Quadratic Polynomial Model Research Articles

Multi-objective optimization of properties on polymer fiber-reinforced desulfurization gypsum-based composite cementitious materials

Epoxy resin, calcium carbonate whisker and polyester fiber were mixed into desulfurized gypsum-based composite cementitious material (DGCM) to prepare polymer fiber-reinforced desulfurization gypsum-based composite cementitious material (PFRDGCM). Based on the response surface methodology (RSM) experimental design, the reliability of polynomial regression models was discussed, and the effects of factor interaction on properties and microscopic morphology of specimens were investigated. Additionally, the optimum mixture ratio of PFRDGCM was determined by the numerical function and verified by experimental results. The results showed that quadratic polynomial models accurately predicted properties of PFRDGCM and reflected the effects of single factor and multi-factor interaction on the compressive strength, water resistance, and durability of PFRDGCM. Moreover, the optimum PFRDGCM ratio for epoxy resin, calcium carbonate whisker, and polyester fiber was 5.49%, 1.02%, and 1.34 %, respectively. The measured test value of compressive strength, water absorption, softening coefficient, dry-wet strength coefficient, and freeze–thaw strength coefficient were 31.79 MPa, 3.58, 0.70, 1.16, and 1.14, respectively, with a relative error value less than 10 %, indicating that the RSM models can provide a reference for the multi-objective optimization of PFRDGCM.

Modeling of tenofovir disoproxil fumarate decontamination using sodium alginate-encapsulated activated carbon: Molecular dynamics, monte carlo and density functional theory

Powdered activated carbon (ACc) made from corn cobs was successfully encapsulated using sodium alginate (ACc-(Na-Alg)). The beads were thoroughly described using TGA/TDA, FTIR, SEM, XRD, and zero charge point measurements (pHpzc). Theoretical evaluations were performed using calculations based on DFT, MD, and MC simulations. We looked at how well ACc-(Na-Alg) beads removed TDF from aqueous medium through adsorption. The pseudo-second-order kinetics and Langmuir isotherm models were found to be suitable for describing the adsorption process after an analysis of the isotherm and kinetics. We have an exothermic, spontaneous adsorption, as shown by thermodynamic analysis. The B.B.D-R.S.M path revealed that the quadratic polynomial model fits the experimental data well (F-value = 3273.253 and R2 = 1.000). Optimal conditions for TDF adsorption were observed at pH = 4, an initial TDF concentration = 0.1 mM, and an ACc-(Na-Alg) dose = 1 g. The manufactured beads were submitted to recycling, and after five cycles of regeneration, they demonstrated significant adsorption efficiency. There was a significant difference between the outcomes of theoretical calculations (DFT, Monte Carlo, and molecular dynamics simulations) and experimental techniques. It can be said that ACc-(Na-Alg) beads have high adsorption capacity and can be used to effectively remove drugs from wastewater.

Investigating the Combined Sensitivity Effects of the Electrodes Density and the Total Suspended Solids on the Turbidity Removal in an Electrocoagulation Reactor

This paper aims to comprehend the sensitivity effect of the electrode density combined with the total suspended solids on the improvement of the turbidity’ removal. To this extent, three electro-coagulation lab-scale reactors (300 mm in length, 100 mm in width and 350 mm in height) equipped with aluminium made electrodes have been built to conduct the experiments with a test volume of 9 L. Consequently, the results have revealed that the removal efficiencies of chemical oxygen demand, total suspended solids, and turbidity are 54.65 %, 71.93% and 34.27% respectively for the reactor No.1. Regarding reactor No.2, the chemical oxygen demand, the total suspended solids, and the Turbidity's removal efficiency are 51.24%, 65.24% and 47.13%, respectively. Finally, the removal efficiencies in reactor No.3 are 54.65 %, 71.93% and 34.27%, respectively, for the chemical oxygen demand, the total suspended solids and the Turbidity, which led to considering implementing a full-scale electrocoagulation process in El Kerma wastewater treatment plant. Moreover, the results obtained have allowed developing statistically a quadratic polynomial model to describe the effluent turbidity. The model outcomes have showed an acceptable coefficient of determination with a significant corresponding probability of < 0.05 regarding the electrode density.

