This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating t...This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating the oil production rate of the ZH86 reservoir block under waterflood recovery.The historical input variables:reservoir pressure,reservoir pore volume containing hydrocarbons,reservoir pore volume containing water and reservoir water injection rate used as inputs for ANN models.To create the NRS-ANN hybrid models,314 data sets extracted from the NRS model,which included reservoir pressure,reservoir pore volume containing hy-drocarbons,reservoir pore volume containing water and reservoir water injection rate were used.The output of the models was the historical oil production rate(HOPR in m^(3) per day)recorded from the ZH86 reservoir block.Models were developed using MATLAB R2021a and trained with 25 models in three replicate conditions(2,4 and 6),each at 1000 epochs.A comparative analysis indicated that,for all 25 models,the ANN outperformed the NRS-ANN in terms of processing speed and prediction performance.ANN models achieved an average of R^(2) and MAE of 0.8433 and 8.0964 m^(3)/day values,respectively,while NRS-ANN hybrid models achieved an average of R^(2) and MAE of 0.7828 and 8.2484 m^(3)/day values,respectively.In addition,ANN models achieved a processing speed of 49 epochs/sec,32 epochs/sec,and 24 epochs/sec after 2,4,and 6 replicates,respectively.Whereas the NRS-ANN hybrid models achieved lower average processing speeds of 45 epochs/sec,23 epochs/sec and 20 epochs/sec.In addition,the ANN optimal model outperforms the NRS-ANN model in terms of both processing speed and accuracy.The ANN optimal model achieved a speed of 336.44 epochs/sec,compared to the NRS-ANN hybrid optimal model,which achieved a speed of 52.16 epochs/sec.The ANN optimal model achieved lower RMSE and MAE values of 7.9291 m^(3)/day and 5.3855 m^(3)/day in the validation dataset compared with the hybrid ANS optimal model,which achieved 13.6821 m^(3)/day and 9.2047 m^(3)/day,respectively.The study also showed that the ANN optimal model consistently achieved higher R^(2) values:0.9472,0.9284 and 0.9316 in the training,test and validation data sets.Whereas the NRS-ANN hybrid optimal yielded lower R^(2) values of 0.8030,0.8622 and 0.7776 for the training,testing and validation datasets.The study showed that ANN models are a more effective and reliable tool,as they balance both processing speed and accuracy in estimating the oil production rate of the ZH86 reservoir block under the waterflooding recovery method.展开更多
The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,inve...The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,investors should consider investing in more secure assets,such as real estate property,cash,gold,and crypto assets.In recent years,among secure assets,cryptoassets are gaining more attention than traditional investments.This study compares the Bitcoin market,the gold market,and American stock indexes(S&P500,Nasdaq,and Dow Jones)before and during the COVID-19 pandemic.For this purpose,the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets.Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin,gold,and stock markets.In particular,we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period.This paper provides practical impacts on risk management and portfolio diversification.展开更多
The aim of the present study was to investigate the modeling and prediction of the high temperature flow characteristics of a cast magnesium(Mg-Al-Ca)alloy by both constitutive equation and ANN model.Toward this end,h...The aim of the present study was to investigate the modeling and prediction of the high temperature flow characteristics of a cast magnesium(Mg-Al-Ca)alloy by both constitutive equation and ANN model.Toward this end,hot compression experiments were performed in 250-450℃and in strain rates of 0.001-1 s^(−1).The true stress of alloy was first and foremost described by the hyperbolic sine function in an Arrhenius-type of constitutive equation taking the effects of strain,strain rate and temperature into account.Predictions indicated that unlike low strain rates and high temperature with dominant DRX activation,in relatively high strain rate and low temperature values,the precision of the models become decreased due to activation of twinning phenomenon.At that moment and for a better evaluation of twinning effect during deformation,a feed-forward back propagation ANN was developed to study the flow behavior of the investigated alloy.Then,the performance of the two suggested models has been assessed using a statistical criterion.The comparative assessment of the gained results specifies that the well-trained ANN is much more precise and accurate than the constitutive equations in predicting the hot flow behavior.展开更多
The aim of this study is to develop processing maps based on two models and compare them with conventional processing maps.The hyperbolic sinus constitutive equation and artificial neural network(ANN)approaches were u...The aim of this study is to develop processing maps based on two models and compare them with conventional processing maps.The hyperbolic sinus constitutive equation and artificial neural network(ANN)approaches were used in this investigation to predict flow stress and to develop processing maps in various conditions.The hot compression tests of InX-750 superalloy were carried out above the gamma prime phase temperature and within the temperature range of 1000-1150℃and strain rate of 0.001-1.000 s^(-1).The processing maps were conducted based upon dynamic material model(DMM)for data by experimental,constitutive equation and ANN approaches.The processing maps drawn by either of the prediction methods show that the method developed by ANN data does not significantly differ from the experimental processing map.The ANN approach is thus a suitable way to predict the flow stress as well as hot working processing map of engineering metals and materials.展开更多
The pulsating heat pipe is a very promising heat dissipation device to address the challenge of higher heat-flux electronic chips,as it is characterised by excellent heat transfer ability and flexibility for miniaturi...The pulsating heat pipe is a very promising heat dissipation device to address the challenge of higher heat-flux electronic chips,as it is characterised by excellent heat transfer ability and flexibility for miniaturisation.To boost the application of PHP,reliable heat transfer performance evaluationmodels are especially important.In this paper,a heat transfer correlation was firstly proposed for closed PHP with various working fluids(water,ethanol,methanol,R123,acetone)based on collected experimental data.Dimensional analysis was used to group the parameters.It was shown that the average absolute deviation(AAD)and correlation coefficient(r)of the correlation were 40.67%and 0.7556,respectively.For 95%of the data,the prediction of thermal resistance and the temperature difference between evaporation and condensation section fell within 1.13K/Wand 40.76K,respectively.Meanwhile,an artificial neural networkmodelwas also proposed.The ANN model showed a better prediction accuracy with a mean square error(MSE)and correlation coefficient(r)of 7.88e-7 and 0.9821,respectively.展开更多
