Response Surface Methodology For Optimization Research Articles

Bioactive Compound Extraction of Hemp (Cannabis sativa L.) Leaves through Response Surface Methodology Optimization

Hemp, commonly known as Cannabis sativa L., is a medicinal plant species of the Cannabaceae family. For the efficient extraction of C. sativa leaves using the conventional stirring process with water as the solvent, three crucial extraction parameters (i.e., extraction duration, liquid–solid ratio, and temperature) were investigated through the response surface methodology (RSM). The concentrations of the extracted bioactive compounds (polyphenols, ascorbic acid, and carotenoids) showed significant variations in the RSM design points, suggesting the importance of finding the optimal extraction conditions in which liquid–solid ratio and extraction temperature were found to have the highest impact. Further analysis was conducted on the optimal extract employing several assays to determine their polyphenol content, total carotenoid content, color evaluation, anti-inflammatory activity, and antioxidant capacity through FRAP, DPPH, and H2O2 assays. Α low extraction time (30 min) at 50 °C and a high liquid–solid ratio (50:1) were required for the highest possible yield of polyphenols. The total polyphenol content was determined to be 9.76 mg gallic acid equivalents/g under optimum conditions, with pelargonin being the most abundant polyphenol (1.51 mg/g) in C. sativa extracts. Ascorbic acid was measured at 282.23 μg/g and total carotenoids at 356.98 μg/g. Correlation analyses revealed that anti-inflammatory activity was negatively correlated with specific polyphenols. As determined by DPPH (27.43 μmol ascorbic acid equivalents (AAE)/g), FRAP (49.79 μmol AAE/g), and H2O2 (230.95 μmol AAE/g) assays, the optimized aqueous extract showed a high antioxidant capacity. Furthermore, it demonstrated considerable anti-inflammatory activity at 17.89%, with the potential to increase to 75.12% under particular extraction conditions. Given the high added-value of the aqueous extracts, the results of this study highlight the potential utility of C. sativa leaves as a source of health-improving antioxidant compounds in the pharmaceutical and food industries.

Response Surface Methodology Optimization of Resistance Welding Process for Unidirectional Carbon Fiber/PPS Composites

The use of thermoplastic composites (TPCs) as one of the lightweight solutions will inevitably encounter problems in connection. Resistance welding has the characteristics of high strength, simplicity, and high reliability, and is considered a very potential hot-melt connection technology. The resistance welding technology of unidirectional carbon fiber-reinforced polyphenylene sulfide composites (UCF/PPS) was systematically studied. The experimental results show that the 100-mesh brass mesh has the best resin wetting effect and heating efficiency, and the PPS/oxidized 100-mesh brass mesh composite resistance element (Ox-RE/PPS) has the highest welding strength. The welding failure mode changes from interface failure and RE failure to interlayer structure damage and fiber fracture. The single-factor experimental results show that the maximum welding strength is reached at 310 °C, 1.15 MPa, and 120 kW/m2. According to the conclusion of the single-factor experiment, the Box–Behnken method was further used to design a three-factor, three-level experiment, and a quadratic regression model was established according to the test results. The results of variance analysis, fitting curve analysis, and perturbation plot analysis proved that the model had high fitting and prediction abilities. From the 3D surface diagram analysis, the influence of power density is the largest, and the interaction between welding temperature and power density is the most significant. Combined with the analysis of Design Expert 13 software, the optimal range of process parameters was obtained as follows: welding temperature 313–314 °C, welding pressure 1.04–1.2 MPa, and power density 124–128 kW/m2. The average strength of resistance welding joints prepared in the optimal range of process parameters was 13.58 MPa.

