Process Parameters Optimization of Laser Cladding for HT200 with 316L Coating Based on Response Surface Method

In order to improve the sealing surface performance of gray cast iron gas gate valves and achieve precise molding control of the cladding layer,as well as to reveal the influence of laser cladding process parameters on the morphology and structure of the cladding layer,we prepared the 316L coating on HT 200 by using Design-Expert software central composite design(CCD)based on response surface analysis.We built a regression prediction model and analyzed the ANOVA with the inspection results.With a target cladding layer width of 3.5 mm and height of 1.3 mm,the process parameters were optimized to obtain the best combination of process parameters.The microstructure,phases,and hardness variations of the cladding layer from experiments with optimal parameters were analyzed by the metallographic microscope,confocal microscope,and microhardness instrument.The experimental results indicate that laser power has a significant impact on the cladding layer width,followed by powder feed rate;scan speed has a significant impact on the cladding layer height,followed by powder feed rate.The HT200 substrate and 316L can metallurgically bond well,and the cladding layer structure consists of dendritic crystals,columnar crystals,and equiaxed crystals in sequence.The optimal process parameter combination satisfying the morphology requirements is laser power(A)of 1993 W,scan speed(B)of 8.949 mm/s,powder feed rate(C)of 1.408 r/min,with a maximum hardness of 1564.3 HV0.5,significantly higher than the hardness of the HT200 substrate.

Optimization of Methylene Blue Dye Adsorption onto Coconut Husk Cellulose Using Response Surface Methodology: Adsorption Kinetics, Isotherms and Reusability Studies

In this study, coconut husk cellulose was employed as a cost-effective and environmentally friendly adsorbent to eliminate methylene blue (MB) dye from aqueous solutions. The successful development of response surface methodology paired with a central composite design (RSM-CCD) enabled the optimization and modelling of the adsorption process. The study investigated the individual and combined effects of three variables (pH, contact time, and initial MB dye concentration) on the adsorption of MB dye onto coconut husk cellulose. The developed RSM-CCD model exhibited a remarkable degree of precision in predicting the removal efficiency of MB dye within the specified experimental parameters. This was demonstrated by the strong regression parameters, with an R2 value of 99.79% and an adjusted R2 value of 99.6%. The study depicted that the optimal parameters for attaining a 98.8827% removal of MB dye using coconut husk cellulose were as follows: an initial MB dye concentration of 30 mg∙L−1, contact time of 120 minutes, and pH 7 at a fixed adsorbent dose of 0.5 g. The Freundlich isotherm model provided the most satisfactory description of the equilibrium adsorption isotherms, suggesting that MB dye adsorption onto coconut husk cellulose occurs on a heterogeneous surface. The experimental results demonstrated a strong agreement with the pseudo-second-order kinetics model, indicating that the number of active sites present on the cellulose adsorbent predominantly influences the adsorption process of MB dye. Additionally, the adsorbent made from coconut husk cellulose exhibited the potential to be reused, as it retained its efficiency for a maximum of three cycles of adsorption of MB dye. The results of this study show that coconut husk cellulose has the potential to be an effective and sustainable adsorbent for removing MB dye from aqueous solutions.

Optimization of Cellulose Nanocrystal Isolation from Ayous Sawdust Using Response Surface Methodology

This study focuses on the extraction of cellulose nanocrystals (CNC), from microcrystalline cellulose (MCC), derived from Ayous sawdust. The process involves multiple steps and a large amount of chemical products. The objective of this research was to determine the effects of factors that impact the isolation process and to identify the optimal conditions for CNC isolation by using the response surface methodology. The factors that varied during the process were the quantity of MCC, the concentration of sulfuric acid, the hydrolysis time and temperature, and the ultrasonic treatment time. The response measured was the yield. The study found that with 5.80 g of microcrystalline cellulose, a sulfuric acid concentration of 63.50% (w/w), a hydrolysis time of 53 minutes, a hydrolysis temperature of 69˚C, and a sonication time of 19 minutes are the ideal conditions for isolation. The experimental yield achieved was (37.84 ± 0.99) %. The main factors influencing the process were the sulfuric acid concentration, hydrolysis time and temperature, with a significant influence (p < 0.05). Infrared characterization results showed that nanocrystals were indeed isolated. With a crystallinity of 35.23 and 79.74, respectively, for Ayous wood fiber and nanocrystalline cellulose were observed by X-ray diffraction, with the formation of type II cellulose, thermodynamically more stable than native cellulose type I.

