EROI of the Ukrainian coal
The concept of EROI (energy return on investment) has become a commonly used synonym for energy efficiency, an alternative to traditional economic evaluation. This work was carried out with the goal of drawing up methodology for determining the significance of the main factors affecting the dynamic...
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irk-123456789-1308982018-03-07T03:02:12Z EROI of the Ukrainian coal Cherevatskyi, D. Atabyekov, O. Social and economic problems of Donbas The concept of EROI (energy return on investment) has become a commonly used synonym for energy efficiency, an alternative to traditional economic evaluation. This work was carried out with the goal of drawing up methodology for determining the significance of the main factors affecting the dynamics of the energy efficiency of Ukrainian coal and assessing the real situation in the coal branch. The coal mining enterprise is represented in the form of distributed flows of coal, symbolizing the energy resources: actually spent at the mine; sent to the metallurgical (by-product) plant; to the power station; spent in the everyday life of all technologically related enterprises personnel, etc. Поняття EROI (energy return on investment) стало загальновживаним синонімом енергетичної рентабельності, певною альтернативою традиційної економічної оцінки. Дослідження виконано з метою складання методичних положень щодо встановлення значимості основних чинників, що впливають на динаміку показників енергетичної ефективності вугілля українських шахт, і оцінки реальної ситуації в галузі. Вугледобувне підприємство представлено як розподілені потоки вугілля, які символізують енергетичні ресурси: що витрачаються по шахті; відправляються на металургійний (коксохімічний) завод; на електростанцію; споживаються в побуті персоналом всіх технологічно пов'язаних підприємств тощо. Понятие EROI (energy return on investment) стало общеупотребительным синонимом энергетической рентабельности, определенной альтернативой традиционной экономической оценки. Работа выполнена с целью составления методических положений по установлению значимости основных факторов, влияющих на динамику изменения показателей энергетической эффективности угля украинских шахт, и оценки реальной ситуации в отрасли. Угледобывающее предприятие представлено в виде распределенных потоков угля, символизирующих энергетические ресурсы: собственно расходуемые на шахте; отправляемые на металлургический (коксохимический) завод; на электростанцию; расходуемые в быту персоналом всех технологически связанных предприятий и т.п. 2017 Article EROI of the Ukrainian coal / D. Cherevatskyi, O. Atabyekov // Економічний вісник Донбасу. — 2017. — № 4 (50). — С. 20-31. — Бібліогр.: 24 назв. — англ. 1817-3772 http://dspace.nbuv.gov.ua/handle/123456789/130898 338.58:620:622.33(477) en Економічний вісник Донбасу Інститут економіки промисловості НАН України |
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Social and economic problems of Donbas Social and economic problems of Donbas |
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Social and economic problems of Donbas Social and economic problems of Donbas Cherevatskyi, D. Atabyekov, O. EROI of the Ukrainian coal Економічний вісник Донбасу |
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The concept of EROI (energy return on investment) has become a commonly used synonym for energy efficiency, an alternative to traditional economic evaluation.
This work was carried out with the goal of drawing up methodology for determining the significance of the main factors affecting the dynamics of the energy efficiency of Ukrainian coal and assessing the real situation in the coal branch.
The coal mining enterprise is represented in the form of distributed flows of coal, symbolizing the energy resources: actually spent at the mine; sent to the metallurgical (by-product) plant; to the power station; spent in the everyday life of all technologically related enterprises personnel, etc. |
| format |
Article |
| author |
Cherevatskyi, D. Atabyekov, O. |
| author_facet |
Cherevatskyi, D. Atabyekov, O. |
| author_sort |
Cherevatskyi, D. |
| title |
EROI of the Ukrainian coal |
| title_short |
EROI of the Ukrainian coal |
| title_full |
EROI of the Ukrainian coal |
| title_fullStr |
EROI of the Ukrainian coal |
| title_full_unstemmed |
EROI of the Ukrainian coal |
| title_sort |
eroi of the ukrainian coal |
| publisher |
Інститут економіки промисловості НАН України |
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2017 |
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Social and economic problems of Donbas |
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http://dspace.nbuv.gov.ua/handle/123456789/130898 |
| citation_txt |
EROI of the Ukrainian coal / D. Cherevatskyi, O. Atabyekov // Економічний вісник Донбасу. — 2017. — № 4 (50). — С. 20-31. — Бібліогр.: 24 назв. — англ. |
| series |
Економічний вісник Донбасу |
| work_keys_str_mv |
AT cherevatskyid eroioftheukrainiancoal AT atabyekovo eroioftheukrainiancoal |
| first_indexed |
2025-07-09T14:25:29Z |
| last_indexed |
2025-07-09T14:25:29Z |
| _version_ |
1837179746504409088 |
| fulltext |
D. Cherevatskyi, O. Atabyekov
20
Економічний вісник Донбасу № 4(50), 2017
UDC 338.58:620:622.33(477)
D. Cherevatskyi,
PhD (Engineering),
O. Atabyekov,
Institute of Industrial Economy of the NAS of Ukraine, Kyiv
EROI OF THE UKRAINIAN COAL
Introduction
Coal production in Ukraine is not only loss making
and subsidized as a result but it is also a very power-
consuming. This became clear as early as the 80th of the
last century. At the time of a considerable reduction of
coal production, consumption of rolled metal grew by
20%, consumption of electricity grew by 4% (specific
consumption grew by 10%), steel intensity of mining
equipment grew by more than 1 mln. tons, etc [1].
The problem of wide-scale liquidation of coal
mines at Donbass became very acute [2]. But the
demonstrative survey of coal mines of Sverdlovantra-
tsite amalgamation [3] which was held in the post-soviet
period was used as an argument in favour of further ex-
ploitation of these coal mines since direct and indirect
electricity inputs for production of 1 ton of anthracite
including those materialized in various stuff, equipment,
buildings, structures, etc. amounted for one eighth/ ninth
of a corresponding electricity costs at a thermal power
station.
The situation in coal production and thermal power
engineering sectors deteriorated considerably since
then.
The problem of whether production of coal in
Ukraine is reasonable or not is still quite topical. This
thesis is substantiated by the article [4]. On the contrary,
the situation in Donbass and requirement of increasing
the anthracite imports from the USA make this problem
even more acute. A methodology in use still does not
give a clear understanding of factors influencing the
energy efficiency of the Ukrainian coal production on a
large scale.