Numerically Optimized Effects of Contact Time Factor of Pulverized and Modified Bio-Adsorbents on Nitrogen Removal Rates in Industrial Wastewater

This study investigated the effect of contact time of two agricultural wastes (cassava peels and ripe plantain peels) used as bio-adsorbents that were modified with base and acids to ascertain their ability to absorb nitrogenous compounds from hospital and abattoir wastewater. The absorbents were prepared using standard procedures into powdered forms and a portion was modified with acid and base respectively. Fourier Infrared spectroscopy was used on the prepared absorbents to determine the organic and functional groups present. Each modified bio-adsorbents was used to inoculate each of the 250 ml flasks containing the wastewater sample. Contact was allowed to be made for 14 hours and the nitrogen removal rate was measured and recorded. The Nitrogen concentration in the wastewater was determined using Ultra-violent spectroscopy. Response surface optimization was used to investigate the effect of contact time on the nitrogen removal rate. Models were generated to analyze the interactions between variables at optimum conditions. The results showed that the bio-adsorbents have the ability to remove nitrogen from wastewater. The nitrogen percentage removal recorded was 57%, 81%, and 77%, 55%, 91% ,and 78% respectively. The R2 from ANOVA was seen to be 96.5%, 97.9%, 97.9%, 98.2%, and 99.7%, and 97.9% repectively. Experimental results were best fitted into linear and quadratic polynomial models. The optimum conditions having desirability of 0.964 showed that the time of 13.558 hrs. The values obtained are a good indicator that the bio adsorbents used in this should be considered by the chemical industries in the process of absorbent design and production.

Optimization of accelerated solvent extraction of bioactive compounds from Eucalyptus intertexta using response surface methodology and evaluation of its phenolic composition and biological activities

Polyphenols-rich extracts were obtained by accelerated solvent extraction (ASE) from E. intertexta leaves and analyzed by HPLC for the first time. Response surface methodology (RSM) based on central composite experimental design (CCD) was used to investigate the effect of three independent variables (temperature, extraction time, and ethanol concentration) on extraction yield, phenolic content, antioxidant, and anti-xanthine oxidase activities. The statistical analysis indicated that experimental data were well fitted to a quadratic polynomial model with regression coefficient values of more than 0.96 for all studied responses. Optimized extraction conditions, obtained by the maximization of a global desirability function, were 26.6% ethanol concentration, dynamic time of 36 min and a temperature of 179 °C. Under these optimal conditions the experimental values of the studied responses were 52.3 g extract/100 g DW extraction yield, 180.7 mg GAE/g extract total phenolic content, 17.2% antioxidant activity, and 55.3% anti-xanthine oxidase capacity. The HPLC analysis of all CCD extracts allowed to quantify twenty-two phenolic compounds in which eight molecules have not been reported previously in the genus of Eucalyptus. 3,4-dihydroxybenzoic, methyl ester, gallic acids, myricitrin, catechin, epicatechin, and polydatin were the most abundant phenolic compounds. The percentages of these compounds in extracts depend on the operating conditions. The best recovery of flavonoids (583 mg/g) was obtained at 100 °C, 10 min, and 44% ethanol, whereas phenolic acids (357 mg/g) were favored at a lower ethanol concentration of 11% and higher temperature and exposure time (180 °C, 36 min). The extraction of stilbenes (148 mg/g) was at its maximum at 140 °C, 1 min, and 27.5%.

Statistical analysis of treatment of rice mill wastewater using the aerobic inverse fluidized bed biofilm reactor (AIFBBR)