Investigation of nanoparticle (Fe3O4) addition to 3rd generation biodiesel (spirulina microalgae)/diesel mixture as an innovative fuel according to different engine variables: An RSM optimization

In order to reduce dangerous air pollution and lessen the hazards associated with climate change, biofuels made from vegetable biomass can be utilized as fuel in diesel engines. However, feedstocks and the crop space needed for their cultivation are unavoidable barriers that hinder its adoption and result in a shortage of farmland for increasing food profits. Nevertheless, microalgae are the most reliable and competent biodiesel supply because they are not edible and do not require cropland. Additionally, the addition of nanoparticles has become popular to improve the negative aspects of biodiesel. Based on these, in this study, multi-purpose optimization research was conducted on the addition of iron oxide (Fe3O4) nanoparticles in three different amounts (25, 50, and 75 ppm) to 20 % synthesized spirulina microalgae biodiesel (SSMB)/80 % diesel mixtures. Tests were conducted at three different compression ratios (14.5:1, 15.5:1, and 16.5:1) (CoR) and four different engine loads (25 %, 50 %, 75 %, and 100 %). The response surface methodology (RSM) optimization process was performed with the test results. According to RSM, a desirability coefficient of 0.8299, 15.80:1 CoR, 68 ppm AoN, and 45 % LoE were the ideal conditions. The optimum responses under these circumstances were 421.9653 g/kWh, 19.4014 %, 0.0939 %, 4.0785 %, 73.1716 ppm and 253.0620 ppm for brake-specific fuel consumption (BSFC), brake-thermal efficiency (BTHE), carbon monoxide (CO), carbon dioxide (CO2), hydrocarbon (HC), and nitrogen oxide (NOx), respectively. RSM could be used successfully because the error rates (maximum 6.5 %) when comparing the test results with the RSM optimization outcomes were within reasonable bounds.

Experimental Study and Response Surface Methodology Optimization of Electro-Fenton Process Reactive Orange 16 Dye Treatment

Experimental Study and Response Surface Methodology Optimization of Electro-Fenton Process Reactive Orange 16 Dye Treatment

Multi-objective optimization and performance assessment of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy interfence system (ANFIS) for estimation of fouling in phosphoric acid/steam heat exchanger

Fouling is a common occurrence in industrial heat exchangers, leading to a decrease of the thermal efficiency. This research addresses the challenge of applying various machine learning algorithms including Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to effectively model the fouling resistance in a heat exchanger in phosphoric acid concentration loop. Subsequently, a multi-objective optimization approach was employed to minimize the fouling resistance. Confirmatory experiments are then conducted in the phosphoric acid concentration plant using optimized variables.The findings of this study indicate that the three models accurately align with one year of operational data, achieving a remarkably high coefficient of correlation (R). Among the models utilized, RSM demonstrates the highest level of prediction accuracy, with an R of 0.9998, accompanied by the lowest mean square error (MSE) of 5.9388 10−13 and root mean squared error (RMSE) of 7.7064 10−7. The RSM optimization process identifies the optimal conditions for variables such as time, acid inlet and outlet temperature, steam temperature, acid density, and volume flow rate, which are determined to be 114.805 h; 72.214 °C; 80.407 °C; 116.784 °C; 1642.47 kg/m3; and 2308.1 m3/h, respectively. The predicted fouling resistance demonstrates a strong correlation with the actual data, with a negligible percentage difference of 2.19 %, further validating the accuracy and reliability of the RSM model.

Development of partitioned rectangular-shaped heat sink for photovoltaic thermal systems and performance evaluation using TiO2/Water-based nanofluids

The current study presents a partitioned rectangular-shaped heat sink for improving the PVT system performance. The experiment was examined under various experimental parameters. Five cooler block types with different dimensions were arranged to share the same total contact area with the panel back surface. These cooler blocks were placed in different arrangements, namely (PVT-I, PVT-II, PVT-III, PVT-IV, PVT-V), and studied comparatively using water. The dimensions and numbers of each cooler block arrangement were altered, while maintaining the same surface area in contact with the photovoltaic panel. Through the response surface methodology (RSM) optimization, the optimal arrangement with the best performance was determined, and then TiO2/nanofluid was applied to that arrangement with different volumetric concentrations. Using water, the highest electrical and thermal efficiencies were obtained by case PVT-III with values of 19.39 % and 63.72 %, respectively. This was followed by PVT-II, PVT-V, PVT-I, and PVT-IV in that order for electrical efficiency; and at the same time by PVT-II, PVT-I, PVT-IV, and PVT-V in that order for thermal efficiency. The RSM method recommended the optimal arrangement case as case PVT-III. By using nanofluid in that case arrangement, the highest electrical efficiency value was recorded upon using 0.3 wt% and operating the system at 1.5 L/m and 900 W/m2. Regarding the thermal efficiency results, in that case, the highest and lowest values were recorded upon using 0.3 wt% and 0.1 wt%, respectively, with values of 72.49 % and 31.19 %. Increasing the volumetric concentrations positively reflected on the system's performance, resulting in high efficiencies being achieved. In addition, the highest electrical efficiencies were obtained at high levels of solar irradiance and flow rates.

