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Owing to bio-polymer’s low-cost, environmental friendliness and mechanically stable nature, calcium alginate microcapsules have attracted much interest for their applications in numerous fields. Among the common production methods, the Electrospraying technique has shown a great potential due to smaller shape capsule production and ease of control of independent affecting parameters. Although one factor at a time (OFAT) can predict the trends of parameter effect on size and sphericity, it is inefficient in explaining the complex parameter interaction of the electrospray process. In the current study, the effects of the main parameters affecting on size and sphericity of the microcapsules using OFAT were optimized to attain calcium alginate microcapsules with an average diameter below 100 µm. Furthermore, we propose a statistical model employing the Surface Responses Methodology (RSM) and Central Composite Design (CDD) to generate a quadratic order linear regression model for the microcapsule diameter and sphericity coefficient. Experimentally, microcapsules with a size of 92.586 µm and sphericity coefficient of 0.771 were predicted and obtained from an alginate concentration of 2.013 w/v, with a flowrate of 0.560 mL/h, a needle size of 27 G and a 2.024 w/v calcium chloride concentration as optimum parameters. The optimization processes were successfully aligned towards formation of the spherical microcapsules with smaller average diameter of less than 100 µm, owing to the applied high voltage that reached up to 21 kV.

Chitosan microcapsules draw attention due to their biodegradability, biocompatibility, antibacterial behavior, low cost, easy processing, and the capability to be used for different applications. This study utilized the electrospraying technique for the chitosan microcapsules formulation. As a novel cross-linking agent, a mixture of oxalic acid and sodium phosphate dibasic was utilized as a collecting solution for the first time in the electrospraying of chitosan microcapsules. Scanning Electron Microscopy (SEM) was utilized to optimize the spherical morphology and size of the experimentally obtained microcapsules. The different parameters, including chitosan concentration, applied voltage, flow rate, and tip-to-collector (TTC) distance, affecting the microcapsules’ size, sphericity, yield, and combined effects were optimized using Surface Responses Methodology (RSM). The Analysis of Variance (ANOVA) was utilized to obtain the impact of each parameter on the process responses. Accordingly, the results illustrated the significant impact of the voltage parameter, with the highest F-values and least p-values, on the capsule size, sphericity, and yield. The predicted optimum conditions were determined as 5 wt% chitosan concentration, 7 mL/h flow rate, 22 kV, and 8 cm TTC distance. The predicted responses at the optimized conditions are 389 µm, 0.72, and 80.6% for the capsule size, sphericity, and yield, respectively. While the validation of the model prediction was conducted experimentally, the obtained results were 369.2 ± 23.5 µm, 0.75 ± 0.04, and 87.3 ± 11.4%, respectively. The optimization process was successfully examined for the chitosan microcapsules manufacturing.

This study explores biogas production through anaerobic digestion of cattle manure (CM) in a Continuous Stirred Tank Reactor (CSTR), using Response Surface Methodology (RSM) to optimize conditions for enhanced methane yield. Cattle manure, a primary substrate in biogas generation, holds untapped methane potential due to its high fiber content, which is only partially degraded in typical single-phase CSTR systems. Experiments were conducted under controlled mesophilic conditions to compare biogas outputs with and without stirring. Key variables, including Volatile Solids (VS), volatile fatty acids (VFA), total alkalinity (TA), and pH, were monitored and optimized using a Box-Behnken design. Results showed that stirring significantly increased methane yield to 0.4713 m3 .kg-1 VS, attributed to uniform microbial activity and enhanced degradation of organic matter. The findings also show that intermittent stirring improves methane yield by 34.36%, achieving a peak value, and offer practical insights into energy-efficient alternatives to continuous mixing, with direct implications for scaling up industrial biogas systems. Statistical analysis via ANOVA confirmed the regression model’s reliability, identifying significant factors influencing biogas production. This study’s findings underscore the efficiency of serial CSTR configurations and optimized operating conditions for sustainable biogas production.

