the impact of various factors, including baffle angle, catalyst concentration, hydraulic retention time (HRT), pH, and initial dye concentration, on the removal of direct red 23 dye. The experimental design employed a central composite design (CCD), with subsequent data analysis using response surface methodology (RSM) and artificial neural network (ANN) models. The findings demonstrate that the adjustable baffle significantly impacts dye removal, achieving maximum efficiency at an optimal angle of 77.5 degrees. The ANN model outperformed the RSM model, with a higher determination coefficient (R 2) of 0.994 compared to 0.928. Furthermore, RSM and genetic algorithms yielded closely aligned optimal conditions, validating their accuracy. The optimised conditions achieved a dye removal efficiency of 89.47%. Significantly, the study also identified degradation as the dominant mechanism over adsorption and highlighted the impressive stability of nano-Fe3O4 during the recycling process. Mineralisation analysis revealed the presence of lightweight organic residual molecules post-treatment. These outcomes demonstrate the effectiveness of adjustable baffles in PFRs, marking a significant advancement in wastewater treatment technologies and underscoring the critical role of baffle orientation and catalyst concentration in optimising dye removal processes.

Mechanical and environmental evaluation of PET plastic-graphene nano platelets concrete mixes for sustainable construction

In recent times, there has been a substantial increase in the annual use of plastics, resulting in a major upward trend. As a result, recycling plastic waste has become a major global issue. The present research aims to explore the feasibility of utilizing Polyethylene terephthalate (PET) as a possible substitute for coarse aggregate in concrete. In addition, the integration of Graphene Nano-platelets (GNPs) into the concrete mixture was conducted using different quantities, aiming to improve its structural integrity. The study employed an experimental research design to carry out its examination. PET was included into concrete at varied ratios, namely 0 %, 5 %, 15 %, 20 %, and 25 %, serving as an additional ingredient alongside coarse aggregate. Additionally, GNPs were introduced into the mixture at various weight percentages, namely 0 %, 0.03 %, 0.05 %, 0.08 %, and 0.1 %. A series of mechanical tests were performed to evaluate various properties of the concrete, including compressive strength (CS), split tensile strength (STS), flexural strength (FS), modulus of elasticity (MoE), ultrasonic pulse velocity (UPV). In addition, an assessment of the concrete's environmental impact was conducted by analysing the carbon content and evaluating its eco-efficiency (ESE). The research study revealed that the incorporation of 5 % PET as a replacement for coarse aggregate, together with the inclusion of 0.1 % GNPs, resulted in the optimal enhancement of CS, STS, FS, MOE, UPV by 9 %, 12.21 %, 4.40 %, 4.40 % and 0.070 % respectively. The Response Surface Methodology (RSM) models were developed, and mathematical equations were generated in order to predict the expected results. The optimization process for all the models was carried out using a multi-objective optimization technique, followed by a subsequent validation process.

Green isolation of cellulosic materials from recycled pulp and paper sludge: a Box-Behnken design optimization

Cellulose was isolated from recycled pulp and paper sludge and used to synthesize cellulose nanocrystals. Response surface methodology and Box-Behnken design model were used to predict, improve, and optimize the cellulose isolation process. The optimal conditions were a reaction temperature of 87.5 °C, 180 min with 4% sodium hydroxide. SEM and TEM results revealed that the isolated cellulose had long rod-like structures of different dimensions than CNCs with short rod-like structures. The crystallinity index from XRD significantly increased from 41.33%, 63.7%, and 75.6% for Kimberly mill pulp sludge (KMRPPS), chemically purified cellulose and cellulose nanocrystals, respectively. The TGA/DTG analysis showed that the isolated cellulosic materials possessed higher thermal stability. FTIR analysis suggested that the chemical structures of cellulose and CNCs were modified by chemical treatment. The cellulose surface was highly hydrophilic compared to the CNCs based on the high water holding capacity of 65.31 ± 0.98% and 83.14 ± 1.22%, respectively. The synthesized cellulosic materials portrayed excellent properties for high-end industrial applications like biomedical engineering, advanced materials, nanotechnology, sustainable packaging, personal care products, environmental remediation, additive manufacturing, etc.