Maximizing enhanced oil recovery via oxidative cracking of crude oil: employing air injection and H2O2 with response surface methodology optimization

The utilization of air injection as a method to enhance oil recovery in oil fields has gained prominence due to its cost-effectiveness and widespread availability, particularly in heavy oil production. This study focuses on optimizing the oxidative cracking process of Algerian crude oil by employing air injection supplemented with H2O2 and analyzing the interaction of key operating parameters like temperature and catalyst amount using response surface methodology. The predicted values derived from the response functions closely aligned with experimental data, demonstrating high accuracy (R2 = 0.9727 for liquid oil, R2 = 0.9176 for residue, and R2 = 0.7399 for gas phases). Using the developed second-order model, optimal conditions were determined through contour and surface plots, as well as regression equation analysis using Design software. At these optimal parameters (14.78 wt% of H2O2, 2 l min−1 of air flow, 100 ml of crude oil at 354.05 °C for 40 min), the oxidative cracking process yielded 96.32% liquid oil, 3.018% residue, and 0.662% gas products. Notably, the experimental produced liquid oil constituted 96.07 vol. %, matching well with the optimization outcomes. Physicochemical analysis of liquid product phase obtained from oxidative cracking process of petroleum confirmed the prevalence of light aliphatic compounds (C2-C11) at 70.59%, alongside 29.41% of C12-C36. The process also resulted in reduced viscosity, density, refractive index, and sulfur content in the liquid phase. The combination of air injection and H2O2 showcases promise in recovering residual oil effectively and contributes to the ongoing advancements in EOR techniques.

Response Surface Methodology for Optimization of Ultrasound-Assisted Antioxidants Extraction from Blackberry, Chokeberry and Raspberry Pomaces.

An investigation of the ultrasound-assisted extraction (UAE) of polyphenol-rich aqueous extracts from blackberry, chokeberry and raspberry pomaces was carried out. The aim of the study was to choose optimal conditions for UAE in order to obtain extracts rich in phenolic compounds. The optimization was carried out based on response surface methodology. The variable conditions were amplitude of ultrasound wave and extraction time, whereas responses were total polyphenol content and antioxidant capacity. Based on the ANOVA analysis, mathematical models were fitted and verified. The most effective conditions of amplitude and time were 98% and 5.00 min, 78% and 10.32 min and 90% and 11.56 min for blackberry pomace, chokeberry pomace and raspberry pomace, respectively. The actual results obtained in optimized conditions were comparable to the results predicted by the models. Additionally, the anthocyanin content in extracts was determined in the high-performance liquid chromatography assay. It was proven that response surface methodology could be a useful tool in the optimization of UAE processes for obtaining polyphenol-rich extracts from berry fruit pomaces.

Optimizing methanol synthesis from CO2 using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability.

Assessment of the performance of linear and nonlinear regression-based methods for estimating in situ catalytic CO2 transformations employing TiO2/Cu coupled with hydrogen exfoliation graphene (HEG) has been investigated. The yield of methanol was thoroughly optimized and predicted using response surface methodology (RSM) and artificial neural network (ANN) model after rigorous experimentation and comparison. Amongst the different types of HEG loading from 10 to 40 wt%, the 30 wt% in the HEG-TiO2/Cu assisted photosynthetic catalyst was found to be successful in providing the highest conversion efficiency of methanol from CO2. The most influencing parameters, HEG dosing and inflow rate of CO2, were found to affect the conversion rate in the acidic reaction regime (at pH of 3). According to RSM and ANN, the optimum methanol yields were 36.3 mg g-1 of catalyst and 37.3 mg g-1 of catalyst, respectively. Through the comparison of performances using the least squared error analysis, the nonlinear regression-based ANN showed a better determination coefficient (overall R2 > 0.985) than the linear regression-based RSM model (overall R2 ∼ 0.97). Even though both models performed well, ANN, consisting of 9 neurons in the input and 1 hidden layer, could predict optimum results closer to RSM in terms of agreement with the experimental outcome.

