Assessment of energy efficiency improvement strategies for ventilation and hoisting systems during the reconstruction of the Molibden mine

Introduction

Mine ventilation systems are essential engineering infrastructures that ensure safe and efficient working conditions in underground environments [1]. Unlike open-pit operations, where natural airflow plays a key role, underground mines are confined spaces with limited air exchange and an increased risk of emergencies [2, 3]. Ventilation systems are especially critical at gassy coal mines, where methane emissions into mine workings are difficult to predict [4, 5]. These emissions pose serious challenges for maintaining stable airflow, as well as for removing hazardous gases, dust, and excess heat generated by mining operations [6–8], particularly in relation to production output and the geometry of mine workings [9].

As the mining industry advances, it becomes increasingly important to assess the risk of accidents, develop scientifically sound methods for evaluating mine aerological hazards [1, 3], and implement modern tools and algorithms for setting appropriate ventilation modes in underground operations [10–12]. The complexity and diversity of system components call for integrated solutions that ensure the core functions of mine ventilation systems [13]. At the same time, engineering decisions must also reflect energy and cost efficiency, both of which play a decisive role in the overall performance of mining operations [14–16].

Ventilation fans—often with installed capacities of 5–7 MW at large-scale mines—and hoisting machines are among the most energy-intensive equipment in the industry. Their performance directly affects overall productivity, as they are critical components of the mine’s material handling and safety infrastructure [17]. Modern hoisting systems are typically equipped with variable-speed electric drives to ensure reliable and efficient operation. Improving energy efficiency and optimizing the operation of both ventilation and hoisting systems therefore remains a pressing priority.

Research object. This study focuses on the Tyrnyauz Tungsten-Molybdenum Plant (TTMP), which is scheduled to resume operations in 2025.

The aim of the study is to analyze the performance of the main mine ventilation fans and hoisting machines and to develop strategies for optimizing their operation.

Methods.  Evaluating the operating modes of mine ventilation systems and fans, as well as the performance of hoisting machines and their electric drives, is a complex engineering task. It requires a combination of theoretical and empirical approaches, the analysis of experimental data, and the use of modern computational techniques. The proposed optimization strategies for ventilation and hoisting systems are based on a techno-economic assessment. The central idea of the study is to determine the appropriate power ratings for the electric drives used in the main ventilation and hoisting systems at the reconstructed mine, with the goal of improving energy efficiency.

Existing ventilation system at the Molibden mine

The Molibden mine is divided into two sections, each with an independent ventilation layout and equipment: the North-Western section and Central Shafts No. 1 and No. 2. Ventilation at the mine is provided by three types of axial-flow fans: VOKD-3.0, VOKD-2.4, and VOD-21. The VOKD-3.0 and VOKD-2.4 models are designed for main ventilation in large underground mines and shafts. Both are based on the TsAGN-K-06 aerodynamic design and have a standardized construction. Airflow is periodically adjusted by turning the impeller blades, while fine-tuning is achieved through intermediate guide vanes. The synchronous motors are excited using thyristor-based excitation systems equipped with reversing devices. The VOD series consists of reversible axial fans intended to replace the older VOK and VOKD models. These fans are built on the K-84 aerodynamic design. Airflow reversal is achieved by changing the direction of the impeller’s rotation, while the intermediate guide vanes are fixed at an angle of 104° relative to the plane of the impeller’s rotation.

Inspection and analysis of the main ventilation fans at the Molibden mine of the Tyrnyauz Tungsten-Molybdenum Plant

The consolidated results of the inspection and performance analysis of the main ventilation fans (MVFs) provide a comprehensive overview of their operation. Table 1 presents key parameters describing the performance of the ventilation fans used in the mine’s ventilation system.

Table 1

Key performance parameters of main ventilation fans at the Molibden mine

 According to Table 1 (item 23), the main ventilation fans operate inefficiently in terms of energy use. Further investigation is required, along with the development of measures aimed at reducing the specific electricity consumption. As shown in Table 1 (items 19, 20, and 21), the installed motor capacities for MVF-4, MVF-1a, MVF-3, and MVF-6 significantly exceed the actual power drawn from the grid. A required motor power assessment should be carried out for the main ventilation fans.