Rice straw, an agricultural waste product generated in huge quantities worldwide, is utilized to remediate diesel pollution as it possesses excellent characteristics as a natural sorbent. This study aimed to optimize factors that significantly influence the sorption capacity and the efficiency of oil absorption from diesel-polluted seawater by rice straw (RS). Spectroscopic analysis by attenuated total reflectance infrared (ATR-IR) spectroscopy and surface morphology characterization by variable pressure scanning electron microscopy (VPSEM) and energy-dispersive X-ray microanalysis (EDX) were carried out in order to understand the sorbent capability. Optimization of the factors of temperature pre-treatment of RS (90, 100, 110, 120, 130 or 140 °C), time of heating (10, 20, 30, 40, 50, 60 or 70 min), packing density (0.08, 0.10, 0.12, 0.14 or 0.16 g cm−3) and oil concentration (5, 10, 15, 20 or 25% (v/v)) was carried out using the conventional one-factor-at-a-time (OFAT) approach. To eliminate any non-significant factors, a Plackett–Burman design (PBD) in the response surface methodology (RSM) was used. A central composite design (CCD) was used to identify the presence of significant interactions between factors. The quadratic model produced provided a very good fit to the data (R2 = 0.9652). The optimized conditions generated from the CCD were 120 °C, 10 min, 0.148 g cm−3 and 25% (v/v), and these conditions enhanced oil sorption capacity from 19.6 (OFAT) to 26 mL of diesel oil, a finding verified experimentally. This study provides an improved understanding of the use of a natural sorbent as an approach to remediate diesel pollution.

One of the attractive iron-based metal organic framework (MOF) MIL-101(Fe) (Material Institute Lavoisier, MIL) and amine-functionalized NH2-MIL-101(Fe) materials were synthesized by conventional solvothermal method and characterized by TGA (Thermo Gravimetric Analysis), SEM (Scanning Electron Microscopy), XRD (X-Ray Diffractograms), BET (Braunner Emmet Teller) and FTIR (Fourier Transform Infrared Spectroscopy). Characterization results indicated that MIL-101(Fe) materials have well defined morphology, good thermal stability, high surface area and also high porosity. These MOF materials were used as photocatalysts for photodegradation of a pesticide, imidacloprid (IMC). The response surface methodology (RSM) was applied in designing the IMC photodegradation experiments for evaluating the interactive effects of independent variables and determining the optimum condition. Central composite design as five independent variables such as initial MIL-101(Fe) concentration, initial IMC concentration, H2O2 concentration, pH and time were coded with low and high level and IMC removal percent was obtained as a response. As for the RSM results on the optimum photocatalytic condition, maximum IMC removal values were determined as 100% for the both catalysts in the end of the 30 min. The adsorption efficiencies of catalysts were also investigated and obtained results showed that amine-functionalized MIL-101(Fe) was the more effective sorbent than MIL-101(Fe).

This study optimized friction stir welding (FSW) parameters for 1.6 mm thick 2024T3 aluminum alloy sheets. A 3 × 3 factorial design was employed to explore tool rotation speeds (1100 to 1300 rpm) and welding speeds (140 to 180 mm/min). Static tensile tests revealed the joints’ maximum strength at 87% relative to the base material. Hyperparameter optimization was conducted for machine learning (ML) models, including random forest and XGBoost, and multilayer perceptron artificial neural network (MLP-ANN) models, using grid search. Welding parameter optimization and extrapolation were then carried out, with final strength predictions analyzed using response surface methodology (RSM). The ML models achieved over 98% accuracy in parameter regression, demonstrating significant effectiveness in FSW process enhancement. Experimentally validated, optimized parameters resulted in an FSW joint efficiency of 93% relative to the base material. This outcome highlights the critical role of advanced analytical techniques in improving welding quality and efficiency.

MnO 2 , as a representative manganese-based catalyst with many kinds of crystal forms, has been widely used to activate PMS. However, the role of morphological scale and crystal structures on the catalytic capability of MnO 2 still lacks further study. In this study, four different crystal forms of MnO 2 (α-MnO 2 , β-MnO 2 , γ-MnO 2 , and δ-MnO 2 ) are succeeded in being fabricated via hydrothermal processes and evaluated by activating PMS for the removal of Reactive Yellow X-RG, typical azo dye. Experiment results indicate that α-MnO 2 with a one-dimensional structure exhibits the best catalytic performance among the four as-prepared MnO 2 , which can be attributed to its broadest crystal interplanar distance (0.692), the highest portion of Mn (III)/Mn (IV) (4.194), and lowest value of average oxidation state AOS (2.696). Correlation analysis confirms that interplanar distance is the most relative factor with the catalytic activity of MnO 2 among the three studied factors ( R 2  = 0.99715). Meanwhile, the morphological scale structure of α-MnO 2 can also account for its highest catalytic ability among the four as-prepared MnO 2 , including its large specific area and advantageous one-dimensional nanostructure. Furthermore, according to the response surface methodology, when the dosage of PMS is 2.369 g/L, the dosage of α-MnO 2 is 0.991 g/L, and the initial dye concentration is 1025 mg/L, the maximum removal rate of Reactive Yellow X-RG is up to 97.38%. Graphical abstract

