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Numerical modeling and optimization of process parameters in continuous extrusion process through response surface methodology, artificial neural network & genetic algorithm

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第6期12卷 230-248页

作者： Tariku Desta Devendra Kumar Sinha Perumalla Janaki Ramulu Ram Sewak Singh Mechanical Design and Manufacturing Engineering Department School of Mechanical Chemical and Materials Engineering Adama Science and Technology UniversityAdamaEthiopia Department of Electronics Engineering School of Electrical Electronics and Computing Adama Science and Technology University Adama Ethiopia

The product of high complex profile,high strength,high productivity and excellent material properties with infinite length can be produced by Continuous Extrusion(CE)process.The numerical simulation of Aluminum(AA 1100)feedstock material at different wheel velocities,product diameter,feedstock temperature,die temperature and friction condition has been carried out using 3D simulation tool Design Environment for Forming(DEFORM-3D)in this paper.The development of mathematical model is carried out to investigate the influence of wheel velocity,extrusion ratio,feedstock temperature,die temperature and friction conditions on total load required for the deformation and extrusion of feedstock material through Response Surface Methodology(RSM).The statistical significance of mathematical model is verified through analysis of variance(ANOVA).The most optimum value of extrusion load has been found to be 136.4 kN through iterative process of Genetic Algorithm(GA)using artificial neural network(ANN).The optimized value of input process variables for minimum value of extrusion load obtained has been found to be 13 Revolutions per Minute(RPM)as wheel velocity,5 mm as product diameter,0.95 as friction condition,650◦C as feedstock temperature and 550◦C as die temperature.This paper with proposed methodology will be helpful for industries working in the area of CE in terms of minimizing energy consumption during production process of bus bars,tubes,wires,cables,sheets,plates,strips,etc.

关键词： Continuous extrusion DEFORM-3D AA1100 response surface methodology artificial neural network genetic algorithm

Tide Forecasting of Tides Around Taiwan by artificial neural network Method and Wavelet Analysis

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China Ocean Engineering 2007年第4期21卷 659-675页

作者： Bang-Fuh CHEN Department of Marine Environment and Engineering National Sun Yat-senUniversity

In maltiresolution analysis （MRA） by wavelet function Daubechies （db）, we decompose the signal to two parts, the low and high frequency content. The high-frequency content of the data is removed first and a new ＂de-noise＂ signal is reconstructed by using inverse wavelet transform. The wavelet spectrum and harmonic analysis were used to analyze the characteristics of tidal data before constructing the input and output structure of ANN model. That is, the concept of tidal constituent phase-lags was introduced and the new ＂de-noise＂ signal was used as the input data set of ANN and the forecasting accuracy of ANN model is significantly improved.

关键词： artificial neural network wavelet analysis tide prediction

Application of an artificial neural network for a Direct Estimation of Atmospheric Instability from a Next-Generation Imager

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Advances in Atmospheric Sciences 2016年第2期33卷 221-232页

作者： Su Jeong LEE Myoung-Hwan AHN Yeonjin LEE Department of Atmospheric Science and Engineering Ewha Women’s University

Atmospheric instability information derived from satellites plays an important role in short-term weather forecasting, especially the forecasting of severe convective storms. For the next generation of weather satellites for Korea＇s multi-purpose geostationary satellite program, a new imaging instrument has been developed. Although this imaging instrument is not de- signed to perform full sounding missions and its capability is limited, its multi-spectral infrared channels provide information on vertical sounding. To take full advantage of the observation data from the much improved spatiotemporal resolution of the imager, the feasibility of an artificial neural network approach for the derivation of the atmospheric instability is investigated. The multi-layer perceptron model with a feed-forward and back-propagation training algorithm shows quite a sensitive re- sponse to the selection of the training dataset and model architecture. Through an extensive performance test with a carefully selected training dataset of 7197 independent profiles, the model architectures are selected to be 12, 5000, and 0.3 for the number of hidden nodes, number of epochs, and learning rate, respectively. The selected model gives a mean absolute error, RMSE, and correlation coefficient of 330 J kg-1, 420 J kg-1, and 0.9, respectively. The feasibility is further demonstrated via application of the model to real observation data from a similar instrument that has comparable observation channels with the planned imager.