Among the different industries, the ricemill industry is identified as one of the major polluting industries because of the large amounts of effluents generated and variations in the concentration of its constituents. The current experimental study aimed to investigate the treatment of ricemill (simulated) using the aerobic inverse fluidized bed biofilm reactor (AIFBBR). Box–Behnken design (BBD) based statiscal response surface methodology (RSM) was used for the current AIFBBR-ricemill treatment system. For the evaluation of the RSM process through the BBD technique, three input variables: Influent COD (800–3200 mg/L), HRT (12–24 h), and % bed volume (15 − 45), and two output variables: %COD and %TOC removal, were selected. The effect of these chosen variables was verified by conducting 15 experiments in a multivariate system for the AIFBBR-ricemill treatment system. The quadratic polynomial models yielded a regression coefficient (R2) of 0.99 (for both COD and TOC) when the experimental data were fitted to it, which indicates the model's suitability for the process. The precision and efficiency of the model were visualized using the ANOVA and lack of fit test. The system has witnessed the maximum COD (88.95%) and TOC (95.68%) removal at optimum parameters of 2804.27 mg/L of influent COD, 23.79 h of HRT, and at a % bed volume of 44.94. Along with the COD and TOC, parameters such as nutrients (NH4+-N, PO43--P, and NO3--N), phenol, and lignin were also monitored in the present study.

Research on modeling and predicting of BDS3 satellite clock bias using the attention mechanism-based LSTM (AttLSTM) neural network model

In the Global Navigation Satellite System (GNSS), the satellite clock bias (SCB) plays an important role in the application of real-time precise point positioning (RT-PPP). Based on the operation of Beidou satellite global service, it is very important to establish a reliable Beidou SCB prediction model. In this research, an attention mechanism-based long short-term memory neural network (AttLSTM) model is applied to SCB prediction. The attention mechanism introduced in modelling can make the model pay less attention to useless information through weight allocation. In this paper, the BeiDou-3 Navigation Satellite System (BDS-3) satellite precision clock product provided by GFZ is used for clock prediction experiments. The proposed AttLSTM model, long short-term memory neural network (LSTM) model and quadratic polynomial (QP) model are compared and evaluated, and 12h and 24h SCB prediction experiments of BDS-3 satellite are set up. The results show that AttLSTM model can achieve high SCB prediction accuracy, and the averaged prediction accuracy of 12h and 24h can reach 1.41ns and 1.75ns. Compared with LSTM and QP models, the prediction accuracy of AttLSTM model is improved by 26.1%, 38.4% for 12h and 29.1%, 43.1% for 24h, respectively. Then, the clock bias predicted by the three models is applied to the static PPP positioning experiment, respectively. Through the analysis of the positioning results of 15 MGEX stations, the averaged positioning accuracy of AttLSTM model in the East, North and Up directions can reach 0.074m, 0.019m and 0.154m, respectively. Compared with LSTM and QP models, the positioning accuracy of AttLSTM model is improved by 42.5% and 44.4% in the East direction, 44.7% and 58.9% in the North direction, and 21.7% and 21.8% in the Up direction.

REVVING UP HEAT-TRANSFER PERFORMANCE OF DOUBLE PIPE HEAT EXCHANGER USING DIVERSE MOLAR Ag-GO HYBRID NANOFLUIDS: AN EMPIRICAL AND NUMERICAL STUDY USING CENTRAL COMPOSITE DESIGN

This study seamlessly integrates empirical and numerical approaches to enhance the efficiency of a double pipe heat exchanger (DPHX) using varied molar concentrations (0.03, 0.06, and 0.09 M) of Ag-doped GO hybrid nanofluids as the working fluids within the heat exchanger's annulus. Remarkable improvements in the heat exchanger's performance were achieved by increasing the molar concentration of Ag-GO hybrid nanoparticles, along with the Reynolds number (ranging from 250 to 1451) and the mass flow rate (ranging from 8 to 47 g/s) of the hybrid nanofluids. The utilization of a 0.09M Ag-GO hybrid nanofluid at a Reynolds number of 1451 and a flow rate of 47 g/s resulted in outstanding enhancements in heat-transfer coefficient (62.9%), and Nusselt number (33.55%) surpassing the base fluid. The empirical results of Nusselt number and heat-transfer coefficient were optimized and analyzed using the central composite design approach (CCD) with response surface method, incorporating the Graetz number, varied molar concentrations of Ag-GO, and thermal conductivity of the hybrid nanofluids as input factors. The optimized second-order polynomial quadratic equation model demonstrated excellent compatibility and optimal performance of the heat exchanger, supported by variance analysis. Additionally, CCD optimization confirmed a notable desirability function (0.99) and emphasized the significant influence of the input factors on the output responses. In conclusion, the Graetz number exhibited prominent influence among the input factors, alongside the molar concentration and thermal conductivity of hybrid nanofluids, effectively enhancing the performance of the DPHX.

