Pattern of H2S concentration in a deep copper mine and its correlation with ventilation schedule

Measurement 140 (2019) 373–381

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Pattern of H2 S concentration in a deep copper mine and its correlation with ventilation schedule Justyna Hebda-Sobkowicz a, Sebastian Gola a,b, Radosław Zimroz a,⇑, Agnieszka Wyłoman´ska c a

Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland KGHM Polska Miedz´ S.A O/ZG, Polkowice-Sieroszowice, 59-101 Kaz´mierzów, Poland c Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wroclaw University of Science and Technology, Janiszewskiego 14a, 50-379 Wroclaw, Poland b

a r t i c l e

i n f o

Article history: Received 22 January 2019 Received in revised form 4 March 2019 Accepted 26 March 2019 Available online 11 April 2019 Keywords: Gas hazards Hydrogen sulphide Mine Process identification Segmentation

a b s t r a c t The quality assessment of the air in a deep underground mine is a challenging issue. It is a time-varying process and it depends on several factors, mainly on technological processes such as blasting, air conditioning, ventilation, machines operations, as well as gas released by rock mass and humidity. The air quality should be monitored and analyzed to understand the process as much as possible in order to facilitate the miner’s work and to improve its safety. One of the most critical parameters of the air quality in the considered mine is the hydrogen sulphide (H2 S) concentration. It is related to the geology of deposit thus one should expect the random nature of the gas concentrations. In this paper, we focused on H2 S concentration analysis based on long term monitoring. Signal segmentation procedure for raw data has been proposed, the segmented data (daily patterns) have been visualized and finally statistically analyzed. It has been found that there are deterministic components in the H2 S data variation, which strongly depend on ventilation operation regimes. This can be a basis for further data analysis and for controlling air quality in the mine in a more effective way. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Natural hazard related to various gases is critically important in deep underground mines [1–5] There are many factors that should be taken into account. The critical issue is methane or coal dust. However, in the considered deep copper ore mine, one may notice the presence of following hazardous gases: CO; CO2 ; H2 S; CH4 .In this paper we are focused on the H2 S concentration analysis. It should be classified as a natural hazard in contrast to, for example, the carbon monoxide (CO) concentration that significantly depends on the blasting schedule. The H2 S concentration in considered mine is an extremely important problem. It exists only in some parts of the mine and in recent years it is permanently monitored by specialized equipment. The origin of the H2 S is organic and it is not related to gases produced by technological processes or machines. It means that its behaviour seems to be random and should depend on the local properties of the deposits. The investigations of the H2 S concentration in the underground mine and tunneling have been studied by [1–6] so far. However, most of the studies were based on physical models, geology, and mining processes consider⇑ Corresponding author. E-mail addresses: [email protected] (J. Hebda-Sobkowicz), [email protected], [email protected] (S. Gola), [email protected] (A. Wyłoman´ska). https://doi.org/10.1016/j.measurement.2019.03.077 0263-2241/Ó 2019 Elsevier Ltd. All rights reserved.

ation. The statistical analysis of the measured data is not so common in the literature. To the best of our knowledge, it is the very first time when the sudden changes in H2 S concentration are explained by the ventilation parameters. In the era of IoT (Internet of Things), sensor technology enables data acquisition of many physical phenomena. Nevertheless, extremely harsh conditions in the deep mine and complex nature of the processes bring a lot of challenges [7]. An industrial/environmen tal/mining data acquired via various SCADA systems require validation, cleaning, resampling, segmentation, de-noising, filtering, detection, approximation, etc [8–12]. After the pre-processsing, signals are analyzed using time series or statistical models in order to provide some characteristic and informative features [13–23]. By visual observation of long term data, one can conclude that there is high variability of H2 S concentration and it is hard to indicate any pattern and build gas hazard prognostic models, indeed. However, in our research, by applying various statistical and data mining techniques, it has been discovered that H2 S concentration is a mixture of two processes - the first one is a random process, it depends on the deposit properties and the rock massive structure. The second component significantly correlates with operational parameters of the ventilation network. Such discovery is significant during daily operation as well as strategic mine planning perspectives.

