Response Surface Methodology Model Research Articles

Study of factors affecting the magnetic sensing capability of shape memory alloys for non-destructive evaluation of cracks in concrete: Using response surface methodology (RSM) and artificial neural network (ANN) approaches

Currently, the field of structural health monitoring (SHM) is focused on investigating non-destructive evaluation techniques for the identification of damages in concrete structures. Magnetic sensing has particularly gained attention among the innovative non-destructive evaluation techniques. Recently, the embedded magnetic shape memory alloy (MSMA) wire has been introduced for the evaluation of cracks in concrete components through magnetic sensing techniques while providing reinforcement as well. However, the available research in this regard is very scarce. This study has focused on the analyses of parameters affecting the magnetic sensing capability of embedded MSMA wire for crack detection in concrete beams. The response surface methodology (RSM) and artificial neural network (ANN) models have been used to analyse the magnetic sensing parameters for the first time. The models were trained using the experimental data obtained through literature. The models aimed to predict the alteration in magnetic flux created by a concrete beam that has a 1 mm wide embedded MSMA wire after experiencing a fracture or crack. The results showed that the change in magnetic flux was affected by the position of the wire and the position of the crack with respect to the position of the magnet in the concrete beam. RSM optimisation results showed that maximum change in magnetic flux was obtained when the wire was placed at a depth of 17.5 mm from the top surface of the concrete beam, and a crack was present at an axial distance of 8.50 mm from the permanent magnet. The change in magnetic flux was 9.50 % considering the aforementioned parameters. However, the ANN prediction results showed that the optimal wire and crack position were 10 mm and 1.1 mm, respectively. The results suggested that a larger beam requires a larger diameter of MSMA wire or multiple sensors and magnets for crack detection in concrete beams.

Performance Evaluation of the Electro-Fenton Process for Distillery Wastewater Treatment

A fair amount of India’s gross domestic product is contributed by distilleries, which are considered the backbone industries of India. Distilleries indeed play key roles in India’s exports. Distillery wastewater is recognized as one of the recalcitrant wastewaters, containing extremely high organic loading and having an adverse impact when released into the environment. The aim of the present study was to optimize the conditions required for attaining improved COD removal efficiency in distillery wastewater through an electro-Fenton (EF) process. The effect of various operating parameters, viz. H2O2 dosage (555–2220 mg L−1), spacing between the iron electrodes (2–6 cm), electrode dipping area (35–65 cm2), initial pH (2–9), and constant voltage supply (5–15 V), were investigated by carrying out the EF process in batch mode. As a result of the EF study, COD removal efficiency of 79.5% for an initial COD of 5500–6000 mg L−1 was achieved for the distillery wastewater under the condition of 1665 mg L−1 H2O2, 2.5 cm of spacing between the electrodes, 55 cm2 of electrode dipping area, pH 3, and constant voltage supply of 5 V. In the same study, the kinetics of the process was also investigated, and it obeyed the pseudo-first-order reaction. The EF process effectively degrades complex organic compounds in distillery wastewater into simpler, potentially less toxic substances, as demonstrated by gas chromatography–mass spectrometry (GC-MS) analysis and pathway elucidation. The central composite design (CCD) of the response surface methodology (RSM) model was used to optimize the COD removal in distillery wastewater through the EF process. In line with the batch experimental results, RSM projections also indicated that the optimum conditions required for attaining a maximum of 70.8% COD removal efficiency in distillery wastewater are found to be 1402 mg L−1 H2O2 dosage, 3 cm electrode spacing, 60 cm2 dipping area, 5 V voltage, and pH 2.18. The research data supported the conclusion that the EF process is feasible for distillery wastewater treatment, which preferably can be applied extensively.

Performance Evaluation of Benzyl Alcohol Oxidation with tert-Butyl Hydroperoxide to Benzaldehyde Using the Response Surface Methodology, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference System Model.