Based on the Residual Oil Hydrodesulfurization Treatment Unit (S-RHT), the n-order reaction kinetic model for residual oil HDS reactions and artificial neural network (ANN) model were developed to determine the sulfur...Based on the Residual Oil Hydrodesulfurization Treatment Unit (S-RHT), the n-order reaction kinetic model for residual oil HDS reactions and artificial neural network (ANN) model were developed to determine the sulfur content of hydrogenated residual oil. The established ANN model covered 4 input variables, 1 output variable and 1 hidden layer with 15 neurons. The comparison between the results of two models was listed. The results showed that the predicted mean relative errors of the two models with three different sample data were less than 5% and both the two models had good predictive precision and extrapolative feature for the HDS process. The mean relative error of 5 sets of testing data of the ANN model was 1.62%—3.23%, all of which were smaller than that of the common mechanism model (3.47%— 4.13%). It showed that the ANN model was better than the mechanism model both in terms of fitting results and fitting difficulty. The models could be easily applied in practice and could also provide a reference for the further research of residual oil HDS process.展开更多
The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological...The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological factors is investigated.The analysis combines a traditional numerical approach with machine learning techniques.Mathematical equations describing the system are transformed into a dimensionless form and then solved using computational methods.The artificial neural network(ANN)model,trained with the Levenberg-Marquardt method,accurately predicts(Nu)values,showing high correlation(R=1),low mean squared error(MSE),and minimal error clustering.Parametric analysis reveals significant effects of parameters,length and location of source(B),(D),heat generation/absorption coefficient(Q),and porosity parameter(ε).Increasing the cooling area length(B)reduces streamline intensity and local Nusselt and Sherwood numbers,while decreasing isotherms,isoconcentrations,and micro-rotation.The Bejan number(Be+)decreases with increasing(B),whereas(Be+++),and global entropy(e+++)increase.Variations in(Q)slightly affect streamlines but reduce isotherm intensity and average Nusselt numbers.Higher(D)significantly impacts isotherms,iso-concentrations,andmicro-rotation,altering streamline contours and local Bejan number distribution.Increased(ε)enhances streamline strength and local Nusselt number profiles but has mixed effects on average Nusselt numbers.These findings highlight the complex interactions between cooling area length,fluid flow,and heat transfer properties.By combining finite volume method(FVM)with machine learning technique,this study provides valuable insights into the complex interactions between key parameters and heat transfer,contributing to the development of more efficient designs in applications such as cooling systems,energy storage,and bioengineering.展开更多
According to the test data of subdivision errors in the measuring cycle of angular measuring system, the characteristics of subdivision errors generated by this system are analyzed. It is found that the subdivision er...According to the test data of subdivision errors in the measuring cycle of angular measuring system, the characteristics of subdivision errors generated by this system are analyzed. It is found that the subdivision errors are mainly due to the rotary-type inductosyn itself. For the characteristic of cyclical change, the subdivision errors in other measuring cycles can be compensated by the subdivision error model in one measuring cycle. Using the measured error data as training samples, combining GA and BP algorithm, an ANN model of subdivision error is designed. Simulation results indicate that GA reduces the uncertainty in the training process of the ANN model, and enhances the generalization of the model. Compared with the error model based on the least-mean-squared method, the designed ANN model of subdivision errors can achieve higher compensating precision.展开更多
The retrieval of the biomass parameters from active/passive microwave remote sensing data （10.2 GHz） is performed based on an iterative inversion of BP neural network model with fuzzy optimization. The BP neural net...The retrieval of the biomass parameters from active/passive microwave remote sensing data （10.2 GHz） is performed based on an iterative inversion of BP neural network model with fuzzy optimization. The BP neural network is trained by a set of the measurements of active and passive remote sensing and the ground truth data versus Day of Year during growth. Once the network training is complete, the model can be used to retrieve the temporal variations of the biomass parameters from another set of observation data. The model was used in weights and microware observation data of wheat growth in 1989 to retrieve biomass parameters change of wheat growth this year. The retrieved biomass parameters correspond well with the real data of the growth, which shows that the BP model is scientific and sound.展开更多
Most of the water quality models previously developed and used in dissolved oxygen （DO） prediction are complex. Moreover, reliable data available to develop/calibrate new DO models is scarce. Therefore, there is a n...Most of the water quality models previously developed and used in dissolved oxygen （DO） prediction are complex. Moreover, reliable data available to develop/calibrate new DO models is scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent decades, computational intelligence techniques, as effective approaches for predicting complicated and significant indicator of the state of aquatic ecosystems such as DO, have created a great change in predictions. In this study, three different AI methods comprising： （1） two types of artificial neural networks （ANN） namely multi linear perceptron （MLP） and radial based function （RBF）; （2） an advancement of genetic programming namely linear genetic programming （LGP）; and （3） a support vector machine （SVM） technique were used for DO prediction in Delaware River located at Trenton, USA. For evaluating the performance of the proposed models, root mean square error （RMSE）, Nash-Sutcliffe efficiency coefficient （NS）, mean absolute relative error （MARE） and, correlation coeffi- cient statistics （R） were used to choose the best predictive model. The comparison of estimation accu- racies of various intelligence models illustrated that the SVM was able to develop the most accurate model in DO estimation in comparison to other models. Also, it was found that the LGP model performs better than the both ANNs models. For example, the determination coefficient was 0.99 for the best SVM model, while it was 0.96, 0.91 and 0.81 for the best LGP, MLP and RBF models, respectively. In general, the results indicated that an SVM model could be employed satisfactorily in DO estimation.展开更多