Demand-Responsive Transportation Vehicle Routing Optimization Based on Two-Stage Method

Demand-responsive transportation(DRT)is a flexible passenger service designed to enhance road efficiency,reduce peak-hour traffic,and boost passenger satisfaction.However,existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs.Consequently,there is a need to develop realtime DRT route optimization methods that integrate both initial and real-time requests.This paper presents a twostage,multi-objective optimization model for DRT vehicle scheduling.The first stage involves an initial scheduling model aimed at minimizing vehicle configuration,and operational,and CO_(2)emission costs while ensuring passenger satisfaction.The second stage develops a real-time scheduling model to minimize additional operational costs,penalties for time window violations,and costs due to rejected passengers,thereby addressing real-time demands.Additionally,an enhanced genetic algorithm based on Non-dominated Sorting Genetic Algorithm-II(NSGA-II)is designed,incorporating multiple crossover points to accelerate convergence and improve solution efficiency.The proposed scheduling model is validated using a real network in Shanghai.Results indicate that realtime scheduling can serve more passengers,and improve vehicle utilization and occupancy rates,with only a minor increase in total operational costs.Compared to the traditional NSGA-II algorithm,the improved version enhances convergence speed by 31.7%and solution speed by 4.8%.The proposed model and algorithm offer both theoretical and practical guidance for real-world DRT scheduling.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Peak Shaving Strategy of Concentrating Solar Power Generation Based on Multi-Time-Scale and Considering Demand Response

According to the multi-time-scale characteristics of power generation and demand-side response(DR)resources,as well as the improvement of prediction accuracy along with the approaching operating point,a rolling peak shaving optimization model consisting of three different time scales has been proposed.The proposed peak shaving optimization model considers not only the generation resources of two different response speeds but also the two different DR resources and determines each unit combination,generation power,and demand response strategy on different time scales so as to participate in the peaking of the power system by taking full advantage of the fast response characteristics of the concentrating solar power(CSP).At the same time,in order to improve the accuracy of the scheduling results,the combination of the day-ahead peak shaving phase with scenario-based stochastic programming can further reduce the influence of wind power prediction errors on scheduling results.The testing results have shown that by optimizing the allocation of scheduling resources in each phase,it can effectively reduce the number of starts and stops of thermal power units and improve the economic efficiency of system operation.The spinning reserve capacity is reduced,and the effectiveness of the peak shaving strategy is verified.

Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

The widespread penetration of distributed energy sources and the use of load response programs,especially in a microgrid,have caused many power system issues,such as control and operation of these networks,to be affected.The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid.In this paper,the optimum operation of distributed generation resources and heat and power storage in a microgrid,was performed based on real-time pricing through the proposed gray wolf optimization(GWO)algorithm to reduce the energy supply cost with the microgrid.Distributed generation resources such as solar panels,diesel generators with battery storage,and boiler thermal resources with thermal storage were used in the studied microgrid.Also,a combined heat and power(CHP)unit was used to produce thermal and electrical energy simultaneously.In the simulations,in addition to the gray wolf algorithm,some optimization algorithms have also been used.Then the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO algorithm.The results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid,considering the demand response program.

Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering domains.To overcome the obstruction problem of large-scale wind power consumption in Northwest China,this paper constructs a bio-inspired computer model.It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load.First,the principle of wind power obstruction with the involvement of a high-energy load is examined in this work.In this step,highenergy load model with different regulation characteristics is established.Then,considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed,a multi-time scale model of coordination optimization is built.An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs,as well as to find the most optimal energy configurationwithin the system.Lastly,we take an example of regional power grid in Gansu Province for simulation analysis.Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.

Cross Validation Based Model Averaging for Varying-Coefficient Models with Response Missing at Random

In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.