The adaptive neuro-fuzzy inference system (ANFIS), central composite experimental design (CCD)-response surface methodology (RSM), and artificial neural network (ANN) are used to model the oxidation of benzyl alcohol using the tert-butyl hydroperoxide (TBHP) oxidant to selectively yield benzaldehyde over a mesoporous ceria-zirconia catalyst. Characterization reveals that the produced catalyst has hysteresis loops, a sponge-like structure, and structurally induced reactivity. Three independent variables were taken into consideration while analyzing the ANN, RSM, and ANFIS models: the amount of catalyst (A), reaction temperature (B), and reaction time (C). With the application of optimum conditions, along with a constant (45 mmol) TBHP oxidant amount, (30 mmol) benzyl alcohol amount, and rigorous refluxing of 450 rpm, a maximum optimal benzaldehyde yield of 98.4% was obtained. To examine the acceptability of the models, further sensitivity studies including statistical error functions, analysis of variance (ANOVA) results, and the lack-of-fit test, among others, were employed. The obtained results show that the ANFIS model is the most suited to predicting benzaldehyde yield, followed by RSM. Green chemistry matrix calculations for the reaction reveal lower values of the E-factor (1.57), mass intensity (MI, 2.57), and mass productivity (MP, 38%), which are highly desirable for green and sustainable reactions. Therefore, utilizing a ceria-zirconia catalyst synthesized via the inverse micelle method for the oxidation of benzyl alcohol provides a green and sustainable methodology for the synthesis of benzaldehyde under mild conditions.

Optimizing porous asphalt mix design for permeability and air voids using response surface methodology and artificial neural networks

Easy infiltration of grouting material in Porous Asphalt Mixture (PAM) is one of the crucial phases in the construction of semi-flexible pavement (SFP). The gradation and compaction primarily influence the pores and their arrangement in PAM, which affects the air voids and permeability of porous mixes and effectiveness of slurry infiltration. Complexity of gradation and compaction makes it very difficult to predict air voids and permeability in PAM. Statistical analysis of these parameters based on experimental data requires very high volume of samples which is missing in literature. Compaction effort has been kept constant in most of them which otherwise is important for stability. Also, machine learning models to predict mix design of PAM has not been explored. This study investigates the PAM using systematic experimentation and modelling approaches such as Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) to establish new and low trial-based guidelines for establishing a relationship between air voids and permeability greater than 0.12 cm/sec, while ensuring the stability of the mix. Using a Simplex Lattice design approach, five distinct types of aggregates were utilized as input variables and twenty-six PAM mixes were generated, which were subjected to 5 compaction levels. A total of 260 mixes were tested and then models were prepared based on RSM and ANN, which considered two output variables: air voids and permeability. RSM and ANN models demonstrated strong agreement between predicted values and actual experiment outcomes. It is observed that optimal permeability in PAM is achieved by adjusting aggregate proportions based on compaction levels. Proportion limits for low compaction suggested 60–70 % coarse aggregates and up to 20 % fines. For higher compaction (25–75 blows), it is recommended to increase the coarse aggregates up to70–90 % and limit fines up to 10 %. All proposed limits satisfied minimum permeability criteria (> 0.12 cm/sec) while ensuring the stability of the mixes. The proposed modelling approach offers greater efficiency and accuracy and reduces trial batch time, minimizing the need for destructive experiments and material wastage.

Optimization of CRDI engine operating parameters using response surface methodology utilizing lemon peel oil biofuel enriched with hydroxy gas

The current study aims to optimize the operational variables of a CRDI engine in Dual-Fuel Mode (DFM). Hydroxy gas (HHO) was generated through water electrolysis using a concentric four-cylinder dry cell electrolyzer with NaOH catalyst, and it was introduced into the inlet manifold as the primary fuel. The pilot fuel consisted of Lemon Peel Oil (LPO) and pure diesel directly injected into the cylinder. An experimental matrix comprising 17 distinct runs was designed using Design of Experiments (DoE), utilizing Box-Behnken Design (BBD) based on Response Surface Methodology (RSM). The RSM model was created depending on the experimental data, and then operating parameters were optimized utilizing the desirability technique. The optimal engine operational variables were identified for the B25 LPO mixture with a constant HHO gas flow rate of 0.5 LPM, a Compression Ratio (CR) of 21.5, an Injection Pressure (IT) of 450 bars, and an inlet air temperature of 87℃. The optimized results were 30.18 % for Brake Thermal Efficiency (BTE), 9.532 g/kW-hr for Carbon Monoxide (CO) emission, 0.253 g/kW-hr for Unburned Hydrocarbon (UHC) emission, and 23.510 g/kW-hr and Nitrogen Oxides (NOx) emission, yielding a total desirability of 0.7641. Empirical validation of the optimized responses at these input conditions demonstrated their conformity to acceptable error ranges.

