Genetic algorithm to simultaneously optimise stope sequencing and equipment dispatching in underground short-term mine planning under time uncertainty

ABSTRACT

The main issue in short-term planning optimisation for underground mining is organising the mining process with limited resources in the form of equipment and materials to satisfy production targets and stable feed grade requirements. In this paper, an integrated optimisation model is proposed based on an individual generation algorithm and an improved Genetic Algorithm to simultaneously optimise stope extraction sequencing and timing, extracted ore grade and equipment dispatching. The model objectives are to shorten the time gap between the stope mining processes and the overall working time. When the uncertainty of equipment working time is taken into account in a short-term scheduling model, the Monte Carlo simulation is applied to evaluate the risk of not meeting the production target. A modification strategy is defined to evaluate equipment failure. Consequently, any available equipment is automatically reassigned to the mining site to replace the broken-down equipment. A case study is used to validate the model in the Sanshandao gold mine of China to formulate an optimal monthly schedule. Compared with the conventional approach, the new model could reduce the variance of ore tonnage and feed grade and improve the equipment allocation efficiency. Discussions are presented to address the uncertainty.     

Analog active filter design using a multi objective genetic algorithm

The selection of the correct values for passive elements, resistors, and capacitors, is an important task in analog active filter design. The classic method of choosing passive elements is a difficult task and can lead to errors. To reduce the incidence of error and human effort evolutionary optimization techniques are used to select the values of capacitors and resistors. However, due to the single objective optimization technique, these are not well suited to optimize different filter parameters. For this reason, the performance of a multi-objective genetic algorithm named non-dominated sorting genetic algorithm II (NSGA-II) against the different single objective algorithms is evaluated. Two analog active filters: A fourth order Butterworth and a second order state variable filter with the operational amplifiers in their cores are used for testing purposes. In both cases two different objects are chosen along with eight components as variables to be optimized. The component values are compatible with the E12, E24 and E96 series using NSGA-II. The computation results are better in terms of design error and allow for better resistor and capacitor choice. To reach the same or better results the NSGA-II needs fewer generations compared with other genetic algorithms for this problem.     

基于改进正弦余弦算法的无线传感器节点部署优化

为了提高无线传感器网络（WSN）的性能，提出了一种基于改进正弦余弦算法（ESCA）的节点部署优化方法。首先，引入双曲正弦调节因子和动态余弦波权重系数，以平衡算法的全局探索与局部开发能力；然后，提出了一种基于拉普拉斯和高斯分布的变异策略，避免算法陷入局部最优。对于基准函数的优化实验结果表明，ESCA相比引力搜索算法、鲸鱼优化算法、基本正弦余弦算法（SCA）及其改进算法具有更高的收敛精度和收敛速度。最后，将ESCA应用于WSN节点部署优化，结果表明其优化覆盖率相比改进粒子群优化算法、外推人工蜂群算法、改进灰狼优化算法和自适应混沌量子粒子群算法分别提高了1.55个百分点、7.72个百分点、2.99个百分点和7.63个百分点，用更少节点便可达到相同目标精度。     

Techno-economic analysis for rustic electrification in Egypt using multi-source renewable energy based on PV/ wind/ FC

A technical-economic investigation based on mathematical modeling, simulation, and optimization approach is employed in this research to assemble an island combined renewable energy systems (CRES) consists of solar PV/Wind/Fuel Cell (FC) of a small-scale countryside area in Egypt. The intent of the proposed island CRES is to boost the share of renewable energy in the energy mix and to study the possibility of using fuel cells as a storage/backup system instead of using battery banks.Three combinations of CRES are presented in this research to select the most optimum one. The combinations of the hybrid systems are PV/FC, PV/WT/FC, and WT/FC. The performance and the total cost of the suggested CRES were optimized using Firefly Algorithm (FA). The results obtained from the FA are compared with those obtained from the Shuffled Frog Leaping Algorithm (SFLA) and the particle swarm optimization (PSO).The selected case study area with latitude and longitude of (29.0214 N, 30.8714 E) is identified for economic viability in this work.The simulation outcomes show that the solar PV/Wind/Fuel Cell combination incorporated with an electrolyzer for hydrogen production grants the excellent performance. The proposed system is economically viable with a levelized cost of energy of 0.47 $/kWh.     

