The optimization of a dust suppression and clean production scheme in a TBM-constructed tunnel based on an orthogonal experiment

Journal Pre-proof The optimization of a dust suppression and clean production scheme in a TBM-constructed tunnel based on an orthogonal experiment Xiaofei Liu, Wen Nie, Wenjie Zhou, Changqi Liu, Qiang Liu, Cunhou Wei

PII:

S0957-5820(19)32381-X

DOI:

https://doi.org/10.1016/j.psep.2020.02.007

Reference:

PSEP 2104

To appear in:

Process Safety and Environmental Protection

Received Date:

26 November 2019

Revised Date:

3 January 2020

Accepted Date:

5 February 2020

Please cite this article as: Liu X, Nie W, Zhou W, Liu C, Liu Q, Wei C, The optimization of a dust suppression and clean production scheme in a TBM-constructed tunnel based on an orthogonal experiment, Process Safety and Environmental Protection (2020), doi: https://doi.org/10.1016/j.psep.2020.02.007

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The optimization of a dust suppression and clean production scheme in a TBM-constructed tunnel based on an orthogonal experiment Xiaofei Liu a,b, Wen Nie a,b,*, Wenjie Zhou a,b, Changqi Liu a,b, Qiang Liu a,b, Cunhou Wei a,b

a State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao b College of Mining and Safety Engineering, Shandong University of Science and Technology,

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Qingdao 266590, China

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Graphical abstract

The full-scale physical model

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established using Solidworks.

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The field airflow-dust coupling simulation

results.

The dust diffusion distances when the

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secondary pressure quantity= 2 m³/s, 4 m³/s, 6

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m³/s, 8 m³/s and 10 m³/s, respectively.

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Highlights



Established a full-scale, 3D model of the ventilation and dust suppression in a tunnel being constructed using the TBM machine.



Simulation experiment results of simulation results of the airflow-dust coupling field was analyzed.

 The orthogonal experiment was designed by considering multivariate factors.

Abstract: This study aims to examine whether clean production can be achieved by using a

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ventilation dust removal system in a tunnel being constructed using a tunnel boring machine (TBM). The section between the Guizhou-Road Station and the Guipaijing Station on the No. 1 line of the Qingdao Metro was selected as the study area, and the effectiveness of a ventilation

dust removal system on the diffusion distance and mean concentration of dust in the excavating

area was investigated through the use of a CFD-based numerical simulation. Firstly, it was found

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that when the first pressure quantity, exhaust quantity, and position of the pressure inlet and exhaust inlet of the ventilation dust removal system all remained unchanged, the secondary

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pressure quantity corresponding to the optimal dust suppression effect was determined to be 8 m³/s. Next, an orthogonal experiment was performed on the position of the pressure inlet and exhaust

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inlet and the forced-to-absorbed airflow quantity ratio in order to examine the effect of different combinations on the dust suppression effect in the tunnel. By selecting the dust diffusion distance, mean dust concentration in the tunnel and dust-collecting efficiency as the evaluation indexes, three favorable schemes were determined, from which the optimal scheme was selected. In the

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optimal combination, the secondary pressure inlet and exhaust inlet were 28 m and 11m away from the tunnel face respectively and the forced-to-absorbed airflow quantity ratio was 1.6. After the scheme’s optimization, the dust diffusion distance in the tunnel was shortened considerably by

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53%, the mean dust concentration was reduced to 1.52E-06 kg/m³, and the dust-collecting efficiency was enhanced by 13.19%. Under these optimal conditions, a ventilation dust removal

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system can maximize the operational efficiency and ensure a clean air environment during any production activities. Key words: TBM construction; dust suppression effect; numerical simulation; dust

concentration; orthogonal experiment;

1. Introduction Due to numerous advantages, including a quick tunneling speed, environmental friendliness and significant comprehensive benefits, the tunnel boring machine (TBM) is currently the most advanced mechanical equipment for tunneling purposes. A TBM can operate in a tunnel with a

complex geomorphology and great depth; such tunnels would be hard to work in using the traditional borehole-blasting method (Geng et al., 2018; Geng et al., 2019). However, a large number of dust particles are produced and distributed in a tunnel when a TBM is being used. Moreover, the dust control level is low and the workers have to operate in a narrowly enclosed space (Xu et al., 2018). The dust particles produced during a TBM’s operational process can seriously threaten the highly efficient production in a mine as well as the safety and occupational health of the workers (Hu et al., 2016). At present, there is misconception that the discharging of dust particles from a tunnel is an effective means of dust control, but this is not true (Wang et al., 2019). The dust particles are mainly produced in the innermost region of a tunnel and only an adequate suppression of the dust diffusion will succeed (Hu et al., 2017). A TBM-matched

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ventilation dedusting system can effectively control the dust produced when a TBM is being used, and can reduce the dust diffusion distance, lower the dust concentration and ensure the occupational health of the workers. Therefore, creating a better dust diffusion suppression method

is an effective means of ensuring safe processes and environmental protection during a TBM tunneling process, and this needs to be done urgently (Chang et al., 2019).