Evaluation of Statistical and Machine Learning Analysis on CRDI Engine using Ternary Fuel doped with Cerium Oxide Nanoadditives

At present, diesel engines are confronted with the formidable task of reconciling aggressive emission regulations with the preservation of intended engine performance. In order to tackle this concern, India can implement a comprehensive strategy that includes waste reduction, recycling programs, and the innovative use of waste-to-energy technologies. The potential synergistic effects of combining fuel mixtures with cerium oxide nanoparticles in CRDI diesel engines remain unexplored. This study employs response surface methodology and MLP regression to investigate these effects. This study explores the experimental design includes variation in blend ratios of karanja biodiesel (upto 20 %), compression ratios (18–22), and injection pressure (400–1200 bar), as well as CeO2 nanoparticle concentrations (50–150 ppm) using 10 % biobutanol. A combination of response surface methodology (RSM) and artificial neural network (ANN) techniques is used to assess and optimise the influence of diverse process parameters on engine responses. The machine learning processor (MLP) regressor model is more capable of forecasting engine responses than RSM modelling, as it has lower MAE, RMSE, and higher R2 values. According to the study, the best blend ratio, CR, Injection Pressure, and CeO2 concentration for the best performance and lowest emissions are 9.97 % biodiesel blend, CR of 20, an injection pressure of 797.4 bar, and CeO2 nanoparticle concentration of 100.1 ppm. The MAE value of the MLP regressor-based model ranged from 0.0011 to 0.0119. In terms of RMSE, MLP regressor-based model outputs are within reasonable boundaries. This study highlights the optimization of engine operating parameters and the application of nanotechnology in biodiesel blends. These findings can lead to the development of durable and eco-friendly fuel alternatives for internal combustion engines, promoting sustainable transportation.

Electrolytic synthesis of γ-Al2O3 nanoparticle from aluminum scrap for enhanced methylene blue adsorption: experimental and RSM modeling

The presence of methylene blue (MB) dye in wastewater has raised concern about human health and environmental ecology due to potential carcinogenic, and mutagenic effects. Therefore, this work aims to remove MB dye from wastewater using γ-Al2O3 nanoparticles synthesized from aluminum scrap via simple electrolytic method. The successful synthesis of the adsorbent was confirmed by a range of spectroscopy and microscopy techniques, including XRD, SEM, FTIR, and BET. The central composite design (CCD) of the response surface methodology (RSM) method was used to optimize the processing parameters such as solution pH, contact time, initial MB concentration, and adsorbent dose. The ANOVA results clearly shows that the quadratic model (p < 0.0001) was sufficient to the best predicting of the removal performance of MB dye (R2 = 0.9862). The optimum condition for the maximum MB dye removal (98.91%) was achieved at solution pH of 8.298, initial MB concentration of 31.657 mg/L, adsorbent dose of 0.387 g/L, and contact time of 46.728 min. Nano-γ-Al2O3 was shown to have a good surface area of 59 mg2/g by BET analysis. The adsorption kinetics follows the pseudo-second-order model (R2 = 0.997). With a maximum adsorption capacity of 137.17 mg/g, the Langmuir isotherm model (R2 = 984) provides the best fit to the adsorption isotherm data, indicating a monolayer adsorption process. Furthermore, thermodynamic analysis demonstrated that the adsorption of MB dye was an endothermic and spontaneous process. The reusability study showed that γ-Al2O3 nano-adsorbent retained 85.08% of its original removal efficiency after five cycles. According to the findings of the study, MB dye molecules were taken up by γ-Al2O3 nano-adsorbent via hydrogen bond formation, Van der Waals interaction, and electrostatic attraction. Therefore, γ-Al2O3 nanoparticles can be used as a potentially eco-friendly and low-cost adsorbent for the removal of MB dye from aqueous solutions.

