Multi-criteria comparative analysis of the pressure drop on coal gangue fly-ash slurry at different parts along an L-shaped pipeline

Disposing of coal gangue and fly-ash on the surface is a risky method with tremendous potential catastrophic consequences for the environment.Backfill mining is a promising practice for turning those hazardous wastes into functional backfill materi-als.Unfortunately,how to efficiently deliver the slurry to the desired places remains under-researched.To address this issue,the computational fluid dynamics software Fluent was used in the current study in addition to a laboratory rheological test to simulate the impact of various parameters on the evolution of pressure at a particular section of the pipeline.Furthermore,the response surface method was employed to investigate how the various components and their corresponding influencing weights interact to affect the pressure drop.This study demonstrates that the pressure drop of the slurry is highly influenced by slurry concentration,speed,and pipe diameter.While conveying speed is the main component in the bend section,pipe diameter takes over in the horizontal and vertical pipe sections.

Prediction of Apple Fruit Quality by Soil Nutrient Content and Artificial Neural Network

The effect of soil nutrient content on fruit yield and fruit quality is very important.To explore the effect of soil nutrients on apple quality we investigated 200 fruit samples from 40 orchards in Feng County,Jiangsu Province.Soil mineral elements and fruit quality were measured.The effect of soil nutrient content on fruit quality was analyzed by artificial neural network(ANN)model.The results showed that the prediction accuracy was highest(R2=0.851,0.847,0.885,0.678 and 0.746)in mass per fruit(MPF),hardness(HB),soluble solids concentrations(SSC),titratable acid concentration(TA)and solid-acid ratio(SSC/TA),respectively.The sensitivity analysis of the prediction model showed that soil available P,K,Ca and Mg contents had the greatest impact on the quality of apple fruit.Response surface method(RSM)was performed to determine the optimum range of the available P,K,Ca,and Mg contents in orchards In Feng County,which were 10∼20 mg⋅kg^(−1),170∼200 mg⋅kg^(−1),1000∼1500 mg⋅kg^(−1),and 80∼200 mg⋅kg^(−1),respectively.The research also concluded that improving the content of available P and available Ca in orchard soil was crucial to improve apple fruit quality in Feng County,Jiangsu Province.

Multi-objective optimization of the cathode catalyst layer micro-composition of polymer electrolyte membrane fuel cells using a multi-scale,two-phase fuel cell model and data-driven surrogates

Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(V_(cell))also PEMFC fabrication and production(C_(stack))costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(L_(Pt)),weight ratio of ionomer to carbon(wt_(I/C)),weight ratio of Pt to carbon(wt_(Pt/c)),and porosity of cathode CL(ε_(cCL)),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R^(2).MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance.

Optimization of Insulin Rapid Amyloid Fibrosis Conditions

Amyloid fibers are now considered as one of the possible therapeutic targets of neurodegenerative diseases due to their unique physicochemical properties and toxicity. To efficiently trace in real time how the drug inhibits protein amyloid fibrosis, it is necessary to study the conditions for rapid amyloid fibrosis. Insulin is selected as the model protein in vitro to explore the process of amyloid formation. The effects of the molar concentration of NaCl, pH and reaction temperature in the single-factor experiment are discussed and the response surface analysis is carried out. Amyloid fibrosis is labeled by Thioflavin-T(ThT). The optimal molar concentration of NaCl, pH and reaction temperature for insulin rapid amyloid fibrosis are 50.0 mmol/L, 2.02 and 54℃, respectively. With the addition of 0.1 mmol/L phenol, the half-time of insulin amyloid fibrosis is shortened from 5.4 h to 1.7 h. The insulin rapid amyloid fibrosis system provides a new approach for screening the protein amyloid fibrosis inhibitors.

