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Neural computing efforts for integrated simulation of ultrasound-assisted hydration kinetics of wheat

Information Processing in Agriculture 2019年第3期6卷 357-374页

作者： S.M.Shafaei A.Nourmohamadi-Moghadami H.Rahmanian-Koushkaki S.Kamgar Department of Biosystems Engineering School of AgricultureShiraz UniversityShiraz 71441-65186Iran Department of Mechanical Engineering of Biosystems Faculty of AgricultureJahrom UniversityJahrom 74135-111Iran

This study is dedicated to examine predictive ability of neural computing environments,based on artificial neural network(ANN)and adaptive neuro-fuzzy inference system(ANFIS)strategies,for integrated simulation of ultrasound-assisted hydration kinetics of wheat *** process was accomplished at five hydration temperatures of 30,40,50,60 and 70C in ultrasonication conditions named control(without ultrasound treatment),US1(25 kHz,360 W)and US2(40 kHz,480 W).The hydration temperature,ultrasonication condition,and hydration time were used as input variables and moisture content was taken as output variable in the neural computing simulation *** account of statistical performance criteria,the distinguished ANFIS simulation environment with coefficient of determination of 0.991,root mean square error of 2.478%d.b.,mean relative deviation modulus of 4.301%and average of absolute values of simulation residual errors of 1.863%*** better performed than the distinguished ANN simulation *** ANFIS simulation results showed that individual or simultaneous increment of hydration temperature and hydration time caused nonlinear increment of moisture content at any given ultrasonication ***,physical perception obtained from the integrated ANFIS simulation results indicated congruency effect(sponge and acoustic cavitation)of cutting-edge ultrasound technology on water *** ANFIS simulation results improved the state of art in domain of studying ultrasoundassisted hydration process of ***,the distinguished ANFIS simulation environment is suggested to be served as an effective step towards management of ultrasound-assisted hydration process of wheat in seed priming,flour milling(tempering),making dough,and wet storage processes.

关键词： Moisture content hydration rate Water absorption hydration time hydration temperature

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Artificial neural network (ANNs) and mathematical modelling of hydration of green chickpea

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Information Processing in Agriculture 2021年第1期8卷 75-86页

作者： Yogesh Kumar Lochan Singh Vijay Singh Sharanagat Ayon Tarafdar Department of Food Engineering National Institute of Food Technology Entrepreneurship and ManagementHaryanaIndia Department of Agriculture and Environmental Sciences National Institute of Food Technology Entrepreneurship and ManagementHaryanaIndia

The present study was aimed to model the hydration characteristics of green chickpea(GC)using mathematical modelling and examine predictive ability of artificial neural network(ANN)*** of GC was performed at different temperatures 25,35,45,55 and 65℃.Different mathematical models were tested for the hydration at different *** ANN modelling,the hydration time and hydration temperature were used as input variables and moisture ratio,moisture content and hydration ratio were taken as output *** model best described the hydration behavior at 25℃;while hydration at high-temperature was better described by Page model and Ibarz et *** optimum temperature obtained for hydration was 35℃.Effective mass diffusion coefficient(D_(e))increased from 1.5510^(-11)-1.7910^(-9) m^(2)/s with the increase in the hydration *** low activation energy(39.66 kJ/moL)shows the low-temperature sensitiveness of *** temperature hydration(25℃)required higher time(>200 min)to achieve the equilibrium moisture content(EMC),however high temperature hydration(35–65℃)reduced the EMC time(150 min).ANN was used to predict the hydration behavior and K fold cross validation was performed to check the over fitting of ANN *** show that the LOGSIGMOID transfer function showed better performance when used at the hidden layer input node in conjunction to both PURELIN and *** was found suitable for moisture ratio(MR)and hydration ratio(HR)prediction,as opposed to PURELIN for moisture content(MC)*** model prediction was obtained when the number of neurons in the hidden layer for MC,MR and HR was 12,8 and 15,*** and ANN modelling results are useful to improve/predict the MC,MR and HR during hydration process of GC at different temperature and other similar process.

关键词： Green chickpea Water absorption hydration temperature ANN modeling

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The Performance Evolution Characteristics of Insitu Concrete at the Permafrost Regions

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Journal of Wuhan University of Technology(Materials Science) 2024年第6期39卷 1534-1540页

作者： NING Zuojun LI Haoxin NIE Kangjun Nueraili Maimaitituersun Aihemaitijiang Tuerhong GAO Xiang School of Transportation Kashi UniversityKashi 844000China KeyLaboratory of Advanced Civil Engineering Materials of Ministry of Education School of Materials Science and EngineeringTongji UniversityShanghai 201804China

This study aimed to investigate the performance evolution characteristics of concrete under permafrost ambient temperatures and to explore methods to mitigate the thermal perturbation by concrete on the permafrost environment.A program was designed to investigate the properties of various concretes at three curing *** compressive strength development pattern of each group was evaluated and the concrete's performance was characterized by compressive strength damage degree,hydration temperature and SEM analysis in a low temperature *** experimental results show that the incorporation of fly ash alone or incombination with other admixtures in concrete under low-temperature curing does not deteriorate its microstructure,and at the same time,it can slow down the hydration rate of cement and significantly reduce the exothermic heat of hydration of *** findings are expected to provide valuable references for the proportioning design of concrete in permafrost environments.

关键词： permafrost ambient mineral admixture compressive strength hydration temperature damage degree

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