The mine planning process converts resources into economically mineable reserves, focusing on value addition and risk reduction. Equipment selection is traditionally addressed late in the process and addresses production capacity, equipment matching and equipment allocation. The primary focus being to reduce the operating cost per unit of material handled. Mineral resource management is an integration of the key functions in the mining process. A focus on resource utilisation plays a key role in the management process and leads to the question whether lower operating costs always add value in the long term. It was determined that traditional equipment selection methods are not effective for all mineral deposits and might even be short sighted, destroying value over the long term. The mine planning process was adapted to allow for an early investigation into the potential for increased recovery. The effect of selectivity in the loading action is simulated in a 3D environment over a range of bench heights. The results are analysed with a grade tonnage curve and the saleable product at each bench height is calculated, taking account of the required product qualities. The concept of financial materiality is applied to classify the resource as either a massive or selective deposit. A massive deposit support the traditional drive for bigger equipment and will benefit from lower operating costs. A selective deposit requires less focus on production capacity, equipment matching and allocation and more on resource recovery. In order to take advantage of the potential indicated in the evaluation, it is necessary to modify the traditional equipment selection techniques. A thorough understanding of the capabilities of the loading equipment is required in an attempt to match these abilities with the geometry of the ore deposit. The objective is to identify the equipment that will ensure the highest mining recovery at the lowest cost. This will be achieved when the loading equipment can attain a mining recovery smaller than the bench height it is mining or if the equipment can be applied economically on small bench heights. The most suitable equipment can only be determined at the hand of a total value chain costing analyses. This means that the production cost i.e. the cost to produce the final product must be evaluated and not the operating cost i.e. the cost to move a unit of material, as is often the case. The proposed mine planning approach and equipment selection technique was used on the Thabazimbi iron ore mine deposits. The results indicated that the NPV of the project could be increased dramatically. It was concluded that the ability to load selectively cannot be calculated mathematically. It is a judgment made on a thorough evaluation of the design and operating features of the shovel in conjunction with the ore body geometric parameters and the loading face conditions. The efficiency of the selected shovel can be manipulated through the application of different bench heights, and the optimum combination can only be determined through a total value chain costing analyses. / Dissertation (MEng (Mining Engineering))--University of Pretoria, 2005. / Mining Engineering / unrestricted

Methods for estimating productivity and costs, and dependent equipment selection process, have needed to be increasingly reliable. Estimated productivity and costs must be as accurate as possible in reflecting actual productivity and costs experienced by mining operations to accommodate the long-term trend for diminishing commodity prices, For loading and hauling equipment operating in open pit mines, some of the interrelated estimating criteria have been investigated for better understanding; and, consequently, more reliable estimates of production and costs, also more effective equipment selection process. Analysis recognizes many of the interrelated criteria as random variables that can most effectively be reviewed, analyzed and compared in terms of statistical mathematical parameters. Emphasized throughout is the need for management of the cyclical loading and hauling system using conventional shovels/excavators/loaders and mining trucks to sustain an acceptable “rhythm” for best practice productivity and most-competitive unit-production costs. Outcomes of the research include an understanding that variability of attributes needs to be contained within acceptable limits. Attributes investigated include truck payloads, bucket loads, loader cycle time, truck loading time and truck cycle time. Selection of “ultra-class” mining trucks (≥ 290 -tonne payload) and suitable loading equipment is for specialist mining applications only. Where local operating environment and cost factors favourably supplement diminishing cost-benefits of truck scale, ultra-class trucks may be justified. Bigger is not always better – only where bigger can be shown to be better by reasons in addition to the modest cost benefits of ultra-class equipment. Truck over-loading may, to a moderate degree, increase productivity, but only at increased unit cost. / From a unit-cost perspective it is better to under-load than overload mining trucks. Where unit production cost is more important than absolute productivity, under-trucking is favoured compared with over-trucking loading equipment. Bunching of mining trucks manifests as a queuing effect – a loss of effective truck hours. To offset the queuing effect, required productivity needs to be adjusted to anticipate “bunching inefficiency”. The “basic number of trucks” delivered by deterministic estimating must provide for bunching inefficiency before application of simulation applications or stochastic analysis is used to determine the necessary number of trucks in the fleet. In difficult digging conditions it is more important to retain truck operating rhythm than to focus on achieving target payload by indiscriminately adding loader passes. Where trucks are waiting to load, operational tempo should be restored by sacrificing one or more passes. Trucks should preferably be loaded by not more than the nominal (modal) number plus one pass. The research has: • Identified and investigated attributes that affect the dispersion of truck payloads, bucket loads, bucket-cycle time, loading time and truck-cycle time. • The outcomes of the research indicate a need to correlate drilling and blasting quality control and truck payload dispersion. Further research can be expected to determine the interrelationship between accuracy of drilling and blasting attributes including accuracy of hole location and direction. • Preliminary investigations indicate a relationship between drill-and-blast attributes through blasting quality control to bucket design, dimensions and shape; also discharge characteristics that affect bucket cycle time that needs further research.

CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / A velocidade de concepÃÃo e de execuÃÃo das infraestruturas tem aumentado devido à utilizaÃÃo de softwares e equipamentos eficientes, o que tem exigido um melhor desempenho de cada etapa. No entanto, apesar dos custos envolvidos com equipamentos serem os itens de maior peso na construÃÃo rodoviÃria, poucos sÃo os sistemas existentes de apoio à decisÃo para a seleÃÃo das frotas. Propostas de seleÃÃo de frotas tÃm sido elaboradas por pesquisadores no Brasil e no mundo, porÃm a maioria consideram apenas caminhÃes e escavadeiras/carregadeiras ou scrapers. Este trabalho tem como objetivo desenvolver um sistema de apoio à decisÃo na seleÃÃo de motoniveladoras para a execuÃÃo de obras de terraplenagem e de pavimentaÃÃo, denominado SADPATROL. Inicialmente, foi feita uma anÃlise da evoluÃÃo tecnolÃgica do equipamento motoniveladora visando verificar a influÃncia destas melhorias sobre o aumento da produtividade. Foram analisadas as equaÃÃes existentes na literatura para o cÃlculo de produtividade e apÃs verificada a representatividade da equaÃÃo utilizada no paÃs esta foi escolhida para compor o SADPATROL, com as devidas alteraÃÃes em suas variÃveis. AtravÃs da anÃlise de cada variÃvel da equaÃÃo foi possÃvel identificar o quanto as mesmas influenciavam no resultado da produÃÃo desta forma, foram implementadas melhorias, como a inserÃÃo de valores padrÃes para as variÃveis do sistema, permitindo a obtenÃÃo de resultados mais precisos, caso o usuÃrio nÃo possua dados medidos em campo. O SADPATROL possui cadastrado em seu banco de dados 46 tipos de motoniveladoras, dos mais variados modelos e tamanhos, de 12 fabricantes distintos. à possibilitado ao usuÃrio calcular a frota com equipamentos prÃprios e verificar se esta atende ao prazo da obra. Caso o prazo nÃo seja atendido pela frota prÃpria ou o usuÃrio nÃo possua equipamentos, o sistema executa as informaÃÃes da obra para todos os equipamentos do banco de dados e fornece a soluÃÃo com as 46 opÃÃes organizadas em frotas, em ordem crescente de quantidade de equipamento. à possibilitado ainda selecionar os equipamentos nos quais se possui informaÃÃo de custo horÃrio, para assim obter a frota de menor custo. Toda a multiplicidade de informaÃÃes necessÃrias à seleÃÃo de motoniveladoras, e suas inter-relaÃÃes e dependÃncias, foram organizadas de forma sistÃmica no software SADPATROL, diminuindo assim o empirismo e a dependÃncia da experiÃncia humana na seleÃÃo de frotas. A presente pesquisa pode ainda contribuir para auxiliar a elaboraÃÃo de normas tÃcnicas e para a disseminaÃÃo do conhecimento sobre o referido equipamento. / The demands on speed and efficiency of infrastructure design and construction has increased due to the use of software and more productive equipment, which has required better performance of equipment planners at each step. However, even considering that the costs associated with equipment are one of the items with the greatest weight in road construction, there are few computerized systems for fleet selection decision support. Fleet selection systems have been developed by researchers in Brazil and worldwide, but most of them consider only trucks and excavators/loaders or scrapers. This research aims at developing a decision support system for the selection of motor graders for construction activities involving earthmoving and paving works named SADPATROL. Initially, an analysis of the motor grader technological evolution was performed in order to evaluate the influence of the improvements on productivity gains. The equations presented in the literature were studied for productivity calculation and, after verifying the suitability of the equation, the equation was incorporated in the SADPATROL system with the necessary changes in its variables. Through the analysis of the variables in the equation, it was possible to identify how they influenced the productivity. Through this analysis, improvements were implemented. These improvements consisted of the inclusion of standard values for the system variables, allowing the achievement of more accurate results if the user has no data for productivity measured in the field. The SADPATROL system has 46 types of motor graders included in its database, from many different models