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In the paper, we will present the experiment details and data acquired in the mine. Next, we propose a procedure for H2 S data processing in order to discover deterministic part of daily variation and finally, we will correlate this pattern with ‘‘averaged” daily parameters of the ventilation (which is strictly controlled in a deep underground mine) to prove that H2 S concentration significantly depends on that. 2. Experiment description A map of the mine considered in the paper looks like a network of corridors, see Fig. 1. Due to geometry and other technical constraints, it is not possible to obtain real-time information about gas concentration in any place of this mine. From a practical point of view a vicinity of mining faces is the most dangerous area. There is no infrastructure (sensors, monitoring systems, electricity etc.). Miners can be equipped with individual gas detectors, however, historical data regarding gas concentration as well as miner location are very limited till now. According to our knowledge, a discussed measurement device with several gases type detectors

and long-term data acquisition is the first one in the history of copper mining in Poland. According to mining’s law regulations authorized mining engineer supervising the team of miners is obligated to measure the quality of air in mining voids by portable equipment in order to give permission to start work there. Nevertheless, the level of gas may change during the shift. Miners can be equipped with individual gas detectors [5,7], however, historical data regarding gas concentration as well as miner location are very limited till now and strategic planning is impeded. 2.1. Mining area for experiment The measurement has been carried out in one of the mining departments in deep copper ore mine. Considered mine is one of the most difficult due to its size, depth (and perspective to be deeper) and harsh environmental condition (high temperature of rock massive and air in mining corridors, harmful gases and other natural hazards). In the considered mine the room and pillar technology with blasting is applied to extract copper ore. To minimize the risk of

Fig. 1. Map of investigated part of the mine with fresh, as well as, used air stream flow and sensors location.

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rock stress and roof collapse, the back-filling technology is applied to fill empty spaces by waste rock (in Fig. 1 presented as the green areas). As shown in Fig. 1, there are two active mining fronts. Fresh and used air streams are pointed out as red and blue arrows. Sensors, presented in Fig. 2(b), have been installed in one of the chambers in mining field F1W, the exact location has been highlighted by arrow 1, see Fig. 1. It is easy to notice that the location of the sensor allows measuring part of the air flow. The main goal of the experiment was to determine the gas hazard and search for preventive measures to minimize it. 2.2. Measurement system The monitoring system used in this experiment is able to measure hazardous gases (CO; CO2 ; H2 S; CH4 ) as well as other physical variables such as humidity, temperature, and air stream flow. As mentioned, mining chamber is not equipped with IT infrastructure, data are stored in SD card and after a couple of weeks (practically at the end of the month) data acquisition is stopped and results of measurement are copied to the computer. The conditions in the mine are harsh, so the system has to have the appropriate certificates, namely, it is able to work up to 50
C and in high humidity [24]. The schematic structure of the monitoring system is presented in Fig. 2(a), whereas a real-life photo of the measurement system installed in the mine is presented in Fig. 2(b). According to our knowledge, a discussed measurement device with several gases type detectors and long-term data acquisition is the first one in the history of copper mining in Poland. 3. Methodology In this section, we present the methodology used in real data analysis. The goal of this analysis is to properly describe the processes responsible for H2 S variation, as well as perform the comparison with the ventilation parameters. In the first step, we proposed to extract segments of data related to the single day. Mining operations are cyclic and in the considered mine a single day is divided into four shifts. According to our knowledge, processes that are realized in each shift might be a bit different during shift 1 (from 6 to 12), during the night (0– 6) or late evening (18–6). So it is better to search for daily (24 h) pattern than for shift pattern (it should be done for each shift separately). After signal segmentation, we have proposed to use three independent techniques for the classification of the segments. These techniques are well known K-means clustering method [26], and two statistical techniques based on Confidence Intervals (CI) construction and Spearman Correlation Coefficient (SCC). Below each of the approaches has been described in details.