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Since a great amount of dust particles are produced during a TBM’s excavating process, any long-term experimental investigation at the construction site may have limitations and a low

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operability, which can affect the construction process (Liu et al.,2014). It is thus inadvisable to carry out field measurements. In order to improve the situation at the construction site and find a

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more desirable construction environment, the interference of other objective factors with the experimental results should be avoided for a more effective investigation. This study employed the numerical simulation method for analyzing and examining (Liu et. al., 2017) TBM-matched ventilation dust removal equipment, which not only reduced the cost of the experiment, but also

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ensured the accuracy, precision and scientificity of the present study. The numerical simulation method has been extensively applied in large-scale construction projects that investigate the dust distribution and airflow dynamics in tunnels. For example, Toraño J et al. employed the k-ε model

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and performed simulations in order to analyze the effects of ventilation on the one-way frictional resistance and flow field distribution around an excavating face and examine the relationship

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between the pressure distribution and air leakage; moreover, the simulation results were validated by a field test. Based on developments in CFD computer simulation technology and the related applications in tunnels, Parra et al. investigated three different types of ventilation systems in a construction tunnel and experimentally verified the accuracy of their simulation results; moreover, they derived the flow patterns of different ventilation systems (Toraño et al., 2011, Parra et al., 2006). Additionally, Hargreaves et al. confirmed that certain characteristics related to various ventilation systems can be successfully identified by CFD computer simulation and a number of laboratory experiments, and made improvements to the ventilation system so as to achieve a more

favorable dust suppression performance (Hargreaves et al., 2007;). Du et al. established the numerical model of the far-pressure-near-adsorption (FPNA) ventilation dedusting system in a tunnel using software and a combined numerical analysis and field test for simulating the change of dust concentration in a fully-mechanized excavating face before and after the use of the FPNA ventilation system. According to their results, the dust removal efficiency of the FPNA ventilation dedusting system reached over 95% (Du et al., 2010). Ming Li and Saiied also performed a related numerical simulation and concluded that the lowest airflow velocity for ensuring normal operations in a tunnel was 0.15 m/s (Li et al., 2016). Wang et al. studied the ventilation system in a long diversion TBM construction tunnel via a CFD computer simulation. By analyzing the impact of air leakages on the airflow and pressure distribution in tunnels with different lengths and

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ventilation distances, they ascertained the relationship between the 100 m leakage rate, the differential pressure at the air leakage port and the volume of air in the air passage (Wang et al., 2011). Wang et al. analyzed the effects of certain parameters, including the air pressure quantity,

air exhaust quantity and the positions of the pressure and exhaust ducts, and ascertained the optimal pressure and exhaust parameters of the FPNA ventilation dedusting system (Wang., 2011,

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Wang et al., 2009). Lu et al. analyzed in depth the dust-producing rules in an excavating face and ascertained the lateral and vertical dust migration rules in a tunnel. Additionally, they highlighted

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that the maximum dust concentration was basically distributed along the direction of the central axis (Lu et al., 2012). Chang, Xu et al. focused on the working face of an underground mine in

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Western Australia, constructed a physical model and analyzed the airflow characteristics and particulate matter (PM) concentration distribution in the working face based on fluid dynamics. Their research results provided insightful guidance for further PM control and auxiliary ventilation design (Chang et al., 2019).

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As can be seen, the above researchers have made certain breakthroughs with regard to tunnel ventilation and dust suppression after some lengthy studies and simulations. However, the established models are all over-simplified and cannot improve optimally the environment of a

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construction site. Moreover, with regard to the influencing factors of ventilation dust removal in a tunnel, previous researchers only performed simple single-variable control experiments; i.e., only

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one factor was set at different values, while the other factors remained unchanged when analyzing the effect on the ventilation dust suppression performances in a tunnel. Therefore, there has not been a comprehensive analysis involving multiple factors and adequate parameter matching, which has led to great differences between research findings and the results of actual projects. In this study, a full-scale physical model was first established in order to significantly improve the actual conditions at a construction site and ensure the practicality and effectiveness of the model (Wang et al., 2019). Next, a numerical simulation-experiment was conducted on the secondary pressure quantity in order to achieve the most favorable dust suppression performance.