Required motor power assessment for main ventilation fans

The motor power for ventilation fans is calculated using the following formula:

P = QH/(102η_fan),       (1)

where Q is the airflow rate, m³/s; Н is the static pressure, kg/m²; η_fan is the fan efficiency.

The motor is sized 10–15% above the calculated value to account for possible voltage drops:

                                                                  P_motor = k_rP,                                                            (2)

where k_r is the power reserve factor, k_r = 1.1–1.15.

The values of Q, Н, and η_fan are presented in Table 2.

Table 3 provides information on the motors installed in the main ventilation fans.

Table 2

Fan specifications in the mine ventilation system

Table 3

Main ventilation fan motor specifications

For MVF-1 (VOKD-2.4 fan), the required motor power assessment yielded the following results:

P = 170.7–463.9 kW; P_motor = 510.3 kW.

The installed motor is model A-13-42-8 with a rated power of 400 kW.

Similar calculations were performed for the other main ventilation fans, and the results are summarized in Table 4.

Table 4

Required motor power assessment results

The calculations showed that the installed motor power significantly exceeds the required power determined by the assessment (see Table 4). It would be reasonable to replace them with lower-power motors. The final decision should be based on a techno-economic analysis, elements of which are presented below.

Brief description and technical characteristics of hoisting systems at the Molibden mine

Two hoisting systems are currently in operation at the Molibden mine, located at the Kapitalnaya and North-West shafts. The Kapitalnaya shaft uses a simple single-rope hoisting system equipped with a drum-type hoist (model ShPM 4×36). However, such systems are characterized by low productivity, poor static balance, and other limitations. To improve the efficiency and reliability of hoisting operations at modern mining enterprises, multi-rope hoisting systems with balanced configurations are widely used. These provide higher productivity and reduce vibration during operation.

The primary function of the hoisting machine is to transport personnel and materials between levels 12 and 4 in a double-deck cage (type 2UKN-3.6-1). The technical specifications of the hoisting system at the Kapitalnaya shaft are presented in Table 5.

Table 5

Technical specifications of the hoisting system at the Kapitalnaya shaft

At the North-West shaft, a multi-rope hoisting machine of the MK-2.25×4 type is installed. The hoist features a single-motor drive operating on a generator–motor system. Its purpose is to transport personnel and materials between levels 4 and 1. The system is statically balanced by two flat-strand tail ropes. The hoist operates with a counterweight. The MK-2.25×4 hoisting machine is equipped with a gearbox. The gearbox is connected to the motor shaft via specially designed extended gear couplings. The technical specifications of the hoisting system are presented in Table 6.

Table 6

Technical specifications of the hoisting system MK-2.25´4 at the North-West shaft

Required motor power assessment for the hoisting machine

At the Kapitalnaya shaft, the following hoisting machines are in operation: a hoist equipped with a DA-170/29-12 electric motor rated at Р = 670 kW; a hoist equipped with a PE-172-5K electric motor rated at Р = 630 kW, operating on a generator–motor system. The estimated required power of the hoist drive motor at the Kapitalnaya shaft is determined by the following formula:

where ρ is a coefficient determined from the dynamic operation characteristics, which depends on the moment of inertia of the hoist, its imbalance, and the velocity multiplier; k is a coefficient accounting for additional resistance-related load; η_g is the gearbox efficiency; Р_useful is the useful power required for lifting a payload of mass m_pl, excluding system losses.

For a hoisting system with a single conveyance and a counterweight:

where ψ is a coefficient accounting for the degree of mass balancing between the payload m_pl and the counterweight; υ_avg is the average conveyance speed, m/s.

The coefficient y can be calculated using the following expression:

where m_cw is the counterweight mass, kg; m_с is the conveyance mass, kg; m_pl is the payload mass, kg.

To account for potential voltage drops in the electrical network, the motor power is selected 10–15% higher than the calculated value:

                                                                P_motor = (1.1–1.15) P_cal                                               (6)

For the hoisting system at the Kapitalnaya shaft (see Table 5): m_cw = 6250 kg; m_с = 4412 kg; m_pl = 3700 kg.

The calculated results of the hoisting motor power assessment are presented in Table 7.

Table 7

Results of required motor power assessment for the hoisting machine

The calculated motor power satisfies the requirements of the motor currently installed on the hoisting machine at the Kapitalnaya shaft.

