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Optimization Techniques and their Applications to Mine Systems [Kõva köide]

(Department of Geological and Mining Engineering and Sciences, Michigan Technological University, MI, US.), (Department of Mining Engineering, NIT Rourkela, Odisha, India.)
  • Formaat: Hardback, 390 pages, kõrgus x laius: 254x178 mm, kaal: 889 g, 114 Tables, black and white; 4 Line drawings, color; 160 Line drawings, black and white; 14 Halftones, black and white; 178 Illustrations, black and white
  • Ilmumisaeg: 30-Sep-2022
  • Kirjastus: CRC Press
  • ISBN-10: 1032060980
  • ISBN-13: 9781032060989
Teised raamatud teemal:
Optimization Techniques and their Applications to Mine SystemsSuurem pilt
  • Formaat: Hardback, 390 pages, kõrgus x laius: 254x178 mm, kaal: 889 g, 114 Tables, black and white; 4 Line drawings, color; 160 Line drawings, black and white; 14 Halftones, black and white; 178 Illustrations, black and white
  • Ilmumisaeg: 30-Sep-2022
  • Kirjastus: CRC Press
  • ISBN-10: 1032060980
  • ISBN-13: 9781032060989
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032060989.html
Märksõnad:
This book describes the fundamental and theoretical concepts of optimization algorithms in a systematic manner, along with their potential applications and implementation strategies in mining engineering. It explains basics of systems engineering, linear programming, and integer linear programming, transportation and assignment algorithms, network analysis, dynamic programming, queuing theory and their applications to mine systems. Reliability analysis of mine systems, inventory management in mines, and applications of non-linear optimization in mines are discussed as well. All the optimization algorithms are explained with suitable examples and numerical problems in each of the chapters.

Features include: Integrates operations research, reliability, and novel computerized technologies in single volume, with a modern vision of continuous improvement of mining systems. Systematically reviews optimization methods and algorithms applied to mining systems including reliability analysis. Gives out software-based solutions such as MATLAB®, AMPL, LINDO for the optimization problems. All discussed algorithms are supported by examples in each chapter. Includes case studies for performance improvement of the mine systems.

