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Bioethanol production has seen an increasing trend in research recently, with a focus on increasing its economic viability. The aim of this study is to develop a low-cost fermentation medium with a minimum of redundant nutritional supplements, thereby minimizing the costs associated with nutritional supplements and seed production. Corn steep liquor (CSL) in glucose fermentation by Saccharomyces Type 1 (ST1) strain and Anchor Instant Yeast (AIY), which are low-cost media, is used as a replacement for yeast extract (YE). The fermentation process parameters were optimized using artificial neural networks (ANN) and the response surface method (RSM). The study shows that for CSL, maximum average ethanol concentrations of 41.92 and 45.16 g/L, representing 82% and 88% of the theoretical yield, were obtained after 36 h of fermentation in a shake flask for ST1 and AIY, respectively. For YE, ethanol concentrations equivalent to 86% and 88% of theoretical yield were obtained with ST1 and AIY, respectively after 48 h. Although ANN better predicted the responses compared to RSM, optimum conditions were better predicted by RSM. This study shows that corn steep liquor is an inexpensive potential nutrient that may have significant cost implications for commercial ethanol 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.

According to Quality by Design (QbD) concept, quality should be built into product/method during pharmaceutical/analytical development. Usually, there are many input factors that may affect quality of product and methods. Recently, Design of Experiments (DoE) have been widely used to understand the effects of multidimensional and interactions of input factors on the output responses of pharmaceutical products and analytical methods. This paper provides theoretical and practical considerations for implementation of Design of Experiments (DoE) in pharmaceutical and/or analytical Quality by Design (QbD). This review illustrates the principles and applications of the most common screening designs, such as two-level full factorial, fractionate factorial, and Plackett-Burman designs; and optimization designs, such as three-level full factorial, central composite designs (CCD), and Box-Behnken designs. In addition, the main aspects related to multiple regression model adjustment were discussed, including the analysis of variance (ANOVA), regression significance, residuals analysis, determination coefficients (R2, R2-adj, and R2-pred), and lack-of-fit of regression model. Therefore, DoE was presented in detail since it is the main component of pharmaceutical and analytical QbD.

Fused deposition modeling (FDM) is gaining distinct advantages because of its ability to fabricate the 3D physical prototypes without the restrictions of geometric complexities, while when it comes to accuracy and efficiency, the advantages of FDM is not distinct, and so how to improve them is worthy of study. Focusing on process parameter optimization, such parameters as line width compensation, extrusion velocity, filling velocity, and layer thickness are selected as control factors, input variables, and dimensional error, warp deformation, and built time are selected as output responses, evaluation indexes. Experiment design is assigned according to uniform experiment design, and then the three output responses are converted with fuzzy inference system to a single comprehensive response. The relation between the comprehensive response and the four input variables is derived with second-order response surface methodology, the correctness of which is further validated with artificial neural network. Fitness function is created using penalty function and is solved with genetic algorithm toolbox in Matlab software. With confirmation test, the results are obtained preferring to the results of the experiment 1 with the best comprehensive response among the 17 experiment runs, which confirms that the proposed approach in this study can effectively improve accuracy and efficiency in the FDM process.

Phlorotannins are secondary metabolites produced mainly by brown seaweeds (Phaeophyceae) and belong to the class of polyphenolic compounds with diverse bioactivities. The key factors in the extraction of polyphenols are the selection of a suitable solvent, method of extraction and selection of optimal conditions. Ultrasonic-assisted extraction (UAE) is one of the advanced energy-saving methods suitable for the extraction of labile compounds. Methanol, acetone, ethanol and ethyl acetate are the most commonly used solvents for polyphenol extraction. As alternatives to toxic organic solvents, a new class of green solvents, natural deep eutectic solvents (NADES), has been proposed for the efficient extraction of a wide range of natural compounds including polyphenols. Several NADES were screened previously for the extraction of phlorotannins; however, the extraction conditions were not optimized and chemical profiling of NADES extract was not performed. The purpose of this work was to study the effect of selected extraction parameters on the phlorotannin content in NADES extract from Fucus vesiculosus, optimization of extraction conditions and chemical profiling of phlorotannins in the NADES extract. A fast and green NADES-UAE procedure was developed for the extraction of phlorotannins. Optimization was performed through an experimental design and showed that NADES (lactic acid:choline chloride; 3:1) provides a high yield (137.3 mg phloroglucinol equivalents per g dry weight of algae) of phlorotannins under the following extraction conditions: extraction time 23 min, 30.0% water concentration and 1:12 sample to solvent ratio. The antioxidant activity of the optimized NADES extract was equal to that of EtOH extract. In total, 32 phlorotannins have been identified (one trimer, two tetramers, six pentamers, four hexamers, six heptamers, six octamers and seven nonamers) in NADES extracts from arctic F. vesiculosus using the HPLC-HRMS and MS/MS technique. It was noted that all the above-mentioned phlorotannins were identified in both EtOH and NADES extracts. Our results suggest that NADES could be considered as an alternative to the conventional techniques for the effective extraction of phlorotannins from F. vesiculosus with high antioxidant potential.

In this research paper, the intelligent learning abilities of the gray wolf optimization (GWO), multi-verse optimization (MVO), moth fly optimization, particle swarm optimization (PSO), and whale optimization algorithm (WOA) metaheuristic techniques and the response surface methodology (RSM) has been studied in the prediction of the mechanical properties of self-healing concrete. Bio-concrete technology stimulated by the concentration of bacteria has been utilized as a sustainable structural concrete for the future of the built environment. This is due to the recovery tendency of the concrete structures after noticeable structural failures. However, it requires a somewhat expensive exercise and technology to create the medium for the growth of the bacteria needed for this self-healing ability. The method of data gathering, analysis and intelligent prediction has been adopted to propose parametric relationships between the bacteria usage and the concrete performance in terms of strength and durability. This makes is cheaper to design self-healing concrete structures based on the optimized mathematical relationships and models proposed from this exercise. The performance of the models was tested by using the coefficient of determination (R 2 ), root mean squared errors, mean absolute errors, mean squared errors, variance accounted for and the coefficient of error. At the end of the prediction protocol and model performance evaluation, it was found that the classified metaheuristic techniques outclassed the RSM due their ability to mimic human and animal genetics of mutation. Furthermore, it can be finally remarked that the GWO outclassed the other methods in predicting the concrete slump (Sl) with R 2 of 0.998 and 0.989 for the train and test, respectively, the PSO outclassed the rest in predicting the flexural strength with R 2 of 0.989 and 0.937 for train and test, respectively and the MVO outclassed the others in predicting the compressive strength with R 2 of 0.998 and 0.958 for train and test, respectively.

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.

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.

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.