Thus the main objective of this work is to review
the current situation in the industry and to make out the
methodology to be used in determining the basic factors
which influence the fluctuations of energy efficiency in-
dexes of the Ukrainian coal mines.
Methods of analysis
One of the initial tasks to be fulfilled as a result of
this research is to achieve the acceptance as a category
of the proposed system of energy efficiency evaluation
of all processes under study.
According to V. Pak, the Ukrainian scientist in me-
chanics and specialist on coal industry, the concept of
exergy should be used in this regard [5]. His suggestion
has possibly been influenced by a book of Polish scien-
tists J. Szargut and R. Petela “Exergy” translated ver-
sion of which was published in the USSR [6, original
edition 7].
Being a category of thermodynamics, exergy as a
derivative of a Greek word “work” having a prefix sig-
nifying the highest degree of something, means a maxi-
mum amount of work which can be accomplish by a sys-
tem when it transforms from the existing state into a
state of equilibrium with all components of the environ-
ment, i.e. with a source and the final receiver of any
stream of energy such as water, vapour, raw materials,
chemical products, various kinds of energy. It is well
known, for example, that a burning fuel extracts more
heat in the oxygen medium than in the open air. At the
same time the summarized exergy of the system is less
since some extra work should be performed for obtain-
ing oxygen from the air.
The situation with coal production is similar. It re-
quires more inputs of coal, materialized in electric and
thermal energy, metal, etc.
The notion of exergy was modified by C.J. Cleve-
land, C.A. Hall , R. Costanza and R.K. Kaufmann into
the notion of EROEI (energy returned on energy in-
vested), or EROI (energy return on investment) almost
20 years ago [8]. This notion came into general use since
then as a synonym of energy efficiency.
"One potentially useful alternative to conventional
economic analysis is net energy analysis, which is the
examination of how much energy is left over after cor-
recting for how much of that energy (or its equivalent
from some other source) is required to generate (extract,
grow or whatever) a unit of the energy in question. Net
energy analysis is sometimes called the assessment of
energy surplus, energy balance, or, as we prefer, energy
return on investment (EROI or sometimes EROEI)" [9,
p. 25].
Keeping the above in view, EROI category is ac-
cepted in this work as the basis to evaluate coal mining
efficiency.
To demonstrate the right approach in evaluation of
EROI the diagram of Grassmann is suitable [6, с. 310].
Grassmann diagrams which are also known as Grass-
mann-Szargut diagrams usually show the system’s
streams of resourses on a scale horizontally and in pro-
portion to their numerical values. The diagrams gra-
phically demonstrate the amounts of energy losses and
their location as well as reallocations between the ele-
ments of the system (object) under study.
A coal mining enterprise can be shown in the form
of distributed coal streams symbolizing energy re-
sources (Fig. 1): one stream of coal consumed at a coal
mine itself, the other one transported to the metallurgical
D. Cherevatskyi, O. Atabyekov
21
Економічний вісник Донбасу № 4(50), 2017
(by-product coking plant), to the power station, and the
one domestically consumed by the personnel of all tech-
nologically combined enterprises, etc.
The elements of the Grassmann diagram are as fol-
lows: Ex – energy net-output of the system; Emet – en-
ergy inputs in metallurgical production, Esh – energy in-
puts in coal mining and coal washery; Eps – energy in-
puts for functioning of thermal power station and trans-
formation of coal into electric energy; Ep – energy in-
puts to meet the requirements of personnel which is en-
gaged in servicing of the system.
Fig. 1. Grassman diagram
The formula corresponding to Grassmann diagram
looks as follows:
pc met p shEx R E E E E= − − − − . (1)
EROI index of coal mining and processing system
equals to:
/ ( ).pc met p shEROI Ex E E E E= − − − (2)
Review of the system from the energy point of
view gives a clear and stable (not depending on the state
of the markets) presentation of the enterprise efficiency.
This includes the understanding of whether a further
existence of the enterprise is feasible from the resource
point of view.
Thus a coal mine needs a certain electric power re-
sources to produce a certain quantity of coal. In order to
deliver a required quantity of electricity to the coal mine
some volume of electricity should be ordered by a coal
mine supplier of electricity from a thermal power sta-
tion. This amount should exceed the amount required to
the coal mine itself by the amount which will be lost
during its transportation in the electricity network. In or-
der to meet the requirements of the electricity supplier,
thermal power station should produce the amount of
electricity ordered by the electricity supplier plus the
amount required for its own needs.
Fuel consumption at a thermal power station de-
pends on its technological efficiency and quality of a
fuel. For the Ukrainian thermal power stations it is three
times higher than the output of a secondary energy re-
sources when calculated in coal equivalent units (tce).
A coal mine consumes not only an electricity but a
thermal energy on a large scale. That is why some quan-
tity of coal produced by a coal mine should be used as a
boiler fuel at a coal mine itself in order to ensure its
operation.
To cover its requirements in metal products a coal
mine transports another portion of produced coal to a
by-product coking plant to use it as a coal blend for co-
king. Besides some coal will be transported to electric
power station and will be used to generate electric en-
ergy to cover the requirements of by-product coking
plant, metallurgical plants, energy transportation losses
in electricity networks plus the requirements in electric
energy of thermal power station itself.
Besides some quantity of coal goes to the power
station for generation of energy used in the process of
coal beneficiation and transportation, as well as in pro-
duction of some auxiliary materials used in various tech-
nological processes at a coal mine, etc.
Some coal is needed to meet the requirements in
electric and thermal energy of metacorporation person-
nel.
If coal mine ensures a sizable net-output of useful
resource, then some unprofitability of it can be consi-
dered acceptable. At the same time a combination of low
economical and energy efficiency and moreover an
energy cannibalism are absolutely impermissible.
Direct inputs of electric energy at the enterprise are
considered a basic index for calculation of coal EROI.
Despite well-known declarations on the unique na-
ture of each of Donbass coal mines, it was proved in the
article [10] that the level of electricity consumption at
the underground coal mining enterprises follows certain
rules.
Statistical analysis of data from 93 separate coal
mines [11] made it possible to find a correlation between
energy, geological и mine technical factors. This analy-
sis was accomplished at the period when the mine list of
the Ukrainian coal industry was quite representative in
quantity.
0.260 0.224 0.679W P H N= ⋅ + ⋅ + ⋅ , (3)
where W – a total electricity consumption at a coal
mine;
P – production capacity of a coal mine;
H – a maximum mining depth;
N – a number of simultaneously developed coal
seams.