Removal of tartrazine and indigo carmine with layered double hydroxide-modified graphite nanocomposite: Isotherm, kinetics and response surface methodology (RSM) optimization

This research in focus investigates the removal of tartrazine (TZ) and indigo carmine (IC) dyes utilizing an AlCuFe-LDH/C nanocomposite as an adsorbent. The composite underwent comprehensive characterization through FE-SEM, EDX, FT-IR, XRD, and BET analyses. To gauge the efficacy of TZ and IC removal with the powdered adsorbent in use, the nanocomposites performance in surface adsorption of dyes species optimized, designed, and tested. Various parameters influencing the dyes adsorption under optimal conditions were considered, with a pH of 6.0 and an adsorption process time of 13 min selected. The concentrations of IC and TZ solutions were set at 11.5 mg/L, while a mass of 0.0143 g of adsorbent was applied. Under these conditions, the adsorbent exhibited impressive capabilities, removing 99.20 % of TZ and 88.5 % of IC. Equilibrium data were fitted to Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich models, with the Langmuir isotherm model found to be the most fitting. Based on the Langmuir isotherm model, the adsorption capacities were determined to be 26.05 mg g−1 for TZ, 26.56 mg g−1 for IC. Additionally, the kinetic evaluation of experiments conducted at various time intervals revealed that the adsorption processes aligned well with second-order kinetic models. Regeneration studies indicated that the adsorbent could successfully retain dyes even after four cycles, with the adsorption percentage of AlCuFe-LDH/C for dyes remaining at 80 % after four adsorption–desorption cycles.

Multivariate modelling of AA6082-T6 drilling performance using RSM, ANN and response optimization

The AA6082-T6 was experimentally studied in the present research with respect to the drilling performance. Drill diameter, cutting speed and feed rate were examined, using a full factorial design. Mathematical modelling of the process was carried out using the Response Surface Methodology (RSM) as well as the Artificial Neural Network (ANN) techniques. The output results in terms of cutting force, torque and surface roughness, revealed high levels of correlation between the experimental and the predicted data. Specifically, the Mean Absolute Percentage Error (MAPE) values using RSM compared to the ones of the experiments, were equal to 2.14%, 3.49% and 6.16% for Fz, Mz and Ra respectively. The equivalent MAPE between the ANN and the experiments were found to be 2.19%, 1.82% and 2.85% accordingly. Moreover, the most significant terms were revealed, being the interaction D × f for the thrust force and the torque with contribution percentages equal to approximately 44% and 42% respectively, and the term D2 for the surface roughness with 51%. The evaluation of the machining parameters, identified their significance, enabling the selection of the optimal cutting parameters, which were obtained by the desirability function, taking into account the importance of the generated surface quality and the reduction of cost. The solutions given by this approach, pointed out the Ø9 tool, coupled with Vc = 50 m/min and f = 0.15mm/rev as a well-balanced combination, whereas the Ø9.9 tool used under the same conditions, yielded the best possible surface quality (appr. 0.2 μm).

Optimizing recombinant production of L-asparaginase 1 from Saccharomyces cerevisiae using response surface methodology.