Cure modeling and optimization of the composition of the polymer matrix for magnetostrictive composites

The paper explores the curing processes of unfilled epoxy resins ED-16. Mathematical modeling of curing processes has been performed and mathematical models of curing processes are presented in the form of full quadratic polynomial models. Separately, the degree of curing of unfilled epoxy compositions is studied. As a result of exploring the obtained models and performing optimization procedures for the ED-16 resin, theoretical aspects of curing high-viscosity epoxy systems were developed and recipe-technological parameters for obtaining a high-strength polymer matrix based on the high-viscosity ED-16 resin were proposed, which makes it possible to obtain epoxy composites with specified parameters structures and properties.

Optimization of Drying Parameters for Total Phenolic Content of Papaya Using Response Surface Methodology.

An optimum condition of the drying process can minimize nutrient losses and maximize the shelf life of food products. Thus, this study is aimed at developing an optimized system for the process conditions to determine the total phenolic content (TPC) of oven-dried papaya slices. The response surface method and central composite design were used to design the experiment, and it was found that the drying conditions had a significant impact on the total phenolic content of papaya slices. TPC was determined in relation to their interactions with the independent variables that include time, temperature, sample thickness, and stage of ripeness. The optimum drying conditions are those with the maximum content of TPC. In order to fit the experimental data, a quadratic polynomial model is created for the output variable, and an analysis of variance is carried out to determine whether or not the model is compatible to determine the optimal drying conditions. Time (10 h), temperature (62.02°C), thickness (9.75 mm) and stages (ripe) were found to be the optimal drying conditions. It was found that temperature had more effect on the amount of TPC than other factors. The numerical findings showed a good agreement with experimental data, with R 2 = 0.9237. It is hoped that the findings will make a contribution to the process of drying food.

A Complex Organomineral Admixture For Cement Saving

The aim of the research is to prepare a composition of a complex organomineral admixture based on industrial waste for cement saving, to experimentally establish the optimum crushing degree of an ash-and-slag waste composition and to estimate the ratio between mineral and organic components of the complex admixture, ensuring the possibility of reducing cement consumption without loss of concrete strength. As a mineral component of the complex admixture the wastes from thermoelectric power stations were used, representing by an ash-and-slag waste composition crushed to a fine powder condition. Optimization of the complex admixture is carried out using mathematical methods of the theory of planning experiments.
 As a result of this research a quadratic polynomial model of influence of crushing fineness of ash-and-slag wastes and cement consumption on concrete strength has been obtained. It is established, that depending on the degree of crushing of ash-and-slag waste composition within values of specific surface of received powder from 2400 to 4000 cm2/g it is possible to reduce cement consumption by 8-18 % without loss in strength of concrete. The introduction of complex organomineral admixtures into concrete is very effective as it saves cement. However, special studies are required in each case. The compositions of the new complex admixtures are tested on the basis of traditional raw materials, and economic efficiency in saving cement is established. It is proposed to use the methodology of mathematical modelling of the technological complex system under study, presented in this article.

Extraction optimization of anti-Parkinson’s medication L-DOPA and bioactive compounds from Mucuna atropurpurea (Roxb.) Wight & Arn. with biomedical potential: RSM based desirability function approach

A statistical optimization tool, Central Composite Design (CCD) part of Response Surface design with the aid of the desirability function (DF) approach was employed to optimize and intensify the anti-Parkinson’s medicine L-3, 4-dihydroxyphenylalanine (L-DOPA), and bio-constituents extraction from Mucuna atropurpurea. The antioxidant and anti-inflammatory activities of an optimized plant extract were measured systematically using a series of assays to determine their relationship with total phenolic and flavonoid contents. Following multiple regression analyses of the experimental results, the optimal conditions were found for the maximum extraction of L-DOPA (112.68 mg/g), TPC (66.505 mgGAE/g), and TFC (132.44 mgQUE/g) were 10.35 min of ultrasonication time, 5.55 h of incubation (shaking) time with 52 °C of extraction temperature. The L-DOPA content was quantified using validated RP-HPLC under the optimized conditions. An estimate of the ANOVA and fit statistics “Suggested” the Quadratic polynomial model fit satisfactory. Validated response surface plots (Ramp function, cube plot, and bar graph of desirability) were the confirmation outputs showing significance to the target response. In conclusion, RSM provides the measures of the influence of factors on each quality characteristic to achieve actual outcomes at the optimal settings and model fit. It also revealed that a multi-target strategy consisting of L-DOPA therapy, antioxidants, iron chelation, and anti-inflammatory properties of Mucuna atropurpurea may provide a targeted new approach toward neuroprotection in Parkinson’s disease. The approach proposed in this research proved to be robust and reliable for L-DOPA investigations, demonstrating its enormous potential for industrial applications.