D. Cherevatskyi, O. Atabyekov
22
Економічний вісник Донбасу № 4(50), 2017
Indexes which are present in the formula are given
in a standardized form (from minus one to plus one,
irrespective of the factors’ nature). Standard errors of
variables were used in the process of standardization as
variability interval. At the same time mathematical ex-
pectations of values are used as the natural value of the
factor. A mean level was equal to zero.
The values of regression indexes show that the
number of developed coal seams (variable N) has the
highest effect on coal mines’ energy consumption. This
has a direct connection with the attitude of bed: the value
of this factor is much higher for steep gradient seams
than in the case of flat-lying seams.
A depth of mining and a production capacity of a
coal mine affect the energy characteristics of mining en-
terprise practically in the same way. At the same time
they do not play the primary role in this process. Evalu-
ations of W by N value explain such a high energy in-
tensity observed at coal mines developing steep-pitch
seams.
Five coal mine clusters were formed as a result of
cluster analysis of three determining variables (P, H and
N) (Table 1).
Table 1
Mean values of variables by mine clusters
(in standardized estimation)
Cluster
No.
Number
of coal
mines
W P H N
1 14 0,021 0,530 1,614 -0,455
2 36 -0,199 -0,301 -0,394 0.101
3 10 0.285 2.098 -0.534 -0.261
4 23 -0.842 -0.838 -0.754 -0.750
5 10 2.339 0.171 0.945 2.261
It is clear that energy consumption in the first clus-
ter is almost on a mean level according to our data base
(45,5±13,4 GWh or 5,6±1,6 thousand tons of coal
equivalent per year). This cluster included the following
industrial objects - 14 coal mines which existed at the
moment. All of them had relatively high production ca-
pacity (740±155 thousand tons) and developed limited
number (2,7±1,9) of deep-lying (1088±165 m) coal
seams.
Energy consumption in the second cluster is below
average (33,8±12,9 GWh or 4,2±1,6 thousand tons of
coal equivalent per year). A production capacity here is
also below average (522±145 thousand tons), depth oc-
currence is less than an average one as per our data base
(587±120 m). At the same time a number of seams
which are developed is sizable (4,4±1,9).
The second cluster has the second by rank grada-
tion average mine wise number of coal seams. By this
index the second cluster is next to the fifth cluster only.
Actually it is explained by the fact that the second clus-
ter also includes some coal mines which develop steep-
pitch seams like “Bulavinskaya”, “Enakievskaya” and
other mines of “Ordzhonikidzeugol” amalgamation. A
number of objects in the cluster is 36.
The third cluster combines 10 objects having the
highest production capacity (1153±146 thousand tons)
and developing a limited number of seams (3,3±1,2),
which are lying at the average depths (672±164 m).
Thus by an electric energy consumption the third cluster
coal mines occupy the position which can be characte-
rized as exceeding by approximately one third the ave-
rage level by a number of objects (51,1±15,0 GWh or
6,3±1,8 thousand tons of coal equivalent per year).
The fourth cluster combines objects with charac-
teristics in the bottom of the range: these are shallow
coal mines (498±147 m) with a minimum number of
coal seams (1,8±0,8) and a low production capacity
(380±113 thousand tons). Energy consumption at such
coal mines constitutes (16,4±7,9 GWh or 2,0±1,0 thou-
sand tons of coal equivalent per year).
The fifth cluster is featured by an impressive depth
of mining (921±132m) and a great number of simulta-
neously developed seams (10,9±1,4). The depth of min-
ing here is approaching the top of the range and the pro-
duction capacities are high enough - 646±138 thousand
tons (at least they are above the average). This substan-
tiates such a high energy inputs in coal production. They
greatly exceed the overall level of energy consumption
in a coal industry (112,5±18,9 GWh or 13,8±2,3 thou-
sand tons of coal equivalent per year). This cluster com-
bines 10 coal mines.
Conversion of electricity units into equivalent fuel
indexes was made taking into account the ratio 1 kWh
=123 grams of coal equivalent, which comes from the
conversion of SI units (1 kwh = 3,6 MJ; 1 kg of coal
equivalent = 7000 kcal or 29,3 MJ).
0.123E W= ⋅ . (4)
where E – electric energy consumption, expressed in the
units of equivalent fuel, thousand tons of equivalent
fuel.
There is a statistical difference (with a probability
of 95%) between all the clusters in the level of energy
consumption and a striking difference of the fifth cluster
from all the other ones.
Beside the general energy consumption clusters
differ from each other by energy consumption in various
technological processes.
About 29% of energy goes on mine ventilation and
only 9% goes on operation of compressors in the first
cluster. At the same time 56% of energy goes on opera-
tion of pneumatic equipment, and 12% of energy is con-
sumed by the ventilators of main ventilation system in
the fifth cluster.
Cluster wise energy intensity characteristics in
kind for the main coal mining processes (Table 2).
D. Cherevatskyi, O. Atabyekov
23
Економічний вісник Донбасу № 4(50), 2017
Table 2
Energy consumption characteristics by technological processes, GWh
Номер кластера
Cluster No.
W W3 W4 W5 W10 W11
1 45,5±13,4 12,9±7,1 5,6±3,7 4,3±4,1 11,0±5,1 3,6±2,3
2 33,8±12,9 7,2±4,4 4,9±3,8 6,7±10,4 7,5±4,5 1,9±1,1
3 51,1±15,0 16,1±7,0 3,9±3,6 2,7±4,0 10,1±4,2 5,4±3,2
4 16,4±7,9 3,8±2,7 2,2±1,4 0,3±0,9 4,5±4,6 2,0±1,6
5 112,5±18,9 13,7±3,8 8,1±4,4 62,2±15,4 11,1±5,4 1,4±1,0
The designations are following:
W3 – Annual energy consumption for Ventila-
tion process, GWh;
W4 – Annual energy consumption for “Mine
hoist” process, GWh;
W5 – Annual energy consumption for “Com-
pressed air supply”, GWh;
W10 – Annual energy consumption for “De-
watering” process, GWh;
W11 – Annual energy consumption in under-
ground transportation processes, GWh.
The above mentioned patterns are typical for those
cases when mine production funds are used to a great
extent and implementation rate is close to one. Produc-
tive capacity implementation rate equal to zero (D = 0)
represents the “idle running” operation of the enterprise
using the mechanics terminology. With some exaggera-
tion we can assume that when a coal mine is in “full idle
run” operation the only really operating equipment are
the ventilation and pumping installations.