Optimization of cerium-based metal–organic framework synthesis for maximal sonophotocatalytic tetracycline degradation

Wastewater treatment is inevitably required to alleviate the pollution of water resources by various contaminants such as antibiotics. MOFs are novel materials with photocatalytic activities. In this study, sonophotocatalytic degradation of tetracycline (TC) by the Cerium-based MOF (Ce-MOF) is optimized by modification of its synthesis route. Ce-MOF synthesis by room temperature (RT), hydrothermal (HT), and sonochemical synthesis (SC) are studied. TC degradation experiments revealed the superiority of SC synthesis. The interplay of main synthesis parameters, namely, initial ligand concentration, ultrasound (US) power and time on sonophotocatalytic activity of Ce-MOF, were investigated by response surface methodology model (RSM) utilizing the central composite experimental design (CCD). The optimum SC synthesis conditions are an initial ligand concentration of 8.4 mmol/L, a sonication power of 50 amplitude, and a US time of 60 min. The optimally synthesized Ce-MOF was characterized by infrared spectroscopy, FTIR, XRD, FE-SEM, TEM, zeta potential analysis, diffuse reflectance spectroscopy, particle size analysis, Mott-Schottky analysis, photocurrent analysis, electrochemical impedance spectra, and photoluminescence spectroscopy. The findings indicate that the removal efficiency of TC can reach up to 81.75% within 120 min in an aqueous solution containing an initial TC concentration of 120 ppm and 1 g/L Ce-MOF at pH of 7. Mineralization efficiency of the process is 71% according to COD measurements. The Ce-MOF catalyst retained its chemical stability and remained active upon TC degradation which makes it a promising candidate for wastewater treatment.

Optimization of Yucca filamentosa fiber based graft copolymer through response surface methodology and evaluation of physico-chemical properties

The study of the physico-chemical modification of Yucca filamentosa (Yf) natural fiber by graft copolymerization with ethylmethacrylate using ferrous ammonium sulfate-potassium persulfate as a redox initiator has been reported in the article. Initially, six process parameters; reaction duration, reaction temperature, solvent amount, pH, FAS:KPS ratio, and monomer concentration were used in the study in a sequential experimental design technique, and the significant process variables affecting the yield of the graft copolymer were identified. The Resolution-V design method identified the significant parameters as the reaction temperature, amount of solvent, and the concentration of monomer. In second phase of the study, the screened variables were utilized in the development of a model through the technique of response surface methodology (RSM) for the prediction of the yields, and its optimization. The developed RSM model fitted well with the experimental data, and predicted for the optimal conditions of reactions as temperature 50 °C, solvent 100 ml, and the monomer 3.05 × 10−3 mol/L; at which the highest graft yield percentage obtained was 124.2%. The techniques of FTIR, SEM, and XRD were used for the characterization graft copolymers. Studies of the various physico-chemical properties showed that the produced graft copolymers were more resistant than the natural fibers.

Removal of levofloxacin from aqueous solutions by using micro algae: optimization, isotherm, and kinetic study

This study was focused on the response surface methodology modeling of the removal of levofloxacin residue using microalgae as adsorbent. The microalgae categories Cladophora was characterized by Fourier transform infrared spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and X-ray Diffraction (XRD) analysis techniques. The efficacy of LEV removal was evaluated by the historical data of different variables (time, pH, algal dosage, and LEV concentration) using Central Composed Design (CCD). The predicted results of the model demonstrated that the expected responses are reasonably close to the actual data, demonstrating the efficiency of this method in producing an accurate prediction. The model was found to be significant based on its coefficient of determination, which was 0.914, along with the fitted and projected values of 0.9029 and 0.8967. Furthermore, the results of the predicted reaction optimization showed that the maximum LEV elimination could be achieved within a time of 90 min, a pH of 6 and 0.2 g/L of micro algae. The model was validated experimentally with 95.45 % of LEV elimination efficiency. In addition, it is observed that the LEV removal process followed Langmuir model and pseudo-second order kinetic in majority of the cases with R2 of 0.9959. As a result of the dead algae's simple use and high adsorption effectiveness, the results demonstrated the possible use of antibiotics in aqueous solutions.

Designing and synthesis of perovskite nanocrystals: a promising wide-spectrum solar light-responsive photocatalyst and lead ion sensor.