L-asparaginase is an essential enzyme used in cancer treatment, but its production faces challenges like low yield, high cost, and immunogenicity. Recombinant production is a promising method to overcome these limitations. In this study, response surface methodology (RSM) was used to optimize the production of L-asparaginase 1 from Saccharomyces cerevisiae in Escherichia coli K-12 BW25113. The Box-Behnken design (BBD) was utilized for the RSM modeling, and a total of 29 experiments were conducted. These experiments aimed to examine the impact of different factors, including the concentration of isopropyl-b-LD-thiogalactopyranoside (IPTG), the cell density prior to induction, the duration of induction, and the temperature, on the expression level of L-asparaginase 1. The results revealed that while the post-induction temperature, cell density at induction time, and post-induction time all had a significant influence on the response, the post-induction time exhibited the greatest effect. The optimized conditions (induction at cell density 0.8 with 0.7mM IPTG for 4h at 30°C) resulted in a significant amount of L-asparaginase with a titer of 93.52μg/mL, which was consistent with the model-based prediction. The study concluded that RSM optimization effectively increased the production of L-asparaginase 1 in E. coli, which could have the potential for large-scale fermentation. Further research can explore using other host cells, optimizing the fermentation process, and examining the effect of other variables to increase production.

Optimization of VAWT installation with spatial and temporal complexity considerations

The constant need for fuel to meet the commercial sector’s ever-increasing demand has driven researchers to discover and optimize renewable energy resources, paving the way for sustainable production of reliable and clean energy resources. The goal of the current work is to close the gap in process parameter optimization needed to convert wind energy wake from traffic on highways into electrical energy utilizing vertical-axis wind turbines (VAWTs). The energy output from the VAWT is analyzed to investigate how it is impacted by the variations in multiple parameter settings. Using the central composite design (CCD), a three-level four-factor array was used to investigate the following parameters: VAWT vertical distance (VD) and horizontal distance (HD) as continuous parameters, while road side (S) and location (L) of VAWT as categorical parameters. To find the most important parameter, response surface methodology (RSM) optimization and an analysis of variance (ANOVA) test are performed. L accounts for 66.67% of the total variable, with S coming in second with 51.80%. Using the best results from RSM and ANOVA, a confirmation test is run, and the results show yields of 88.75% ± 0.05% and 87.5% ± 0.05%, respectively. Therefore, RSM and ANOVA can be utilized equally for optimization at the same VAWT design. Lastly, the findings of the economic and environmental evaluation demonstrate that, in comparison to the basic settings, VAWT operating at optimal settings can save up to 180% and 200% more energy and reduce carbon emissions, respectively.

Application of response surface methodology for optimization of removal sunset yellow from aqueous by Granular Ferric Hydroxide (GFH)

Color is the first pollutant that is detected in wastewater and can be discharged into the wastewater through various industries. Sunset yellow is an azo dye that is used in the food, health and pharmaceutical industries. Due to its low price and good quality, this color is used in industries more than natural colorants. The aim of this study was to investigate the effect of granular ferric hydroxide (GFH) on the removal of sunset yellow color from aqueous solution using the response surface methodology (RSM). In this study, GFH was employed to remove sunset yellow dye from the aqueous solution. Due to saving time, cost and getting to the optimum conditions, the effect of important factors on adsorption was optimized by the RSM. The morphology of the adsorbent was determined by scanning electron microscopy. The results revealed that optimum conditions for sunset yellow removal by GFH with design experiment were dye concentration= 24 mg/L, GFH dose= 2.4 g/L, reaction time= 25 min. In addition, based on the results, the best-fitted model for this study was the quadratic model (R2 =0.966, R2adj=0.79 and R2pred=0.936). According to the obtained results, GFH was successful in sunset yellow removal. Therefore, it is suggested to use this process to remove sunset yellow from aqueous solutions.

Investigating the Emissions and Performance of Ethanol and Biodiesel Blends on Al2O3 Thermal Barrier Coated Piston Engine Using Response Surface Methodology Design - Multiparametric Optimization

The Response Surface Methodology (RSM) optimization technique to examine the effect of load, Tomato Methyl Ester (TOME), and Ethanol injection enhanced diesel on engine performance and exhaust gas emissions with normal piston and Al2O3 coated piston. TOME biodiesel (10, 20, and 30%) and Ethanol (10, 20, and 30%) were chosen to increase BTE while minimizing BSFC, NOx, CO, smoke, and HC. The RSM technique was used to operate the engine by load (0-100%). The results revealed that engine load, TOME, and ethanol concentration all exhibited a considerable effect on the response variables. The (ANOVA) results for the established quadratic models specified that each model, furthermore, an ideal was discovered by optimizing an experiment's user-defined historical design. The present research efforts to improve the performance of a diesel engine by using a thermal barrier-coated piston that runs on biodiesel blends. Al2O3 is the chosen material for TBC due to its excellent thermal insulation properties. B20E30 has a 4% higher brake thermal efficiency than diesel, but B10E20 and B30E20 mixes have a 3.6% and 12% reduction in (BSFC). The B20 blends lowered CO and HC emissions by 6% to 8% respectively. In terms of performance and emissions, biodiesel blends performed similarly to pure diesel, and the combination was optimized through a design of experiment tool.