In this paper, the artificial neural network(ANN) model was used to evaluate the degree of intensive urban land use in Nanjing City, China. The construction and application of the ANN model took into account the compr...In this paper, the artificial neural network(ANN) model was used to evaluate the degree of intensive urban land use in Nanjing City, China. The construction and application of the ANN model took into account the comprehensive, spatial and complex nature of urban land use. Through a preliminary calculation of the degree of intensive land use of the sample area, representative sample area selection and using the back propagation neural network model to train, the intensive land use level of each evaluation unit is finally determined in the study area. Results show that the method can effectively correct the errors caused by the limitations of the model itself and the determination of the ideal value and weights when the multifactor comprehensive evaluation is used alone. The ANN model can make the evaluation results more objective and practical. The evaluation results show a tendency of decreasing land use intensity from the core urban area to the periphery and the industrial functional area has relatively low land use intensity compared with other functional areas. Based on the evaluation results, some suggestions are put forward, such as transforming the mode of urban spatial expansion, strengthening the integration and potential exploitation of the land in the urban built-up area, and strengthening the control of the construction intensity of protected areas.展开更多
An artificial neural network model for backside bead width was established and three control methods——PID, fuzzy and neuron were designed, simulated and tested. The test results of bead on plate weld of GTAW indicat...An artificial neural network model for backside bead width was established and three control methods——PID, fuzzy and neuron were designed, simulated and tested. The test results of bead on plate weld of GTAW indicate that the artificial neural network (ANN) modeling and learning control method have more advantages than the conventional method. They show that the ANN modeling and learning control method is an effective approach to real time control of welding dynamics and ideal quality.展开更多
The thermo-physical properties of nanofluids are highly dependent on the used base fluid.This study explores the influence of the mixing ratio on the thermal conductivity and viscosity of ZnO-CuO/EG(ethylene glycol)-W...The thermo-physical properties of nanofluids are highly dependent on the used base fluid.This study explores the influence of the mixing ratio on the thermal conductivity and viscosity of ZnO-CuO/EG(ethylene glycol)-W(water)hybrid nanofluids with mass concentration and temperatures in the ranges 1-5 wt.%and 25-60C,respectively.The characteristics and stability of these mixtures were estimated by TEM(transmission electron microscopy),visual observation,and absorbance tests.The results show that 120 min of sonication and the addition of PVP(polyvinyl pyrrolidone)surfactant can prevent sedimentation for a period reaching up to 20 days.The increase of EG(ethylene glycol)in the base fluid leads to low thermal conductivity and high viscosity.Thermal conductivity enhancement(TCE)decreases from 21.52%to 11.7%when EG:W is changed from 20:80 to 80:20 at 1 wt.%and 60C.A lower viscosity of the base fluid influences more significantly the TCE of the nanofluid.An Artificial Neural Network(ANN)has also been used to describe the effectiveness of these hybrid nanofluids as heat transfer fluids.The optimal number of layers and neurons in these models have been found to be 1 and 5 for viscosity,and 1 and 7 for thermal conductivity.The corresponding coefficient of determination(R^(2))was 0.9979 and 0.9989,respectively.展开更多
In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,2020.The proposed model is...In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,2020.The proposed model is based on the existing data(training data)published in the Saudi Arabia Coronavirus disease(COVID-19)situation—Demographics.The Prey-Predator algorithm is employed for the training.Multilayer perceptron neural network(MLPNN)is used in this study.To improve the performance of MLPNN,we determined the parameters of MLPNN using the prey-predator algorithm(PPA).The proposed model is called the MLPNN–PPA.The performance of the proposed model has been analyzed by the root mean squared error(RMSE)function,and correlation coefficient(R).Furthermore,we tested the proposed model using other existing data recorded in Saudi Arabia(testing data).It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia.The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789.The number of recoveries will be 2000 to 4000 per day.展开更多
Groundwater resources have considerable influences on the human population and socioeconomic development of Vietnam and the Mekong River Delta(MRD). This paper presents an overview of the relationship between climate ...Groundwater resources have considerable influences on the human population and socioeconomic development of Vietnam and the Mekong River Delta(MRD). This paper presents an overview of the relationship between climate change and groundwater in the MRD, including the challenges, strategies and technical measures. Our results showed that groundwater levels are related to other climate and hydrological variables(i.e., rainfall, river levels, etc.); therefore, the impacts of climate change on the groundwater resources of the Mekong delta are significant, especially on groundwater recharge. Based on the results of this study, it is recommended that groundwater development in the future should focus on reducing groundwater harvesting, enhancing groundwater quantity by establishing artificial works and exploiting surface water. This study suggests that the Artificial Neural Network(ANN) model is an effective tool for forecasting groundwater levels in periods of 1 month and 3 months for aquifers in the natural and tidal regime areas of the delta.展开更多
