The document proposes using machine learning techniques to improve control of mine ventilation systems. Specifically, it suggests using neural networks to define the functional dependencies between regulator positions and required airflow values over time. This would account for changes in the ventilation network and reduce issues like hunting. The researchers trained networks on historical data to predict regulator angles from required airflow. Adding a temporal parameter reflecting previous system states improved long-term predictions. The proposed control algorithm aims to efficiently and safely meet airflow requirements using the neural network models.

1. Applying machine learning techniques to mine
ventilation control systems
Aleksey Kashnikov
Department of aerology and thermophysics
Mining Institute of the Ural Branch of the Russian Academy
of Sciences
Perm, Russia
alexey.kashnikov@gmail.com
Lev Levin
Department of aerology and thermophysics
Mining Institute of the Ural Branch of the Russian Academy
of Sciences
Perm, Russia
aerolog_lev@mail.ru
Abstract — The purpose of the research is determination of
mine ventilation system regulators positions providing required
airflow on ventilated directions. Currently regulators positions are
set iteratively that causes hunting. It is proposed to use historical
data of the system for defining regulators functional dependencies
on required airflow values with consideration of temporal
variability of a ventilation network. The problem is solved by a
regression model based on neural networks. Consequently, a set of
model parameters is defined and the control algorithm of the
system is modified for using a historical data set.
Keywords — regression, neural networks, airflow, ventilation
network, air distribution, automatic ventilation control system, mine
I. INTRODUCTION
A mine ventilation system provides diluting toxic and
flammable gases in underground air, removing dust, keeping
temperature values on comfortable and safe levels, supplying
oxygen in working areas. The traditional approach to mine
ventilation is to supply a constant volume of air to a mine
assuming maximal demand during each planning period.
However, air requirement varies depending on operations
actually being performed at every moment.
Considering the significant contribution of ventilation
expenses in mine operational costs (up to 40%) energy
efficiency could be remarkably improved by dynamically
controlled ventilation based on actual situation while providing
safe working conditions [2].
II. VENTILATION SYSTEM IMPROVEMENT
Shift-based control is one way to reduce operational costs. In
reference to potash mines, a production shift requires the most
considerable airflow due to rock destruction followed by gas
emission. In maintenance shifts demand for air is determined
only by human and possibly diesel vehicle presence in a
ventilated area. In some cases it is economically reasonable to
halt operations on a set of working zones providing only
minimal air flow. Consequently, demand for air noticeably
changes during mine operating shift-by-shift and day-by-day.
Mining Institute (Perm, Russia) researches in the area results
in design of automatic control ventilation system which was
implemented in the Mine#3 of Belaruskali (Republic of
Belarus). Airflow redistribution is performed by six regulators
installed on ventilated headings on two levels (pic.1).
Pic. 1. A schema of Belaruskali's Mine#3 and regulator locations
The control algorithm iteratively rotate leafs of each
regulator until values of specified and actual airflow passing
through regulators become equal.
Since all regulators are operating in the single ventilation
network changing leaf position of a regulator effects in airflow
redistribution in the whole network. As far as the mine
ventilation network of existing mines is slow response the
process of adjusting regulators could lead to hunting for up to
one hour. It results in several issues. First, such fluctuations
speed up equipment wearing. Second, it makes impossible
further control deepening (for example, involving actual
location of diesel equipment or personnel or intrashift operating
cycles and downtimes) because the period of system adjusting
exceeds the period of updating air requirements.
Researchers in Canada and USA propose using of steady-
state air distribution model for calculating regulator leaf
positions. After been calculated the parameters are sent to
control system and regulators are adjusted according to these
values [1]. Nevertheless, the approach has essential drawbacks.