Response surface modeling-based source contribution analysis and VOC emission control policy assessment in a typical ozone-polluted urban Shunde,China

To develop a sound ozone（O_3） pollution control strategy,it is important to well understand and characterize the source contribution due to the complex chemical and physical formation processes of O_3.Using the ＂Shunde＂ city as a pilot summer case study,we apply an innovative response surface modeling（RSM） methodology based on the Community Multi-Scale Air Quality（CMAQ） modeling simulations to identify the O_3 regime and provide dynamic analysis of the precursor contributions to effectively assess the O_3 impacts of volatile organic compound（VOC） control strategy.Our results show that Shunde is a typical VOC-limited urban O_3 polluted city.The ＂Jiangmen＂ city,as the main upper wind area during July 2014,its VOCs and nitrogen oxides（NO_x） emissions make up the largest contribution（9.06%）.On the contrary,the contribution from local（Shunde） emission is lowest（6.35%） among the seven neighbor regions.The local VOCs industrial source emission has the largest contribution comparing to other precursor emission sectors in Shunde.The results of dynamic source contribution analysis further show that the local NO_x control could slightly increase the ground O_3 under low（10.00%） and medium（40.00%）reduction ratios,while it could start to turn positive to decrease ground O_3 under the high NO_x abatement ratio（75.00%）.The real-time assessment of O_3 impacts from VOCs control strategies in Pearl River Delta（PRD） shows that the joint regional VOCs emission control policy will effectively reduce the ground O_3 concentration in Shunde.

Co-inhibition of methanogens for methane mitigation in biodegradable wastes

The inhibition effects and mechanisms of chlorinated methane and acetylene on methanogenesis in the anaerobic digestion process of the biodegradable wastes were investigated.It was found that both chloroform and acetylene could effectively inhibit methanogens while the biodegradability of the wastes was not affected.Acetylene inhibited the activity of methanogens,while chloroform inhibited metabolic process of methanogenesis.A central composite design（CCD） and response surface regression analysis（RSREG） were employed to determine the optimum conditions and interaction effects of chloroform and acetylene in terms of inhibition effciency,production of volatile fatty acids（VAF） and molar ratio of propionic acid to acetic acid.Chloroform had significant effect on enhancing the production of VFA（F = 121.3;p 〈 0.01）,and acetylene promoted the inhibition effciency（F = 99.15;p 〈 0.05） more effectively than chloroform（F = 9.72;p 〉 0.05）.In addition,a maximum molar ratio of propionic acid to acetic acid of 1.208 was estimated under the optimum conditions of chloroform concentration of 9.05 mg/kg and acetylene concentration of 3.6×10^-3（V/V）.Hence,methanogens in the wastes can be inhibited while the stabilization process of the biodegradable wastes can still work well,as propionic acid generated during the inhibition process could hardly be utilized by methanogens.

Operation analysis and parameter optimization of the conveying device for uniform crushed straw throwing and seed-sowing machines

Uniform crushed straw throwing and seed-sowing machines can achieve the processes of straw chopping,straw transport,sowing,fertilization,and straw mulching at the same time,which is widely used in many areas of China.Conveying device is one of the important components used to convey,elevate and throw straw.However,the problems of high power consumption and congestion affect the promotion of the machine.Therefore,the conveying device of uniform crushed straw throwing and seed-sowing machine was analyzed in order to determine its device operation mechanism.Kinematic and dynamic analyses of particles of crushed rice straw during lifting and dispersion are used to develop a flexible-body model of rod-shaped and agglomerate-shaped crushed straw and a coupling model including the mechanical structure of the device.By integrating computational fluid dynamics and the discrete element method,the gas-solid coupling theory in numerical simulations and motion analysis of crushed straw particles is used to determine how the flow field and motion characteristics affect the conveying performance.Besides,regression equations to describe the relationships between the factors and each assessment index were established by using the regression analysis and response surface analysis with the software Design-Expert.The effect of throwing blade speed X_(1),conveying volume of crushed straw X_(2),and pipeline diameter X_(3) on the throwing speed of crushed straw Y_(1) and specific power consumption Y2 were investigated.The highest throwing speed of crushed straw and lowest specific power consumption are the optimization goal.The results of optimization showed that the predict the best optimal parameters were 2000 r/min throwing blade rotational speed,1.4 kg/s conveying volume,and 220 mm pipeline diameter,the planter achieved a throwing speed of 12.2 m/s and specific power consumption of 9179 m^(2)/s^(2).And then a field test verification was conducted.The planter achieved a throwing speed 12.4 m/s and specific power consumption 9070 m^(2)/s^(2) while selecting the best optimal parameters.Thus,the optimal parameters can provide a high-performance operation and satisfy the actual operation requirements The results provide a theoretical basis and data support for seeding technology innovation and equipment optimization to ensure uniform crushed straw throwing in dense rice stubble fields.

