耕地生态位及其数学测度模型构建研究

生态位理论是生态学重要的基础理论之一。以生态位理论为基础,提出了耕地生态元和耕地生态位的概念,并对耕地生态位的主体、本质、类型、特征及其演替规律作了系统阐述;构建了耕地生态位数学测度模型,包括耕地生态位宽度模型、耕地生态位扩充压缩度模型、耕地生态位商模型和耕地数量生态位重心模型,从而为区域耕地数量和质量变化研究提供了新的理论与方法。

未确知测度模型在矿石质量控制过程中的应用

结合矿山设计和生产实际 ,针对矿物和其有用成分分布得不均匀的矿床 ,需要在采场进行矿石混合配料 ,而建立了质量控制的 UM(未确知测度数学 )模型 ,并研究和分析了混合配矿的结果 。

Anomaly detection of earthquake precursor data using long short-term memory networks

Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.

Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics

Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.

A New Model for Prediction of Mean Liquid Circulating Velocity in Bubble Columns

A new model without any fitting parameter for estimating the mean liquid recirculating velocity has been derived from previous work directly. The prediction agrees with experimental data reasonably well. Accurency of prediction from the new model is comparable with the models reported in the literature. However, the new model has a potential capability to predict the average liquid recirculation velocity at elevated pressure bubble columns since n and c is developed under pressure. However this needs to be further tested experimentally.

Mathematical Model Prediction of Heat Losses from a Pilot Sirosmelt Furnace

A mathematical model was developed for simulating heat transfer through the sidewall, bottom and top of a pilot scale TSL （Top-Submerged-Lance） Sirosmelt furnace. With a feed rate of about 50 kg/h, the furnace has been used for investigating the technical feasibility of a variety of pyrometallurgical processes for smelting nonferrous and ferrous metals and for high temperature processing of solid wastes including electronic scraps, etc. The model was based on numerical solution of energy transport equations governing heat conduction in multi-layered linings in the sidewall, bottom and top lid of the furnace as well as convection and radiation of heat from the furnace outer surfaces to the ambient. Imperfect contacts between two neighboring solid lining layers due to air gap formation were considered. Temperature profiles were determined across the furnace bottom, top lid and three sections of the furnace sidewall, from which the heat loss rates through the corresponding parts of the furnace were calculated. The modelling results indicate that approximately 88% of heat is lost from the furnace sidewall, 7-8% from the bottom and 4-5% from the top lid. With increasing melt bath temperature, the proportion of total heat loss from the bottom decreases whereas that from the top lid increases and that from the sidewall is little changed. For a bath temperature of 1,300℃, total absolute heat loss rate from the furnace was found to be close to 12 kW.

Modeling on Heat and Mass Transfer in Stored Wheat during Forced Cooling Ventilation

A mathematical model based on the theory of heat and mass transfer in porous media was developed to simulate the evolution of grain temperature and moisture content in a wheat storage bin during ventilation with cooling air at the constant temperature and humidity.Unlike the previous works on this aspect,the present work was not focused on cooling the stored grain by ventilation with ambient air,but with the refrigerated air.Validation was performed by comparing between predicted and measured grain temperature and grain moisture content for two cases.Predicted data were in reasonable good agreement with measured ones.The model and the parameter values used in the model are applicable for predicting temperature and moisture of stored grains under ventilation conditions.