This paper presents experimental performance and artificial neural network modeling of a large-scale greenhouse solar dryer for drying of natural rubber sheets. The dryer consists of a parabolic roof structure covered...This paper presents experimental performance and artificial neural network modeling of a large-scale greenhouse solar dryer for drying of natural rubber sheets. The dryer consists of a parabolic roof structure covered with polycarbonate sheets on a concrete floor. The dryer is 9.0 m in width, 27.0 m in length and 3.5 m in height. Nine 15-W DC fans powered by three 50-W PV modules were used to ventilate the dryer. To investigate its performance, the dryer was used to dry six batches of natural rubber sheets. For each batch, 750 kg of rubber sheets were dried in the dryer. Results obtained from the experiments showed that drying temperatures varied from 32 ~C to 55 ~C and the use of the dryer led to a considerable reduction of drying time, as compared to the open air sun drying. In addition, the quality of the product from the dryer was high-quality dried products. A multilayer neural network model was developed to predict the performance of this dryer. The predictive power of the model was found to be high after it was adequately trained.展开更多
Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data,and noisy data can affect the modeling performance.On the other hand in training phase of any Artificial Int...Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data,and noisy data can affect the modeling performance.On the other hand in training phase of any Artificial Intelligence(AI) based model,each training data set is usually a limited sample of possible patterns of the process and hence,might not show the behavior of whole population.Accordingly,in the present paper,wavelet-based denoising method was used to smooth hydrological time series.Thereafter,small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smooth time series to form different denoised-jittered data sets.Finally,the obtained pre-processed data were imposed into Artificial Neural Network(ANN) and Adaptive Neuro-Fuzzy Inference System(ANFIS)models for daily runoff-sediment modeling of the Minnesota River.To evaluate the modeling performance,the outcomes were compared with results of multi linear regression(MLR) and Auto Regressive Integrated Moving Average(ARIMA)models.The comparison showed that the proposed data processing approach which serves both denoising and jittering techniques could enhance the performance of ANN and ANFIS based runoffsediment modeling of the case study up to 34%and 25%in the verification phase,respectively.展开更多
Pomegranate peels are disposed of as waste even though it is an eminent source of total hydrolysable tannins(THT)and possesses a great worth for recycling.The present study was carried out with the hypothesis that pul...Pomegranate peels are disposed of as waste even though it is an eminent source of total hydrolysable tannins(THT)and possesses a great worth for recycling.The present study was carried out with the hypothesis that pulsed system ultrasound-assisted extraction(PSUAE)technique optimization would aid in retaining high extract yield and THT recovery yield from pomegranate peel extract(PPE)with enhanced antioxidant potential.The extraction was performed at various conditions regarding solvent concentration(30-90% of acetone),solvent-feed ratio(10-30 mL/g),extraction time(10-20 min),and ultrasound amplitude(40-80%)for maximum extract yield,THT and antioxidant activity(AA).To model and optimize the process conditions tools namely response surface methodology(RSM)and artificial neural network(ANN)were applied.For solvent concentration,solvent-feed ratio,extraction time,and amplitude the optimal conditions attained with aid of RSM and ANN were:56%,26.5 mL/g,15 min,55%,and 45%,23 mL/g,15 min,50% respectively.The extract yield and THT content determined with RSM and ANN optimized values were 51.2%,86.4 mg TAE/g,and 62%,98.1 mg TAE/g,respectively.The mean square error value displayed the minimum and R^(2) exhibited maximum value in case of ANN compared to RSM model.PSUAE significantly augmented the AA of PPE to 77.2±0.50% compared to 49.7±0.35% of conventional extraction.FTIR and HPLC analysis established that PPE produced from PSUAE contains a significant amount of THT compounds(gallic acid and tannic acid derivative).SEM elucidated that the sonication effect resulted in peel cell wall disruptions ensuing in elevated extraction of THT.Therefore,this pulsed mode of ultrasound extraction could be regarded as an easy,cost-effective,and competent technique for the extraction of preferred natural THT from pomegranate peels that possess high prospective to be applied in food and nutraceutical formulations.展开更多
Due to recent advances in the field of artificial neural networks(ANN)and the global sensitivity analysis(GSA)method,the application of these techniques in structural analysis has become feasible.A connector is an imp...Due to recent advances in the field of artificial neural networks(ANN)and the global sensitivity analysis(GSA)method,the application of these techniques in structural analysis has become feasible.A connector is an important part of a composite beam,and its shear strength can have a significant impact on structural design.In this paper,the shear performance of perfobond rib shear connectors(PRSCs)is predicted based on the back propagation(BP)ANN model,the Genetic Algorithm(GA)method and GSA method.A database was created using push-out test test and related references,where the input variables were based on different empirical formulas and the output variables were the corresponding shear strengths.The results predicted by the ANN models and empirical equations were compared,and the factors affecting shear strength were examined by the GSA method.The results show that the use of ANN model optimization by GA method has fewer errors compared to the empirical equations.Furthermore,penetrating reinforcement has the greatest sensitivity to shear performance,while the bonding force between steel plate and concrete has the least sensitivity to shear strength.展开更多
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)modelling.Hydration of GC was perfor...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)modelling.Hydration of GC was performed at different temperatures 25,35,45,55 and 65℃.Different mathematical models were tested for the hydration at different temperatures.In 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 variables.Peleg model best described the hydration behavior at 25℃;while hydration at high-temperature was better described by Page model and Ibarz et al.model.The 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 temperature.The low activation energy(39.66 kJ/moL)shows the low-temperature sensitiveness of GC.Low 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 model.Results show that the LOGSIGMOID transfer function showed better performance when used at the hidden layer input node in conjunction to both PURELIN and TANSIGMOID.TANSIGMOID was found suitable for moisture ratio(MR)and hydration ratio(HR)prediction,as opposed to PURELIN for moisture content(MC)data.Satisfactory model prediction was obtained when the number of neurons in the hidden layer for MC,MR and HR was 12,8 and 15,respectively.Mathematical 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.展开更多
基金National Natural Science Foundation of China grants no.41972326 and 51774258.