Response Surface Methodology as an Approach for Optimization of Vinegar Fermentation Conditions Using Three Different Thermotolerant Acetic Acid Bacteria

This study aimed to investigate optimal fermentation conditions of biological acetic acid fermentation for vinegar production. Optimization was performed on 3 acetic acid bacteria strains namely VMA1, VMA7 and VMAO using Response Surface Methodology (RSM). A Box-Behnken-Design (BBD) was achieved with three different independent process parameters involving: fermentation temperature, original alcohol concentration and original acetic acid concentration and one dependent variable (acetic acid yield). The results showed that the mathematical models describe correctly the relationship between responses and factors (F values of the models (p R2 (coefficient of correlation) respectively 0.96, 0.94, 0.98, and adjusted R2 0.95, 0.92, 0.98). The maximum acidity was obtained respectively at fermentation temperatures, original alcohol concentrations and original acetic acid concentrations ranging from [37.5°C - 45°C], [16% - 20% (v/v)], [1.5% - 2% (w/v)] for VMA1, [40°C - 45°C], [14.5% - 20% (v/v)], [1.7% - 2% (w/v)] for VMA7 and [42°C - 45°C], [17% - 20% (v/v)], [1.5% - 2% (w/v)] for VMAO. The use of these acetic strains in the production of vinegar may seriously lead to a decrease or even an ablation of the costs related to the cooling of bioreactors especially in warm and hot countries, in the context of global warming.

Optimization of Photo-Fenton Catalyst Preparation Based Bamboo Carbon Fiber by Response Surface Methodology

In this paper,the residue from bamboo factory has been used to design photo-Fenton catalyst,which has the advantages of low cost and magnetic recycling.The photo-Fenton catalytic performance of the biocarbon-based catalyst was excellent and its optimal preparation process was also explored by response surface methodology.First,bamboo-carbon fiber was selected as the photo-Fenton catalyst carrier.Subsequently,the surface of the car-bon fiber was modified,with which dopamine,nano-Fe_(3)O_(4) and nano-TiO_(2) were successively loaded by hydro-thermal method.After the single factor tests,four factors including dopamine concentration,ferric chloride mass,P25 titanium dioxide mass and liquid-solid ratio were selected as the characteristic values.The degradation efficiency of photo-Fenton catalyst to methylene blue(MB)solution was treated as the response value.After the analysis of the response surface optimization,it was shown that the significance sequence of the selected 4 factors in terms of the MB degradation efficiency was arranged as follows:dopamine concentration>liquid-solid ratio>P25 titanium dioxide quality>ferric chloride quality.The optimal process parameters of fiber-carbon catalyst were affirmed as follows:the 1.7 mg/mL concentration of dopamine,the 1.2 g mass of ferric chloride,the 0.2 g mass of P25 titanium dioxide and the liquid-solid ratio of 170 mL/g.The experiment-measured average MB degra-dation efficiency performed by the optimized catalyst was 99.3%,which was nearly similar to the model-predicted value of 98.9%.It showed that the prediction model and response surface model were accurate and reliable.The results from response surface optimization could provide a good reference to design bamboo-based Fenton-like catalyst with excellent catalytic performance.

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 for Microbial Degumming of Ramie with Bacillus subtilis DZ_(5) in Submerged Fermentation by Orthogonal Array Design and Response Surface Methodology

As a kind of natural fiber,ramie fiber has distinctive advantages in textile application,but the application is limited due to the traditional degumming mode.Compared with the traditional degumming process,the microbial degumming process has many advantages.To obtain the optimal conditions for degumming ramie with Bacillus subtilis DZ_(5)(BS DZ_(5)),a combined statistical approach of orthogonal array design(OAD)and response surface methodology(RSM)was used.The influences of initial pH of the bacteria medium,culture temperature,shaking speed,degumming time and inoculum size on submerged fermentation degumming were evaluated by using fractional factorial design.The main factors in the analysis were culture temperature,shaking speed and initial pH.The residual gum mass fraction was used as the optimization index,and the optimal conditions for degumming were determined by central composite design and RSM.Thus with only a limited number of experiments,an optimal ramie microbial degumming condition was found as the culture temperature of 40℃,the initial pH in the culture medium of 8.5,the shaking speed of 205 r/min,the degumming time of 96 h and the inoculum size of 5%.After microbial degumming of ramie under the optimal conditions,there was only 10.6%residual gum by mass in the fiber.In addition,the effective degumming of BS DZ_(5)was also confirmed by a scanning electron microscope(SEM).