This book is aimed primarily at professionals, graduate students, and researchers in mining engineering.
Preface xi
Author biographies xiii
Chapter 1 Introduction to mine systems
1(10)
1.1 Definition of a system
1(1)
1.2 Types of system
1(3)
1.3 System approach
4(1)
1.4 System analysis
5(1)
1.5 Elements of a mining system
5(1)
1.6 Definition and classification of optimization problem
6(3)
1.6.1 Based on the existence of constraints
6(1)
1.6.2 Based on the nature of the equations involved
7(1)
1.6.3 Based on the permissible values of the decision variables
8(1)
1.6.4 Based on the number of the objective function
8(1)
1.7 Solving optimization problems
9(2)
1.7.1 Classical optimization techniques
9(1)
1.7.1.1 Direct methods
9(1)
1.7.1.2 Gradient methods
9(1)
1.7.1.3 Linear programming methods
9(1)
1.7.1.4 Interior point method
9(1)
1.7.2 Advanced optimization techniques
9(2)
Chapter 2 Basics of probability and statistics
11(44)
2.1 Definition of probability
11(2)
2.2 Additional theory of probability
13(1)
2.3 Probability distributions
14(2)
2.4 Common probability distribution functions
16(13)
2.4.1 Uniform distribution
16(2)
2.4.2 Normal distribution
18(8)
2.4.3 Poisson distribution
26(1)
2.4.4 Exponential distribution
27(2)
2.5 Conditional probability
29(1)
2.6 Memory less property of the probability distribution
30(2)
2.7 Theorem of total probability for compound events
32(1)
2.8 Bayes' rule
33(1)
2.9 Definition of statistics
34(2)
2.10 Statistical analyses of data
36(19)
2.10.1 Common tools of descriptive statistics
36(1)
2.10.1.1 Arithmetic Mean
36(1)
2.10.1.2 Median
37(2)
2.10.1.3 Mode
39(1)
2.10.1.4 Standard deviation
40(2)
2.10.1.5 Mean Absolute Deviation
42(1)
2.10.1.6 Skewness
43(1)
2.10.1.7 Coefficient of variation
43(1)
2.10.1.8 Expectation or expected value
44(2)
2.10.1.9 Variance and covariance
46(2)
2.10.1.10 Correlation coefficient
48(1)
2.10.2 Standard analysis tools of inferential statistics
48(1)
2.10.2.1 Hypothesis Tests
48(7)
Chapter 3 Linear programming for mining systems
55(44)
3.1 Introduction
55(1)
3.2 Definition of Linear Programming Problem (LPP)
55(1)
3.3 Solution algorithms of LPP
56(15)
3.3.1 Graphical method
56(3)
3.3.1.1 Multiple Solutions
59(1)
3.3.1.2 Unbounded solution
60(1)
3.3.2 Simplex method
61(7)
3.3.3 Big-M method
68(3)
3.4 Sensitivity analysis
71(11)
3.4.1 Graphical method of sensitivity analysis
72(4)
3.4.2 Sensitivity analysis of the model using simplex method
76(6)
3.5 The dual problem
82(9)
3.5.1 Formulation of dual problem for a given primal LPP
82(4)
3.5.2 Dual simplex algorithm
86(5)
3.6 Case Study of the application of LPP in optimization of coal transportation from mine to power plants
91(8)
Chapter 4 Transportation and assignment problems in mines
99(46)
4.1 Definition of a transportation problem
99(1)
4.2 Types of transportation problem
99(1)
4.3 Solution algorithms of a transportation model
100(19)
4.3.1 Initial basic feasible solution
102(1)
4.3.1.1 The north-west corner method
102(1)
4.3.1.2 Matrix minimum method
103(1)
4.3.1.3 Vogel Approximation Method (VAM)
104(3)
4.3.2 Determination of optimal solution
107(1)
4.3.2.1 The Modified Distribution method
107(6)
4.3.2.2 Stepping Stone Method
113(3)
4.3.3 Solution algorithm of an unbalanced transportation model
116(2)
4.3.4 Solution algorithm of a transportation model with prohibited routes
118(1)
4.3.5 Solution algorithm for degeneracy problem
119(1)
4.4 Assignment problem
119(11)
4.4.1 The Hungarian Assignment Method (HAM)
120(10)
4.5 Case study on the application of transportation model in mining system
130(15)
Chapter 5 Integer linear programming for mining systems
145(38)
5.1 Definition
145(1)
5.2 Formulation of ILP
145(2)
5.3 Solution algorithms of an ILP
147(16)
5.3.1 Cutting plane method or Gomory's cut method
147(8)
5.3.2 Branch and bound (B&B) algorithm
155(8)
5.4 Case Study of the application of Mixed Integer Programming (MIP) in production scheduling of a mine
163(20)
Chapter 6 Dynamic programming for mining systems
183(32)
6.1 Introduction
183(1)
6.2 Solution algorithm of dynamic programming
183(1)
6.3 Example 1: Maximising Project NPV
184(6)
6.3.1 Backward recursion algorithm
185(3)
6.3.2 Forward recursion algorithm
188(2)
6.4 Example 2: Decision on ultimate pit limit (UPL) of two-dimensional (2-D) blocks
190(10)
6.5 Example 3: Stope boundary optimization using dynamic programming
200(4)
6.6 Case Study of dynamic programming applications to determine the ultimate pit for a copper deposit
204(11)
Chapter 7 Network analysis for mining project planning
215(26)
7.1 Introduction
215(1)
7.2 Representation of network diagram
215(1)
7.3 Methods of determining the duration of a project
216(12)
7.3.1 Critical Path Method (CPM)
216(9)
7.3.2 Program Evaluation and Review Technique (PERT)
225(1)
7.3.2.1 PERT analysis algorithm
225(3)
7.4 Network crashing
228(13)
Chapter 8 Reliability analysis of mining systems
241(48)
8.1 Definition
241(1)
8.2 Statistical concepts of reliability
241(1)
8.3 Hazard function
241(1)
8.4 Cumulative hazard rate
242(1)
8.5 Reliability functions
243(12)
8.5.1 Reliability calculation with an exponential distribution function
243(4)
8.5.2 Reliability calculation with a normal probability density function
247(3)
8.5.3 Reliability calculation with a Weibull distribution probability density function
250(3)
8.5.4 Reliability calculation with a Poisson distribution probability mass function
253(1)
8.5.5 Reliability calculation for a binomial distribution
254(1)
8.6 Mean time between failure (MTBF) and mean time to failure (MTTF)
255(2)
8.7 Maintainability and mean time to repair (MTTR)
257(2)
8.8 Reliability of a system
259(11)
8.8.1 System reliability on a series configuration
259(2)
8.8.2 System reliability on parallel configuration
261(2)
8.8.3 System reliability of a combination of series and parallel system
263(1)
8.8.4 System reliability of k-out-of-n configuration
264(2)
8.8.5 System reliability of bridge configuration
266(2)
8.8.6 System reliability of standby redundancy
268(2)
8.9 Availability
270(2)
8.10 Improvement of system reliability
272(6)
8.10.1 Redundancy optimization
272(6)
8.11 Reliability analysis to a mine system: A Case Study
278(11)
8.11.1 Introduction
278(1)
8.11.2 Data
278(1)
8.11.3 Exploratory data analysis
278(1)
8.11.4 Estimating the best fit probability density function (PDF) for TBF and TTR
279(5)
8.11.5 Reliability analysis for estimation of maintenance schedule
284(5)
Chapter 9 Inventory management in mines
289(32)
9.1 Introduction
289(1)
9.2 Costs involved in inventory models
290(1)
9.3 Inventory models
291(30)
9.3.1 Deterministic model
292(1)
9.3.1.1 Basic economic order quantity (EOQ) model
292(4)
9.3.1.2 EOQ model with planned shortages
296(5)
9.3.1.3 EOQ model with price discounts
301(5)
9.3.1.4 Multi-item EOQ model with no storage limitation
306(3)
9.3.1.5 Multi-item EOQ model with storage limitation
309(2)
9.3.2 Fixed time-period model
311(3)
9.3.3 Probabilistic EOQ model
314(7)
Chapter 10 Queuing theory and its application in mines
321(46)
10.1 Introduction
321(1)
10.2 Kendall notation
322(1)
10.3 Probability distributions commonly used in queuing models
323(2)
10.3.1 Geometric distribution
323(1)
10.3.2 Poisson distribution
324(1)
10.3.3 Exponential distribution
324(1)
10.3.4 Erlang distribution
324(1)
10.4 Relation between the exponential and Poisson distributions
325(2)
10.5 Little's law
327(1)
10.6 Queuing Model
327(24)
10.6.1 M/M/l Model
327(1)
10.6.1.1 Time-dependent behaviour of the flows of dump trucks
328(6)
10.6.2 M/M/s queuing system
334(10)
10.6.3 Infinite server queue model (M/M/∞)
344(2)
10.6.4 (M/M/s): (FCFS)/K/K queuing system
346(5)
10.7 Cost models
351(6)
10.8 Case Study for the application of queuing theory for shovel-truck optimization in an open-pit mine
357(10)
Chapter 11 Non-linear algorithms for mining systems
367(16)
11.1 Introduction
367(1)
11.2 Stationary points
367(1)
11.3 Classifications of non-linear programming
368(9)
11.3.1 Unconstrained optimization algorithm for solving non-linear problems
368(5)
11.3.2 Constrained optimization algorithm for solving non-linear problems
373(4)
11.4 Case study on the application of non-linear optimization for open-pit production scheduling
377(6)
Bibliography 383(4)
Index 387
Amit Kumar Gorai is an Associate Professor in the Department of Mining Engineering at National Institute of Technology, Rourkela, India. Prior to join at NIT Rourkela, Dr. Gorai has worked at Birla Institute of Technology Mesra, Ranchi for over 7 years. He has published over 60 research articles on reliability, machine learning, remote sensing, environment, etc. In addition, Dr. Gorai has written 1 guide book, A Complete Guide for Mining Engineers and 1 edited book, Sustainable Mining Practices. Dr. Gorai has received his Ph.D. from Indian School of Mines, Dhanbad. He has worked as an Endeavour Executive Fellow at the University of New South Wales, Sydney, Australia, and Raman Postdoctoral Fellow at Jackson State University, MS, USA. He has been teaching Mine Systems Engineering at NIT Rourkela for last 5 years. He has completed several sponsored research projects in the field of environmental modelling for different government organizations. His current research area is systems optimization, machine learning, GIS and Remote Sensing. Gorai is a life member of the Institution of Engineers India (IEI), The Mining, Geological & Metallurgical Institute (MGMI) of India.