Multiparameter collaborative optimization of the vibrating screen based on the behavior of oil sunflower seed penetrating screen holes

The behavior of oil sunflower seeds penetrating screen holes is an important factor that affects the screening performance of oil sunflower seeds.In this study,a double-deck reverse-motion vibrating screening device for oil sunflower seed screening was designed.The force condition and motion law of the oil sunflower seeds on the screen surface were analyzed.This study compared the effect of particle filling amount of discrete element model of oil sunflower seeds on the simulation effects.The screening process was numerically simulated using the coupled Discrete Element Method and Multibody Dynamics(DEM-MBD)technique with the screening percentage of oil sunflower seeds as the index.The influence of the operating parameters of the vibrating screen on the screening effect was analyzed using a multiparameter collaborative optimization scheme.The results of this study can provide a reference for the numerical simulation of crop screening behavior and the development of screening devices.

Experiments and modeling of mechanism analysis of maize picking loss

Maize picking is the main form of maize harvest in China.Maize picking loss accounts for a large proportion of the current maize harvest loss.An experimental study and a theoretical analysis were conducted to explore the influencing factors and rules of maize picking loss.First,the boundary conditions,established by analyzing the mechanism of maize picking,determined the influences of maize picking loss.Then,single-factor experiments and a central composite design(CCD)method were used to determine the influence of various factors and their interactions on maize picking loss.Finally,the models of kernel loss and ear loss were set up to determine the optimal parameter combination of maize picking harvest.Field experiment verification was conducted.The results indicated that the optimal parameters of the maize picking harvest were the rotational speed of pulling rollers of 1120 r/min,operating speed of 1.94 m/s,the inclination of the header of 18°and clearance between the picking plates of 30 mm.By establishing these optimal parameters,the kernel loss rate was 0.065%,and the ear loss rate was 0%.The obtained experimental results and regression models could be used to predict the performance of the maize picking harvest,guide the adjustment of header working parameters,and provide a theoretical basis for reducing the mechanical loss of maize harvesting.

Formation mechanism for the laying angle of hemp harvester based on ANSYS-ADAMS

Aiming at the problem of large differences in the laying angle and posture of plants cut by the hemp harvester,which is unfavorable for the subsequent picking-up,this paper analyzed the laying process and laying angles,and built a conveyorplant rigid-flexible coupling model for simulating the laying of hemp plant.Moreover,the operating parameters were tested and optimized based on the central composite design theory,and carried out multi-objective optimization with the minimum laying angle as the response index.Firstly,the formation mechanism of the laying angle of hemp harvester was studied.Secondly,a test was designed with the quadratic orthogonal rotational combination test method,with the data being processed by Design-Expert.A regression mathematical model of the laying angle was built,and the influence of the interactions between factors on the laying angle was analyzed with the response surface method.Furthermore,multi-objective optimization was conducted on the regression model according to the actual production design requirements.As a result,the best combination was obtained,that is,when the forward speed is 0.7 m/s,speed ratio 1.40,and stubble height 95 mm,the minimum laying angle can be obtained,namely 124.9°.The optimization parameters were verified by the simulation and field tests.The simulation test showed that the simulated laying angle is 125.2°,with a relative error of 0.24%from the theoretical value,under the best combination of parameters.The field test showed that the average laying angle of hemp plant is 121.8°,with a relative error of 2.5%from the theoretical value,under the best combination of parameters.The results may provide a reference for the structural improvement and operating parameter control of hemp harvesters.