2. They should periodically alter a model of a ventilation network
(it could be done only manually). In conditions of intensive
mining activities a ventilation network changes continuously
(annual production of mines in Russia and former Soviet Union
countries reaches 20 mn tonnes by ore). Furthermore, the steady-
state air distribution model does not account for dynamic
impulses induced by people and vehicles movements, hoisting,
intraday natural pressure oscillation and local reconfigurations
of ventilation network.
III. PREDICTION MODEL
The described problem could be solved by using machine
learning techniques based on neural networks for regulator
adjusting. A training set consists of historical data of system
operating.
Let 𝑄𝑖
𝑟𝑒𝑞
, 𝑖 = 1. . 𝑁 be required airflow for the i-th regulator,
𝑄𝑖
𝑓𝑎𝑐𝑡
(𝑡) – actual airflow for the i-th regulator at the moment t,
𝛼𝑖 – leaf angle of the i-th regulator, N – quantity of regulators.
The task is to construct the function F such that
𝐹: ( 𝑄1
𝑟𝑒𝑞
, … , 𝑄 𝑁
𝑟𝑒𝑞
) → (𝛼1, … , 𝛼 𝑁)
on the acceptable region of ( 𝑄1
𝑟𝑒𝑞
, … , 𝑄 𝑁
𝑟𝑒𝑞
) depending on
conditions of a particular mine.
Primarily the network containing one hidden level with 12
neurons, hyperbolic tangent as activation functions, 𝑄𝑖
𝑓𝑎𝑐𝑡
(𝑡) for
a different set of angles with the whole mine airflow 𝑄 𝑎𝑙𝑙
𝑓𝑎𝑐𝑡
(𝑡) >
∑ 𝑄𝑖
𝑓𝑎𝑐𝑡
(𝑡)𝑁
𝑖=1 as the input layer and a set of angles as the output
layer was built. Adding the whole mine airflow is reasoned by
the fact that the same airflow on a particular direction could be
provided by different angles.
Calculation results in a low quality of approximation (an
error on a training set exceeds 20%). Therefore, the next step
was to construct different networks for every regulator (but with
the identical structure). The output level of every network is
presented by the corresponding angle. Modelling results
demonstrated a low error (less than 3%) on a test set formed by
data for nearby periods (several weeks) (pic.2).
Pic. 2. Approximated values of regulator#4 angles on test set (after
three months)
However, applying the model for angles prediction on
distant periods (after three months) leads to dramatic error
growth (up to 10%). To overcome the issue introducing the past
state of the system to the model is proposed. The input level of
the network is enlarged by a parameter which is a relation of a
regulator airflow to the whole mine airflow divided by a
corresponding angle and averaged over a preceding period.
𝑝𝑖(𝑡𝑗) = ∑
𝑄𝑖
𝑓𝑎𝑐𝑡
(𝑡𝑗−𝑘)/𝑄 𝑎𝑙𝑙
𝑓𝑎𝑐𝑡
(𝑡𝑗−𝑘)
𝛼𝑖(𝑡𝑗−𝑘)
𝑀
𝑘=1
where M stands for a count of averaged elements.
Gradual variation of the parameter reflects air redistribution
between controlled directions if such variant was not included in
a training set. Modified networks demonstrates good
approximation on distant periods (about 2%) (pic.3).
Pic. 3. Approximated values of regulator#4 angles on test set (after
three months) calculated using a temporal parameter
IV. CONTROL ALGORITHM
While modifying the control algorithm using revealed
dependencies expressed in neural network models it should be
considered that there is a chance to overestimate the required
airflow (that means inefficient ventilation) or underestimate the
required value (that means safety violation). While the first case
is undesirable, the second one is unacceptable. According to this
point, the control algorithm of the automatic ventilation system
should be expressed as follows:
For each required airflow 𝑄𝑖
𝑟𝑒𝑞
Repeat
Calculate and set 𝛼𝑖;
Get 𝑄𝑖
𝑓𝑎𝑐𝑡
;
Correct weights in neural networks
〈〈𝑄𝑖
𝑓𝑎𝑐𝑡
, 𝑄 𝑎𝑙𝑙
𝑓𝑎𝑐𝑡
, 𝑝𝑖〉, 𝛼𝑖〉 for each i.
Unitl ∃𝑖: |𝑄𝑖
𝑟𝑒𝑞
− 𝑄𝑖
𝑓𝑎𝑐𝑡
| ≤ 𝛿,
where 𝛿 stands for acceptable tolerance.
An additional temporal parameter p enables decreasing
number of network correction iterations and thus reduce
duration of possible regulator fluctuations.

3. V. SUMMARY
The main results of the current research are as follows:
 A neural network model is designed to predict angles of
regulator leafs depending on required airflow.
 An additional temporal parameter for a network input
layer calculated on a preceding system state enables
adjusting the described neural network for long-term
prediction.
 The developed control algorithm for automatic
ventilation systems meets efficiency and safety
requirements.
REFERENCES
[1] Allen, C. Ventilation on demand (VOD) project -Vale Inco Ltd., Coleman
Mine /Allen, C., Keen, B. //Proceedings 12th U.S./North American Mine
Ventilation Symposium, Reno, NV, USA, 9-11 June, 2008, pp. 45-50.
[2] Hardcastle S. Strategic mine ventilation control: a source of potential
energy savings / Hardcastle S., Kocsis C., Lacroix R. // Proceedings of
Montreal Energy & Mines, Montreal, April 29- May 2, 2007, pp.255-263.