Bamboo Fiber Modified Asphalt Mixture Proportion Design and Road Performances Based on Response Surface Method

In order to comprehensively utilize the remaining bamboo residue of bamboo products,this paper presents a research on recycling the bamboo fibers from bamboo residue for improving the performance of the asphalt mixtures.First of all,the basic performance parameters of sinocalamus affinis fiber,phyllostachys pubescens fiber,green bamboo fiber were tested and analyzed,and the optimal content and length were put forward.Then,the mix ratio design of the bamboo fiber modified asphalt mixture was further designed through the response surface method,and was verified the rationality of the mix ratio.Finally,the mixture specimens were made according to the experimental design mix ratio,and the high temperature,low temperature performance and moisture susceptibility of the bamboo fiber modified mixtures asphalt were tested.The results showed that the high temperature performance,low temperature performance and moisture susceptibility of bamboo fiber modified asphalt mixtures were improved compared with the performance of SBS modified asphalt mixture.When the length of bamboo fiber is 7.25 mm and the content of 0.22%,the road performance of the asphalt mixture was optimal.Consequentially,the decomposition of bamboo residue into bamboo fiber and its application in asphalt pavement can improve the reuse of bamboo waste,with remarkable environmental benefits and great promotion value.

Mechanical and Permeability Analysis and Optimization of Recycled Aggregate Pervious Concrete Based on Response Surface Method

In this paper,the effects of different influencing factors and factor interaction on the compressive strength and permeability of recycled aggregate pervious concrete(RAPC)were studied based on the response surface method(RSM).By selecting the maximum aggregate size,water cement ratio and target porosity as design variables,combined with laboratory tests and numerical analysis,the influences of three factors on the compressive strength and permeability coefficient of RAPC were revealed.The regression equation of compressive strength and permeability coefficient of recycled aggregate pervious concrete were established based on RSM,and the response surface model was optimized to determine the optimal ratio of RAPC under the conditions of meeting the mechanical and permeability properties.The results show that the mismatch item of the model is not significant,the model is credible,and the accuracy and reliability of the test are high,but the degree of uncorrelation between the test data and the model is not obvious.The sensitivity of the three factors to the compressive strength is water cement ratio>maximum coarse aggregate particle size>target porosity,and the sensitivity to the permeability coefficient is target porosity>maximum coarse aggregate particle size>water cement ratio.The absolute errors of the model prediction results and the model optimization results are 1.28 MPa and 0.19 mm/s,and the relative errors are 5.06%and 4.19%,respectively.With high accuracy,RSM can match the measured results of compressive strength and permeability coefficient of RAPC.

Optimization of Supercritical Extraction Process of Laoshan Black Tea by Response Surface Methodology

[Objectives]Laoshan black tea was subjected to supercritical CO_(2) extraction. [Methods]The extraction conditions of Laoshan black tea were studied by an orthogonal experiment and optimized by response surface methodology. [Results] The optimum extraction conditions of black tea extract by supercritical CO_(2) extraction were as follows: extraction pressure 23.53 MPa, extraction time 1.73 h, and extraction temperature 49.75 ℃, with which the extract yield could reach 5.15% theoretically. [Conclusions] Based on the traditional extraction process, a supercritical extraction method optimized by response surface methodology and a unique extraction process were formed, which enriches the extraction processes and methods of natural raw materials.

Response Surface Methodology-Based SERS for Determination of Gymnodimine

Gymnodimine (GYM), a fast-acting marine toxin, is destructive to aquaculture and human health through contaminated shellfish. The current detection methods in GYM have definite drawbacks in operation, such as the demand for delicate instruments and the consumption of time. Therefore, silver colloid was utilized as a surface-enhanced Raman scattering (SERS) desirable substrate for sensitive and rapid detection of GYM in lake and shellfish samples. The theoretical spectrum of GYM is calculated by density functional theory (DFT), and the substrate performance is evaluated by a rhodamine 6 G probe. Under the optimal SERS experimental condition calculated by the response surface methodology, the low limit of detection of 0.105 μM with R2 of 0.9873 and a broad linearity range of 0.1 - 10 μM was achieved for GYM detection. In addition, the substrate was satisfyingly applied to detect gymnodimine in the lake and shellfish matrix samples with LOD as low as 0.148 μM and 0.170 μM, respectively. These results demonstrated a promising SERS platform for detecting marine toxins in seafood for food safety and pharmaceutical research.