Thermal and Dielectric Properties of Wolfberries as Affected by Moisture Content and Temperature Associated with Radio Frequency and Microwave Dehydrations

Knowledge of the thermal and dielectric properties of wolfberries is essential for understanding the heat transfer and the interaction between the electromagnetic field (10-3000 MHz) and the sample during radio frequency (RF) and microwave (MW) drying. The thermal and dielectric properties of wolfberries were determined as influenced by moisture content from 15.1% to 75.2%, w.b.) and temperature from 25 to 85 °C. The results showed that as the moisture content increased from 15.1% to 75.2% (w.b.), the true density of wolfberries decreased, but the specific heat capacity and thermal conductivity increased with increasing temperature and moisture content. The dielectric properties (DPs) of wolfberries decreased with increasing frequency from 10 to 3000 MHz. The dielectric constant increased with increasing temperature at lower a moisture content (below 45% w.b.) but decreased with increasing temperature at a high moisture content (above 60% w.b.). The cubic and quadratic polynomial models (R2 = 0.977 - 0.997) were best for fitting the dielectric constant and loss factor at four representative frequencies of 27, 40, 915, and 2450 MHz, respectively. The penetration depth increased with the decreased frequency, temperature, and moisture content, and was greater at RF frequencies than MW range, making the RF heating more effective for drying bulk wolfberries. These findings offered essential data before optimizing RF or MW dehydration protocols for wolfberries via computer simulation.

Process intensification of biopolymer polyhydroxybutyrate production by pseudomonas putida SS9: A statistical approach

There are numerous elements of daily life where plastic is employed, yet it is uncertain exactly when it will deteriorate. Poly-(3-hydroxybutyrate) (PHB), a biodegradable polymer, is viewed as a possible substitute for synthetic plastics made from petroleum. With Pseudomonas putida SS9, the current study sought to enhance operational conditions and nutritional factors to enhance PHB production. To maximize the impacts of operational factors, a combination of response surface modeling (RSM) and artificial neural networks (ANN) has been applied. PHB content was used as the response while the interaction effects of the factors were examined. The optimal parameters for PHB synthesis were further tested in a lab scale fermentor. Under optimal conditions, 13.83 g/L of C, 0.57 g/L of N, 0.59 g/L of P, the maximal productivity of PHB obtained with Pseudomonas putida SS9 is 12.89 g/L after 84 h. A mean square value of 15.7 with P < 0.0001 were obtained from the ANOVA results of quadratic polynomial model using RSM. The same construct was employed in MATLAB software to train a feed-forward ANN using the back-propagation approach, generating 12.88 g/L. The data indicated that a properly trained ANN model outperforms the RSM model in prediction. Furthermore, employing dairy waste (cheese whey) as a low-cost feedstock resulted in an equally proportionate PHB yield of 12.02 g/L. Therefore, cheese whey appeared to be a viable alternative carbon source over optimized synthetic media.