关键词： CAPE artificial neural network instability geostationary imager

Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network(ANN) and multiple linear regressions(MLR)

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Journal of Integrative Agriculture 2017年第7期16卷 1634-1644页

作者： Ali Mohammadi Torkashvand Abbas Ahmadi Niloofar Layegh Nikravesh Department of Soil Science Science and Research Branch Islamic Azad University Department of Soil Science University of Tabriz

Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. In the present study, the predictive capabilities of multiple linear regressions （MLR） and artificial neural networks （ANNs） are evaluated to estimate fruit firmness in six months, including each of nutrients concentrations （nitrogen （N）, potassium （K）, calcium （Ca） and magnesium （Mg）） alone （P1）, com- bination of nutrients concentrations （P2）, nutrient concentration ratios alone （P3）, and combination of nutrient concentrations and nutrient concentration ratios （P4）. The results showed that MLR model estimated fruit firmness more accuracy than ANN model in three datasets （P1, P2 and P4）. However, the application of P3 （N/Ca ratio） as the input dataset in ANN model improved the prediction of fruit firmness than the MLR model. Correlation coefficient and root mean squared error （RMSE） were 0.850 and 0.539 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between 6-mon-fruit firmness and nutrients concentration.

关键词： artificial neural network firmness fruit kiwi multiple linear regression nutrient

Development and application of a GIS-based artificial neural network system for water quality prediction: a case study at the Lake Champlain area

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Journal of Oceanology and Limnology 2020年第6期38卷 1835-1845页

作者： LU Fang ZHANG Haoqing LIU Wenquan Shandong Provincial Key Laboratory of Marine Environment and Geological Engineering Ocean University of ChinaQingdao 266100China Laboratory for Marine Geology Qingdao National Laboratory for Marine Science and TechnologyQingdao 266000China Key Laboratory of Marine Environment and Ecology Ministry of EducationQingdao 266100China Key Laboratory of Marine Sedimentology and Environmental Geology First Institute of OceanographyMinistry of Natural Resources(MNR)Qingdao 266061China

artificial neural network(ANN)models have been extensively applied in the prediction of water resource variables,and Geographical Information System(GIS)includes powerful functions to visualize spatial data.In order to provide an efficient tool for environmental assessment and management that combines the advantages of these two modules,a GIS-based ANN water quality prediction system was developed in the present study.The ANN module and ArcGIS Engine module,along with a dynamic database,were imbedded in the system,which integrates water quality prediction via the ANN model and spatial presentation of the model results.The structure of the ANN model could be modified through the graphical user interface to optimize the model performance.The developed system was applied to a real case study for the prediction of the total phosphorus concentration in the Lake Champlain area.The prediction results were verified with the monitoring data,and the performance of the developed model was further evaluated through graphical techniques and quantitative statistical methods.Overall,the developed system provided satisfactory prediction results,and spatial distribution maps of the predicted results were obtained,which coincided with the monitored values.The developed GIS-based ANN water quality prediction system could serve as an efficient tool for engineers and decision makers.

关键词： water quality prediction Geographical Information System(GIS) artificial neural network integration system development

Application of artificial neural network to calculation of solitary wave run-up

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Water Science and Engineering 2010年第3期3卷 304-312页

作者： You-xing WEI Deng-ting WANG Qing-jun LIU College of Harbour Coastal and Offshore Engineering Hohai University Nanjing 210098 P. R. China River and Harbor Engineering Department Nanjing Hydraulic Research Institute Nanjing 210024 P. R. China

The prediction of solitary wave run-up has important practical significance in coastal and ocean engineering, but the calculation precision is limited in the existing models. For improving the calculation precision, a solitary wave run-up calculation model was established based on artificial neural networks in this study. A back-propagation （BP） network with one hidden layer was adopted and modified with the additional momentum method and the auto-adjusting learning factor. The model was applied to calculation of solitary wave run-up. The correlation coefficients between the neural network model results and the experimental values was 0.996 5. By comparison with the correlation coefficient of 0.963 5, between the Synolakis formula calculation results and the experimental values, it is concluded that the neural network model is an effective method for calculation and analysis of solitary wave ran-up.