Snehamoy Chatterjee is an Assistant Professor of Mining Engineering in the Department of Geological and Mining Engineering and Sciences at Michigan Technological University. Before joining Michigan Tech, Chatterjee was working as an Assistant Professor at the National Institute of Technology, Rourkela, India. Chatterjee specializes in ore reserve estimation, short- and long-range mine planning, mining machine reliability analysis, mine safety evaluation, and the application of image analysis and artificial intelligence in mining problems. He received his Ph.D. in Mining Engineering from the Indian Institute of Technology Kharagpur, India. Chatterjee worked as a Post-Doctoral Fellow at the University of Alaska Fairbanks, and as a research associate at COSMO Stochastic Mine Planning Laboratory, McGill University, Canada, where he focused on mine planning optimization and ore-body modeling under uncertainty. Presently, Chatterjee is actively involved in research work in the field of resource modeling, production planning, and online quality monitoring, integrating multiple data types. He is teaching courses and advising students on topics related to mine planning, mineral resource modeling, mining machine reliability, and vision-based online quality monitoring. He has completed several sponsored research and industry projects in these fields for different government organizations and mining companies in India and the USA.

Chatterjee is an active member of the International Associate of Mathematical Geosciences (IAMG), the Society for Mining, Metallurgy, and Exploration, Inc. (SME), the Society of Mining Professors (SOMP). He has served as a co-convener and a technical committee member for several international mining conferences. He is also a reviewer for more than twenty journals and has received The Editor's Best Reviewer Awards 2014 from Mathematical Geosciences Journal. He is the recipient of the 2015 APCOM Young Professional Award at the 37th APCOM in Fairbanks, Alaska. He is an editorial board member of International Journal of Mining, Reclamation, and Environment and Artificial Intelligence Research journal, and associate editor of Results in Geophysical Sciences.