A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak

In blasting operation,the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak.Therefore,predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome.Since many parameters affect the blasting results in a complicated mechanism,employment of robust methods such as artificial neural network may be very useful.In this regard,this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran.Back propagation neural network（BPNN） and radial basis function neural network（RBFNN） are adopted for the simulation.Also,regression analysis is performed between independent and dependent variables.For the BPNN modeling,a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN,architecture 636-2 with spread factor of 0.79 provides maximum prediction aptitude.Performance comparison of the developed models is fulfilled using value account for（VAF）,root mean square error（RMSE）,determination coefficient（R2） and maximum relative error（MRE）.As such,it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error.Also,sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak,respectively.On the other hand,for both of the outputs,specific charge is the least effective parameter.

Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments

Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surficial sediments（SSs）. Artificial neural network was used to build a model（the determination coefficient square r2 is 0.9977） to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance（ANOVA）, F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model（MRAM）. The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn（–1.993 μmol/mol）. Organic materials（OMs） and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components（Fe, Mn oxides and OMs） in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.

Microstructure quantification of Cu-4.7Sn alloys prepared by two-phase zone continuous casting and a BP artificial neural network model for microstructure prediction

Microstructures of Cu-4.7Sn(%) alloys prepared by two-phase zone continuous casting(TZCC)technology contain large columnar grains and small grains.A compound grain structure,composed of a large columnar grain and at least one small grain within it,is observed and called as grain-covered grains(GCGs).Distribution of small grains,their numbers and sizes as well as numbers and sizes of columnar grains were characterized quantitatively by metallographic microscope.Back propagation(BP) artificial neural network was employed to build a model to predict microstructures produced by different processing parameters.Inputs of the model are five processing parameters,which are temperatures of melt,mold and cooling water,speed of TZCC,and cooling distance.Outputs of the model are nine microstructure quantities,which are numbers of small grains within columnar grains,at the boundaries of the columnar grains,or at the surface of the alloy,the maximum and the minimum numbers of small grains within a columnar grain,numbers of columnar grains with or without small grains,and sizes of small grains and columnar grains.The model yields precise prediction,which lays foundation for controlling microstructures of alloys prepared by TZCC.