Perovskites are an emerging material with a variety of applications, ranging from their solar light conversion capability to their sensing efficiency. In current study, perovskite nanocrystals (PNCs) were designed using theoretical density functional theory (DFT) analysis. Moreover, the theoretically designed PNCs were fabricated and confirmed by various characterization techniques. The calculated optical bandgap from UV-Vis and fluorescence spectra were 2.15 and 2.05eV, respectively. The average crystallite size of PNCs calculated from Scherrer equation was 15.18nm, and point of zero charge (PZC) was obtained at pH 8. The maximum eosin B (EB) removal efficiency by PNCs was 99.56% at optimized conditions following first-order kinetics with 0.98 R2 value. The goodness of the response surface methodology (RSM) model was confirmed from analysis of variance (ANOVA), with the experimental F value (named after Ronald Fisher) of 194.66 being greater than the critical F value F0.05, 14, 14 = 2.48 and a lack of fit value of 0.0587. The Stern-Volmer equation with a larger Ksv value of for Pb2+ suggests its greater sensitivity for Pb2+ among the different metals tested.

Optimization of Enzyme-Assisted Extraction of Rosemary Essential Oil Using Response Surface Methodology and Its Antioxidant Activity by Activating Nrf2 Signaling Pathway.

Rosemary essential oil (REO) is widely recognized as a food flavoring and traditional herb and possesses potential antioxidant activity. However, its low yield rate and unclarified antioxidant mechanism warrant further investigation. In this study, an enzyme pretreatment-assisted extraction method with Box-Behnken design (BBD) and response surface methodology (RSM) models was employed to optimize the main factors of REO, and its antioxidant molecular mechanism under oxidative stress was elucidated in hydrogen peroxide-induced human lung carcinoma (A549) cells. The optimized yield (4.10%) of REO was recorded with the following optimum conditions: enzyme amount 1.60%, enzyme digestion pH 5.0, enzyme digestion temperature 46.50 °C, and enzyme digestion time 1.7 h. Meanwhile, 1.8-cineole (53.48%) and β-pinene (20.23%) exhibited radical scavenging activity higher than that of BHA and BHT. At the cellular level, REO (12.5-50 µg/mL) increased the levels of cell viability, CAT, SOD, and GSH significantly while reducing the contents of ROS, MDA, and GSSG, when compared to H2O2 exposure. Mechanically, REO relieved oxidative stress via activating the Nrf2 signaling pathway and enhancing the protein expression of Nrf2, NQO-1, and HO-1, which was further verified by molecular docking between the main component 1.8-cineole and the Kelch domain of KEAP1. Therefore, REO could be considered as a potent natural antioxidant with a potential strategy in the food and pharmaceutical industries.

Experimental investigation of utilizing coconut shell ash and coconut shell granules as aggregates in coconut coir reinforced concrete

In the agricultural industry, coconut shells are one of the most generated wastes worldwide. In particular, studies in the Philippines show that the maximum capacity of the organic decomposition processes of agricultural products is exceeded due to the increasing agricultural activities. On the other hand, in the construction industry, cement production accounts for billions of tons of carbon dioxide emissions yearly. To address the said biowaste disposal problem, and environmental implications of the growing construction industry, this study evaluated the potential of utilizing coconut shell ash (CSA) and coconut shell granules (CSG) in concrete production as alternatives to cement and sand, being the conventional aggregates, respectively. Additionally, coconut coir (CC) was incorporated as fiber reinforcements in concrete. The experiment consisted of 15 mix designs using different proportions of CSA and CSG, ranging from 0% to 20%, and CC fiber reinforcements, ranging from 0% to 2%. The tests conducted on fresh concrete involved measuring its slump and unit weight, while the 28-day cured samples were tested for compressive and tensile strengths. Results showed that high concentrations of CSA, CSG and CC, when combined in the concrete mix, leads to poor workability; on the other hand, the modified mixes generally had lower unit weights than the conventional concrete. These are associated to the higher absorption rate, but lower density of the said agri-wastes than the conventional aggregates. In terms of the compressive and tensile strengths, all modified mixes produced lower strengths than the conventional concrete. Nonetheless, Response Surface Methodology (RSM) was utilised to model the relationship between the different independent variables considered in the study, namely CSA, CSG and CC contents, and their corresponding response to the compressive and tensile strengths. Based on the generated RSM Model, the optimum combination for obtaining the maximum strength consisted of 2% CC, resulting in a compressive and tensile strength of 23.046 MPa and 3.315 MPa, respectively. Overall, CSG-CSA coconut coir reinforced concrete is found to be a viable sustainable alternative for structures requiring low-strength, non-structural concrete, such as concrete slab patios and pathways.