If we know the electricity consumption for these
processes, we can determine the requirements of a coal
mine when production capacity implementation rate
equals zero.
A power production function of a coal mine – is a
correlation between the production output and energy
consumption.
Fig. 2 gives graphs of actual electric power con-
sumption for production of coal at two coal mines be-
longing to marginal fourth and fifth clusters. Standard-
ized coal production at mine S forms the ratio of the an-
nual coal output to production capacity of the mine.
Fig. 2. Correlation between a production development and electric power consumption
at coal mines belonging to various clusters
Coal mine “Kharkovskaya” situated at Sverdlovsk
of Lugansk region has a capacity of 320 thousand
tons/year and belongs to the fourth cluster. Coal mine
named after Karl Marx situated at Enakievo has a capac-
ity of 900 thousand tons and belongs to the fifth cluster.
The production function of a coal mine shown in
its totality forms a logarithmic function graph beginning
in the point having coordinates (Е(D=0); D=0) and going
through the point having coordinates (Е(D=P); D=P),
where Es – summarizes the basic inputs of coal used for
functioning of an enterprise.
( )e SD K Ln E C= + , (5)
where D – Annual coal output, thousand tons;
Ke and С – Logarithmic function coefficients;
Еs – total standardized coal consumption for
production needs which is in correspondence with a to-
tal coal mine output.
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1,8
2
0 2 4 6 8 10 12
S
ta
n
d
a
rd
iz
e
d
c
o
a
l o
u
tp
u
t,
u
n
it
fr
a
ct
io
n
s
Annual electric power input,
thousand tons of coal equivalent
им. К. Маркса Харьковская
D. Cherevatskyi, O. Atabyekov
24
Економічний вісник Донбасу № 4(50), 2017
3s MET p P ShE E F f N F= + + + , (6)
where Е3 – Total electric power consumption in the
system which includes energy resource production at a
thermal power station and energy losses in the electric
networks;
FMET – Consumption of coal in metallurgical
production;
fPNP – Consumption of coal by personnel for
domestic needs;
FSh – Coal consumption for coal mine needs.
Table 3 shows the electric energy consumption at a
coal mine in two cases: when a coal mine production
reaches its maximum which corresponds to its produc-
tion capacity and in the “idle-run” operation. Based on
this data one can make a coal mine production function
typical for the particular cluster.
Table 3
Mean cluster wise electric power consumption,
thousand tons of coal equivalent
Cluster
No.
Electricity
consumption when
D=P
Electricity
consumption when
D=0
1 5.6 2.0
2 4.2 1.8
3 6.3 2.5
4 2.0 0.8
5 13.8 5.5
Based on the review of observation data published
in [3, p. 16-17], a conclusion can be made with regards
to electric power consumption in coal dressing pro-
cesses, its transportation by rail road, as well as a vo-
lume of electric power materialized in consumed mate-
rials, buildings and structures and equipment which was
put into action during the period of the year. Percentage-
wise, these figures constitute, respectively: 11,1±3,0;
5,1±0,9; 65,3±8,6; 6,6±1,2; and 10,6±1,8% of direct
electric power consumption at a coal mine.
Still these indexes are typical for coal mines with
flat pitch coal seams.
According to the research made by DonUGI spe-
cialized laboratory of wood and material stores [12, p. 8]
there exist considerable differences in consumption of
materials depending on bed attitudes. On the basis of
long term observations, functioning of coal mines deve-
loping flat pitch seams goes under the following pattern
as far as a daily material consumption is concerned:
125.5 25.3 2.5 1,3П
MAT пр pQ D L L= + ⋅ + ⋅ + ⋅ , t, (7)
where QMAT – A daily consumption of materials at a
coal mine, tons/day;
D1 – A daily extraction of coal at a coal mine,
thousand tons/day;
Lпр – An average daily penetrating face ad-
vance, m/day;
Lp – An average daily overhaul advance in
mine workings, m/day.
For coal mines developing steep pitch seams
151.4 150 2.0 1.2К
МAT пр pQ D L L= + ⋅ + ⋅ + ⋅ , t. (8)
All elements of the above formulas may be divided
into 2 groups: the one in which consumption is calcu-
lated in tons of materials and a group of elements refer-
ring to a running meter of mine workings which are be-
ing advanced or overhauled. As a rule a nomenclature of
the latter combines the metallurgical products such as
arch support, rails, pipes.
According to standard requirements of a relatively
small coal mine having a daily output of 1 thousand tons
(or around 250 thousand tons of coal per year) a daily
supply of about 51 tons of mixed cargo and 20 tons of
metal products is needed i.e. a total requirement is
71 tons if coal mine develops flat pitch coal seams and
169 tons of loads if a coal mine develops steep pitch
seams.
Because of geological conditions the difference in
material consumption of an average mine having an an-
nual capacity of 700 thousand tons is about 3,4 times.
At the same time coal mines developing steep pitch
seams consume 2,5 times more electricity.
That’s why in case of coal mine with steep pitch
seams the ratio between the electricity consumption ma-
terialized in goods and consumed directly requires cor-
rection: this ratio should be increased 1,4 times com-
pared to coal mines developing flat pitch seams. At the
same time, keeping in view the abovementioned in-
creased electricity consumption for own needs of coal
mines with steep pitch seams, specific costs for the other
categories at these mines should be reduced.
Data on coal mines of both types is given in
Table 4.
Table 4
Electricity consumption rate, unit fractions,
from direct electricity consumption at a coal mine
Coal mine with
flat pitch seams
Coal mine with
steep pitch seams
Coal dressing 0,11 0,04
Railroad transportation 0,05 0,02
Materials 0,65 0,92
Equipment 0,11 0,04
Buildings and struc-
tures 0,07 0,03
Costs of electric power supply to a coal mine are as
follows:
3
(1 ) (1 ) (1 )
PS
E
E
⋅ + ρ ⋅ + λ ⋅ + φ=
η
. (9)
where ρ – electricity inputs for processes of coal
dressing and transportation, inputs of electricity materi-
alized in goods, metal products, buildings and struc-
tures, equipment;
λ – electricity losses in its transportation;
D. Cherevatskyi, O. Atabyekov
25
Економічний вісник Донбасу № 4(50), 2017
φ – electricity inputs for own needs of a ther-
mal power station;
ηPS – energy efficiency of thermal power station
with regards to fuel consumption.