关键词： solitary wave run-up artificial neural network back-propagation （BP） network,additional momentum method auto-adjusting learning factor

Retrieval of Water Vapor Profiles with Radio Occultation Measurements Using an artificial neural network

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Advances in Atmospheric Sciences 2005年第5期22卷 759-764页

作者：王鑫吕达仁 Laboratory for Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences

A new method applying an artificial neural network （ANN） to retrieve water vapor profiles in the troposphere is presented. In this paper, a fully-connected, three-layer network based on the backpropagation algorithm is constructed. Month, latitude, altitude and bending angle are chosen as the input vectors and water vapor pressure as the output vector. There are 130 groups of occultation measurements from June to November 2002 in the dataset. Seventy pairs of bending angles and water vapor pressure profiles are used to train the ANN, and the sixty remaining pairs of profiles are applied to the validation of the retrieval. By comparing the retrieved profiles with the corresponding ones from the Information System and Data Center of the Challenging Mini-Satellite Payload for Geoscientific Research and Application （CHAMP-ISDC）, it can be concluded that the ANN is relatively convenient and accurate. Its results can be provided as the first guess for the iterative methods or the non-linear optimal estimation inverse method.

关键词： radio occultation water vapor artificial neural network back-propagation

Applying artificial neural network to predict the viscosity of microalgae slurry in hydrothermal hydrolysis process

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能源与人工智能(英文)

能源与人工智能(英文) 2021年第2期4卷 181-187页

作者： Hao Chen Qian Fu Qiang Liao Xun Zhu Akeel Shah Key Laboratory of Low-grade Energy Utilization Technologies and Systems Chongqing UniversityMinistry of EducationChongqing 400044China Institute of Engineering Thermophysics School of Energy and Power EngineeringChongqing UniversityChongqing 400044China

Estimation of the viscosity of microalgae slurry is the premise for the design of industrial reactors in microalgal biofuel production.To accurately predict the viscosity of microalgae slurry(Chlorella pyrenoidosa),an artificial neural network(ANN)model is designed in this study.In the ANN model,the mass fraction of microalgal cell,shear rate,temperature,and retention time during the hydrothermal hydrolysis process are used as the input variables,and the viscosity of microalgae slurry is obtained as the output variable.Comparisons show that the ANN model is in excellent agreement with the experimental data.The mean square error(MSE),Mean Absolute Error(MAE),and goodness of fit(R 2)are 0.725,0.484 and 0.991,respectively.The results provide a proof-of-concept for using ANN models to estimate the viscosity of microalgae slurry.In particular,the developed ANN model can accurately predict the viscosity of microalgae slurry in a hydrothermal hydrolysis process,which cannot be accurately predicted by a standard curve fitting method.

关键词： artificial neural network Viscosity prediction Microalgae slurry Hydrothermal hydrolysis process Curve fitting

Simultaneous determination of captopril and hydrochlorothiazide by time-resolved chemiluminescence with artificial neural network calibration

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Journal of Pharmaceutical Analysis 2011年第1期1卷 32-38页

作者： Han-Chun Yao Min Sun Xiao-Feng Yang Zhen-Zhong Zhang Hua Li School of Pharmaceutical Science Zhengzhou University Zhengzhou 450001 China Department of Chemistry Northwest University Xi'an 710069 China Institute of Analytical Science Northwest University Xi'an 710069 China.

The combined use of chemometrics and chemiluminescence（CL）measurements,with the aid of the stopped-flow mixing technique,developed a simple time-resolved CL method for the simultaneous determination of captopril（CPL）and hydrochlorothiazide（HCT）.The stopped-flow technique in a continuous-flow system was employed in this work in order to emphasize the kinetic differences between the two analytes in cerium（IV）-rhodamine 6G CL system.After the flow was stopped,an initial rise of CL signal was observed for HCT standards,while a direct decay of CL signal was obtained for CPL standards.The mixed CL signal was monitored and recorded on the whole process of continuous-flow/stopped-flow,and the obtained data were processed by the chemometric approach of artificial neural network.The relative prediction error（RPE）of CPL and HCT was 5.9% and 8.7%,respectively.The recoveries of CPL and HCT in tablets were found to fall in the range between 95% and 106%.The proposed method was successfully applied to the simultaneous determination of CPL and HCT in a compound pharmaceutical formulation.

关键词： time-resolved chemiluminescence artificial neural network captopril hydrochlorothiazide