A modified cross-correlation algorithm for PIV image processing of particle-fluid two-phase flow

PIV (Particle Image Velocimetry) technique for flow field measurement has achieved popular self-identify through over ten years development, and its application range is becoming wider and wider. PIV post-processing techniques have a great influence on the success of particle-fluid two-phase flow field measurement and thus become a hot and difficult topic. In the present study, a Phase Respective Identification Algorithm (PRIA) is introduced to separate low-density solid particles or bubbles and high-density tracer particles from the PIV image of particle-fluid two-phase flow. PTV (Particle Tracking Velocimetry) technique is employed to calculate the velocity fields of low-density solid particles or bubbles. For the velocity fields of high-density solid particles or bubble phase and continuous phase traced by high-density smaller particles, based on the thought of wavelet transform and multi-resolution analysis and the theory of cross-correlation of image, a delaminated processing algorithm (MCCWM) is presented to conquer the limitation of conventional Fourier transform. The algorithm is firstly testified on synthetic two-phase flows, such as uniform steady flow, shearing flow and rotating flow, and the computational results from the simulated particle images are in reasonable agreement with the given simulated data. The algorithm is then applied to images of actual bubble-liquid two-phase flow and jet flow, and the results also confirmed that the algorithm proposed in the present study has good performance and reliability for post-processing PIV images of particle-fluid two-phase flow.     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

A simulation study of reduction kinetics for sponge iron production in a rotary hearth furnace

ABSTRACT

A mathematical model has been developed by coupling genetic algorithm (GA) with heat and material balance equations to estimate rate parameters and solid-phase evolution related to the reduction of iron ore-coal composite pellets in a multi-layer bed Rotary hearth Furnace (RHF). The present process involves treating iron ore-coal composite pellets in a crucible over the hearth in RHF. The various solid phases evolved at the end of the process are estimated experimentally, and are used in conjunction with the model to estimate rate parameters. The predicted apparent activation energy for the wustite reduction step is found to be lower than those of the reduction of higher oxides. The thermal efficiency is found to decrease significantly with an increase in the carbon content of the pellet. Thermal efficiency was also found to increase mildly up to three layers. Multilayer bed remains as a potential design parameter to increase thermal efficiency.     

A novel approach for efficient stance detection in online social networks with metaheuristic optimization

In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.     

Affective Experience Measurement in Outdoor Environment via Machine Visual Cognition

Outdoor environments with quality landscapes can benefit people’s physical and mental health. Real-time assessment on individuals’ environmental affective experience can improve the scientism in measuring the quality of outdoor environments. Existing measurement methods are often insufficient for the cases of a larger site area or sample size. The machine visual cognition of Artificial Intelligence can realize the recognition of facial expressions and the changes in video images, which supports high-precision and long-cycle measurements on individuals’ affective experience in outdoor environments. Taking an urban community square as the study site, this research simultaneously collects participants’ facial data from video images and their electrodermal activity data, wherein Convolutional Neural Network algorithm model is trained with a deep learning algorithm, i.e. codec–SVM optimized model, whose reliability is tested through an additional experiment. The research reveals that: 1) The accuracy rate of the main and additional experiments in measuring individuals’ affective experience is 82.01% and 65.08%, respectively; 2) The additional experiment verifies the application potential of the codec–SVM optimized model; And 3) the model works more effective for outdoor scenarios with varying usage behaviors and open views. Therefore, machine visual cognition can be used for emotion measurement in a larger site area or sample size and contributes to the effectiveness of landscape optimization efforts, especially as an instrumental tool to study the affective experience of the ones who have communication or reading disability. The findings also demonstrate the model’s great potential in building Smart Cities with refined public services.     

AMBO: All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.