Coal mines are heavy users of not only an electric
power energy but of thermal energy and motor oil as
well.
Based on the regression analysis it is determined
that summary inputs of fuel (boiler coal, petrol and
diesel fuel) are comparable with electricity consumption
[13, 14]. With a high degree of accuracy this depen-
dence can be presented in the following way:
1.133ShF E= ⋅ . (10)
*MET MET METF f Q= , (11)
where fMET – specific consumption of energy re-
sources in metallurgical production, thousand tons of
equivalent fuel;
QMET – annual consumption of metal rolled
stock at a coal mine, thousand tons.
The data provided in the work [15, p. 113] shows
that on the whole 1,3 tons of coal equivalent is con-
sumed in production of 1 ton of rolled stock. At the same
time this data involves net-inputs of electricity. It does
not take into account fuel inputs at generating plants.
Taking into account the data given in the work [16,
p. 52] a summarized specific consumption of material-
ized coal if recalculated considering all cycles of metal-
lurgical treatment can be considered equal to 2,1 tons of
equivalent fuel for 1 ton of rolled stock consumed by a
coal mine. However, there is a reason to divide costs and
under fMET understand only the consumption of coal for
the production of coke – 640 kg per ton of coal/ton of
rolled metal [16, p. 157], and the remaining part of the
costs included in the electricity consumption.
The following dependences defining the respective
requirements in metal-roll by coal mines developing
flat-pitch and steep-pitch seams respectively are based
on generalized statistical data.
3
110 (2.5 1.3 )П
MET pQ l P s k−= ⋅ ⋅ ⋅ ⋅ + ⋅ ,
thousand tons, (12)
3
110 (2.0 1.2 )К
MET pQ l P s k−= ⋅ ⋅ ⋅ ⋅ + ⋅ ,
thousand tons, (13)
where l1 – specific ratio of a coal output and prepara-
tory works made during the year (6,5 m/thousand tons);
kp – Ratio coefficient between face advance ac-
tivities and underground overhauling activities (accord-
ing to previous experience this ratio can be admitted as
0,5 on the average).
Evaluation of energy inputs for participation of
personnel in production processes is a matter of special
attention. Instead of evaluating a direct labour it is sug-
gested in the work to calculate energy inputs in domestic
needs of company personnel. Coal consumption clearly
prevails in total energy consumption for domestic heat-
ing but at the same time one needs to consider inputs of
electric and thermal energy as well.
According to the terms of the Mining Code of
Ukraine [17, art. 43], all personnel of coal mining (coal
processing) and mine building companies are enabled to
acquire a free coal for domestic needs in quantity of 5,9
tons of coal for 1 person which can be equated with 4,2
tons of coal equivalent.
310P P PF N f−= ⋅ ⋅ , (14)
where FP – An annual consumption of furnace and
chimney fuel for personnel needs, thousand tons of coal
equivalent;
Np – Number of free coal receivers, men;
fР – Specific consumption of furnace/chimney
fuel, tons of coal equivalent/men.
During the period of intensive exploitation of mine
fund objects, cluster wise direct labour inputs by
coal mines are equal (with a probability of 0,95) to
2,7 ± 0,3; 3,8 ± 0,4; 2,3 ± 0,8; 4,4 ± 0,9; 3,3 ± 0,2 per-
sons / 1000 tons of produced coal. Any changes in the
production output do not lead to linear changes in per-
sonnel number.
Empirical dependence for coal mines developing
flat pitch seams looks as follows:
(1 0.398)bП aK
Sh ShN B P s −= ⋅ ⋅ , (15)
where NSh
b – A current number of mine personnel,
men;
BSh
a – Labour coefficient in production of coal
at a coal mine having a high degree of production capac-
ity utilization, men/thousand tons.
The dependence for coal mines developing steep
pitch seams (the fifth cluster) looks as follows:
(1 0.516)bК aК
Sh ShN B P s −= ⋅ ⋅ . (16)
Workers of metallurgical, by-product coking plants
and thermal power stations involved in processes asso-
ciated with coal production can be considered a person-
nel participating in coal production on the basis of out-
sourcing agreements. To estimate the number of this
personnel it is acceptable to use data on labour-inten-
siveness of various operations/processes at the corre-
sponding enterprises.
As far as metallurgical companies are concerned,
the labour-intensiveness of metallurgical production is
equal to 7,4 persons per 1 thousand tons of steel (the data
was provided by Monitor Company Group, analytical
firm which made a development strategy of Donetsk Re-
gion).
According to the data published in article [18], a
labour content in operation of power generating enter-
prises in its dependence from the annual quantity of pro-
cessed fuel is as follows:
5 20.539 7 10 94PSN d d−= ⋅ − ⋅ ⋅ − , (17)
where NPS – Number of personnel involved in servic-
ing of energy generating company, men;
d – Annual consumption of fuel (coal) at the
thermal power station, thousand tons of equivalent fuel.
D. Cherevatskyi, O. Atabyekov
26
Економічний вісник Донбасу № 4(50), 2017
Though the personnel involved in metallurgical
production and energy generation is not on the coal mine
staff, their involvement in coal production provides the
ground for considering this personnel as consumers of
domestic coal.
The total number of personnel in production sys-
tem is as follows:
*( ) ( )P Sh PS MET METN N N d N Q= + + , (18)
where NPS(d) – Labour-intensiveness in servicing of
power station in respect of a coal mine requirements,
persons;
NMET (QMET) – Labour-intensiveness in ser-
vicing of metallurgical production in respect of a coal
mine requirements, persons/
Graph-analytic method is most suitable for defin-
ing a production function and determining the coeffi-
cient Ke. This means the requirement to calculate the
value Es which forms the combined energy inputs of a
coal mine in D = 0 and D = P modes. After this the log-
arithmic dependence graph should be made with the
help of Excel MS software.
The following formula describes a quantity of use-
ful coal mine production:
SEx R E= − . (19)
It is acceptable to use a simplified dependence [19]
in order to recalculate a quantity of produced coal given
in physical terms into coal equivalent terms
(31505.7 332.8 )
1000 29.3
r dQ A= − ⋅β =
⋅
. (20)
where β – Transfer coefficient from physical units
into units of coal equivalent;
Qr – The lowest heating power of coal at a par-
ticular mine, determined on the basis of coal ash content,
kJ/kg;
Аd – Coal ash content, percent.