Methodology for estimating the economic benefit of optimal utilization of processing equipment

A study of the relationship between energy consumption and ore production volumes at the Molibden mine has shown that the specific energy consumption is strongly dependent on the mine’s daily productivity. An analysis of data over an eight-year period revealed that the correlation between monthly ore production volumes and energy consumption ranged from 0.309 to 0.730. This indicates that an increase in ore output tends to result in a reduction in specific energy consumption.

An analysis of the ore production dataset showed a high degree of variability, as evidenced by a large standard deviation. Skewness and kurtosis were also observed, indicating an uneven distribution of values. Approximately 50% of the recorded values were significantly below the average, which suggests a predominance of periods with relatively low productivity. Therefore, reducing specific energy consumption at the Molibden mine requires limiting the duration of low-productivity operation and ensuring maximum equipment utilization.

To better understand how equipment utilization affects energy consumption, it is also necessary to analyze the structure of energy use at the mine. This involves disaggregating the total energy consumption into individual processes (such as drilling, blasting, transportation, crushing, and beneficiation) and examining the energy consumption of each technological stage. This analysis would allow for identifying the most energy-intensive operations and determining optimal operating modes for their implementation [18–20]. In addition, the potential for implementing automated production control systems should be considered. These systems can help maintain stable and high equipment utilization and minimize energy losses resulting from inefficient operating conditions.

Table 8 presents the results of the statistical analysis of the ore production dataset. Both the complete dataset (including all recorded values) and a truncated version (excluding values below the mean) were analyzed. This approach was used to evaluate the impact of excluding low-productivity periods on the overall statistical characteristics of the ore mass distribution.

Table 8

Results of ore production parameter calculations for the original data set (Q ) and the truncated data set (Q‘)

The statistical parameters of the truncated data set were calculated using a theoretical method based on the Gram–Charlier distribution. The initial moment S of the random variable Q ³ m_Q is defined as:

where Р(Q ≥ m_Q) is the probability that the values of Q in the truncated data set exceed the mean value m_Q of the original data set:

and f(Q) is the theoretical differential probability density function of the random variable Q.

When the mean value of the extracted ore increases by

                                                             ∆m_Q = m′_Q− m_Q                                                         (9)

the specific energy consumption decreases by the amount Dw:

                                               ∆ω = (a₂m_Q + b₂) − (a₂m′_Q + b₂) = a₂∆m_Q,                                    (10)

where a₂ and b₂ are the coefficients of the regression equation: w = a₂Q+b₂ (see Table 1);

and m_ω is the mean specific energy consumption.

The annual energy savings due to improved equipment utilization is calculated as:

                                                                  ∆W = ∆ωm_Q∙12.                                                   (12)

Table 9 presents the results of the eight-year calculation.

Table 9

Results of calculations for changes in specific power consumption Δω and electricity losses ΔW

The relationships between the mean extracted ore mass and energy savings, along with the corresponding approximating functions, are presented in Fig. 1.

Fig. 1. Energy savings and extracted ore volume, along with their corresponding approximating functions: 1 – annual variation in energy savings; 2 – annual variation in extracted ore mass; trend lines represent polynomial models used for approximation of dependencies (1) and (2)

The expected energy savings are defined as the expected value of ∆W:

where m_∆W = 2, 294, 213 kWh.

The cost of electricity under a two-rate tariff scheme is calculated as:

                                                                  C = P_max a + Wb.                                                            (14)

The monetary savings are then determined as:

                                                                  ∆С = С₁ – С₂,                                                             (15)

where С₁ is the electricity cost at the current consumption level: C₁ = P_max a + W₁b.

The electricity cost at full equipment utilization is as follows:

C₂ = P_max a + W₂b.

where a = 4300 rubles/kW and b = 6.0 rubles/kWh.

Thus,

                                                                  ∆С = (W₁ – W₂)b = ∆Wb.                                                         (16)

Economic benefit from replacing fan motor drives

According to the calculations (see Table 4), the electric motors installed in MVF-4, MVF-1a, and MVF-3 are significantly oversized relative to the actual load. For example: MVF-4: P_cal = 642.9 kW; P_rated = 1250 kW; MVF-1a and MVF-3: P_cal = 221.1 kW; P_rated = 500 kW. It is proposed to replace the motor of MVF-4 with an SDVV-15-39-10 motor rated at 800 kW, and the motors of MVF-1a and MVF-3 with SDV-15-34-12 motors rated at 400 kW.