Using the Box-Behnken Response Surface Method to Optimize the Preparation and Characterization of Lavender and Fennel Volatile oilβ-cyclodextrin Form Inclusion Complex

Objective:The objective of this study is to study the best inclusion technology of Lavender-and fennel-mixed volatile oil by beta?cyclodextrin(β-CD) and characterize the final product thereafter.Methods:Using the saturated water solution method,the volatile β-CD inclusion complex was produced.The effect of volatile oil weight ratio,inclusion temperature and inclusion time on the inclusive quality was studied by measuring the yield of inclusion and inclusion rate of volatile oil as evaluation indexes.The preparation method of inclusion complex was then optimized by the Box-Behnken response surface method.The inclusion complex was characterized by ultraviolet spectrophotometry,thin-layer chromatography,thermogravimetry and differential thermal analysis,and the microscopic imaging method.Results:The optimized conditions were the weight ratio of β-CD to volatile oil was 8.13:1(g/ml).The inclusion temperature was 44°C.The inclusion time was 1 h.Conclusion:We were able to produce an inclusion complex with high inclusion rate of volatile oil and high yield of inclusion using the preparation method mentioned above.Furthermore,the method can also improve the stability of volatile oil in abnormal savda munziq.This study can provide a good reference for the development of new preparations.

Optimization of Rhododendri Daurici oil liposome by Box-Behnken design and response surface method

Objective:To prepare the liposomes of mangrove oil,Optimization of the formulation of mangrove oil liposomes by Box Behnken response surface methodology.Methods:Preparation of Rhododendron oil liposomes by ethanol injection probe ultrasound,Determination of gemacrone by HPLC.The ratio of lecithin to cholesterol(X1),drug lipid ratio(X2)and phospholipid concentration(X3)were used as independent variables,and encapsulation efficiency(Y)was used as dependent variable,the formulation was optimized by Box Behnken response surface method,and the entrapment efficiency was predicted.The entrapment efficiency,particle size,polydispersity index(PDI),Zeta potential and drug loading of the optimized liposomes were evaluated.Results:The optimal prescription and preparation of Folium Rhododendri Daurici oil liposome was confirmed as follows:X1=7.28:1、X2=11.34:1、X3=9.32mg·mL-1,the encapsulation efficiency was(82.55±1.66)%,the particle size was(130.531±46)nm,the polydispersity index was 0.185±05,Zeta potential was(21.970±36)mV,the drug loading was(5.941±0.12)%.Conclusion:The Box Behnken response surface method is accurate to obtain the optimal formulation of mangrove oil liposomes,it has high precision and good prediction effect.And the preparation process of mangrove oil liposomes is stable and feasible.

Optimization of the extraction process of total flavonoids from Trichosanthis Fructus by Box-Behnken response surface method combined with differential spectrophotometry

Objective:To optimize the extraction process of total flavones in Trichosanthis Fructus(composed of Trichosanthis pericarpium and Trichosanthis semen in certain proportion).Methods:The effects of the mixture ratio of Trichosanthis pericarpium and Trichosanthis semen,ethanol concentration,ultrasonic extraction time and extraction temperature on the extraction rate of total flavonoids in Trichosanthis Fructus were investigated.The extraction process of total flavonoids in Trichosanthis Fructus was optimized by Box-Behnken response surface method combined with differential spectrophotometry.Results:The optimum extraction conditions of total flavonoids in Trichosanthis Fructus were as follows:The mixture ratio of Trichosanthis pericarpium and Trichosanthis semen was 4:6,the ethanol concentration was 70%,the ultrasonic extraction time was 60min and the extraction temperature was 40℃.Conclusion:Box-Behnken response surface method combined with differential spectrophotometry can optimize the extraction process of total flavonoids from Trichosanthis Fructus,which can provide reference for the extraction and application of total flavonoids in Trichosanthis Fructus.

Box-Behnken response surface method combined with fingerprint to optimize the extraction process of total anthraquinone from Cassia seeds

Objective:The Box-Behnken response surface method combined with fingerprints was used to optimize the extraction process of total anthraquinone from Cassia seeds.Methods:A three-factor,three-level response surface test was conducted based on the single-factor test with comprehensive evaluation as the measurement index.The comprehensive evaluation indexes included the extraction rate of total anthraquinone of Cassia seeds or the equivalent amount of herbs per gram of total anthraquinone of Cassia seeds,the normalized value of peak areas of 5 index components such as aurantio obtusin in the fingerprint of each sample to 16 shared peaks and the similarity of fingerprints(the reference fingerprint was established by the extraction solvent for the determination of Cassia seeds content in the Chinese Pharmacopoeia 2020 edition)with the weights of 0.2,0.5 and 0.3,respectively.Results:The best extraction process was obtained:the liquid-to-material ratio was 20:1(mL·g-1),the extraction solvent was mixture of 60%ethanol-ethyl acetate(2:1),and the extraction time was 15.12 min.The results of five sets of validation experiments showed that the overall evaluation of total anthraquinone of cassia seeds by the best process was 0.528(RSD=0.45%),and the prediction result of response surface method model was 0.531,and the relative error with the prediction result was 0.531.The relative error of the predicted results was 0.56%,and the best extraction process was consistent with the model prediction,and the obtained best process could be used for the extraction of total anthraquinone from Cassia seeds.Conclusion:The Box-Behnken response surface method combined with the fingerprint technique can be used to find the best reaction conditions and examine the interactions among the factors in a comprehensive and accurate manner,which can provide reference for the optimization and evaluation of the extraction process of Chinese medicine.