Finally, by means of an orthogonal test, the effects of three factors, namely the distance between the secondary pressure inlet and the tunnel face, the distance between the exhaust inlet and the tunnel face and the forced-to-absorbed airflow quantity ratio, were fully analyzed in order to determine the optimal ventilation dust removal scheme (Liu et al., 2019).

2. The mathematical model The airflow in a tunnel can be regarded as a turbulent flow of fluid and described using the k-ε two-equation model for the constant turbulence of fluid (Rahm et al., 2016). Moreover, the energy equation was also taken into account in the simulation. The following assumptions were made in advance. Firstly, since the tunnel in the study was fairly long, the effect of natural wind around the tunnel’s entrance was not taken into account (Benni et al., 2016). Secondly, the

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ventilating air in the tunnel can be modeled as a three-dimensional and viscous Newtonian fluid, while energy and pressure losses in the pipe were not taken into account. Thirdly, the disturbances from the slag transporters, rear support equipment and other devices in the tunnel on the flow field

were ignored. Fourthly, the effects of the resistance of the traffic wind and natural wind in the tunnel were also ignored. The migration behaviors of the airflow field and dust fields in the

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TBM’s operating face were a steady-state turbulent flow. Based on the gas-particle two-phase flow theory and airflow-dust flow characteristics, time-averaging equations, which can fully reflect the

established (Chang, et al., 2019).

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migration behaviors of the airflow field and dust field in the TBM’s operating face, were

The continuity equation of the gas phase can be written as:

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    (v j )   (  vj )  S   nk mk t x j x j

(1)

The continuity equation of the particle phase can be written as: (2)

nk   )  (nk vkj )   (nk vkj t x j x j

(3)

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k    ( k vkj )  Sk  ( k vkj )  nk mk t x j x j

The momentum equation of the gas phase can be written as:

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  p  ji (  vi )  (  v j vi )      gi  t x j xi xi

v j  v   vj vi)  



mk (nk vki  nk vi)  vi  nk mk   rk

k

vki  vi   vi S  FMi  (  vj vi  vi  vj   rk x j

 n vm   m n v   vn m k i

k

k ki

i

k

k

(4)

The momentum equation of the particle phase can be written as:

   (nk vki )  (nk vkj vki )  nk gi  nk (vi  vki ) /  rk  (vi  vki )nk mk / mk  FMi / mk  (nk vkj vki  t x j x j

vkj nk vki  vki nk vkj  nk vkj vki )  (nk vi  nk vki ) /  rk  (vi nk mk  nk vimk  mk nk vi  nk vimi  vki nk mk 

nk vki mk  mk nk vki  nk vki mk ) / mk 

 (nk vki ) t

(5)

Where: 𝜌 denotes the fluid density, with a unit of kg/m3; 𝑡 denotes the tangential direction; x denotes the x-axis direction; 𝑣 denotes the velocity along the y-axis direction, with a unit of m/s; i and j are tensor coordinates; p denotes the pressure, with a unit of Pa; n denotes the number density of particles; S denotes the displacement source item; m denotes the particle mass, with a unit of kg; 𝑚̇ denotes the derivative of the particle mass m, with a unit of kg; 𝑚̇′ denotes the mean turbulent mass, with a unit of kg; 𝜏 denotes the time, with a unit of s; 𝑟 denotes the radial position; 𝑔 denotes the gravitational acceleration, with a unit of m/s2; 𝐹𝑀 denotes the applied external force, with a unit of N; and 𝑘 denotes the k-th particle. Furthermore: 𝜌 = 𝜌 + 𝜌′, where

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𝜌 denotes the mean density, 𝜌′ denotes the fluctuating density, and 𝜌′ denotes the mean fluctuating density; 𝑣 = 𝑣 + 𝑣 ′, where 𝑣 denotes the mean velocity, 𝑣 ′ denotes the fluctuating

velocity, and 𝑣 ′ denotes the mean fluctuating velocity; and 𝑛 = 𝑛 + 𝑛′ , where 𝑛 denotes the mean number density of the particles, 𝑛′ denotes the fluctuating mean number density of the particles, and 𝑛′ denotes the mean fluctuating number density of the particles (Xu et al., 2017).