The economic benefit of motor replacement is calculated as:

                                                                  ∆R = 0.12∆K + ∆C_W,                                                          (17)

where

∆K = K₁ – K₂;

Here, K₁ is the total cost of the currently installed motors: 8.47 million rubles; K₂ is the total cost of the proposed replacement motors: 8.20 million rubles; DK = 0.27 million rubles. The motor cost values are presented in Table 10.

Table 10

Motor costs

The cost of saved electricity is calculated using the two-rate tariff С.

Motor power losses, kW:

                                                                  ∆Р = ∆Р₁ − ∆Р₂,                                                           (18)

where DР₁ and DР₂ are the power losses of the existing and replacement motors, respectively.

Electricity losses, kWh:

                                                                  ∆W = ∆РT,                                                          (19)

where T is the annual operating time of the fans, T = 8570 h.

Main electrical losses in the stator winding, kW:

where r₁ is the active resistance of the stator winding, Ω, calculated as,

where r_υ is the conductor resistivity adjusted for temperature (75 or 130 °C): r_υ(75)=(1/47)×10⁻⁶, Ω·m; r_υ(130)=(1/39)×10⁻⁶, Ω·m; w₁ is the number of winding turns; l_avg = 2(l₁+l₂) is the average turn length; n_eq_e is the effective conductor cross-section, mm²; a is the number of parallel branches in the winding.

Excitation losses, kW:

where ∆U_bc = 1 V – voltage drop at the brush contact; η_v =0.80–0.85 – excitation system efficiency; and r_v is the field winding resistance, Ω,

Mechanical losses, comprising bearing friction and ventilation losses, kW:

where υ_r is the rotor peripheral speed, m/s; and l₁ is stator length, m.

Additional load losses, kW:

                                                             P_add = 0.005P_rated                                            (25)

Total motor losses, kW:

                                                             ∆P₁ = P_e + P_v + P_mech + P_add.                                                      (26)

The results of the motor loss calculations are provided in Table 11.

Total electricity losses and the corresponding are presented in Table 12.

Table 11

Power loss calculation results for all motors

Table 12

Total energy losses and economic benefit

Conclusion

1. The main mine ventilation fans at the Molibden mine operate with excessive specific energy consumption. An inspection of the fan units revealed that actual energy consumption significantly exceeds the standard values (for example, for MVF-4 the actual consumption was 5.79 kW/m³ compared to the standard 1.86 kW/m³). Required motor power assessments showed that the installed motors are oversized (e.g., 1250 kW for MVF-4 versus the calculated requirement of 642.9 kW). It is proposed to replace the motors with more appropriately rated units (e.g., 800 kW instead of 1250 kW for MVF-4), which will reduce energy consumption and operating costs. The expected annual economic benefit from motor replacement is 4.9 million rubles due to reduced energy losses.
2. An eight-year data analysis revealed an inverse correlation between ore output and specific energy consumption. A 10–15% increase in productivity leads to a 2–5% decrease in specific energy consumption. Reducing periods of low equipment utilization and implementing automated control systems can help minimise energy losses. The estimated annual energy savings amount to 4.87 million rubles.
3. The Kapitalnaya shaft currently operates with an outdated single-rope hoisting system (ShPM 4×36), which has limited productivity and lacks static balance. It is recommended to upgrade to modern multi-rope systems (e.g., MK-2.25×4), which provide higher performance, reduced vibration, and a statically balanced design. Required motor power assessments confirmed that the installed motor capacity (670 kW) meets the calculated requirements. In addition, modernizing the control systems could further improve the overall efficiency of the hoisting operation.
4. A theoretical method based on the Gram–Charlier distribution was used to calculate the statistical parameters of the truncated dataset. Equations were derived to calculate and forecast ore production volumes and the corresponding energy savings.
5. The implementation of the proposed measures will not only improve energy efficiency but also enhance environmental performance by reducing emissions. The findings of this study may be applicable to other mining operations with similar working conditions.
6. Future research may explore the use of artificial intelligence for forecasting equipment operating modes and optimizing energy consumption in real time. A detailed breakdown of energy use across key processes (e.g., crushing, transportation) is needed to enable targeted efficiency improvements.