.rR Q D= = ⋅ β (21)
The consumption of energy resources at a coal
mine depends on a number of variables. It is essential to
determine to which extend each of the variables affects
it. To achieve this it is suggested in the work to use the
above model for making a multi-factor experiment by
Box-Wilson method of experiment planning theory [20,
p. 80].
A coal mine when described by its production
function is a device at the outlet of which a response
function (or criterion function when one says about op-
timization) is formed under the influence of inlet signals
(factors).
Numerical factors can be denoted by any of a great
number of acceptable values. Box-Wilson method per-
mits deviation of factors on two levels only: the lower
one which is designated by minus 1 and the upper one –
by plus 1.
To reduce the labour inputs for analysis, it would
be appropriate in the first place to conduct a screening
experiment making use of software Statistica® [21]. The
program enables the maximum of 11 factors to be used
for analysis. On the assumption of this it is reasonable
to form the composition of variables used in the varia-
tion in the following way:
F1 –
power energy inputs in coal beneficiation
processes;
F2 –
power energy inputs in coal transportation by
railroads;
F3 – power energy materialized in goods;
F4 –
power energy materialized in buildings and
structures;
F5 – power energy materialized in equipment;
F6 –
coal consumption for thermal energy gener-
ation;
F7 –
labour inputs in coal mining at a specific
mine;
F8 –
electric power energy losses in electric net-
works;
F9 –
consumption of coal used for personnel do-
mestic needs;
F10 –
specific fuel consumption at thermal power
station;
F11 – coal inputs in coke production.
Factor variation levels correspond to those indi-
cated in Table 5. As an object of analysis on the stage of
a screening experiment the following coal mine was
chosen: the one belonging to the third cluster with a pro-
duction capacity of 1150 thousand tons per year and a
respective electric power consumption of 2,5 and 6,3
thousand tons of coal equivalent per year which corre-
spond to a coal mine operation in the idle run mode and
with a full load of production funds.
Table 5
Values of factor setting levels
Nomenclature
of a factor
The upper
level (+1)
The
basic
level
The
lower
level (-1)
Variation
interval
F1, unit fractions. 0,08 0,11 0,14 0,03
F2, unit fractions. 0,04 0,05 0,06 0,01
F3, unit fractions. 0,57 0,65 0,73 0,08
F4, unit fractions. 0,05 0,065 0,08 0,015
F5, unit fractions. 0,09 0,105 0,12 0,015
F6, unit fractions. 1,03 1,135 1,24 0,105
F7, men./th.tons 1,5 2,3 3,1 0,8
F8, unit fractions. 0,05 0,085 0,12 0,035
F9, t.e.f./man. 0 2,1 4,2 2,1
F10, gr.c.e./kWh 200 300 400 100
F11, t/t 0,40 0,525 0,65 0,125
Appropriateness of applying a fractional factorial
experiment matrix for factor variation is substantiated
by the necessity to reduce research labour inputs since a
comprehensive plan involving 11 factors variated on
two levels would have constituted 2048 tests.
A matrix containing 16 tests (fractional factor ex-
periment having a view of 2 (11-7)) and generated by soft-
ware Statistica® is designated for systematic variation of
variables.
D. Cherevatskyi, O. Atabyekov
27
Економічний вісник Донбасу № 4(50), 2017
When the factors affecting the third factor in a big
way are selected in the course of the screening experi-
ment it is required to start the experiments at the coal
mines representing the marginal clusters, the fourth and
the fifth ones. To achieve this the selected factors should
be supplemented by another factor, a qualitative one, the
lower level of which embodies a coal mine of the fourth
factor, and the upper level – a coal mine of the fifth clus-
ter.
The results of experiment at coal mines represent-
ing the marginal clusters are aimed at presenting a full
spectrum of the Ukrainian coal mines characteristics.
Results
The results of screening experiment, fulfilled with
the use of a software pack Statistica 6.0®, show that one
factor only, the tenth one, which is a specific fuel inputs
at the thermal power station, has a statistically signifi-
cant effect on function Е (s) in the idle run mode.
The following dependence reflects the values of
the variable
(D 0) 1020.408 4.614E F= = + ⋅ . (22)
Two factors, the tenth and the ninth, the same spe-
cific fuel consumption at thermal power station and rates
of coal allocated for domestic needs of the personnel,
have statistically sizable effect on accumulated energy
consumption at coal mine operating at full swing.
Energy loads at the coal mine operating at full
swing are formed according to the following depend-
ence
( ) 9 1063.639 8.341 11.934D PE F F= = + ⋅ + ⋅ . (23)
Thus a production function of coal mines belong-
ing to the third cluster can be formed by two sets of data,
estimated in thousands of tons of coal equivalent: when
setting factor values at the upper level E(D = 0) = 25.0;
E (D = P) = 83.9; and setting factor values at the lower
level E(D = 0) = 15.8; E(D = P) = 43.4. Corresponding pro-
duction functions are given on the Fig. 3.
Fig. 3. Production functions of coal mine belonging to the third cluster
(Level +1 and Level -1)
Coefficient Ке for the first production function is
equal to 1138, for the second one – 950; coefficients C
are 3141 and 3057 respectively. It is clear that the elas-
ticity of production functions in standardized imaging
becomes less with the increase of effecting factor va-
lues.
Such effect can actually be achieved if one applies
the system which includes the elimination of benefit for
the mine personnel which is a free provision of coal
(zero rate) plus improving the effectiveness of thermal
power stations by reducing fuel consumption rates.
According to the estimates a maximum energy ef-
ficiency of a coal mine is achieved when it operates at a
maximum capacity. Depending on the attendant factors
EROI of a coal varies from 17,5:1 to 8,6:1.
Keeping in view the obtained data on effect of va-
rious factors it makes sense to carry out the next expe-
riment. This time it is a three-phase hypothetical (com-
puter) experiment with one qualitative factor by matrix
of a comprehensive factor experiment 23 consisting of 8
tests. The first factor F1 is a cluster type (+1 corresponds
to a coal mine of the fifth cluster, and -1 corresponds to
a coal mine of the fourth cluster). The second factor
F2 – is a distribution rate of a free coal provided to the
personnel of a coal mine. The third factor F3 – is a fuel
consumption efficiency at a thermal power station.