Parametric optimization to establish eco-friendly nanofluid minimum quantity lubrication (NMQL) practice for turning superalloy Inconel 718

Purpose The purpose of this paper, an experimental study, is to investigate the optimal machining parameters for turning of nickel-based superalloy Inconel 718 under eco-friendly nanofluid minimum quantity lubrication (NMQL) environment to minimize cutting tool flank wear (Vb) and machined surface roughness (Ra). Design/methodology/approach The central composite rotatable design approach under response surface methodology (RSM) is adopted to prepare a design of experiments plan for conducting turning experiments. Findings The optimum value of input turning parameters: cutting speed (A), feed rate (B) and depth of cut (C) is found as 79.88 m/min, 0.1 mm/rev and 0.2 mm, respectively, with optimal output response parameters: Vb = 138.633 µm and Ra = 0.462 µm at the desirability level of 0.766. Feed rate: B and cutting speed: A2 are the leading model variables affecting Vb, with a percentage contribution rate of 12.06% and 43.69%, respectively, while cutting speed: A and feed rate: B are the significant factors for Ra, having a percentage contribution of 38.25% and 18.03%, respectively. Results of validation experiments confirm that the error between RSM predicted and experimental observed values for Vb and Ra is 3.28% and 3.75%, respectively, which is less than 5%, thus validating that the formed RSM models have a high degree of conformity with the obtained experimental results. Practical implications The outcomes of this research can be used as a reference machining database for various metal cutting industries to establish eco-friendly NMQL practices during the turning of superalloy Inconel 718 to enhance cutting tool performance and machined surface integrity. Originality/value No study has been communicated till now on the turning of Inconel 718 under NMQL conditions using olive oil blended with multi-walled carbon nanotubes-based nanofluid. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0317/

Machine learning approaches for the treatment of textile wastewater using sugarcane bagasse (Saccharum officinarum) biochar.

Most dyes present in wastewater from the textile industry exhibit toxicity and are resistant to biodegradation. Hence, the imperative arises for the environmentally significant elimination of textile dye by utilising agricultural waste. The achievement of this objective can be facilitated through the utilisation of the adsorption mechanism, which entails the passive absorption of pollutants using biochar. In this study, we compare the efficacy of the response surface methodology (RSM), the artificial neural network (ANN), the k-nearest neighbour (kNN), and adaptive neuro-fuzzy inference system (ANFIS) in removing crystal violet (CV) from wastewater. The characterisation of biochar is carried out by scanning electron microscope (SEM) and Fourier transform infrared (FTIR). The impacts of the solution pH, adsorbent dosage, initial dye concentration, and temperature were investigated using a variety of models (RSM, ANN, kNN, and ANFIS). The statistical analysis of errors was conducted, resulting in a maximum removal effectiveness of 97.46% under optimised settings. These conditions included an adsorbent dose of 0.4mg, a pH of 5, a CV concentration of 40.1mg/L, and a temperature of 20°C. The ANN, RSM, kNN, and ANFIS models all achieved R2 0.9685, 0.9618, 0.9421, and 0.8823, respectively. Even though all models showed accuracy in predicting the removal of CV dye, it was observed that the ANN model exhibited greater accuracy compared to the other models.

Insightful properties-performance study of Ti–Cu–O heterojunction sonochemically embedded in mesoporous silica matrix for efficient tetracycline adsorption and photodegradation: RSM and ANN-based modeling and optimization