Study on the Removal of Iron and Manganese from Groundwater Using Modified Manganese Sand Based on Response Surface Methodology

This study used modified manganese sand as an adsorbent to explore its adsorption effect on iron and manganese ions from groundwater. The effects of pH, manganese sand dosage, and the initial concentration of Fe/Mn on the removal rate of iron and manganese ions were studied through single-factor experiments. Based on the above three factors, a quadratic polynomial model between the adsorption rate and the above factors was established to determine the optimal adsorption conditions. The response surface analysis showed that pH had the most significant effect on the adsorption process. The optimum conditions for the adsorption of iron and manganese ions by modified manganese sand were pH = 7.20, the dosage of manganese sand = 3.54 g/L, and the initial concentration ratio of Fe/Mn = 3.80. The analysis of variance showed that the RSM model could accurately reflect the adsorption process of manganese sand. In addition, we confirmed that the relative error between model predictions and experimental values was close to 1%, proving that the response surface model was reliable. The kinetic data of the manganese sand were described well with the pseudo-second-order model. The isothermal adsorption of iron and manganese ions by modified manganese sand was fitted well using the Langmuir equation.

Influencing parameter optimisation of CRDI engine fuelled with lemongrass biodiesel blends

Based on the central composite design of the experiment of 50 input combinations, the behaviour of common rail direct injection compressed ignition engine was examined, and maximum engine performance and minimal emission were found using multi-objective RSM. The optimal input combination was identified after thorough testing. It consisted of 12.3 kg engine load, 60% lemongrass biodiesel, 1000 bar FIP, 6obTDC FIT, and 0% exhaust gas recirculation. A second order quadratic model with R 2 values of 0.99, 0.99, 0.84, 0.98, 0.82, 0.93 and 0.94 for Torque, BMEP, BTE, mechanical efficiency, BSFC, CO and NOx, respectively, was developed by taking into account all of the input parameters for all of the investigated responses. This model can be used to predict output responses within the experimental analysis ranges, with a maximum combined desirability of 0.863. ANN model may be successfully implemented for better prediction of the engine each output response than RSM-based prediction. Highlights CCD-based DOE was made in the design expert 13 version by considering major influencing CRDI engine parameters. RSM was used to create a second-order polynomial quadratic model for forecasting torque, BMEP, mechanical efficiency, BTE, BSFC, CO and NOx. Furthermore, there was no apparent difference between the fitted values and the experimental data. The optimisation was done to enhance the BMEP, Torque, BTHE, and mechanical efficiency and minimise the BSFC, CO and NOx. The optimum combination for the best performance is obtained when running the engine at the following settings such as 100% EL, 60% fuel blend, 6obTDC of FIT, 1000 bar FIP and 0% EGR. The ANOVA study's findings indicate that EL has a 99% impact on BMEP and torque. The effect of EL on BTHE is 60.3%. EL affects 94.4% of mechanical efficiency. EL controls 55.9% of SFC. CO is influenced by EL, EGR and IT, respectively, by 60.58%, 10% and 5.3%. EGR and IT both affect NOx by 50.5% and 23.9%, respectively. CRDI engine output response prediction accuracy of ANN is better than RSM

Effects of citric acid supplementation on growth performance, intestinal morphology and microbiota, and blood parameters of geese from 1 to 28 days of age

This study was conducted to investigate the effects of citric acid (CA) supplementation on growth performance, intestinal morphology, intestinal microbiota, and blood parameters of geese from 1 to 28 d of age and evaluate the optimum additional level of citric acid. A total of 180 one-day-old male goslings were randomly allotted to 5 treatment groups of 36 birds with 6 replications. The control group was fed a basal diet, and the other groups were fed the basal diet supplemented with 0.25, 0.50, 1.00, and 2.00% of citric acid, respectively. The results showed that goslings fed the diet supplemented with 1.00% CA had higher final body weight (FBW) and average daily gain (ADG) than other groups (P < 0.05). The CA supplementation at 0.25 to 1.00% improved the morphology of duodenum or jejunum (P < 0.05). The jejunal content pH value was significantly reduced with the addition of CA compared with the control group (P < 0.05). As citric acid levels increased, the IgA concentrations in plasma increased and then decreased, and the goslings fed 1.00% CA supplementation had the highest IgA concentrations (P < 0.05). The supplementation of 1.00% and 2.00% CA in diet significantly reduced the malondialdehyde (MDA) concentration in plasma (P < 0.05). No significant difference was found on some indices related to liver function in plasma (P > 0.05), while creatinine significantly increased by the 2.00% CA supplementation (P < 0.05). Besides, the higher Coliform level in cecal content and worse intestinal morphology were observed when CA supplementation was up to 2.00%. Hence, the dietary CA supplementation (especially 1.00%) in goslings improves the growth performance, intestinal morphology, immunity and antioxidant, while excessive CA addition may cause negative effects. According to the quadratic polynomial model, the addition of CA in diet for obtaining maximum average daily feed intake (ADFI) should be 1.09% (10.9 g/kg diet) for goslings from 1 to 28 d of age.

