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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.

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.

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.

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.

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%.

Natural fibre-based composites are replacing traditional materials in a wide range of structural applications that are used in different environments. Natural fibres suffer from thermal shocks, which affects the use of these composites in cold environment. Considering these, a goal was set in the present research to investigate the impact of cryogenic conditions on natural fibre composites. Composites were developed using polyester as matrix and jute-fibre and waste Teak saw-dust as reinforcement and filler, respectively. The effects of six parameters, viz., density of saw-dust, weight ratio of saw-dust, grade of woven-jute, number of jute layers, duration of cryogenic treatment of composite and duration of alkaline treatment of fibres on the mechanical properties of the composite was evaluated with an objective to maximise hardness, tensile, impact and flexural strengths. Taguchi method was used to design the experiments and response-surface methodology was used to model, predict and plot interactive surface plots. Results indicated that the duration of cryogenic treatment had a significant effect on mechanical properties, which was better only up to 60 min. The models were found to be statistically significant. The study concluded that saw-dust of density 300 kg/m3 used as a filler with a weight ratio of 13 wt.% and a reinforcement of a single layer of woven-jute-fibre mat of grade 250 gsm subjected to alkaline treatment for 4 h in a composite that has undergone 45 min of cryogenic treatment presented an improvement of 64% in impact strength, ca. 21% in flexural strength, ca. 158% in tensile strength and ca. 28% in hardness.

There has been increased deconstruction and demolition of reinforced concrete structures due to the aging of the structures and redevelopment of urban areas resulting in the generation of massive amounts of construction. The production volume of waste concrete is projected to increase rapidly over 100 million tons by 2020. However, due to the high cement paste content, recycled aggregates have low density and high absorption ratio. They are mostly used for land reclamation purposes with low added value instead of multiple approaches. This study was performed to determine an effective method to remove cement paste from recycled aggregates by using the abrasion and substituting the process water with acidic water. The aim of this study is to analyze the quality of the recycled fine aggregates produced by a complex method and investigate the optimum manufacturing conditions for recycled fine aggregates based on the design of experiment. The experimental parameters considered were water ratio, coarse aggregate ratio, and abrasion time and, as a result of the experiment, data concerning the properties of recycled sand were obtained. It was found that high-quality recycled fine aggregates can be obtained with 8.57 min of abrasion-crusher time and a recycled coarse aggregate ratio of over 1.5.

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.

In recent years, response surface methodology (RSM) has been used for modelling and optimising a variety of water and wastewater treatment processes. RSM is a collection of mathematical and statistical techniques for building models, evaluating the effects of several variables, and obtaining the values of process variables that produce desirable values of the response. This paper reviews the recent information on the use of RSM in different water and wastewater treatment processes. The theoretical principles and steps for its application are first described. The recent investigations on its application in coagulation–flocculation, adsorption, advanced oxidation processes, electro-chemical processes and disinfection are reviewed. The limitations of the methodology are highlighted. Attempts made to improve the RSM by combining it with other modelling techniques are also described.

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.