Computer model of hybrid compensator of fast varying loads

A computer model of the hybrid compensator of fast varying loads was developed in this dissertation. The computer model was implemented in MFC Visual C++ compiler version 6.0. The classification of fast varying loads and effects of their operation in distribution systems were described in this dissertation. The power properties of fast varying loads were expressed in terms of the Currents\u27 Physical Components Theory. Therefore, the Currents\u27 Physical Components Theory was applied to systems with non-periodic voltages and currents. The supply current of the fast varying loads was decomposed into the useful and the useless components using an algorithm based in the Currents\u27 Physical Components Theory. The current components resulting from this decomposition were used to generate the reference signals for compensator control. The systems of equations that modeled the integrated operation of the distribution system, the fast varying load and the hybrid compensator were developed in detail. The computer model was developed with sequential subroutines that allowed both the analytical solution of the compensation of fast varying loads and the incorporation of the hybrid compensator in the compensation of fast varying loads. The performance of the computer model was verified by comparison between the analytical results with the results obtained with the effect of the hybrid compensator. Finally, the computer model provided reduction of the active and reactive power variation and reduction of the distorted component of the supply current of the fast varying loads tested

Electrified Powertrains for a Sustainable Mobility: Topologies, Design and Integrated Energy Management Strategies

This Special Issue was intended to contribute to the sustainable mobility agenda through enhanced scientific and multi-disciplinary knowledge to investigate concerns and real possibilities in the achievement of a greener mobility and to support the debate between industry and academic researchers, providing an interesting overview on new needs and investigation topics required for future developments

Replacing combustion engines with hydrogen fuel cells to power mining haul trucks: challenges and opportunities

With the proven advantage of higher energy density in hydrogen fuel cells over batteries, there is potential to apply fuel cells to power mining haul trucks. This study aims to evaluate the technical and economic feasibility of hydrogen fuel cell electric mine trucks as an alternative to current mine haul trucks. Specifically, the project: (1) developed an economic framework for evaluating the integration of renewable energy powered haul trucks into mining; and (2) applied vehicle drivetrain and energy simulation in Matlab/Simulink to elucidate the challenges and opportunities of incorporating hydrogen fuel cell technology into the current form factors of mine haul trucks. First, the study uses an optimization model to characterize the impact of production, market and policy parameters on a mining firm’s decision of what types of trucks (with or without renewable technology) to deploy to minimize its overall costs, including costs associated with greenhouse gas emissions. Second, is an investigation of the significant technical challenges and opportunities associated with integrating hydrogen fuel cells in mining haul trucks using the vehicle drivetrain model and simulation experiments. The results show that even with green energy government incentives and levies for greenhouse gas emission, the cost of operating green energy trucks needs to be competitive to ensure they minimize a mining firm’s cost. However, to utilize a hydrogen fuel cell truck in the mine, a new vehicle frame is likely required to support the integration of the technology. This would require financial and technical investments by original equipment manufacturers and mining firms to make the transition --Abstract, page iii

Dynamic impact of ageing dump truck suspension systems on whole-body vibrations in high-impact shovel loading operations

Surface mining operations typically deploy large shovels, with 100+ tons per pass capacity, to load dump trucks in a phenomenon described as high-impact shovel loading operations (HISLO). The HISLO phenomenon causes excessive shock and vibrations in the dump truck assembly resulting in whole body vibration (WBV) exposures to operators. The truck suspension system performance deteriorates with time; therefore their effectiveness in attenuating vibrations reduces. No research has been conducted to study the impact of ageing suspension mechanisms on the magnitudes of WBV in HISLO operations. This study is a pioneering effort to provide fundamental and applied knowledge for understanding the impact of ageing on the magnitudes of WBV exposures. The effects of underlying ageing processes on a suspension performance index are mathematically modeled. The effects of scheduled maintenance and corrective maintenance on improving the performance index (PI) are also modeled. Finally, the proposed mathematical ageing model is linked to the truck operator\u27s exposure to WBVs via a virtual prototype CAT 793D truck model in the MSC ADAMS environment. The effects of suspension system ageing in increasing the WBV levels are examined in the form of both the vertical and horizontal accelerations under HISLO conditions. This study shows that the hydro-pneumatic suspension strut ageing results in deteriorating stiffness-damping parameters. The deteriorating suspension performance (with time) introduces more severe and prolonged WBVs in HISLO operations. The RMS accelerations increase significantly with time (suspension ageing). The vertical RMS accelerations increase to severe magnitudes of over 3.45, 3.75, and 4.0 m/s2 after 3, 5, and 7 years, respectively. These acceleration magnitudes are well beyond the ISO limits for the human body\u27s exposure to WBVs. This pioneering research effort provides a frontier for further research to provide safe and healthy working environments for HISLO operations --Abstract, page iii