文摘This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating the oil production rate of the ZH86 reservoir block under waterflood recovery.The historical input variables:reservoir pressure,reservoir pore volume containing hydrocarbons,reservoir pore volume containing water and reservoir water injection rate used as inputs for ANN models.To create the NRS-ANN hybrid models,314 data sets extracted from the NRS model,which included reservoir pressure,reservoir pore volume containing hy-drocarbons,reservoir pore volume containing water and reservoir water injection rate were used.The output of the models was the historical oil production rate(HOPR in m^(3) per day)recorded from the ZH86 reservoir block.Models were developed using MATLAB R2021a and trained with 25 models in three replicate conditions(2,4 and 6),each at 1000 epochs.A comparative analysis indicated that,for all 25 models,the ANN outperformed the NRS-ANN in terms of processing speed and prediction performance.ANN models achieved an average of R^(2) and MAE of 0.8433 and 8.0964 m^(3)/day values,respectively,while NRS-ANN hybrid models achieved an average of R^(2) and MAE of 0.7828 and 8.2484 m^(3)/day values,respectively.In addition,ANN models achieved a processing speed of 49 epochs/sec,32 epochs/sec,and 24 epochs/sec after 2,4,and 6 replicates,respectively.Whereas the NRS-ANN hybrid models achieved lower average processing speeds of 45 epochs/sec,23 epochs/sec and 20 epochs/sec.In addition,the ANN optimal model outperforms the NRS-ANN model in terms of both processing speed and accuracy.The ANN optimal model achieved a speed of 336.44 epochs/sec,compared to the NRS-ANN hybrid optimal model,which achieved a speed of 52.16 epochs/sec.The ANN optimal model achieved lower RMSE and MAE values of 7.9291 m^(3)/day and 5.3855 m^(3)/day in the validation dataset compared with the hybrid ANS optimal model,which achieved 13.6821 m^(3)/day and 9.2047 m^(3)/day,respectively.The study also showed that the ANN optimal model consistently achieved higher R^(2) values:0.9472,0.9284 and 0.9316 in the training,test and validation data sets.Whereas the NRS-ANN hybrid optimal yielded lower R^(2) values of 0.8030,0.8622 and 0.7776 for the training,testing and validation datasets.The study showed that ANN models are a more effective and reliable tool,as they balance both processing speed and accuracy in estimating the oil production rate of the ZH86 reservoir block under the waterflooding recovery method.
基金supported by the Department of Economics and Management,University of Luxembourgfinancial support from the Department of Economics and Management,University of Luxembourg.
文摘The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,investors should consider investing in more secure assets,such as real estate property,cash,gold,and crypto assets.In recent years,among secure assets,cryptoassets are gaining more attention than traditional investments.This study compares the Bitcoin market,the gold market,and American stock indexes(S&P500,Nasdaq,and Dow Jones)before and during the COVID-19 pandemic.For this purpose,the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets.Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin,gold,and stock markets.In particular,we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period.This paper provides practical impacts on risk management and portfolio diversification.
文摘The aim of the present study was to investigate the modeling and prediction of the high temperature flow characteristics of a cast magnesium(Mg-Al-Ca)alloy by both constitutive equation and ANN model.Toward this end,hot compression experiments were performed in 250-450℃and in strain rates of 0.001-1 s^(−1).The true stress of alloy was first and foremost described by the hyperbolic sine function in an Arrhenius-type of constitutive equation taking the effects of strain,strain rate and temperature into account.Predictions indicated that unlike low strain rates and high temperature with dominant DRX activation,in relatively high strain rate and low temperature values,the precision of the models become decreased due to activation of twinning phenomenon.At that moment and for a better evaluation of twinning effect during deformation,a feed-forward back propagation ANN was developed to study the flow behavior of the investigated alloy.Then,the performance of the two suggested models has been assessed using a statistical criterion.The comparative assessment of the gained results specifies that the well-trained ANN is much more precise and accurate than the constitutive equations in predicting the hot flow behavior.
基金the support by the Faculty of Engineering,Hakim Sabzevari University,Sabzevar,Iran。
文摘The aim of this study is to develop processing maps based on two models and compare them with conventional processing maps.The hyperbolic sinus constitutive equation and artificial neural network(ANN)approaches were used in this investigation to predict flow stress and to develop processing maps in various conditions.The hot compression tests of InX-750 superalloy were carried out above the gamma prime phase temperature and within the temperature range of 1000-1150℃and strain rate of 0.001-1.000 s^(-1).The processing maps were conducted based upon dynamic material model(DMM)for data by experimental,constitutive equation and ANN approaches.The processing maps drawn by either of the prediction methods show that the method developed by ANN data does not significantly differ from the experimental processing map.The ANN approach is thus a suitable way to predict the flow stress as well as hot working processing map of engineering metals and materials.