Optimized extraction of polyphenols with antioxidant and anti-biofilm activities and LC-MS/MS-based characterization of phlorotannins from Sargassum muticum

The Sargassum genus is rich in glycoglycerolipids, phytosterols, terpenoids, polyphenols, nitrogenous compounds, and other components, and it exhibits various biological activities. Response surface methodology optimization based on Box-Behnken design was applied to extract polyphenols with antioxidant and anti-biofilm activities from Sargassum muticum. Results indicated that the optimal extraction conditions were an ethanol concentration of 56%, a temperature of 58 °C, a time of 5 h, and a liquid/material ratio of 40 mL/g. Under these conditions, the contents of polyphenols and phlorotannins were 10.76 ± 0.42 mg/g and 1.62 ± 0.04 mg/g, respectively. The EtOH extract of S. muticum was further purified with organic solvents of different polarities, and the ethyl acetate fraction (HA-PTs) exhibited the highest purity of polyphenols and phlorotannins, along with the best antioxidant and anti-biofilm activities. In phytochemical analysis, the main substances identified were polysaccharides, phytosterols, polyphenols, flavonoids, and phlorotannins. According to UHPLC-DAD-ESI-MS/MS analysis, four types of phlorotannins were identified from HA-PTs: dibenzodioxine-1,3,6,8-tetraol, tetraphlorethol, pentaphlorethol, and fucopentaphlorethol. These results indicate that the polyphenols of S. muticum have the potential to become new natural antioxidants and antibacterial agents.

Experimental investigation, performance analysis, and optimization of hot air convective drying of date fruits via response surface methodology

A laboratory-scale hot air convective dryer (HACD) was used to dry date fruits surplus at 0.5 ± 0.02 kg kg−1 to a safe moisture level of 0.35 kg kg−1 under drying temperatures (T) of 50–70 °C with airflow rates (Q) of 0.18 and 0.28 m3 h−1. Kinetics, modeling, energy, economic feasibility, and color difference of the date fruits were analyzed and then optimized using response surface methodology (RSM) with two drying factors and eight responses. The drying time to reach the desired moisture level ranged from 9 to 28 h under different drying conditions. The proposed mathematical model expressed the moisture behavior over drying time with the highest R2 = 0.9998 and 0.9999, respectively, for 0.18 and 0.28 m3 h−1. Effective moisture diffusivity (Deff) was calculated using the starting accessibility parameter and varied between 9.9 × 10−8 and 3.3 × 10−7 m2 s−1. The energy analysis revealed that the specific energy consumption (SEC) varies between 22.04 and 43.05 kWh.kg−1 and the drying efficiency (η) are 16.91, 16.14, and 25.38% for 50, 60, and 70 °C, respectively. The RSM optimization using a multi-level design with two centurium points denoted the optimum responses for the highest drying temperature. The lowest payback period (Pb) of the drying system was estimated to be 0.45 years for T = 70 °C and Q = 0.28 m3 h−1. The optimum drying time, Deff, total energy consumption (Et), SEC, η, and total color change (ΔE) were estimated to be 9 h, 3.3 × 10−7 m2 s−1, 33.91 kWh, 22.04 kWh.kg−1, 27.69%, and 1.69, respectively.