This study aims to provide a comprehensive evaluation of the photocatalytic properties and performance of the Cu–Ti–O heterojunction sonochemically embedded in the mesoporous silica matrix. Various characterization analyses and adsorption/photodegradation experiments were performed to assess the potential of the sample for tetracycline (TC) removal. The characterization results indicated that sonication contributes to better dispersion of Ti–Cu–O species, resulting in more uniform particle sizes, stronger semiconductors-silica interaction, and less agglomeration. Furthermore, sonication significantly affected the optical nanocomposite features, leading to an improvement in charge carrier separation and a decrease in the band gap of Ti–Cu–Si (S) by approximately 2.6 eV. Based on the textural results, the ultrasound microjets increased the surface area and pore volume, which facilitate mass transfer and provide suitable adsorption sites for TC molecules. Accordingly, Cu–Ti–Si (S) demonstrated higher adsorption capacity (0.051 g TC/g adsorbent) and eliminated TC significantly faster (0.0054 L.mg−1.min−1) than a non-sonicated sample during 120 min of irradiation, resulting in 2.84 times improvement in the constant rate. In addition, experimental results were accurately modeled using a central composite design in combination with response surface methodology (RSM) and artificial neural networks (ANN) to predict and optimize TC photodegradation. Both RSM and ANN models revealed excellent predictability for TC degradation efficiency, with R2 = 99.47 and 99.71%, respectively. At optimal operational conditions (CTC = 20 ppm, photocatalyst dosage = 1.15 g.L−1, pH = 9, and irradiation time = 100 min), more than 95% and 87% of TC were degraded within the UV (375 W) and simulated solar light (400 W) irradiation periods, respectively. It was observed that the Cu–Ti–Si (S) nanocomposite maintained remarkable stability after four cycles with only a negligible 3% loss of activity, owing to the superior interaction between the bimetallic heterojunction and the silica matrix.

Optimizing design of a free piston Stirling engine using response surface methodology and grey relation analysis

The performance analysis and optimization of a γ-type free-piston Stirling engine (FPSE) are conducted using Response Surface Methodology (RSM) and Grey Relation Analysis (GRA). The input-output parameters table is determined by the RSM. A regression model is presented to investigate the influence of structural parameters of FPSE on its performance. The relation coefficients for the four output parameters obtained from the RSM are determined to be 0.9980, 0.9983, 0.9999, and 0.9995, respectively, thus unequivocally demonstrating the exceptional precision of the RSM model. Also, the relationship between displacer and piston amplitudes, operating frequency, and output power and these parameters of Stirling engine is presented via 3D surface plots. The performance of the FPSE is optimized using the desirability approach and GRA, respectively, followed by a comparison of four groups of optimization results to determine the most suitable model. The maximum output power achieved is 78.02 W, with corresponding operating frequency, displacer amplitude, and piston amplitude values of 60.24 Hz, 2.55 mm, and 7.27 mm. Finally, by comparing pre- and post-optimization, the output power is increased by 36.85%. The GRA exhibits superior optimization compared to the RSM, thus providing valuable guidance for enhancing the optimization of FPSE in this study.

Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm

Plant materials are a rich source of polyphenolic compounds with interesting health-beneficial effects. The present study aimed to determine the optimized condition for maximum extraction of polyphenols from grape seeds through RSM (response surface methodology), ANFIS (adaptive neuro-fuzzy inference system), and machine learning (ML) algorithm models. Effect of five independent variables and their ranges, particle size (X1: 0.5–1 mm), methanol concentration (X2: 60–70% in distilled water), ultrasound exposure time (X3: 18–28 min), temperature (X4: 35–45 °C), and ultrasound intensity (X5: 65–75 W cm−2) at five levels (− 2, − 1, 0, + 1, and + 2) concerning dependent variables, total phenolic content (y1; TPC), total flavonoid content (y2; TFC), 2, 2-diphenyl-1-picrylhydrazyl free radicals scavenging (y3; %DPPH*sc), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) free radicals scavenging (y4; %ABTS*sc) and Ferric ion reducing antioxidant potential (y5; FRAP) were selected. The optimized condition was observed at X1 = 0.155 mm, X2 = 65% methanol in water, X3 = 23 min ultrasound exposure time, X4 = 40 °C, and X5 = 70 W cm−2 ultrasound intensity. Under this situation, the optimal yields of TPC, TFC, and antioxidant scavenging potential were achieved to be 670.32 mg GAE/g, 451.45 mg RE/g, 81.23% DPPH*sc, 77.39% ABTS*sc and 71.55 μg mol (Fe(II))/g FRAP. This optimal condition yielded equal experimental and expected values. A well-fitted quadratic model was recommended. Furthermore, the validated extraction parameters were optimized and compared using the ANFIS and random forest regressor-ML algorithm. Gas chromatography-mass spectroscopy (GC–MS) and liquid chromatography–mass spectroscopy (LC–MS) analyses were performed to find the existence of the bioactive compounds in the optimized extract.