基金This work is funded by National Natural Science Foundation of China(No.51906216).
文摘The pulsating heat pipe is a very promising heat dissipation device to address the challenge of higher heat-flux electronic chips,as it is characterised by excellent heat transfer ability and flexibility for miniaturisation.To boost the application of PHP,reliable heat transfer performance evaluationmodels are especially important.In this paper,a heat transfer correlation was firstly proposed for closed PHP with various working fluids(water,ethanol,methanol,R123,acetone)based on collected experimental data.Dimensional analysis was used to group the parameters.It was shown that the average absolute deviation(AAD)and correlation coefficient(r)of the correlation were 40.67%and 0.7556,respectively.For 95%of the data,the prediction of thermal resistance and the temperature difference between evaporation and condensation section fell within 1.13K/Wand 40.76K,respectively.Meanwhile,an artificial neural networkmodelwas also proposed.The ANN model showed a better prediction accuracy with a mean square error(MSE)and correlation coefficient(r)of 7.88e-7 and 0.9821,respectively.
文摘Based on the Residual Oil Hydrodesulfurization Treatment Unit (S-RHT), the n-order reaction kinetic model for residual oil HDS reactions and artificial neural network (ANN) model were developed to determine the sulfur content of hydrogenated residual oil. The established ANN model covered 4 input variables, 1 output variable and 1 hidden layer with 15 neurons. The comparison between the results of two models was listed. The results showed that the predicted mean relative errors of the two models with three different sample data were less than 5% and both the two models had good predictive precision and extrapolative feature for the HDS process. The mean relative error of 5 sets of testing data of the ANN model was 1.62%—3.23%, all of which were smaller than that of the common mechanism model (3.47%— 4.13%). It showed that the ANN model was better than the mechanism model both in terms of fitting results and fitting difficulty. The models could be easily applied in practice and could also provide a reference for the further research of residual oil HDS process.
基金Deanship of Scientific Research at King Khalid University,Abha,Saudi Arabia,for funding this work through theResearch Group Project underGrant Number(RGP.2/610/45)funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R102)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological factors is investigated.The analysis combines a traditional numerical approach with machine learning techniques.Mathematical equations describing the system are transformed into a dimensionless form and then solved using computational methods.The artificial neural network(ANN)model,trained with the Levenberg-Marquardt method,accurately predicts(Nu)values,showing high correlation(R=1),low mean squared error(MSE),and minimal error clustering.Parametric analysis reveals significant effects of parameters,length and location of source(B),(D),heat generation/absorption coefficient(Q),and porosity parameter(ε).Increasing the cooling area length(B)reduces streamline intensity and local Nusselt and Sherwood numbers,while decreasing isotherms,isoconcentrations,and micro-rotation.The Bejan number(Be+)decreases with increasing(B),whereas(Be+++),and global entropy(e+++)increase.Variations in(Q)slightly affect streamlines but reduce isotherm intensity and average Nusselt numbers.Higher(D)significantly impacts isotherms,iso-concentrations,andmicro-rotation,altering streamline contours and local Bejan number distribution.Increased(ε)enhances streamline strength and local Nusselt number profiles but has mixed effects on average Nusselt numbers.These findings highlight the complex interactions between cooling area length,fluid flow,and heat transfer properties.By combining finite volume method(FVM)with machine learning technique,this study provides valuable insights into the complex interactions between key parameters and heat transfer,contributing to the development of more efficient designs in applications such as cooling systems,energy storage,and bioengineering.
文摘According to the test data of subdivision errors in the measuring cycle of angular measuring system, the characteristics of subdivision errors generated by this system are analyzed. It is found that the subdivision errors are mainly due to the rotary-type inductosyn itself. For the characteristic of cyclical change, the subdivision errors in other measuring cycles can be compensated by the subdivision error model in one measuring cycle. Using the measured error data as training samples, combining GA and BP algorithm, an ANN model of subdivision error is designed. Simulation results indicate that GA reduces the uncertainty in the training process of the ANN model, and enhances the generalization of the model. Compared with the error model based on the least-mean-squared method, the designed ANN model of subdivision errors can achieve higher compensating precision.
文摘The retrieval of the biomass parameters from active/passive microwave remote sensing data （10.2 GHz） is performed based on an iterative inversion of BP neural network model with fuzzy optimization. The BP neural network is trained by a set of the measurements of active and passive remote sensing and the ground truth data versus Day of Year during growth. Once the network training is complete, the model can be used to retrieve the temporal variations of the biomass parameters from another set of observation data. The model was used in weights and microware observation data of wheat growth in 1989 to retrieve biomass parameters change of wheat growth this year. The retrieved biomass parameters correspond well with the real data of the growth, which shows that the BP model is scientific and sound.