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Method 1. In K-means algorithm the assignation into a given class is based on the minimization of the criterion that within each cluster calculates the average Euclidean distance between the points in that cluster and observations from the cluster mean. The optimal number of classes we chose using the Silhouette algorithm [27]. In the real data analysis we perform the classification on the daily H2 S segments and select the class which contains the most daily segments and daily segments are the most similar themselves. The median value of these segments has been calculated and set as the pattern of the data behaviours. It is worth to mention that the median is more resistant to outliers value than the sample mean. Method 2. The method based on the CI is constructed at a confidence level, usually 5%, selected by the user. A confidence level of 5% means that there is a probability of at least 95% that the result is reliable. To calculate the Confidence Intervals we use the quantiles of time series related to daily segments. The set of trajectories of time series will be the database for calculating the quantiles for each time. In our consideration, the 95% CI (in other words CI with the confidence level of 5%) has been considered with two different cut-off thresholds (cot) (5%; 10%). The classification is made as follows. We check if the time series corresponding to given segment falls into considered CI. If up to 5% of data from this time series does not fall into 95% CI then this segment is classified as the class 1. If up to 10% of data of this segment does not fall into IC then it belongs to class 2. Otherwise, we have class number 3. Similar as in the previous case we select the representative class which has the most segments and which segments are the most similar themselves. The median value of segments of the chosen class has been established as the pattern of the data behaviors. Method 3. In this method, we applied Spearman’s Correlation Coefficient (SCC). It describes both the strength and direction of the relationship. The SCC measures monotonic relationships. Thus it is superior to the classical Pearson Correlation Coefficient, which only measures the linear dependencies. In a monotonic relationship, the variables tend to change together, but not necessarily at a constant rate. The Spearman’s q has been defined as follows [28]

q¼1

P 3 ni¼1 jdi j ; nðn2  1Þ

where di is the difference between the ranks of the two columns, which in our case correspond to different daily segments, and n is the length of each column. The ranks are assigned according to the order of occurrence of the given numbers. This tool has been used to create the symmetric Spearman Correlation matrix (SCM), which contains the Spearman Correlation Coefficients between segments. In the next step we take the mean of SCM for each segment and take the absolute value, what gives the positive, overall, averaged Spearman correlation (ASC) between segments. We calculate

Fig. 2. Measurement system used in the experiment [25].

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then the probability density function (pdf), which presents the distribution of ASC. Then we can perform segments classification. The given segment belongs to the class 1 if the corresponding ASC (in absolute value) is smaller or equal to the threshold th1 , otherwise, it falls to the class number 2. The th1 is calculated on the basis of the distribution for all values of ASC, namely for our ASC data we observe 2 modes distribution and the threshold is selected as the local minimum between existing modes. Finally we select the representative class which has segments the most similar to the median value of segments in the given class, namely we calculate the Mean Square Error (MSE) of the given segment with the median value and if it is smaller than 2% then we treat it as the consistent enough and include in the H2 S concentration pattern. To validate the obtained results from each method we compare them graphically and the final pattern of H2 S concentration has been established. Using the H2 S pattern we try to recognize the key changes in the process. We check if the discovered pattern corresponds to the daily pattern of the ventilation. The visual inspection indicates that H2 S concentration behaves similarly as the air forced by the ventilation flow. In order to confirm this hypothesis, we calculate the cross-correlation coefficients. The cross-correlation is a measure of similarity of two series as a function of the displacement of one relative to the other. The definition of the cross-correlation between two time series X ¼ fx1 ; . . . ; xn g and Y ¼ fy1 ; . . . ; yi g, is given by [29] n X ðxi  xÞðyih  yÞ i¼1 ffisffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; Rxy ðhÞ ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n n X X ð xi  xÞ ðyih  yÞ i¼1

i¼1

where x and y are the means of the corresponding series. Using the cross-correlation we have been looking for the delay of h, where the maximum cross-correlation is achieved. This knowledge can give us the information about the shifting of a given signal over time, indispensable to make the processes most correlated. In other words, it checks if one of the processes is similar to another and if so how long should be taken to observe the waveform detected in signal 1 in signal 2. In practice, this can be confirmed by expert knowledge. The overall process of data analysis is presented in Fig. 3.

4. Data analysis The vector of observations contains the concentration of the hydrogen sulphide ðH2 SÞ in deep underground copper ore mine. The data are taken from the period 28.10.2014 to 28.12.2014. The frequency of data acquisition is one second. The vector of observations includes missing values thus the pre-processing was needed. For cases where the gaps have a small time interval, we fill the missing observations using interpolation based on adjacent values, otherwise, we do not take such period into account. Raw signal describing H2 S variation is presented in Fig. 4. As one can see the longest lack of the data appears from the period 23.11.2014. to 30.11.2014. It is a period when the measurement system has been disassembled for calibration and data collection. In the further analysis, we do not take into account these missing days. As one can see in Fig. 4, there is the visible occurrence of periodic jumps of H2 S concentration. However, based on the raw data it is hard to conclude if they exhibit the specific behaviour, which may be adequate to the nature of the phenomena. In order to describe the existing changes in the H2 S concentration we started from the data segmentation, precisely described in Section 3.

