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Creation of the Information System Based on Experimental Data for Control of the MMF Operating Modes to Improve the Efficiency of Ventilation in Mines

The paper presents the results of two experiments conducted using the main mine fan in BKPRU-2 mine (PJSC Uralkali, Russia). The obtained data processing showed that a statistical verification of the measured data on their falsity and independence should be performed to effectively control the ventilation process. The mathematical dependences are provided for calculating the total natural draft during measurements, which significantly affects the ventilation process. This enables the control of ventilation in mines.


Introduction
In order to ensure safe operation of mines, fresh air must be supplied in the volume calculated for each individual underground mining enterprise using main mine fans (MMF). The parameters controlled during MMF operation are the MMF capacity QMMF and pressure hMMF.
Ventilation requires from 30% to 50% of the total amount of electricity used in the underground mining enterprise. Therefore, in addition to the air supply provided in the required volume, this process should be performed without overstating the volume air flow supplied to the mine opening. To control MMF operation, automated air control systems are currently being introduced, in which data on the volume flow QMMF and pressure hMMF of the air entering the mine are transferred from sensors to the control system.
The ventilation process is inertial, and MMF operation is affected by a significant number of factors. Thus, the required volume of air supply should be controlled in real time. At the same time, a change in the state of ambient air should be predicted. MMF operation is affected by the total natural draft, which can both increase and prevent the ventilation flow into the mine [1-4, etc.]. To develop the control system, it is required to create the information system that provides reliable, accurate and sufficient measurement results. At the same time, false data and data that do not satisfy the requirement for homogeneity of variance should be deleted from the continuously updated experimental database. The verification of homogeneity of variances in the sampling population of experimental data in the compared series and the absence of random and systematic errors will ensure the accuracy of further statistical processing and efficient control of ventilation.