Effect of seawater salinity, pH, and temperature on external corrosion behavior and microhardness of offshore oil and gas pipeline: RSM modelling and optimization

This research aims to investigate the effects of seawater parameters like salinity, pH, and temperature on the external corrosion behaviour and microhardness of offshore oil and gas carbon steel pipes. The immersion tests were performed for 28 days following ASTM G-1 standards, simulating controlled artificial marine environments with varying pH levels, salinities, and temperatures. Besides, Field emission scanning electron microscopy (FESEM) analysis is performed to study the corrosion morphology. Additionally, a Vickers microhardness tester was used for microhardness analysis. The results revealed that an increase in salinity from 33.18 to 61.10 ppt can reduce the corrosion rate by 28%. In contrast, variations in seawater pH have a significant effect on corrosion rate, with a pH decrease from 8.50 to 7 causing a 42.54% increase in corrosion rate. However, the temperature of seawater was found to be the most prominent parameter, resulting in a 76.13% increase in corrosion rate and a 10.99% reduction in the microhardness of offshore pipelines. Moreover, the response surface methodology (RSM) modelling is used to determine the optimal seawater parameters for carbon steel pipes. Furthermore, the desirability factor for these parameters was 0.999, and the experimental validation displays a good agreement with predicted model values, with around 4.65% error for corrosion rate and 1.36% error for microhardness.

Optimization of Flexural Performance of PETG Samples Produced by Fused Filament Fabrication with Response Surface Method.

Additive manufacturing (AM), particularly fused filament fabrication (FFF), has gained significant attention for its design flexibility and cost-effectiveness. This study focuses on optimizing FFF parameters that employ response surface methodology (RSM) to enhance the flexural performance of polyethylene terephthalate glycol (PETG) parts. Three essential parameters-layer height, print speed, and nozzle temperature-were varied, and their effects on flexural strength, flexural modulus, flexural toughness for ultimate strength, flexural toughness at 5% strain, and strain at ultimate strength were evaluated. Based on a Box-Behnken design, the experiments revealed significant effects of these parameters on the mechanical responses. The analysis of variance (ANOVA) indicates that layer height predominantly affects flexural modulus and toughness, while nozzle temperature significantly impacts flexural strength. The RSM models exhibited high accuracy, with R2 values exceeding 99%. Optimal parameter combinations yield remarkable improvements: flexural strength reached 39.55 MPa, flexural modulus peaked at 1344.60 MPa, flexural toughness for ultimate strength reached 218.22 J/mm3, flexural toughness at 5% strain reached 381.47 J/mm3, and strain at ultimate strength reached 3.50%. Validation experiments confirm the effectiveness of the optimization, with errors below 3.17%.

Remediation of cationic dye from wastewater using a new environmentally friendly adsorbent: A response surface methodology and artificial neural network modeling study

Remediation of cationic dye from wastewater using a new environmentally friendly adsorbent: A response surface methodology and artificial neural network modeling study

Low-cost optimization of industrial textile landfill sludge re-dewatering using ferrous sulfate and blast furnace slag

This study was based on an industrial sludge landfill with a scale of 1 million cubic meters, which had been filled for more than 10 years. It focused on the secondary dewatering of industrial textile landfill sludge (LS) with a total organic carbon (TOC) content greater than 50% and a volatile suspended solids to suspended solids (VSS/SS) ratio of 0.59. A response surface methodology (RSM) model was established using the coagulant ferrous sulfate (FeSO4) and conditioning agents such as hydrated magnesium oxide (MgO), blast furnace slag (BFS), and calcium oxide (CaO). By solving the RSM equations for the respective indicators, the optimal dosages of FeSO4, MgO, and BFS were determined to be 90 mg/g of dry sludge (DS), and for CaO 174.85 mg/g DS. Further examinations of the dewatering performance, apparent properties, extracellular polymeric substances (EPS) components, rheological characteristics, moisture distribution, and pollutant content variation led to the development of a green waste-based dewatering agent composed of FeSO4 and BFS. In small-scale diaphragm plate and frame filter press tests, the optimal water content (WC) was 69.11%. In the final production-scale experiments, it was 65.72%, with the actual application cost being only 13.07 $/ton DS. Additionally, when FeSO4 and BFS were used together, the combined action of Fe and Si could significantly reduce the biotoxicity of heavy metals (HMs), cut down 75.2% of the LS's TOC, and effectively reduced the leaching of organic substances from the leachate, which was beneficial for subsequent disposal. In conclusion, the combined use of FeSO4 and BFS for the secondary dewatering of industrial textile LS was economically efficient, effective in dewatering, and had significant harm reduction effects, making it a worthwhile for waste treatment.