文摘Most of the water quality models previously developed and used in dissolved oxygen （DO） prediction are complex. Moreover, reliable data available to develop/calibrate new DO models is scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent decades, computational intelligence techniques, as effective approaches for predicting complicated and significant indicator of the state of aquatic ecosystems such as DO, have created a great change in predictions. In this study, three different AI methods comprising： （1） two types of artificial neural networks （ANN） namely multi linear perceptron （MLP） and radial based function （RBF）; （2） an advancement of genetic programming namely linear genetic programming （LGP）; and （3） a support vector machine （SVM） technique were used for DO prediction in Delaware River located at Trenton, USA. For evaluating the performance of the proposed models, root mean square error （RMSE）, Nash-Sutcliffe efficiency coefficient （NS）, mean absolute relative error （MARE） and, correlation coeffi- cient statistics （R） were used to choose the best predictive model. The comparison of estimation accu- racies of various intelligence models illustrated that the SVM was able to develop the most accurate model in DO estimation in comparison to other models. Also, it was found that the LGP model performs better than the both ANNs models. For example, the determination coefficient was 0.99 for the best SVM model, while it was 0.96, 0.91 and 0.81 for the best LGP, MLP and RBF models, respectively. In general, the results indicated that an SVM model could be employed satisfactorily in DO estimation.
基金Under the auspices of Special Financial Grant and General Financial Grant from the China Postdoctoral Science Foundation(No.2015T80127,2014M561040)National Natural Science Foundation of China(No.41371172,41401171,41471143)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(No.164320H101)
文摘In this paper, the artificial neural network(ANN) model was used to evaluate the degree of intensive urban land use in Nanjing City, China. The construction and application of the ANN model took into account the comprehensive, spatial and complex nature of urban land use. Through a preliminary calculation of the degree of intensive land use of the sample area, representative sample area selection and using the back propagation neural network model to train, the intensive land use level of each evaluation unit is finally determined in the study area. Results show that the method can effectively correct the errors caused by the limitations of the model itself and the determination of the ideal value and weights when the multifactor comprehensive evaluation is used alone. The ANN model can make the evaluation results more objective and practical. The evaluation results show a tendency of decreasing land use intensity from the core urban area to the periphery and the industrial functional area has relatively low land use intensity compared with other functional areas. Based on the evaluation results, some suggestions are put forward, such as transforming the mode of urban spatial expansion, strengthening the integration and potential exploitation of the land in the urban built-up area, and strengthening the control of the construction intensity of protected areas.
文摘An artificial neural network model for backside bead width was established and three control methods——PID, fuzzy and neuron were designed, simulated and tested. The test results of bead on plate weld of GTAW indicate that the artificial neural network (ANN) modeling and learning control method have more advantages than the conventional method. They show that the ANN modeling and learning control method is an effective approach to real time control of welding dynamics and ideal quality.
基金This project is supported by Yulingn Zhai and the National Natural Science Foundation of China(No.51806090)the Basic Research Project of Yunnan Province(No.202001AT070081).
文摘The thermo-physical properties of nanofluids are highly dependent on the used base fluid.This study explores the influence of the mixing ratio on the thermal conductivity and viscosity of ZnO-CuO/EG(ethylene glycol)-W(water)hybrid nanofluids with mass concentration and temperatures in the ranges 1-5 wt.%and 25-60C,respectively.The characteristics and stability of these mixtures were estimated by TEM(transmission electron microscopy),visual observation,and absorbance tests.The results show that 120 min of sonication and the addition of PVP(polyvinyl pyrrolidone)surfactant can prevent sedimentation for a period reaching up to 20 days.The increase of EG(ethylene glycol)in the base fluid leads to low thermal conductivity and high viscosity.Thermal conductivity enhancement(TCE)decreases from 21.52%to 11.7%when EG:W is changed from 20:80 to 80:20 at 1 wt.%and 60C.A lower viscosity of the base fluid influences more significantly the TCE of the nanofluid.An Artificial Neural Network(ANN)has also been used to describe the effectiveness of these hybrid nanofluids as heat transfer fluids.The optimal number of layers and neurons in these models have been found to be 1 and 5 for viscosity,and 1 and 7 for thermal conductivity.The corresponding coefficient of determination(R^(2))was 0.9979 and 0.9989,respectively.
文摘In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,2020.The proposed model is based on the existing data(training data)published in the Saudi Arabia Coronavirus disease(COVID-19)situation—Demographics.The Prey-Predator algorithm is employed for the training.Multilayer perceptron neural network(MLPNN)is used in this study.To improve the performance of MLPNN,we determined the parameters of MLPNN using the prey-predator algorithm(PPA).The proposed model is called the MLPNN–PPA.The performance of the proposed model has been analyzed by the root mean squared error(RMSE)function,and correlation coefficient(R).Furthermore,we tested the proposed model using other existing data recorded in Saudi Arabia(testing data).It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia.The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789.The number of recoveries will be 2000 to 4000 per day.
文摘Groundwater resources have considerable influences on the human population and socioeconomic development of Vietnam and the Mekong River Delta(MRD). This paper presents an overview of the relationship between climate change and groundwater in the MRD, including the challenges, strategies and technical measures. Our results showed that groundwater levels are related to other climate and hydrological variables(i.e., rainfall, river levels, etc.); therefore, the impacts of climate change on the groundwater resources of the Mekong delta are significant, especially on groundwater recharge. Based on the results of this study, it is recommended that groundwater development in the future should focus on reducing groundwater harvesting, enhancing groundwater quantity by establishing artificial works and exploiting surface water. This study suggests that the Artificial Neural Network(ANN) model is an effective tool for forecasting groundwater levels in periods of 1 month and 3 months for aquifers in the natural and tidal regime areas of the delta.