An Overview of EGS Development and Management Suggestions

The world is facing the energy challenge to over-reliance to fossil-fuels, the development of renewable energy is inevitable. From a clean and economic view, enhanced geothermal system (EGS) provides an effective mean to utilize geothermal energy to generate. Different form the conventionalhydro geothermal, the host rock of EGS is Hot Dry Rock (HDR), which buries deeper with high temperature (more than 180°C). The generationof EGS is promising. The development of EGS can be combined with the tech Power to geothermal energy. Exceed power is supposed to drive fluid working in HDR layer to obtain geothermal energy for generation. The whole article can be divided into three parts. In the first art, evaluation indexes of EGS as well as pilot EGs Projects (e.g. Fenton Hill and Basel) and exiting EGS project (e.g. Paralana and Newberry) are summarized, which points a general impression on EGS site. The dominate indexes are heat flow, geothermal gradient and thermal storage. The second part is focused on the simulation methods and working fluids selection of EGS. A detailed comparison of the main simulation software (e.g. TOUGH2 and FEHM) is carried out. With the respect of working fluid selection, the comparison between water and CO2 is researched and CO2 is a preferred option for EGS development for less fluid loss and less dissolution to HDR. The art of CO2-EGS is introduced clearly in this part. The third part is about the addition consideration of EGS plant operation, it excludes auxiliary plant support and HSE management

Computational dynamics and virtual dragline simulation for extended rope service life

The dragline machinery is one of the largest equipment for stripping overburden materials in surface mining operations. Its effectiveness requires rigorous kinematic and dynamic analyses. Current dragline research studies are limited in computational dynamic modeling because they eliminate important structural components from the front-end assembly. Thus, the derived kinematic, dynamic and stress intensity models fail to capture the true response of the dragline under full operating cycle conditions. This research study advances a new and robust computational dynamic model of the dragline front-end assembly using Kane\u27s method. The model is a 3-DOF dynamic model that describes the spatial kinematics and dynamics of the dragline front-end assembly during digging and swinging. A virtual simulator, for a Marion 7800 dragline, is built and used for analyzing the mass and inertia properties of the front-end components. The models accurately predict the kinematics, dynamics and stress intensity profiles of the front-end assembly. The results showed that the maximum drag force is 1.375 MN, which is within the maximum allowable load of the machine. The maximum cutting resistance of 412.31 KN occurs 5 seconds into digging and the maximum hoist torque of 917. 87 KN occurs 10 seconds into swinging. Stress analyses are carried out on wire ropes using ANSYS Workbench under static and dynamic loading. The FEA results showed that significant stresses develop in the contact areas between the wires, with a maximum von Mises stress equivalent to 7800 MPa. This research study is a pioneering effort toward developing a comprehensive multibody dynamic model of the dragline machinery. The main novelty is incorporating the boom point-sheave, drag-chain and sliding effect of the bucket, excluded from previous research studies, to obtain computationally dynamic efficient models for load predictions --Abstract, page iii

