Unascertained measurement classifying model of goal collapse prediction

Based on optimized forecast method of unascertained classifying,a unascer- tained measurement classifying model (UMC) to predict mining induced goaf collapse was established,The discriminated factors of the model are influential factors including over- burden layer type,overburden layer thickness,the complex degree of geologic structure, the inclination angle of coal bed,volume rate of the cavity region,the vertical goaf depth from the surface and space superposition layer of the goaf region.Unascertained mea- surement (UM) function of each factor was calculated.The unascertained measurement to indicate the classification center and the grade of waiting forecast sample was determined by the UM distance between the synthesis index of waiting forecast samples and index of every classification.The training samples were tested by the established model,and the correct rate is 100%.Furthermore,the seven waiting forecast samples were predicted by the UMC model.The results show that the forecast results are fully consistent with the ac- tual situation.

Modeling the effects of mechanical parameters on the hydrodynamic behavior of vertical current classifiers

This study modeled the effects of structural and dimensional manipulations on hydrodynamic behavior of a bench vertical current classifier. Computational fluid dynamics （CFD） approach was used as modeling method, and turbulent intensity and fluid velocity were applied as system responses to predict the over- flow cut size variations. These investigations showed that cut size would decrease by increasing diameter and height of the separation column and cone section depth, due to the decrease of turbulent intensity and fluid velocity. As the size of discharge gate increases, the overflow cut-size would decrease due to freely fluid stream out of the column. The overflow cut-size was significantly increased in downward fed classifier compared to that fed by upward fluid stream. In addition, reforming the shape of angular overflow outlet＇s weir into the curved form prevented stream inside returning and consequently unselec- tire cut-size decreasing.

Development of surrogate-optimization models for a novel transcritical power cycle integrated with a small modular reactor

In recent years,various types of surrogate optimization models have been proposed to reduce the computational time and to improve the emulation accuracy.In this study,by leveraging an ANN surrogate model developed earlier,a comprehensive and efficient optimization algorithm is conceived for the global optimal design of an integrated regenerative methanol transcritical cycle.It combines a unique converging/diverging classifier model into the surrogate model to form a surrogate-based model,which significantly improves the prediction accuracy of the objective function.Six binary classifiers are explored and the multi-layer feed-forward(MLF)neural network classifier is selected.In addition,within the five global optimizers being explored,the basinhopping(BH)and dual-annealing(DA)are selected.The optimal surrogate-based model and global optimizers are then combined to form a unique surrogate-optimizer model.The surrogate-optimizer model is slightly outperformed by the physics-based model in terms of the optimization results,the time consumption of the surrogate-optimizer model during the optimization searching process is 99%less than that of the physicsbased model.As the results,the surrogate-optimizer model is slightly outperformed by the physics-based model in terms of the optimization results,where the Levelized Cost of Energy(LCOE)of the Surrogate-DA and Surrogate-BH models are 77.912 and 78.876$/MWh,respectively,compared to the 77.190$/MWh of the Baseline model with fairly close penalties between them.In the meantime,the time consumption of the surrogate-optimizer model during the optimization searching process is 99%less than that of the physics-based model.