Fig. 3. Block diagram – real data analysis.

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H 2 S [ppm]

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50 0 00:00

H 2 S [ppm]

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Median - class 1 Median - class 2

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Fig. 6. K-means classification.

To perform the segmentation, we divide the whole timeline into twenty-four hours segments (the choice of the period’s length has been described in Section 3), from 00:00 to 23:59 and hold on the data in one matrix, sized 54-days  86400-seconds. To make the differences and similarities more visible we plot the one-day trajectories as the overlapped functions. As one can see in Fig. 5, surprisingly, the daily concentration of H2 S have some repetitive structure in most of the cases. However, there are some outliers. To identify and distinguish the most frequent behaviours we perform classification using 3 different methods (described in Section 3), which finally enable us to extract the most frequent pattern of data behaviour. We suspect that all 3 presented methods should give similar daily H2 S pattern, if so then we accept the result and consider it as the proper one. 4.2. K-means classification (method 1) Method 1 is based on the K-means classification, described in Section 3. We perform the clustering on the normalized data to be more focused on the differences in the shape of the trajectories than on the scale. We normalize data dividing each segment by their maximum values. The number of classes has been chosen by the Silhouette algorithm [27], which suggests considering 2 separate classes. After the classification, we obtain the class number that the given segment belongs. In the next step we plot these classes separately. In Fig. 6 we present 3 panels. The top and the middle panels show two classes obtained from the K-means classification. In the bottom subplot, the median values of segments in each class have been plotted, what presents that the main differences between classes occur during the afternoon, where segments from class 1 exhibit more constant and low (below 10 ppm) concentraAll of the daily segments overlapped 100 80

H 2S [ppm]

12:00

Class 2 (30 members)

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4.1. Segmentation

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Class 1 (24 members)

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Time [sek] Fig. 5. H2 S concentration variation – 54 overlapped daily segments.

23:59

Table 1 K-means classification – day of the week verification. Day of the week Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Quantity in class 1

Quantity in class 2

6 0 4 2 3 3 6

1 8 4 6 5 5 1

tions. As one can see the most numerous class is class number 2 (with 30 segments). The segments in class 2 are more similar to each other than the segments in class 1, where we can observe small values (around 2 ppm) of H2 S concentration as well as a big jumps (close to 100 ppm). Therefore, as the daily H2 S pattern from method 1, we will consider the median value calculated based on segments from class 2. Based on the results presented in Table 1 one can conclude that the most frequent days in class 1 are Mondays and Sundays, whereas in class 2 there are Tuesdays and Thursdays. It means that the middle of the week has a different structure than the day when the miners have the day off (Sunday), as well as, differ from the day of the beginning of the week (Monday). It gives the preliminary conjectures that there is some correlation between miner’s schedule and H2 S concentration. 4.3. Classification based on confidence intervals with the specific thresholds (method 2) Similar as before, in order to obtain the number of one-day segments which represents the most frequent pattern of data behaviour, we firstly normalize the data and then create the 95% CI (described in Section 3) with two different cut-off thresholds, namely cot ¼ 0—5% and cot ¼ 0—10%, described in Section 3. In Fig. 7 the considered CIs have been plotted in black. In Fig. 8 the representative daily segments which do not exceed (the first row with subplots) and which exceed (the second row with subplots) the considered confidence intervals are presented. The column subplots show the results for different values of the cut-off thresholds: cot ¼ 0–5%, and cot ¼ 0–10% respectively. In the next step we separate segments according to the adopted cut-off threshold. As a result, we received 26 segments which do not exceed the CI with cot ¼ 0—5% and 37 segments which do not exceed the CI with cot ¼ 0—10%. In Fig. 9 we present the overall summary based on the medians value for the 95% CI and each cot. As one can see the differences

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J. Hebda-Sobkowicz et al. / Measurement 140 (2019) 373–381 Table 2 The 95% Confidence Interval with cot ¼ 5% – day of the week verification.

All segments overlapped 1 95% CI

H 2S (normalized)

Day of the week 0.8

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

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Time [sek] Fig. 7. The 95% confidence interval calculated from all segments.

Exceed 95% CI (cot 0-5%) (28)

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Fig. 8. The 95% Confidence Interval – segments classification.