Long-term deformation analysis of Shuibuya concrete face rockfill dam based on response surface method and improved genetic algorithm

Due to the size effects of rockfill materials, the settlement difference between numerical simulation and in situ monitoring of rockfill dams is a topic of general concern.The constitutive model parameters obtained from laboratory triaxial tests often underestimate the deformation of high rockfill dams.Therefore, constitutive model parameters obtained by back analysis were used to calculate and predict the long-term deformation of rockfill dams.Instead of using artificial neural networks (ANNs), the response surface method (RSM) was employed to replace the finite element simulation used in the optimization iteration.Only 27 training samples were required for RSM, improving computational efficiency compared with ANN, which required 300 training samples.RSM can be used to describe the relationship between the constitutive model parameters and dam settlements.The inversion results of the Shuibuya concrete face rockfill dam (CFRD) show that the calculated settlements agree with the measured data, indicating the accuracy and efficiency of RSM.

Distributed Collaborative Response Surface Method for Mechanical Dynamic Assembly Reliability Design

Because of the randomness of many impact factors influencing the dynamic assembly relationship of complex machinery, the reliability analysis of dynamic assembly relationship needs to be accomplished considering the randomness from a probabilistic perspective. To improve the accuracy and efficiency of dynamic assembly relationship reliability analysis, the mechanical dynamic assembly reliability（MDAR） theory and a distributed collaborative response surface method（DCRSM） are proposed. The mathematic model of DCRSM is established based on the quadratic response surface function, and verified by the assembly relationship reliability analysis of aeroengine high pressure turbine（HPT） blade-tip radial running clearance（BTRRC）. Through the comparison of the DCRSM, traditional response surface method（RSM） and Monte Carlo Method（MCM）, the results show that the DCRSM is not able to accomplish the computational task which is impossible for the other methods when the number of simulation is more than 100 000 times, but also the computational precision for the DCRSM is basically consistent with the MCM and improved by 0.40-4.63% to the RSM, furthermore, the computational efficiency of DCRSM is up to about 188 times of the MCM and 55 times of the RSM under 10000 times simulations. The DCRSM is demonstrated to be a feasible and effective approach for markedly improving the computational efficiency and accuracy of MDAR analysis. Thus, the proposed research provides the promising theory and method for the MDAR design and optimization, and opens a novel research direction of probabilistic analysis for developing the high-performance and high-reliability of aeroengine.

Optimizing electrocoagulation process for the treatment of biodiesel wastewater using response surface methodology

The production of biodiesel through a transesterification method produces a large amount of wastewater that contains high levels of chemical oxygen demand （COD） and oil and grease （O＆G）. Currently, flotation is the conventional primary treatment for O＆G removal prior to biological treatments. In this study, electrocoagulation （EC） was adopted to treat the biodiesel wastewater. The effects of initial pH, applied voltage, and reaction time on the EC process for the removal of COD, O＆G, and suspended solids （SS） were investigated using one factor at a time experiment. Furthermore, the Box-Behnken design, an experimental design for response surface methodology （RSM）, was used to create a set of 15 experimental runs needed for optimizing of the operating conditions. Quadratic regression models with estimated coefficients were developed to describe the pollutant removals. The experimental results show that EC could effectively reduce COD, O＆G, and SS by 55.43%, 98.42%, and 96.59%, respectively, at the optimum conditions of pH 6.06, applied voltage 18.2 V, and reaction time 23.5 min. The experimental observations were in reasonable agreement with the modeled values.