Insights into water-lubricated transport of heavy and extra-heavy oils: Application of CFD, RSM, and metaheuristic optimized machine learning models

With diminishing light crude oil reserves, the focus shifts to heavy and extra-heavy crude oil, posing challenges with high viscosity impeding flow. Water-lubricated technology addresses this issue in oil transmission lines. This study introduces a novel method integrating response surface methodology (RSM), computational fluid dynamics (CFD), and optimized machine learning (ML) models to analyze pipeline pressure gradients (PG) in oil–water two-phase flows downstream of T-junctions. The present study uses the D-optimal technique for simulation design to optimize CFD computational demands efficiently. This study breaks new ground by proposing a framework that leverages support vector machines (SVMs). The proposed framework incorporates metaheuristic optimization algorithms (genetic algorithm (GA) and particle swarm optimization (PSO)) to achieve superior PG prediction accuracy. The optimized ML models outperformed RSM models for predicting PG. Results indicated that oil-to-water viscosity ratio and oil inlet velocity significantly affect PG, followed by water inlet velocity and surface tension between phases. In contrast, the oil-to-water density ratio, oil entry angle at the T-junction, and wall contact angle have minimal impact. Furthermore, statistical metrics and visual comparison tools identified the PSO-optimized SVM model based on linear kernel function as the most effective (MAPE = 13.2 % and R = 0.9949). The hybrid methodology presented in this research holds significant promise for optimizing heavy oil transfer efficiency in applications involving water-lubricated technologies.

Optimization of ultrasound-assisted extraction of phenolics from Cordia dichotoma leaves using response surface methodology

In this study, ultrasound-assisted extraction (UAE) was employed to extract phenolic compounds from Cordia dichotoma (C. Dichotoma) leaves. The extraction process was optimized using response surface methodology (RSM). The C. Dichotoma tree is found in tropical and subtropical areas and has medicinal properties. The obtained extracts were subjected to phytochemical screening using standard tests, antioxidant potential, and total phenolic content (TPC) of C. Dichotoma leaf extract by 2,2-diphenylpicrylhydrazyl assay and Folin-Ciocalteu assay respectively. The impact of process parameters like different solvents, solute-to-solvent ratio, ultrasonic power, and sonication time on TPC and antioxidant activity were studied. UAE offers a more rapid and efficient extraction process when compared to conventional extraction techniques such as Soxhlet extraction due to effective mass transfer. RSM models identified that ultrasonic power of 200 W and 30:1 (mL/g) solvent-to-solute ratio, using methanol, and a sonication time of 15 min are the optimal conditions for extraction of phenolics. FTIR analysis of methanolic extract confirmed the presence of phenolic functional groups. The compounds obtained from C. Dichotoma leaf extracts were identified by GCMS. UAE proves to be a promising technique for obtaining bioactive compounds with antioxidant potential from Cordia dichotoma leaves which can be potential applications in pharmaceutical, nutraceuticals, and functional food industries.

Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network

This study aims to develop two models for thermodynamic data on hydrogen generation from the combined processes of dimethyl ether steam reforming and partial oxidation, applying artificial neural networks (ANN) and response surface methodology (RSM). Three factors are recognized as important determinants for the hydrogen and carbon monoxide mole fractions. The RSM used the quadratic model to formulate two correlations for the outcomes. The ANN modeling used two algorithms, namely multilayer perceptron (MLP) and radial basis function (RBF). The optimum configuration for the MLP, employing the Levenberg–Marquardt (trainlm) algorithm, consisted of three hidden layers with 15, 10, and 5 neurons, respectively. The ideal RBF configuration contained a total of 80 neurons. The optimum configuration of ANN achieved the best mean squared error (MSE) performance of 3.95e−05 for the hydrogen mole fraction and 4.88e−05 for the carbon monoxide mole fraction after nine epochs. Each of the ANN and RSM models produced accurate predictions of the actual data. The prediction performance of the ANN model was 0.9994, which is higher than the RSM model's 0.9771. The optimal condition was obtained at O/C of 0.4, S/C of 2.5, and temperature of 250 °C to achieve the highest H2 production with the lowest CO emission.