Response Surface Optimization of Ultrasound-assisted Aqueous Two-phase Extraction of Sweet Potato Leaf Polysaccharides

[Objectives]The ultrasound-assisted aqueous two-phase extraction of sweet potato leaf polysaccharides was studied.[Methods]With the yield of sweet potato leaf polysaccharides as the index,the aqueous two-phase extraction system was determined,and the optimal extraction conditions were optimized by single-factor experiments and response surface methodology.[Results]The optimal parameters were ethanol concentration 25.68%,liquid-to-material ratio 55.83,and ultrasonic treatment time 38.33 min.Under these conditions,the yield of sweet potato leaf polysaccharides could reach 20.646 mg/g.[Conclusions]The ethanol/ammonium sulfate aqueous system is a rapid and efficient method for extracting sweet potato leaf polysaccharides,which is of great significance for the application of sweet potato leaf extract as a natural food additive.

Economic Analysis of Demand Response Incorporated Optimal Power Flow

Demand Response(DR)is one of the most cost-effective and unfailing techniques used by utilities for consumer load shifting.This research paper presents different DR programs in deregulated environments.The description and the classification of DR along with their potential benefits and associated cost components are presented.In addition,most DR measurement indices and their evaluation are also highlighted.Initially,the economic load model incorporated thermal,wind,and energy storage by considering the elasticity market price from its calculated locational marginal pricing(LMP).The various DR programs like direct load control,critical peak pricing,real-time pricing,time of use,and capacity market programs are considered during this study.The effect of demand response in electricity prices is highlighted using a simulated study on IEEE 30 bus system.Simulation is done by the Shuffled Frog Leap Algorithm(SFLA).Comprehensive performance comparison on voltage deviations,losses,and cost with and without considering DR is also presented in this paper.

Electricity-Carbon Interactive Optimal Dispatch of Multi-Virtual Power Plant Considering Integrated Demand Response

As new power systems and dual carbon policies develop,virtual power plant cluster(VPPC)provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems.To solve the coordinated optimal operation and low-carbon economic operation problem in multi-virtual power plant,a multi-virtual power plant(VPP)electricity-carbon interaction optimal scheduling model considering integrated demand response(IDR)is proposed.Firstly,a multi-VPP electricity-carbon interaction framework is established.The interaction of electric energy and carbon quotas can realize energy complementarity,reduce energy waste and promote low-carbon operation.Secondly,in order to coordinate the multiple types of energy and load in VPPC to further achieve low-carbon operation,the IDR mechanism based on the user comprehensive satisfaction(UCS)of electricity,heat as well as hydrogen is designed,which can effectively maintain the UCS in the cluster within a relatively high range.Finally,the unit output scheme is formulated to minimize the total cost of VPPC and the model is solved using theCPLEX solver.The simulation results showthat the proposed method effectively promotes the coordinated operation among multi-VPP,increases the consumption rate of renewable energy sources and the economics of VPPC and reduces carbon emissions.

Catalytic Hydrothermal Liquefaction of Water Hyacinth Using Fe3O4/NiO Nanocomposite: Optimization of Reaction Conditions by Response Surface Methodology