H 2S [ppm]

Median values of segments which do not exceed 95% CI (for each cot) cot = 5% cot = 10%

20 10 0 00:00

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Median values of segments which exceed 95% CI (for each cot) H 2S [ppm]

Do not exceed 95% CI

cot = 5% cot = 10%

20 10 0 00:00

06:00

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4 6 6 3 4 3 0

from the days in the middle of the week and it does not appear within the days which represent the general behaviour of H2 S concentration.

4.4. Classification based on Spearman’s correlation matrix (method 3) In the last method, we start from creating the symmetric Spearman Correlation matrix, SCM, (described in Section 3) sized 54  54 days. The matrix has been plotted in Fig. 10. The first column of the matrix contains the correlations of the Tuesday (28.10.2014) with all 54 days, the next one is a correlation of Wednesday (29.10.2014) with all 54 days and so one. The last column is Saturday (27.12.2014). As one can see in Fig. 10, there is a visible cyclic strong (correlation close to 1) and weak correlation (close to 0). In Fig. 11, the upper subplot, the ASC (described in Section 3) has been plotted. As one can see there are some days which are weakly correlated with the other days of the week (correlation close to 0). In order to find all days with weak and strong correlations we set up the threshold which appropriates separate the largest and the smallest correlations, namely th1 ¼ 0:1935 and in the next step we plot the segments which fall into the correlation’s intervals created by using the given threshold. The given threshold has been found using the distribution of the ASC, which have been plotted in Fig. 10, the bottom subplot. In Fig. 12, left subplots, we can see the normalized daily H2 S segments which correlation does not exceed (upper subplot) or exceed (bottom subplot) the given threshold 0:1935. The additional trajectory plotted in black is the median value of all segments in the considered class. The right panels show the Mean Square Error values for residuals of the median with each segment for each class. As one can see the smallest MSE values we can observe for segments with the AC equals or greater than 0.1935 and it is also the most numerous class. There are 41 members

23:59

Time [sek]

Correlation coefficients

between median values of segments called as the pattern (these ones which fall into the considered CI) and median values of segments called as the outliers (these ones which do not fall into the considered CI) is easy to notice. The outliers segments have the median value with the much smaller amplitude and smaller standard deviation than the median of segments which fall into given CI. All of the considered cot values successfully separated segments. The CI with cot ¼ 0—5% is the most rigorous and the most commonly used in the statistical analysis of real data. Therefore, we treat the 95% CI with cot ¼ 0—5% as the effect for distinguishing H2 S behaviours. In Table 2 one can find that the most frequent days for the 95% CI with cot ¼ 0—5% are Tuesdays and Wednesdays. Similar as before, the day when the miners have the day off (Sunday) differs

Segments (days start from Tuesday)

Fig. 9. The Confidence Interval with different cot values – summary.

50 45 40 35 30 25 20 15 10 5 5

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Segments (days start from Tuesday) Fig. 10. Spearman Correlation matrix of given 54 segments.

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Correlation (ASC) for each segment H 2S [ppm]

ASC

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Fig. 11. The average Spearman correlation.

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Fig. 12. ASC and MSE classification.

Value

and almost all of them have MSE smaller than 0.02, whereas, in the second class, the values of MSE are higher. As the daily pattern from method 3 we consider daily segments from the class with the AC P 0:1935 without the segments with MSE greater than 0.02. In Table 3 one can find that the most frequent days for the chosen most representative segments for method 3 are Tuesdays and Wednesdays. Moreover, Tuesdays prevail in the patterns, in the 3 performed methods. Whereas the data set corresponding to Sundays is far from the H2 S daily pattern. In Fig. 13, upper plot one can see the final segments of the H2 S daily pattern chosen as the representatives of each method. The results obtained from 3 performed methods of extracting the H2 S pattern coincide with each other. To obtain the final pattern of the daily H2 S behaviour we chose the common 19 trajectories which are the members of the daily