Dynamic model of the flip-flow screen-penetration process and influence mechanism of multiple parameters

• A dynamic model of the flip-flow screen-penetration process was established. • The effect of main structural parameters on dynamic indexes was explored. • Quadratic polynomial model of parameters and dynamic index were obtained. • The significance of parameters in screen-penetration process were clarified. Flip-flow screening is an important method for classifying fine particles. The traditional research of flip-flow screening focuses on the final screening results, and neglects the screen-penetration process. However, the screen-penetration process directly affects the final screening effect. In this paper, a dynamic model of the flip-flow screen-penetration process was proposed and clarified the influence mechanism of main structural parameters on the screening process. First, based on theoretical derivation and regression fitting, the mathematical model of particle screen-penetration rate and screening time was established, and the dynamic evaluation index was obtained. Then, the effect of main structural parameters, namely, the excitation frequency, displacement excitation amplitude, and stretching amount of the screen plate, on the dynamic indexes, was explored. Furthermore, a quadratic polynomial model of the main structural parameters and the two-stage dynamic evaluation index were established by using the response surface method. The explicitness and interactivity of the effects of parameters were elucidated. This work is of great significance for the accurate control of the screening process.

Biosynthesis of Silver Nanoparticles Using White Propolis Extract as a Reduction Agent and Optimized by Box-Behnken Design

In this study, it was aimed to optimize the biosynthesis of silver nanoparticles with a Box-Behnken design. The white propolis extract was utilized as the reduction and stabilizing agent. The synthesized silver nanoparticles using white propolis extract solution were optimized by Box-Behnken design considering the effect of certain independent variables such as microwave power, time, and concentration of AgNO3 (silver nitrate). A quadratic polynomial model was used in mathematical modeling and response surface analysis was performed to determine the independent variable-response relationship. The optimum synthesis conditions were determined as 10 mM of AgNO3 concentration, 0.3 of VExt/VAg, 150 watts of microwave power, and 35 seconds. The optimized silver nanoparticles were characterized using FTIR (Fourier Infrared) spectroscopy, UV-VIS (Ultraviolent visible) spectrophotometry, and DLS (Dynamic Light Scattering). In addition, the antibacterial activity of the optimized silver nanoparticles was tested against Staphylococcus aureus (S. aureus), Klebsiella pneumonia (K. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa), and Enterococcus faecalis (E. faecalis) strains. It was observed that synthesized silver nanoparticles had higher antibacterial activity compared to propolis extract.

Optimization, Kinetics Isotherm, and Reusability Studies of Methylene Blue Dye Adsorption using Acrylic Acid Grafted Rubber Hydrogel

Hydrogel based on liquid natural rubber (LNR) crosslinked with acrylic acid (AAc) was synthesised and applied for the adsorption of methylene blue (MB) in aqueous solutions. The LNR/AAc hydrogel was prepared by free-radical polymerisation using potassium persulfate (KPS) and N,N-methylenebisacrylamide (MBA) as the initiator and the crosslinking agent, respectively. The effect of three variables (i.e., AAc:LNR weight ratio, KPS concentration and MBA concentration) on the hydrogel preparation for MBA removal was further investigated using response surface methodology (RSM). A quadratic polynomial model with the analysis of variance (ANOVA) results yielding R2 value of 0.9833 was consequently obtained. The optimum conditions for the hydrogel preparation were identified as AAc:LNR weight ratio of 2.59 g/g, KPS concentration of 0.08 M and MBA concentrations of 0.08 M and resulting a high percentage of MB removal about 92.57% was obtained. Therefore, the kinetic and isotherm models of MB removal were represented by the pseudo-second order and Freundlich model, respectively, and reusability studies were also investigated resulting in the hydrogel can be used up to four cycles.