Electrocoagulation for the decolorization of textile wastewater in single-channel reactor: Response surface methodology for optimization and a novel model exploitation

This work focuses on the multi-objective optimization of the electrocoagulation process. The goal is to decolorize synthetic Reactive Black 5 wastewater using a single-channel reactor operating in a closed-circuit. Response Surface Methodology (RSM) is employed for the optimization. The Central Composite Design (CCD) is used to study the empirical relationship between two responses (decolorization efficiency (η) and energy consumption (WC)) and four independent variables (recirculation flow rate (Qv), current intensity (I), electrolysis time (τe) and active electrode area (Sa)), while the desirability function (D) is applied to determine optimal conditions. In addition, a novel approach for the exploitation of the RSM model is developed to set correlations between the limit of η (ηLim) and corresponding independent variables’ limit values (below of which η < ηLim). The developed correlations showed a good agreement between the graphical data and the estimated values (R2 > 0.99). The CCD model fits well the experimental data with a R2 of 0.94 and 0.95, for η and WC, respectively. The analysis of variance allowed to build quadratic models for both responses. For a maximal overall desirability D of 0.88, optimal conditions are Qv = 70 L h−1, I = 100 mA, τe = 34.6 min and Sa = 621 cm2. Under these conditions, η = 70.9% and WC = 4.6 Wh.m−3 were achieved. The sludge characterization using X-ray diffraction, Scanning Electron Microscopy, and Fourier Transform InfraRed reveals the formation of the amorphous Al(OH)3 without other precipitated substances and authenticates the successful sorption of RB5 onto the Aluminum hydroxide flocs.

An environmentally friendly multifunctional soy protein adhesive composed of rosin acid derivative inspired from organic-inorganic hybrid structure

Soy protein adhesives have the advantages of low price and renewability but are limited by terrible bonding strength and poor water resistance, which restricts the practical application. Herein, inspired by the structure of mussels and organic-inorganic hybrid strengthening mechanism, a novel and bio-based soy protein adhesive (SPI-RA-CSA) based on rosin acid derivative and calcium sulfoaluminate was generated. According to the optimization of response surface methodology (RSM), the best proportion of adhesive SPI-RA6-CSA1 was chosen. The dry and wet shear strengths of SPI-RA6-CSA1 adhesive reached 1.92 and 1.35 MPa, respectively, which were 120% and 210% higher than original soy protein adhesive. Meanwhile, SPI-RA6-CSA1 adhesive showed excellent coating performance even if it was coated on wet wood veneer. In addition, SPI-RA6-CSA1 adhesive effectively inhibited the formation of mold and exhibited outstanding antimicrobial ability for Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) with antimicrobial rates of 87.36% and 85.73%, respectively. What's more, SPI-RA6-CSA1 adhesive demonstrated prominent flame retardant properties reducing the harm of fire accidents. SPI-RA6-CSA1 adhesive also possessed the characteristics of low energy consumption and environmentally friendliness by Life Cycle Assessment (LCA). Therefore, this simple strategy provides workable guidance for the production of multifunctional and green soy protein adhesive.

Bacillus cereus-mediated biofermentation of Sardine offal waste: A novel approach to enhance nutritional value by Response Surface Methodology optimization

The rising protein demand in the aquaculture sector has significantly impacted fishmeal supply and pricing. Excessive use of fishmeal can lead to environmental issues and negatively impact marine biodiversity and human food security. Consequently, finding alternative fishmeal in aquaculture is crucial for economic and environmental sustainability. The present study aimed to determine how Bacillus cereus (MT355408) could enhance nutritional value of Sardine fish waste, which could replace fish meal in the market. Solid-state fermentation (SSF) represents a biotechnological method that utilizes microbes to convert discarded fish byproducts into valuable products. The bacterial ability to produce enzymes was studied and optimised for its maximum production to be used as an inoculum for the SSF technique. Different prebiotic sources were also studied for better upliftment of bacteria in the solid-state surface. A single-factor analysis was conducted to investigate the influence of varying prebiotic concentrations, inoculum quantity, and fermentation duration on protein breakdown. After studying the single-factor tests, a further response surface model was employed for better yield. The results indicated that the highest protein yield could be achieved with a fermentation time of 132.893 hours, a prebiotic quantity of 25%, and an inoculum quantity of 5.3%. The study's findings also affirmed that the model was vital in enhancing the crude protein content during fermentation. In conclusion, the model's results contribute valuable insights into fermentation processes, offering practical implications for enhancing protein content and digestibility in similar contexts.