Using the H2 S pattern we try to recognize the most important changes in the process. We check if the discovered pattern corresponds to the daily pattern of the ventilation. We take under consideration the basic data from ventilation station measurement system, namely the air flow forced by the ventilators (main ventilation station located at the top of the shaft) and the air pressure, both during work in deep underground copper ore mine from the period 28.10.2014 to 17.11.2014. The frequency of data acquisition was not constant. The values of the ventilator parameter have been recorded when the parameter state changed. Therefore, the validation of the data was necessary. The data has been plotted in Fig. 14. In the top panel we present H2 S concentration. The middle panel presents the ventilator’s pressure. The negative values of this parameter mean that ventilator creates an under-pressure. The last subplot presents the ventilator’s air flow. One can see that both of them significantly differ from the H2 S behaviour. As one can see the two considered ventilator’s parameters are negatively correlated. When the ventilator’s flow has the local maximum value then the ventilator’s pressure takes the minimum

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

H 2 S daily pattern

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Quantity (26 days in all) 3 7 5 3 4 3 0

H 2S daily pattern vs. ventilation daily pattern

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1

Value

Normalized H 2S

AC >= 0.1935, 41 trajectories 1

H 2 S (normalized)

1

0.1

MSE

Normalized H 2S

AC < 0.1935, 13 trajectories

pattern from all 3 considered methods. The median value from all those 19 trajectories has been plotted in Fig. 13, bottom subplot. In the next section, we will attempt to describe the H2 S concentration pattern and find the causes of the cyclic variation appearing.

flow

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Fig. 14. The H2 S concentration pattern and the ventilator’s parameters pattern behavior.

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Normalized H 2S

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Fig. 15. The normalized ventilation flow (top panel), the normalized H2 S concentration (middle panel) and the cross-correlation between H2 S pattern and ventilators flow (bottom panel). Normalized daily pattern of H 2S with the normalized shifted 14.2 min right flow of the ventilator 0.9 0.8

Normalized values

0.7 0.6 0.5 0.4 0.3 0.2 0.1

Based on long-term measurements and simple analytic techniques (segmentation, clustering, correlation) we have shown that there is determinism in the variability of H2 S concentration in the air in mine excavation. Moreover, we have distinguished pattern via 3 different techniques, based on different assumptions. The obtained results are very similar. We have compared this daily H2 S concentration pattern with the operational regime of the ventilator station using the crosscorrelation method and discovered that there is a delay in time between the processes. Based on the presented results one can interpret that the delay between processes is related to the time needed to move the air from the H2 S sensor location to the flow sensor location in the shaft operated under pressure regime. Based on our results one may conclude that the schedule of the ventilation work in deep underground ore mine significantly influences on the level of the H2 S concentration. As it turns out, the increase in the work of the ventilators causes (with the delay of 14 min) higher concentrations of the H2 S. Therefore, surprisingly, ventilation negatively affects the occurrence of this dangerous chemical compounds in the air of the underground mine. The turning on of the ventilators causes the excessive release of this chemical compound from the rock mass. The presented analysis concerns only ‘‘working days”, the concentration of gas during the weekend is different (ventilation regime is different) and will be the subject of the future work. Daily pattern estimation allows for the current determination of anomalous changes in H2 S concentration (the anomaly is expressed in the residuum between daily concentration and daily concentration pattern).

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Time [sek]

Fig. 16. The H2 S pattern and shifted ventilators air flow – overlapped.

value and vice versa. Thus, we decided to study one of them, namely the ventilators air flow. In order to check if the ventilator’s regimes influence on the concentration of the H2 S we calculate the cross-correlation (described in Section 3) between the vector of H2 S and vector of ventilators air flow, considering their normalized values and the same period of time. The results of the cross-correlation have been presented in Fig. 15, the bottom panel. Cross-correlation takes the highest value for the lag equal to 14:2 minutes. It suggests that the shifting of the ventilators air flow in time by 14:2 minutes will cause a high correlation between vectors. The delay is related to distance between mining face, sensor and ventilation shaft. The normalized signals i.e. H2 S concentration signal and ventilators air flow signal have been shifted according to discovered delay and presented in Fig. 16. As one can see the points of growth for both functions mostly coincide themselves. Presented results show that the schedule of the ventilation work in deep underground ore mine significantly influences on the level of the H2 S concentration. As it turns out, the increase in the work of the ventilators causes (with a delay of 14 min) higher concentrations of the H2 S. Therefore, surprisingly, ventilation negatively affects the occurrence of this dangerous chemical compounds in the air of the underground mine. The turning on of the ventilators causes the excessive release of this chemical compound from the rock mass, so further ventilation fights with the excess that has just been freed, because of the ventilation inclusion. 6. Discussion and conclusions In order to conclude the presented results we would like to highlight a few important points.

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