The simultaneous effects of three continuous factors: solvent concentration (50–100%), treated times (25–85 min), treated temperatures (25–55 °C), and two categorical factors: type of solvents (methanol or ethanol) and ultrasonic frequency (28 kHz or 40 kHz) on ultrasonic-assisted extraction yield from waste orange peels were evaluated and optimized by response surface methodology. Fourier Transform Infrared (FTIR) spectroscopy with a wavelength of 500 cm−1 to 4000 cm−1 was employed to rapidly identify the orange extracts. The significant polynomial regression models on crude extraction, sediments after evaporation, and precipitation yield were established (p < 0.05). Results revealed that solvent concentration affected crude extraction and precipitation yield linearly (p < 0.01). The optimal and practical ultrasound-assisted extraction conditions for increasing the precipitation yield were using 61.42% methanol with 85 min at 55 °C under 40 kHz ultrasonic frequency. The spectra of extracts showed a similar fingerprint of hesperidin.

Producing high-strength self-compacting concrete (SCC) requires a low water-cement ratio (W/C). Hence, using a superplasticizer is necessary to attain the desired self-compacting properties at a fresh state. The use of low W/C results in very brittle concrete with a low deformation capacity. This research aims to investigate the influence of crumb rubber (CR), fly ash (FA), and nanosilica (NS) on SCC’s workability and mechanical properties. Using response surface methodology (RSM), 20 mixes were developed containing different levels and proportions of FA (10–40% replacement of cement), CR (5–15% replacement of fine aggregate), and NS (0–4% addition) as the input variables. The workability was assessed through the slump flow, T500, L-box, and V-funnel tests following the guidelines of EFNARC 2005. The compressive, flexural, and tensile strengths were determined at 28 days and considered as the responses for the response surface methodology (RSM) analyses. The results revealed that the workability properties were increased with an increase in FA but decreased with CR replacement and the addition of NS. The pore-refining effect and pozzolanic reactivity of the FA and NS increased the strengths of the composite. Conversely, the strength is negatively affected by an increase in CR, however ductility and deformation capacity were significantly enhanced. Response surface models of the mechanical strengths were developed and validated using ANOVA and have high R2 values of 86–99%. The optimization result produced 36.38%, 4.08%, and 1.0% for the optimum FA, CR, and NS replacement levels at a desirability value of 60%.

Titanium and its alloys exhibit numerous uses in aerospace, automobile, biomedical and marine industries because of their enhanced mechanical properties. However, the machinability of titanium alloys can be cumbersome due to their lower density, high hardness, low thermal conductivity, and low elastic modulus. The wire electrical discharge machining (WEDM) process is an effective choice for machining titanium and its alloys due to its unique machining characteristics. The present work proposes multi-objective optimization of WEDM on Ti6Al4V alloy using a fuzzy integrated multi-criteria decision-making (MCDM) approach. The use of MCDM has become an active area of research due to its proven ability to solve complex problems. The novelty of the present work is to use integrated fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal situation (TOPSIS) to optimize the WEDM process. The experiments were systematically conducted adapting the face-centered central composite design approach of response surface methodology. Three independent factors—pulse-on time (Ton), pulse-off time (Toff), and current—were chosen, each having three levels to monitor the process response in terms of cutting speed (VC), material removal rate (MRR), and surface roughness (SR). To assess the relevance and significance of the models, an analysis of variance was carried out. The optimal process parameters after integrating fuzzy AHP coupled with fuzzy TOPSIS approach found were Ton = 40 µs, Toff = 15 µs, and current = 2A.