Resilience estimation of the mining fleet (Case study: Sungun copper mine)
-RESEARCH PAPER-
Resilience estimation of the mining fleet (Case study: Sungun copper
mine)
Adel Mottahedi a, Farhang Sereshki a, Mohammad Ataei a, *, Abbas Barabadi b, Ali Nouri
Qarahasanlou c
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
Department of Engineering and Safety, UiT the Arctic University of Norway, Tromsø, Norway
c
Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran
a
b
ABSTRACT
Article History:
Received: 09 October 2019,
Revised: 03 October 2020,
Accepted: 08 January 2021.
In recent years, the application of the resilience concept has increased in various domains. Resilience depicts the ability of a system to return
to its normal operational status after failure events or disruptions. According to the literature survey, there are various studies, which have
been done in the field of engineering and non-engineering systems, and there is no study about applying the resilience concept in the field of
the mining industry. In this paper, first, the resilience concept is introduced, and the resilience of the mining fleet of Sungun copper mine is
estimated later on. For this aim, performance indicators of the system (i.e., reliability, maintainability, and supportability) are used. The results
showed that the resilience of the entire system for one hour of its function is equal to 83.1%, and this value decreases to 37.1% after 10 hours.
It means if there is a failure in the system, it will have 83.1% and 37.1% probabilities to be resilient against the failure event after 1 hour and 10
hours of system function.
Keywords: Maintainability, Mining, Reliability, Resilience, Supportability
1. Introduction
Mining is one of the most significant parts of human industries. This
industry is consisted of many complicated processes like ore mining, ore
processing, and so on, to supply the primary requirements of other
industrial sectors. In the field of ore mining, systems like fan systems,
loading and haulage systems, drilling systems, supporting systems, water
drainage systems, and so on have worked together to produce the final
product of mine and increase productivity. Out of schedule stoppage of
these systems due to the failures or disruptions may cause to decreasing
both mining safety and productivity.
Resilience has driven from Resilire (a Latin word), which refers to
bounce back, flexibility, etc. [1]. In 1625, the resilience concept was used
scientifically for the first time [2]. This concept has migrated from the
natural and physical sciences into the other sciences [3]. US National
Infrastructure Advisory Council (NIAC) defined resilience as the
“system’s ability to anticipate, absorb, adapt to, and rapidly recover from
a potentially disruptive event” [4]. The schematic view of resilience is
shown in Fig. 1. As can be seen, the system is performing its requested
function in an initial stable state (𝐹1 level) until failure occurring at the
time 𝑡2 . After the failure event, the performance level of the system
decrease (system degradation) until the function level of the system
reaches to 𝐹2 at time 𝑡3 . The system may stay at the degraded state for a
while (𝑡2 − 𝑡3 ) based on the system supportability. However, by the
initiation of recovery actions at time t3, the system return to its desired
performance level (𝐹3 ) at time 𝑡4 .
There are many definitions of system resilience in the literature; most
of them are general definitions. Orwin and Wardle [5] defined resilience
as the recovery speed of a system to return to its pre-failure status.
Allenby and Fink [6] defined resilience as the system's ability to preserve
its functions and structure in case of disruptions (internal or external),
and to degrade when it must. Haimes [7] defined resilience as the
system's ability to withstand a critical disruption within acceptable
degradation parameters and to recover with a suitable time and
reasonable costs and risks. Youn et al. [8] defined resilience as the sum
of the system reliability (passive survival rate) and system restoration
(proactive survival rate). Pregenzer [9] defined resilience as the system's
ability to absorb continuous and unpredictable change and still maintain
its vital functions. Ayyub [10] defined resilience as the ability of the
system to prepare for and adapt to changing conditions and withstand
and recover rapidly from disruptions. However, numbers of resilience
definitions have been presented for more specific domains as follow (see
Fig. 2):
(a) Engineering resilience: System’s ability to predict, absorb, adapt,
and/or quickly recover from a disruptive event [11].
(b) Ecological resilience: The ability of an ecosystem to absorb
changes of state variables, driving variables, and parameters, that
is, to persist after disturbance [12].
(c) Economic resilience: Ability and adaptive response that enables
firms and regions to avoid maximum potential losses [11].
(d) Social resilience: The ability of a society to absorb failures and
reorganize while retaining the same function, structure, identity,
and feedbacks [13].
(e) Psychological resilience: Dynamic process wherein individuals
display positive adaptation despite experiences of significant
adversity or trauma [14].
All of these definitions have emphasized that a resilient system should
be able to withstand the failures and absorb failures' impacts. These
abilities are about preparedness activities or pre-failure features of the
* Corresponding author. E-mail address: (M. Ataei).
Journal Homepage: ijmge.ut.ac.ir
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A. Mottahedi et al. / Int. J. Min. & Geo-Eng. (IJMGE), 55-2 (2021) 151-156
system. These are activities that make the system reliable, robust,
flexible, and adaptable. However, the post-failure features of system or
recovery activities are also significant for having a resilience system.
These activities help the system to return to its normal performance
status. System supportability and maintainability levels have a critical
effect on the system recovery process. Haimes [7] and Ayyub's [10]
definitions have considered both pre-and post-failure features of the
system. Preparedness and recovery activities are both vital for having a
resilience system [15].
𝑡1
(1)
𝑅 = ∫ [100 − 𝑄(𝑡)]𝑑𝑡
𝑡0
System performance function (Q(t))
Disruption
Resilience reduction (R)
100%
Robustness
Rapidity
t0
t1
Time (t)
Fig. 3. The measure of the resilience reduction (adapted from [16]).
Fig. 1. System’s performance transition in resilience.
Orwin and Wardle [5] presented a deterministic quantitative metric
for measuring the resilience of soil’s biota against exogenous
disruptions. They have introduced Equation (2) as follow:
2|𝐷0 |
(2)
Rsilience =
−1
(|𝐷0 | + |𝐷𝑥 |)
As can be seen in Fig. 4, 𝐷0 is the difference between the control (𝐶0 )
and the disturbed soil (𝑃0) at the end of the disturbance (𝑡0 ), and 𝐷𝑥 is
the difference between the control (Cx) and the disturbed soil (𝑃𝑥 ) at t (...truncated)