文摘This paper presents experimental performance and artificial neural network modeling of a large-scale greenhouse solar dryer for drying of natural rubber sheets. The dryer consists of a parabolic roof structure covered with polycarbonate sheets on a concrete floor. The dryer is 9.0 m in width, 27.0 m in length and 3.5 m in height. Nine 15-W DC fans powered by three 50-W PV modules were used to ventilate the dryer. To investigate its performance, the dryer was used to dry six batches of natural rubber sheets. For each batch, 750 kg of rubber sheets were dried in the dryer. Results obtained from the experiments showed that drying temperatures varied from 32 ~C to 55 ~C and the use of the dryer led to a considerable reduction of drying time, as compared to the open air sun drying. In addition, the quality of the product from the dryer was high-quality dried products. A multilayer neural network model was developed to predict the performance of this dryer. The predictive power of the model was found to be high after it was adequately trained.
基金financially supported by a grant from Research Affairs of Najafabad Branch,Islamic Azad University,Iran
文摘Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data,and noisy data can affect the modeling performance.On the other hand in training phase of any Artificial Intelligence(AI) based model,each training data set is usually a limited sample of possible patterns of the process and hence,might not show the behavior of whole population.Accordingly,in the present paper,wavelet-based denoising method was used to smooth hydrological time series.Thereafter,small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smooth time series to form different denoised-jittered data sets.Finally,the obtained pre-processed data were imposed into Artificial Neural Network(ANN) and Adaptive Neuro-Fuzzy Inference System(ANFIS)models for daily runoff-sediment modeling of the Minnesota River.To evaluate the modeling performance,the outcomes were compared with results of multi linear regression(MLR) and Auto Regressive Integrated Moving Average(ARIMA)models.The comparison showed that the proposed data processing approach which serves both denoising and jittering techniques could enhance the performance of ANN and ANFIS based runoffsediment modeling of the case study up to 34%and 25%in the verification phase,respectively.
文摘Pomegranate peels are disposed of as waste even though it is an eminent source of total hydrolysable tannins(THT)and possesses a great worth for recycling.The present study was carried out with the hypothesis that pulsed system ultrasound-assisted extraction(PSUAE)technique optimization would aid in retaining high extract yield and THT recovery yield from pomegranate peel extract(PPE)with enhanced antioxidant potential.The extraction was performed at various conditions regarding solvent concentration(30-90% of acetone),solvent-feed ratio(10-30 mL/g),extraction time(10-20 min),and ultrasound amplitude(40-80%)for maximum extract yield,THT and antioxidant activity(AA).To model and optimize the process conditions tools namely response surface methodology(RSM)and artificial neural network(ANN)were applied.For solvent concentration,solvent-feed ratio,extraction time,and amplitude the optimal conditions attained with aid of RSM and ANN were:56%,26.5 mL/g,15 min,55%,and 45%,23 mL/g,15 min,50% respectively.The extract yield and THT content determined with RSM and ANN optimized values were 51.2%,86.4 mg TAE/g,and 62%,98.1 mg TAE/g,respectively.The mean square error value displayed the minimum and R^(2) exhibited maximum value in case of ANN compared to RSM model.PSUAE significantly augmented the AA of PPE to 77.2±0.50% compared to 49.7±0.35% of conventional extraction.FTIR and HPLC analysis established that PPE produced from PSUAE contains a significant amount of THT compounds(gallic acid and tannic acid derivative).SEM elucidated that the sonication effect resulted in peel cell wall disruptions ensuing in elevated extraction of THT.Therefore,this pulsed mode of ultrasound extraction could be regarded as an easy,cost-effective,and competent technique for the extraction of preferred natural THT from pomegranate peels that possess high prospective to be applied in food and nutraceutical formulations.
文摘Due to recent advances in the field of artificial neural networks(ANN)and the global sensitivity analysis(GSA)method,the application of these techniques in structural analysis has become feasible.A connector is an important part of a composite beam,and its shear strength can have a significant impact on structural design.In this paper,the shear performance of perfobond rib shear connectors(PRSCs)is predicted based on the back propagation(BP)ANN model,the Genetic Algorithm(GA)method and GSA method.A database was created using push-out test test and related references,where the input variables were based on different empirical formulas and the output variables were the corresponding shear strengths.The results predicted by the ANN models and empirical equations were compared,and the factors affecting shear strength were examined by the GSA method.The results show that the use of ANN model optimization by GA method has fewer errors compared to the empirical equations.Furthermore,penetrating reinforcement has the greatest sensitivity to shear performance,while the bonding force between steel plate and concrete has the least sensitivity to shear strength.
文摘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)modelling.Hydration of GC was performed at different temperatures 25,35,45,55 and 65℃.Different mathematical models were tested for the hydration at different temperatures.In 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 variables.Peleg model best described the hydration behavior at 25℃;while hydration at high-temperature was better described by Page model and Ibarz et al.model.The 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 temperature.The low activation energy(39.66 kJ/moL)shows the low-temperature sensitiveness of GC.Low 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 model.Results show that the LOGSIGMOID transfer function showed better performance when used at the hidden layer input node in conjunction to both PURELIN and TANSIGMOID.TANSIGMOID was found suitable for moisture ratio(MR)and hydration ratio(HR)prediction,as opposed to PURELIN for moisture content(MC)data.Satisfactory model prediction was obtained when the number of neurons in the hidden layer for MC,MR and HR was 12,8 and 15,respectively.Mathematical 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.