and sizes and from 12 different manufacturers. The user is able to plan the fleet with its own equipment and verify if it satisfies the construction project deadline. If the deadline cannot be satisfied using the available fleet or the user has no equipment, the system performs the construction planning using all database equipment and provides the optimal solution with 46 options organized into fleets in increasing order according to the amount of equipment needed. It is also possible for the user to select the equipment using time cost information and, thus, achieve the lowest cost fleet. The significant amount of information necessary for motor graders selection and its inter-relationships and dependencies were organized in a systematic way in the SADPATROL system, reducing, the empiricism and the dependence on human expertise in selecting fleets. This research may also help in assisting in the development of technical standards and expanding the dissemination of knowledge about such equipment.

This dissertation focuses on the material transport system design problem (MTSDP), integrating decisions of technology selection and flow network design. This research is motivated by the design of material transport systems (MTS) in manufacturing plants. The objective is to design a MTS with minimum lifetime costs, subject to service requirements, flow network restrictions, and limited resources. We characterize the MTSDP from the perspectives of task requirements, transport technology, and space utilization. A classification is proposed for transport technologies such that instances in the same class share the same properties, and a decision framework is proposed to emphasize the inter-relationships of three major decisions: task clustering, network connecting, and technology selection. We consider fixed and variable costs, arc capacities, and empty travel in our formulations. We propose two solution approaches for the MTSDP. The first is the compact formulation (CF) approach where the three major decisions are handled by a mixed integer non-linear programming (MINLP) formulation. Relaxation techniques are applied to linearize the model. The solution of the resulting linear formulation (MILP) provides a lower bound to that of MINLP. A tightened formulation reduces the computational time by a factor of 3.85. The experiment also shows that when control system costs are significant, designs with multiple-task clusters are more economical than those restricted to single-task clusters. The other approach is clustering/set partition (CSP), where the three decisions are decomposed and solved sequentially. In an example MTS design problem, three methods are compared: CSP, a GREEDY approach from the literature, and enumeration. CSP finds the optimal solution, while GREEDY results in 31% greater costs. A similar comparison with another example is made for the CF and CSP approaches. We apply the CSP approach in a case problem, using data from an auto parts manufacturer. We include flow path crossing constraints and perform experiments to determine solution quality over a range of small problem sizes. The largest difference from optimality is 3.34%, and the average is 0.98%. More importantly, based on these experiments, it seems there is no evidence that the difference percentage grows with an increase in the number of tasks.

Диссертационное исследование посвящено разработке рекомендаций для улучшения рабочего процесса проектирования инженерных сетей ОВ. В работе рассматриваются: этапы разработки цифровой модели здания, основы технологии проектирования, рекомендации по разработке систем отопления и вентиляции средствами программы Autodesk Revit и Linear, а также методика по разработке и реализация алгоритма, автоматизирующего процесс формирования документации. / The dissertation research is devoted to the development of recommendations for improving the workflow of designing engineering networks of the OB. The paper discusses: the stages of developing a digital building model, the basics of design technology, recommendations for the development of heating and ventilation systems using the Autodesk Revit and Linear programs, as well as a methodology for developing and implementing an algorithm that automates the process of generating documentation.