This research aimed at optimizing the reaction conditions for the catalytic hydrothermal liquefaction (HTL) of water hyacinth using iron oxide/nickel oxide nanocomposite as catalysts. The iron oxide/nickel oxide nanocomposite was synthesized by the co-precipitation method and used in the hydrothermal liquefaction of water hyacinth. The composition and structural morphology of the synthesized catalysts were determined using X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic absorption spectroscopy (AAS). The particle size distribution of the catalyst nanoparticles was determined by the Image J software. Three reaction parameters were optimized using the response surface methodology (RSM). These were: temperature, residence time, and catalyst dosage. A maximum bio-oil yield of 59.4 wt% was obtained using iron oxide/nickel oxide nanocomposite compared to 50.7 wt% obtained in absence of the catalyst. The maximum bio-oil yield was obtained at a temperature of 320°C, 1.5 g of catalyst dosage, and 60 min of residence time. The composition of bio-oil was analyzed using gas chromatography-mass spectroscopy (GC-MS) and elemental analysis. The GC-MS results showed an increase of hydrocarbons from 58.3% for uncatalyzed hydrothermal liquefaction to 88.66% using iron oxide/nickel oxide nanocomposite. Elemental analysis results revealed an increase in the hydrogen and carbon content and a reduction in the Nitrogen, Oxygen, and Sulphur content of the bio-oil during catalytic HTL compared to HTL in absence of catalyst nanoparticles. The high heating value increased from 33.5 MJ/Kg for uncatalyzed hydrothermal liquefaction to 38.6 MJ/Kg during the catalytic HTL. The catalyst nanoparticles were recovered from the solid residue by sonication and magnetic separation and recycled. The recycled catalyst nanoparticles were still efficient as hydrothermal liquefaction (HTL) catalysts and were recycled four times. The application of iron oxide/ nickel oxide nanocomposites in the HTL of water hyacinth increases the yield of bio-oil and improves its quality by reducing hetero atoms thus increasing its energy performance as fuel. Iron oxide/nickel oxide nanocomposites used in this study are widely available and can be easily recovered magnetically and recycled. This will potentially lead to an economical, environmentally friendly, and sustainable way of converting biomass into biofuel.

Improvement of xylanase production by Aspergillus niger XY-1 using response surface methodology for optimizing the medium composition

Objective: To study the optimal medium composition for xylanase production by Aspergillus niger XY-1 in solid-state fermentation (SSF). Methods: Statistical methodology including the Plackett-Burman design (PBD) and the central composite design (CCD) was employed to investigate the individual crucial component of the medium that significantly affected the enzyme yield. Results: Firstly, NaNO3, yeast extract, urea, Na2CO3, MgSO4, peptone and (NH4)2SO4 were screened as the significant factors positively affecting the xylanase production by PBD. Secondly, by valuating the nitrogen sources effect, urea was proved to be the most effective and economic nitrogen source for xylanase production and used for further optimization. Finally, the CCD and response surface methodology (RSM) were applied to determine the optimal concentration of each sig-nificant variable, which included urea, Na2CO3 and MgSO4. Subsequently a second-order polynomial was determined by multiple regression analysis. The optimum values of the critical components for maximum xylanase production were obtained as follows: x1 (urea)=0.163 (41.63 g/L), x2 (Na2CO3)=?1.68 (2.64 g/L), x3 (MgSO4)=1.338 (10.68 g/L) and the predicted xylanase value was 14374.6 U/g dry substrate. Using the optimized condition, xylanase production by Aspergillus niger XY-1 after 48 h fermentation reached 14637 U/g dry substrate with wheat bran in the shake flask. Conclusion: By using PBD and CCD, we obtained the optimal composition for xylanase production by Aspergillus niger XY-1 in SSF, and the results of no additional expensive medium and shortened fermentation time for higher xylanase production show the potential for industrial utilization.

INTEGRATION SHAPE AND SIZING OPTIMIZATION OF COMPOSITE WING STRUCTURE BASED ON RESPONSE SURFACE METHOD

An effective optimization method for the shape/sizing design of composite wing structures is presented with satisfying weight-cutting results. After decoupling, a kind of two-layer cycled optimization strategy suitable for these integrated shape/sizing optimization is obtained. The uniform design method is used to provide sample points, and approximation models for shape design variables. And the results of sizing optimization are construct- ed with the quadratic response surface method （QRSM）. The complex method based on QRSM is used to opti- mize the shape design variables and the criteria method is adopted to optimize the sizing design variables. Compared with the conventional method, the proposed algorithm is more effective and feasible for solving complex composite optimization problems and has good efficiency in weight cutting.

DYNAMIC RESPONSE OPTIMIZATION DESIGN FOR ENGINEERING STRUCTURES BASED ON RELIABILITY

In many practical structures, physical parameters of material and applied loads have random property.To optimize this kind of structures,an optimum mathematical model was built.This model has reliability constraints on dynamic stress and displacement and upper & lower limits of the design variables. The numerical characteristic of dynamic response and sensitivity of dynamic response based on probability of structure were deduced respectively. By equivalent disposing, the reliability constraints were changed into conventional forms. The SUMT method was used in the optimization process.Two examples illustrate the correctness and practicability of the optimum model and solving approach.