According to the experimental conditions it is ad-
mitted that a coal mine of the fourth cluster has a pro-
duction capacity of 380 thousand tons of coal per year,
direct inputs of electric power energy at a coal mine
D = 949,82ln(Es) - 3057,4D = 1138,1ln(Es) - 3141,2
0
200
400
600
800
1000
1200
1400
0 20 40 60 80 100
Co
al
o
ut
pu
t,
th
ou
sa
nd
to
ns
Summary inputs of energy resources, thousand tons of coal
equivalent
Уровень +1 Уровень -1
D. Cherevatskyi, O. Atabyekov
28
Економічний вісник Донбасу № 4(50), 2017
operating in the idle run mode is 0,8 thousand tons of
coal equivalent and 2 thousand ton of equivalent fuel
when the coal mine operates at a full swing.
The capacity of the fifth cluster coal mine is 650
thousand tons, corresponding direct electricity inputs
are 5,5 and 13,8 thousand tons of equivalent fuel. The
values of those factors which are statistically of no im-
portance for the response function i.e. electricity inputs
for coal beneficiation, coal railway transportation and
those materialized in products, etc. are fixed at the aver-
age level.
The experiment results bring one to the conclusion
that only two factors, the first and the third ones, as well
as their interaction, have statistically sizable effect on
energy consumption of coal mining process. The diffe-
rence between coal mines representing clusters which
are marginal in terms of energy inputs is so sizable that
they overlap the effect of the second factor.
The effect of the first and third clusters correlation
F1F3 is also of essence: it is positive which means that
the response function value increases when factors are
set on one and the same level.
The dependence of response functions from the
factors are as follows:
(D 0) 1 3
1 3
23.726 16.931 6.151
4.629 ;
E F F
F F
= = + ⋅ + ⋅ +
+ ⋅
(24)
(D ) 1 3
1 3
65.582 42.630 15.685
11.363 .
PE F F
F F
= = + ⋅ + ⋅ +
+ ⋅
(25)
The marginal sets of indexes for coal mines of the
fourth and the fifth clusters, calculated in thousands tons
of coal equivalent, amount: when the factor values are
set on the upper level E(D = 0) = 51.4; E(D = P) = 135.3;
when factor values are set on the lower level –
E(D = 0) = 5.3; E(D = P) = 18.6.
The higher capacity and more energy consuming
coal mines of the fifth cluster have more elastic produc-
tion function. That is why smaller coal mines develop-
ing flat pitch seams require a greater increase of energy
inputs (in relative terms) in order to achieve a sizable
increase of coal output. For the coal mines which de-
velop steep pitch seams an increase of coal output does
not require such a high increase of energy inputs.
The effect of the second significant factor F3 is of
no less importance: coal mines are not self-sufficient: a
low effectiveness of thermal power stations’ operation
reduces greatly the effectiveness of the entire coal min-
ing industry. And actually the more energy consuming a
coal mine production is, the more the fuel inputs at the
thermal power stations effect a coal mine operation.
This is reflected by plus or minus symbol showing the
factors’ mutual effect.
Table 6 shows at which conditions the optimum
operation of coal mines belonging to different clusters
at various effectiveness of thermal power station opera-
tion can be achieved. Optimality conditions are calcu-
lated with the help of “Search for a solution” module of
Excel MS software.
Table 6
Optimal coal mine operation modes
Optimization conditions Optimal standardized
output, fractions of one
Optimal energy input,
thousand tons of coal equivalent
EROI
of a system
Coal mine of the 4th cluster, high ef-
fectiveness of TPS 0,709 201 9,60
Coal mine of the 5th cluster, high ef-
fectiveness of TPS 0,948 412 4,79
Coal mine of the 4th cluster, low effec-
tiveness of TPS 0,756 208 8,00
Coal mine of the 5th cluster, low effec-
tiveness of TPS 0,989 377 3,08
To achieve the highest possible energy efficiency
it is required to control and adjust the operation modes
at the fourth cluster coal mines which have a more rigid
production function as far as a resource aspect is con-
cerned. The optimal values of standardized coal produc-
tion at such mines varies between 0,71 and 0,76. As to
the fifth cluster coal mines the basic condition for
achieving an effective operation mode is the increase of
an annual coal output to reach a rated level of production
capacity.
This goal is hard or more likely impossible to
achieve since it requires to attract very sizable invest-
ments and involve a lot of additional manpower. Since
these enterprises are loss-making they do not have any
resources of their own to achieve the above. Budget re-
sources are limited and are not sufficient to support even
more efficient coal mines. This is the reason why the
majority of state-owned coal mines have low production
loads and their energy efficiency is at a very low level.
The following Grassmann diagrams (Fig. 4) are in-
dicative of the operating efficiency of the fourth and
fifth clusters’ coal mines when they operate in the opti-
mal operation modes at a high efficiency of thermal
power station operation.
Actually these are the descriptions of how the pro-
duced coal is distributed. The fourth cluster coal mine
consumes 7 thousand tons of equivalent fuel (in coal)
for its own technological needs. Besides it sends: 4 thou-
sand tons of equivalent fuel to electric power station, 4
thousand tons of equivalent fuel to the by-product cok-
ing plant and 7 thousand ton – for the domestic needs of
personnel. EROI of coal mine having a useful coal out-
put at the level of 201 thousand tons of equivalent fuel
equals 9,60.
D. Cherevatskyi, O. Atabyekov
29
Економічний вісник Донбасу № 4(50), 2017
Fig. 4 Comparative Grassmann diagrams for coal mines of the fourth and the fifth clusters
when thermal power stations operate with a high efficiency
Out of 650 thousand tons of equivalent fuel pro-
duced by the hypothetical fifth cluster coal mine,
44 thousand tons of equivalent fuel (in coal) is con-
sumed by the coal mine directly (in a boiler house) or
indirectly for its own technological needs. Besides,
26 thousand tons of equivalent fuel goes to electric
power station, 7 thousand tons goes for production of
coke and another 9 thousand tons goes for domestic
needs of personnel. The actual useful output of coal
amounts for 412 thousand tons of equivalent fuel. And
EROI of this coal mine constitutes 4,79.
When electric power stations operate inefficiently,
the inputs Eps,, i.e. the amount of fuel to be used at the
energy generating enterprise increases to 13 and upto 80
thousand tons of equivalent fuel depending on whether
a coal mine belongs to the fourth or to the fifth cluster.
