Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method

One of the most critical and complicated steps in mine design is a selection of suitable mining method based upon geological,geotechnical,geographical,safety and economical parameters.The aim of this study is developing a Monte Carlo simulation to selection the optimum mining method by using effective and major criteria and at the same time,taking subjective judgments of decision makers into consideration.Proposed approach is based on the combination of Monte Carlo simulation with conventional Analytic Hierarchy Process(AHP).Monte Carlo simulation is used to determine the confdence level of each alternative’s score,is calculated by AHP,with the respect to the variance of decision makers’opinion.The proposed method is applied for Jajarm Bauxite Mine in Iran and eventually the most appropriate mining methods for this mine are ranked.

APPLICATION OF THE PROBABILISTIC FRACTURE MECHANICS METHOD OF PREDICTING THE FATIGUE LIFE OF TUBULAR JOINTS

In this paper .fatigue strength of T-type tubularjoints subjected to in-planebending or out-of-plane bending load is investigated By, considering material constantsand initial crack sizes as random variables and applying Monte Carlo simulation method, we have giyen a statistical analvsis on fatigue life. Simultaneously, linearregression analyses of computed resuldts are performed and compared with that of theknown experimental data.

Resistivity and critical temperature of （Lal-xPrx）0.7Ca0.3MnO3

An empirical formula of the critical temperature that is concentration dependent for polycrystalline （La1-xPrx）0.7Ca0.3MnO3 is presented in this paper. With this formula, the temperature dependence of resistance is simulated for various values of x by using the random resistor network model and the Monte Carlo method. The hysteresis effect in p-T curves is reasonably explained. The simulation results are in good agreement with the relevant experimental measurements.

Probabilistic fragility analysis:A tool for assessing design rules of RC buildings

In this work, fragility analysis is performed to assess two groups of reinforced concrete structures. The first group of structures is composed of buildings that implement three common design practices; namely, fully infilled, weak ground story and short columns. The three design practices are applied during the design process of a reinforced concrete building. The structures of the second group vary according to the value of the behavioral factors used to define the seismic forces as specified in design procedures. Most seismic design codes belong to the class of prescriptive procedures where if certain constraints are fulfilled, the structure is considered safe. Prescriptive design procedures express the ability of the structure to absorb energy through inelastic deformation using the behavior factor. The basic objective of this work is to assess both groups of structures with reference to the limit-state probability of exceedance. Thus, four limit state fragility curves are developed on the basis of nonlinear static analysis for both groups of structures. Moreover, the 95% confidence intervals of the fragility curves are also calculated, taking into account two types of random variables that influence structural capacity and seismic demand.

Non-Homogeneous Poisson Processes Applied to Count Data:A Bayesian Approach Considering Different Prior Distributions

This article discusses the Bayesian approach for count data using non-homogeneous Poisson processes, considering different prior distributions for the model parameters. A Bayesian approach using Markov Chain Monte Carlo (MCMC) simulation methods for this model was first introduced by [1], taking into account software reliability data and considering non-informative prior distributions for the parameters of the model. With the non-informative prior distributions presented by these authors, computational difficulties may occur when using MCMC methods. This article considers different prior distributions for the parameters of the proposed model, and studies the effect of such prior distributions on the convergence and accuracy of the results. In order to illustrate the proposed methodology, two examples are considered: the first one has simulated data, and the second has a set of data for pollution issues at a region in Mexico City.

Reliability Assessment of Integrated Electricity and Natural Gas Transmission Systems in Presence of Power-to-gas and Combined Heat and Power Technologies

Assessing the reliability of integrated electricity and gas systems has become an important issue due to the strong dependence of these energy networks through the power-to-gas(P2G)and combined heat and power(CHP)technologies.The current work,initially,presents a detailed energy flow model for the integrated power and natural gas system in light of the P2G and CHP technologies.Considering the simultaneous load flow of networks,a contingency analysis procedure is proposed,and reliability is assessed through sequential Monte Carlo simulations.The current study examines the effect of independent and dependent operation of energy networks on the reliability of the systems.In particular,the effect of employing both P2G and CHP technologies on reliability criteria is evaluated.In addition,a series of sensitivity analysis are performed on the size and site of these technologies to investigate their effects on system reliability.The proposed method is implemented on an integrated IEEE 24-bus electrical power system and 20-node Belgian natural gas system.The simulation procedure certifies the proposed method for reliability assessment is practical and applicable.In addition,the results prove connection between energy networks through P2G and CHP technologies can improve reliability of networks if the site and size of technologies are properly determined.

Low-carbon benefit analysis on DG penetration distribution system

In response to the severe energy and environmental issues,CO_(2)emission reduction and low-carbon development are inevitable.China has become the biggest CO_(2)emitter in the world since 2006.As a major CO_(2)emission source in China,the power industry is facing greater pressure for carbon emission abatement.By applying various low-carbon power technologies and mechanisms,the potential for CO_(2)emission reduction in power systems is considerable.This paper proposes a pseudo-sequential Monte Carlo simulation method for the low-carbon benefit evaluation of distribution system including distributed wind turbines,solar array and battery energy storage systems.The hourly sequential models and state variation models for wind turbines,solar array and battery are established.The non-power components are sequentially sampled and the components of the wind turbine,the solar array and the battery are non-sequentially sampled.The failure modes and effect analysis(FMEA)procedure for the system are discussed and the heuristic load shedding approach is used,then the low-carbon benefit evaluation procedure is illustrated.Based on this,the system state transition sampling method for calculating the loss of expected energy index with high DG penetration is proposed.The state transition models for non-active elements and DGs are established.The validity of the proposed method is demonstrated by a study case.The method can be used for the low-carbon benefit analysis on DG penetration distribution networks.