Results and Discussion
In 2016 and 2017, a series of full-scale experiments was performed in BKPRU-2 mine (PJSC Uralkali, Russia) in order to develop a technique for database creation based on processing the data obtained in the MMF parameter measurements. During each of the two experiments (summer and winter), the MMF performance QMMF and corresponding values of static pressure hMMF were measured at different rotational speeds of the fan runner (ni). Each of the experiments included 9 series of measurements, and each of the series included 8-10 experimental values of the MMF performance QMMF and corresponding values of static pressure in the mine hMMF.
The nine series of measurements in each of the two experiments (summer and winter) took about 20 minutes (two minutes per each series of measurements). About ten QMMF and hMMF values were recorded. The experiment was performed within a short time period to avoid significant changes in the parameters of ambient air (temperature, atmospheric pressure, relative humidity, etc.), which have a considerable effect on the total natural draft hе in the mine [1][2][3][4][5][6].
The experiments were carried out on July 6, 2016 and February 9, 2017, in summer and in winter, respectively. The ambient air parameters in summer were as follows: atmospheric pressure Ра =737.0 mm Hg (98258.6 Pa) and temperature t=21.8° C (294.95 K). In winter, the parameters were as follows: Ра = 779.8 mm Hg (103962.13Pa) and t = -2.1° C (271.05 K).
The results of the first and second experiments are summarized in Tables 1 and 2, respectively.
The experimental values of the MMF performance QMMF at different rotational speeds (ni) of the runner are shown in Fig. 1. For example, the top curves in the graphs correspond to the first rows in Tables 1 and 2, which indicate the fan speed n=375 rpm.
Some measured values plotted in the graphs ( Fig. 1) should be noticed. The preliminary analysis of the obtained sampling populations of experimental values (Tables 1 and 2) indicates that the measurement results are apparently affected by random factors which cannot be allowed for during the experiments. Therefore, the creation of experimental databases should include a procedure of initial statistical verification of the measurement results for data randomness and independence to verify their reliability at a given level of significance α (with confidence coefficient p=1 − α).   Since false data may cause wrong conclusions, primary processing of the experimental results starts with their exclusion in accordance with the requirements for mathematical statistics [7,8].
Verification of data for validity is performed as follows.
In each series of measurements, data on the obtained sampling population should be arranged in ascending order: Next, we need to calculate the experimental values of the Student's statistics τexp, that is, the characteristics min exp τ and max exp τ [7,8]: where x is the average value of experimental data, which is calculated from the initial sampling population of the volume m by the following formula: S is the corrected (due to a small volume of the considered samples) sample mean squared deviation that shows distribution of the experimental values of the random variable X for the sampling mean x : S is also calculated from the initial sampling population of experimental data. The   τcr(α,k), which can be found in statistical tables for a given number of degrees of freedom k=m−1 and a given value of the significance level . In the information systems, critical values are recorded in the database.
The significance level  is the probability of rejecting the null hypothesis when it is true, which is known as type I error. The value = 0.05 was adopted in the calculations below.
If the inequality τexp ≤ τcr (α, k) is satisfied, then Н0 hypothesis is not rejected. We assume that if the probability p=1−α, the test value xmin (or xmax) is not false and is left in the sample. Otherwise, the test value is deleted from the initial sampling population.
In this case, after excluding the next of the false experimental values, the characteristics x and S 2 must be recalculated for the remaining sampling. Thus, false data is deleted from each of the series separately.
The corrected variance S 2 is used to estimate the variance for the experimental data remaining in the test series. Thus, each of the experimental data series is processed in succession.
Next, we compare the variances of different series. The variances must be homogeneous [7,8]. The homogeneity of variances indicates that the data are obtained with equal accuracy.
The homogeneity of variances in the entire sampling population of experimental data in the compared series is a prerequisite for creating the information system database. Only in this case it can be used to develop the information system for practical purposes, including the automated ventilation process. The comparison can be performed, for example, by pairwise comparing the variances of series.
As a result, the series with homogeneous variances remain in the resulting population, which ensure the reliability of subsequent calculations and analysis of the results for a given α.
The homogeneity of variances is estimated using the Fisher's criterion F. The experimental value of the Fisher's criterion F exp is calculated by the formula [7,8]: where the numerator shows the largest variance. The experimental value of the Fisher's criterion must be compared with the critical value Fcr(α;k1;k2), which can be found in statistical tables for a given value of the significance level α and given values of the numbers of degrees of freedom k1, k2 [7,8].
If F exp >Fcr(α;k1;k2), then the variances in the compared series should be assumed inhomogeneous (different). This means that the measurements are performed with different degrees of accuracy, and they cannot be included in a single database. Therefore, a series with a variance different from that of other series in data processing must be deleted from the experimental database, and the corresponding experiments should be repeated if required. The where Rmine is the aerodynamic resistance of the mine, H•s2/m8, which can be determined from equation [9]: The total natural draft he obtained in the experimental data processing will enable determining the degree of its effect on the ventilation process. The positive total natural draft he, which improves MMF operation, can reduce the fan performance and thereby increase the energy efficiency of ventilation. In case of negative total natural draft, MMF performance should be increased by the draft value to ensure ventilation in the safe mode, but with minimal energy consumption. Table 3 summarizes the results of the primary experimental data processing for two experiments. Due to the deletion of inhomogeneous variances, the average values of the total natural draft he increase. The limits of the confidence interval for natural draft in the summer experiment are positive, which indicates the effect of negative natural draft [10]. In the winter experiment, the lower limit of the interval he is negative, whereas its upper limit is positive. Due to the fact that the upper limit of the interval he is of greater significance, with the probability of 95% in the mine, negative total natural draft takes place in the winter experiment, but its value is much smaller than that in the summer experiment.

Conclusion
The summer and winter experiments can be performed with different accuracy, which is unacceptable for statistical analysis, that is, the variances of the two compared sampling populations should be homogeneous.
Thus, the experiments have shown that a significant number of different factors that affect the fan performance, total natural draft that varies within a wide range (Table 3) in particular, must be taken into account to efficiently control the MMF operating modes. Therefore, it is impossible to consider the results of measurements only. Real time deletion of false data by statistical methods, timely averaging of the measured QMMF and hMMF values, that is, creation of the information system based on the data obtained, are crucial for efficient control of ventilation in the mine with regard to disturbing effects.