In this case a net energy production of the system con-
stitutes 208 and 376 thousand tons of equivalent fuel
respectively. And EROI is 8,0 and 3,08 respectively.
Discussions
According to the available information EROI of
the American coal is 80:1, and the average EROI world-
wide is 46:1, a Chinese coal has the ratio of 27:1 (ac-
cording to the data of 2007) [22]. Research in the sphere
of various processes in the energy sector of the national
coal mining sector can be concluded in the following
way: EROI index of the best national enterprises ope-
rating in the most favourable geological conditions and
with the most efficient thermal power station is about
18:1. The actual profitability in the Ukrainian fuel and
energy complex is much worse.
In the pre-depression and prewar 2006, a cumula-
tive curve of EROI distribution by groups of coal mines
excavating Steam Coal and Anthracite had the form,
given in Fig. 5.
A general situation with anthracite coal has been
improved by few high-performance coal mines forming
an integral part of Sverdlovanthracite and Rovenkian-
thracite amalgamations. One of them has an EROI index
which corresponds to an average global level of energy
efficiency by coal, two other mines have EROI index on
the level of Chinese coal mining industry. The rest of
coal mines do not show any improvement as far as the
energy characteristics of coal are concerned. There is a
great number of coal mines in both groups having a low
coal output which leads to a sort of energy cannibalism
in coal mining industry.
For the first group a total reported coal production
amounted 18,5 million tons, and EROI turned to be
9,3:1, as for the second group a reported coal production
is 15,5 million tons and EROI is 10,1:1. A general EROI
index of the Ukrainian steam coals is equal to 9,6:1 (ac-
cording to the data of 2006).
180 190 200 210 220 230
Шахта из IV
кластера
201 7 4 4 7
Ex
Esh
Eps
Emet
Ep
350 400 450 500
Шахта из V
кластера
412 44 26 7 1
Ex
Esh
Eps
Emet
Ep
D. Cherevatskyi, O. Atabyekov
30
Економічний вісник Донбасу № 4(50), 2017
Fig. 5. Cumulative curves, characterizing energy cost-effectiveness of various coal grades mines
Such a low efficiency of the national coal mining
industry is substantiated by unfavourable geological
conditions (great depths and high gassiness of coal
seams), neglect of technical facilities especially of state-
owned coal as well as by the backwardness of thermal
power engineering.
It should not be overlooked that Donbass basin is
one of the oldest in the world, its history goes back to
200 years ago. The deepest coal mine in the world,
“Shakhterskaya glubokaya”, is situated here. Mining ac-
tivities are conducted at this mine at the depths exceed-
ing the minus 1546 meters mark. No other country
“takes risk” of developing such deposits. And until re-
cently Ukraine was one a dozen of the biggest coal pro-
ducing countries in the world. In view of the abovesaid,
coal mining enterprises of Donbass are more likely the
productions of hard, liquid and gaseous coal wastes
(more than 5 т on 1 ton of excavated fossil) than on the
contrary [23].
As shown in the article, a high fuel capacity of elec-
tric power energy production at the thermal power sta-
tions is one of the most powerful factors affecting the
energy profitability of the Ukrainian coal.
Sigmund Freud believed that the technical expan-
sion of Civilization is akin to a culturally acceptable
form of sadism. But est modus in rebus: the desire for
sustainable development requires the legislative re-
striction of enterprises with a low EROI [24].
It makes sense to update the obtained results to the
recent situation in the coal mining industry of Ukraine,
but even the given data is suitable for taking decisions
on restructuring of capital assets in coal mining industry
as well as developing policies on fuel provision of the
national energy sector.
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0
10
20
30
40
50
0 20 40 60 80 100
ER
O
I
Cumulative production, percent
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A-Coal S-coal
D. Cherevatskyi, O. Atabyekov
31
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Череватський Д. Ю., Атабєков О. І. EROI
українського вугілля
Поняття EROI (energy return on investment)
стало загальновживаним синонімом енергетичної
рентабельності, певною альтернативою традиційної
економічної оцінки.
Дослідження виконано з метою складання ме-
тодичних положень щодо встановлення значимості
основних чинників, що впливають на динаміку по-
казників енергетичної ефективності вугілля україн-
ських шахт, і оцінки реальної ситуації в галузі.
Вугледобувне підприємство представлено як
розподілені потоки вугілля, які символізують енер-
гетичні ресурси: що витрачаються по шахті; відпра-
вляються на металургійний (коксохімічний) завод;
на електростанцію; споживаються в побуті персона-
лом всіх технологічно пов'язаних підприємств тощо.
Ключові слова: EROI, вугільна шахта, коксохі-
мічний завод, електростанція, персонал, витрата
енергетичних ресурсів.
Череватский Д. Ю., Атабеков О. И. EROI
украинского угля
Понятие EROI (energy return on investment)
стало общеупотребительным синонимом энергети-
ческой рентабельности, определенной альтернати-
вой традиционной экономической оценки.
Работа выполнена с целью составления методи-
ческих положений по установлению значимости ос-
новных факторов, влияющих на динамику измене-
ния показателей энергетической эффективности
угля украинских шахт, и оценки реальной ситуации
в отрасли.
Угледобывающее предприятие представлено в
виде распределенных потоков угля, символизиру-
ющих энергетические ресурсы: собственно расходу-
емые на шахте; отправляемые на металлургический
(коксохимический) завод; на электростанцию; рас-
ходуемые в быту персоналом всех технологически
связанных предприятий и т.п.
Ключевые слова: EROI, угольная шахта, коксо-
химический завод, электростанция, персонал, рас-
ход энергетических ресурсов.
Cherevatskyi D., Atabyekov O. EROI of the
Ukrainian coal
The concept of EROI (energy return on invest-
ment) has become a commonly used synonym for en-
ergy efficiency, an alternative to traditional economic
evaluation.
This work was carried out with the goal of drawing
up methodology for determining the significance of the
main factors affecting the dynamics of the energy effi-
ciency of Ukrainian coal and assessing the real situation
in the coal branch.
The coal mining enterprise is represented in the
form of distributed flows of coal, symbolizing the en-
ergy resources: actually spent at the mine; sent to the
metallurgical (by-product) plant; to the power station;
spent in the everyday life of all technologically related
enterprises personnel, etc.
Keywords: EROI, coal mine, by-product coke
plant, power station, personnel, energy consumption.
Received by the editors: 15.12.2017
and final form 22.12.2017
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