Optimization of High-Protein Glutinous Rice Flour Production Using Response Surface Method

A response surface method was employed to study the effect of α-amylase concentration, hydrolysis temperature and time on the production of high protein glutinous rice flour(HPGRF). The suspension of glutinous rice flour(15%) that contained 6.52% protein was gelatinized and subsequently hydrolyzed by thermostable α-amylase. The hydrolysis yielded 0.144–0.222 g/g HPGRF with 29.4%–45.4% protein content. Hydrolysis time exerted a significant effect, while enzyme concentration and hydrolysis temperature showed insignificant effect on the protein content and production yield of HPGRF. The result of response surface method showed that the optimum condition for the production of HPGRF that contained at least 36% protein was treating gelatinized 15% glutinous rice flour suspension with 0.90 Kilo Novo α-amylase Unit(KNU)/g α-amylase at 80 oC for 99 min. By carrying out the predicted hydrolysis condition, HPGRF with 35.9% protein and 61.8% carbohydrates was resulted. The process yielded 0.172 g/g HPGRF. HPGRF contained higher amount of essential amino acids compared to glutinous rice flour. HPGRF had higher solubility and lower swelling power, and also showed no pasting peak compared with glutinous rice flour.

Response surface method using grey relational analysis for decision making in weapon system selection

A proper weapon system is very important for a na- tional defense system. Generally, it means selecting the optimal weapon system among many alternatives, which is a multiple- attribute decision making （MADM） problem. This paper proposes a new mathematical model based on the response surface method （RSM） and the grey relational analysis （GRA）. RSM is used to obtain the experimental points and analyze the factors that have a significant impact on the selection results. GRA is used to an- alyze the trend relationship between alternatives and reference series. And then an RSM model is obtained, which can be used to calculate all alternatives and obtain ranking results. A real world application is introduced to illustrate the utilization of the model for the weapon selection problem. The results show that this model can be used to help decision-makers to make a quick comparison of alternatives and select a proper weapon system from multiple alternatives, which is an effective and adaptable method for solving the weapon system selection problem.

An Intelligent Method for Structural Reliability Analysis Based on Response Surface

As water depth increases, the structural safety and reliability of a system become more and more important and challenging. Therefore, the structural reliability method must be applied in ocean engineering design such as offshore platform design. If the performance function is known in structural reliability analysis, the first-order second-moment method is often used. If the performance function could not be definitely expressed, the response surface method is always used because it has a very clear train of thought and simple programming. However, the traditional response surface method fits the response surface of quadratic polynomials where the problem of accuracy could not be solved, because the true limit state surface can be fitted well only in the area near the checking point. In this paper, an intelligent computing method based on the whole response surface is proposed, which can be used for the situation where the performance function could not be definitely expressed in structural reliability analysis. In this method, a response surface of the fuzzy neural network for the whole area should be constructed first, and then the structural reliability can be calculated by the genetic algorithm. In the proposed method, all the sample points for the training network come from the whole area, so the true limit state surface in the whole area can be fitted. Through calculational examples and comparative analysis, it can be known that the proposed method is much better than the traditional response surface method of quadratic polynomials, because, the amount of calculation of finite element analysis is largely reduced, the accuracy of calculation is improved, and the true limit state surface can be fitted very well in the whole area. So, the method proposed in this paper is suitable for engineering application.

Advanced response surface method for mechanical reliability analysis

Based on the classical response surface method （RSM）, a novel RSM using improved experimental points （EPs） is presented for reliability analysis. Two novel points are included in the presented method. One is the use of linear interpolation, from which the total EPs for determining the RS are selected to be closer to the actual failure surface; the other is the application of sequential linear interpolation to control the distance be- tween the surrounding EPs and the center EP, by which the presented method can ensure that the RS fits the actual failure surface in the region of maximum likelihood as the center EPs converge to the actual most probable point （MPP）. Since the fitting precision of the RS to the actual failure surface in the vicinity of the MPP, which has significant contribution to the probability of the failure surface being exceeded, is increased by the presented method, the precision of the failure probability calculated by RS is increased as well. Numerical examples illustrate the accuracy and efficiency of the presented method.

Optimization of a fermented pumpkin-based beverage to improve Lactobacillus mali survival and-glucosidase inhibitory activity:A response surface methodology approach

The aim of this research was to develop an optimum fermentation and composition model for a new fermented pumpkin-based beverage with high probiotic survival and-glucosidase inhibitory activity.Relationship between fermentation temperature,inoculum and ingredient concentration with response variables(fermentation time at the fermentation endpoint pH 4.5,survival rate of Lactobacillus mali K8 in pumpkin-based beverage treated with simulated gastrointestinal tract enzyme fluids,-glucosidase inhibitory activity and sensory overall acceptability after 4 weeks of refrigerated storage)was investigated using response surface methodology.Optimal formulation was obtained at an approximation of 40%pumpkin puree concentration,8 Log CFU/mL inoculum and at 35℃.The product derived from this optimum formula reached the fermentation endpoint after 28.34±0.10 h and the quality change during 4 weeks storage was studied.The product achieved 88.56±0.67%of L.mali survival after treatment with simulated gastric and intestinal juices;demonstrated 95.89±0.30% α-glucosidase inhibitory activity,as well as scored 6.99±0.40 on sensory overall acceptability after 4 weeks of storage.These findings illustrated that the model is effective in improving probiotic survival and α-glucosidase inhibitory activity with excellent sensory acceptability,thus may offer a dietary means for the management of hyperglycaemia.