Development of an integrated mining and processing optimization system

Low-grade mineral deposits lead to a very high tonnage excavation with the adherent economical and environmental problems belong to gas emissions and minerals recovery costs, which, accompanied by the higher operational and equipment costs and the higher demand for the mineral resources, lead to increasing of mineral commodities prices, especially metals. These challenges can be overcome through mine planning optimization. Therefore, an approach for the global optimization of the integrated mining and processing operations is designed by a dynamic and simulation model construction. By applying a case study and through mining selectivity strategy, deeply investigation of the ore parameters (especially mineral liberation grain size and hardness), and proper arrangements for the plant facilities, mineral production is realized, with better quality, lower environmental impacts, lower costs, and higher economic benefits.:Table of Content List of Figures ………………………………………………………………………….……… V List of Tables …………………………………………………………………………….…… IX List of symbols and Abbreviations …………………………………………………............ XII List of Appendices …………………………………………………………..……............ XVIII 1. Justification and Importance of the Mine Planning Optimization ……………………….. 1 1.1 Introduction ............................................................................................................................... 1 1.2 Urgent need for general mine planning optimization ............................................................... 2 1.2.1 Overall costly low-grade ore deposits ................................................................................... 2 1.2.2 World markets ........................................................................................................................ 3 1.2.3 Sustainability requirements in mining, environmental and social issues .............................. 5 1.2.4 The strategic importance of the mining industry ................................................................... 6 2. State of the Science and General Outline for Mine Planning Optimization Concepts …... 8 2.1 The mine planning optimization concepts ................................................................................ 8 2.1.1 Improvements for the interconnected mining and processing operations ............................. 8 2.1.2 Urgent demand for the unit-operations cost reduction through holistic optimization ......... 12 2.1.3 Expenditures of size reduction operations ........................................................................... 13 2.1.4 The Mill as a critical point in the product supply chain ...................................................... 17 2.2 Critical review of researches for the (Mine-to-Mill) optimization field ................................. 18 2.2.1 Mill throughput optimization ............................................................................................... 18 2.2.2 Intelligent assistant systems and processes automation and monitoring …………………. 19 2.2.3 Scheduling software and operationally holistic modules ……………………………...…. 20 2.3 The aim of work and the thesis layout .................................................................................... 22 3. Suggested Approach for a Holistic Mine-to-Mill Optimization ……………………….… 25 3.1 Introduction and scope …………………………………………………………………….. 25 3.2 The methodology plan …………..………………………………………………………….. 26 3.3 Assignment of the operational parameters inter-acting the integrated optimization ……….. 29 3.3.1 Mining and processing activities …………………………………………………………. 29 3.3.2 Mining and processing operational parameters …………………………………………... 31 3.3.3 Mining and processing special indicators ………………………………………………… 42 3.4 Introduction to the dynamic modeling and simulation softwares ………………………...… 45 3.5 Particular concepts belonging to the chosen modeling software ………………………...…. 46 3.6 Main tools, components and constituents of the used software …………………………..… 49 3.7 Assumed case study for the model construction ……………………………………….…… 51 4. Calculation Basics for Applying Dynamic Modeling and Simulation for the Mining and Processing Operations ……………………………………………………………………….... 53 4.1 The modeling construction strategy ………………………………………………………… 53 4.2 Construction of the [Reference-Mode] model …………………………………………….... 54 4.2.1 Dynamic modeling and simulation for the drilling and blasting operation ………………. 54 4.2.2 Dynamic modeling and simulation for the loading and hauling operations …………..….. 62 4.2.3 Dynamic modeling and simulation for the crushing and grinding operations …………..... 71 5. Case Study Application and the Model Output and Assessment ……………………...… 82 5.1 Main physical properties of the ore deposit under study ………………………………..….. 82 5.2 Principal technological and operational parameters within the case study ……………....… 83 5.3 Processing of the data from the case study ………………………………………………… 86 5.4 [Reference-Mode] model results and assessment ………………………………………...… 87 5.4.1 Preliminary main results of the mining activities sub-models ………………………...….. 87 5.4.2 Preliminary main results of the processing activities sub-model ……………………..….. 97 5.4.3 Further model optimization requirements ……………………………………………….. 105 6. The Model Optimization, Validation and Practical Applications ………………..…….. 107 6.1 Model further optimization plan …………………………………………………….…….. 107 6.2 The ore deposit characteristics and details …………………………………………….….. 108 6.2.1 Tonnage distribution and cut-off-grade for the ore deposit ……………………………... 108 6.2.2 Liberation size and microscopic grain size distribution for the ore deposit …………….. 112 6.3 Mining selectivity and processing mixing scenarios …………………………………….... 113 6.3.1 Blending triangle design for choice of the annual mining contribution scenarios ……… 113 6.3.2 Planed processing strategies according to the pre- and post-grinding mixing ………..… 115 6.4 An Excel calculation tool for preparing the new detailed inputs to the modified model .… 118 6.4.1 The need for new prepared and detailed inputs to the modified model ……………….… 118 6.4.2 Description and benefits of the designed Excel calculation tool ……………………..…. 118 6.4.3 The main outputs of the Excel calculation tool ……………………………………….… 120 6.4.4 The Excel calculation tool outputs as inputs to the modified Vensim model ………….... 120 6.5 The model modification through the new added mathematical and functions ……………. 123 6.6 [Controlled] model results and the comparable discussion of the processing strategies ..… 129 6.6.1 General notifications for the model handling and the results presentation …………….... 129 6.6.2 Results of the mining section of the model …………………………………………….... 130 6.6.3 Results of the processing section of the model ……………………………………….…. 132 6.6.4 Comparison between the three data processing and arrangement methodologies ……..... 142 6.6.5 Comparison between scenarios ………………………………………………………….. 149 6.6.6 Extreme cases versus the chosen Organized Method ………………………………….... 153 6.7 Optimization evolution overview across the operations improvement steps …………...… 157 7. Conclusion and Recommendations …………………………………………………...… 163 References …………………………………………………………………………………… 168 Appendices ……………………………………………………………………………...…… 17