Statistical Modification Analysis of Helical Planetary Gears based on Response Surface Method and Monte Carlo Simulation

Tooth modification technique is widely used in gear industry to improve the meshing performance of gearings. However, few of the present studies on tooth modification considers the influence of inevitable random errors on gear modification effects. In order to investigate the uncertainties of tooth modification amount variations on system＇s dynamic behaviors of a helical planetary gears, an analytical dynamic model including tooth modification parameters is proposed to carry out a deterministic analysis on the dynamics of a helical planetary gear. The dynamic meshing forces as well as the dynamic transmission errors of the sun-planet 1 gear pair with and without tooth modifications are computed and compared to show the effectiveness of tooth modifications on gear dynamics enhancement. By using response surface method, a fitted regression model for the dynamic transmission error（DTE） fluctuations is established to quantify the relationship between modification amounts and DTE fluctuations. By shifting the inevitable random errors arousing from manufacturing and installing process to tooth modification amount variations, a statistical tooth modification model is developed and a methodology combining Monte Carlo simulation and response surface method is presented for uncertainty analysis of tooth modifications. The uncertainly analysis reveals that the system＇s dynamic behaviors do not obey the normal distribution rule even though the design variables are normally distributed. In addition, a deterministic modification amount will not definitely achieve an optimal result for both static and dynamic transmission error fluctuation reduction simultaneously.

Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles

In this paper, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), in a microwave-assisted extraction method to determine the amount of zinc in fish samples were investigated. The experiments were carried out based on a 3-level, 4-variable Box–Behnken design. The amount of zinc was considered as a function of four independent variables, namely irradiation power, irradiation time, nitric acid concentration, and temperature. The RSM results showed the quadratic polynomial model can be used to describe the relationship between the various factors and the response. Using the ANN analysis, the optimal configuration of the ANN model was found to be 4-10-1. After predicting the model using RSM and ANN, two methodologies were then compared for their predictive capabilities. The results showed that the ANN model is much more accurate in prediction as compared to the RSM.

Reliability analysis for aeroengine turbine disc fatigue life with multiple random variables based on distributed collaborative response surface method

The fatigue life of aeroengine turbine disc presents great dispersion due to the randomness of the basic variables,such as applied load,working temperature,geometrical dimensions and material properties.In order to ameliorate reliability analysis efficiency without loss of reliability,the distributed collaborative response surface method(DCRSM) was proposed,and its basic theories were established in this work.Considering the failure dependency among the failure modes,the distributed response surface was constructed to establish the relationship between the failure mode and the relevant random variables.Then,the failure modes were considered as the random variables of system response to obtain the distributed collaborative response surface model based on structure failure criterion.Finally,the given turbine disc structure was employed to illustrate the feasibility and validity of the presented method.Through the comparison of DCRSM,Monte Carlo method(MCM) and the traditional response surface method(RSM),the results show that the computational precision for DCRSM is more consistent with MCM than RSM,while DCRSM needs far less computing time than MCM and RSM under the same simulation conditions.Thus,DCRSM is demonstrated to be a feasible and valid approach for improving the computational efficiency of reliability analysis for aeroengine turbine disc fatigue life with multiple random variables,and has great potential value for the complicated mechanical structure with multi-component and multi-failure mode.

A new look at the response surface method for reliability analysis using chaos theory

To overcome excessive computation errors and convergence failures encountered in an iterative calculation of the reliability index using the response surface method （RSM） for some nonlinear limit state functions, this study investigates an essential factor based on chaotic dynamics theory. The bifurcation diagrams of the reliability index are presented for some typical nonlinear limit state functions, and the computation results from the mapping functions due to the RSM iterations show the complicated dynamic phenomena such as the periodic oscillation, as well as bifurcation and chaos. From the numerical examples, it is concluded that the parameter of selection range fplays an important role in the convergence of the RSM iteration, and an improved RSM iterative algorithm is proposed with regard to the incorporation of the iterative sequential function of selection rangef The proposed method is shown to be efficient and to yield accurate results.