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The above equation set was enclosed using the realizable 𝑘 − 𝜀 model. The detailed enclosure process can be described below.

 t    k 

 k    Gk     x j 

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(  k ) (  kui )    t xi x j

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𝑘 equation — the kinetic energy equation of turbulence can be written as:

(6)

𝜀 equation — the energy dissipation rate equation of turbulence can be written as:

 C1 E   C2

 t     

      x j 

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 (  )  (  ui )    t xi x j

(7)

2

k  v

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where 𝑢 denotes the velocity along the x-axis direction; 𝜇𝑡 denotes the coefficient of the eddy viscosity, 𝜇𝑡 denotes the 𝑘 equation, 𝐺𝑘 denotes the generation item of the turbulent kinetic

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energy induced by the gradient of the mean motion velocity, 𝜀 denotes the Prandtl number of the turbulent flow in the equation and 𝐶2 is a constant. Furthermore: 𝐺𝑘 = 𝜇𝑡 𝐸 2 , where 𝐸 = 𝜂

𝑘

√2𝐸𝑖𝑗 𝐸𝑖𝑗 ; and 𝐶1= 𝑚𝑎𝑥 [0.43, 𝜂+5], where 𝜂 = 𝐸 𝜀 . In the actual calculation process, 𝐶2 = 1.9, 𝜎𝑘 = 1.0, 𝜎𝜀 = 1.2 and

t   C

k2



(8)

In Eq. (8), 𝐶𝜇 denotes the function related to the mean strain rate and can be further written

as: 𝐶𝜇 =

1

1

𝐴0 +𝐴𝑠 𝑈 ∗ 𝑘/𝜀

, where 𝐴0 = 4.0, 𝐴𝑠 = √6 𝑐𝑜𝑠 𝜑, 𝜑 = 𝑎𝑟𝑐𝑐𝑜𝑠( √6𝑊), 𝑊 denotes the

𝐸 𝐸 𝐸

3

1 𝜕𝑢

𝜕𝑢

𝑘𝑗 power (𝑊 = (𝐸𝑖𝑗 𝐸𝑗𝑘 )1/2 and 𝐸𝑖𝑗 = 2 (𝜕𝑥 𝑖 + 𝜕𝑥𝑗)) , 𝑈 ∗ denotes the internal energy (with a unit of J, 𝑖𝑗 𝑖𝑗

𝑗

𝑖

𝑈 ∗ = √𝐸𝑖𝑗 𝐸𝑖𝑗 + 𝑒̃𝑖𝑗 𝑒̃𝑖𝑗 , 𝑒𝑖𝑗 = 𝑒𝑖𝑗 − 𝜀𝑖𝑗𝑘 𝜔𝑘 and 𝑒̃𝑖𝑗 = 𝑒𝑖𝑗 − 2𝜀𝑖𝑗𝑘 𝜔𝑘 ), 𝜔 denotes the fluid’s auto-rotational velocity, and 𝑒𝑖𝑗 denotes the time-averaging tensor of the rotation rate observed in the reference coordinate system at an angular velocity of 𝜔𝑘 . In the flow field without a rotation, 𝑒̃𝑖𝑗 𝑒̃𝑖𝑗 =0, i.e., the effect of the rotation was introduced (Wang et.al., 2017). Since the variable forced-to-absorbed airflow quantity ratio was the research object in this study, the ventilation quantity was investigated via the airflow velocity in a numerical simulation.

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Therefore, the following formula was introduced: （9）

𝑄 = 𝐴𝑣

where Q denotes the airflow quantity, A denotes the cross-sectional area at the air outlet, and v denotes the airflow velocity.

3. The establishment of the geometrical model of the tunnel

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In this study, a model of the section from the Guizhou-Road Station and the Guipaijing

Station on the No. 1 line, Qingdao Metro, China, was established, as shown in Fig. 1. Since a

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section of the tunnel passed through II-IV2 surrounding rock-granite, the TBM was combined with the borehole-blasting method for construction purposes. By reference to the field exploration

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and drawing-based numerical measurement results, the full-scale model of this section was established using Solidworks (a three-dimensional modeling software). This project employed a TBM (DSU0630) with a fully-enclosed structure for construction purposes. The TBM was 8 m in length and included 10 trolleys. The operating diameter of the

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cutter was 6.1 m, the diameters of the first and second air pressure cylinders were 0.8 m and 0.7 m respectively, and the diameter of the absorbed air cylinder was 0.7 m. Based on the above parameters, a TBM model with a length of 130 m was established, which mainly consisted of a

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main engine, a driving system, a slag conveyor, a ventilation dust removal device including forced and absorbed air cylinders, and a related support devices. When the TBM operated in the

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tunnel, the crushed sludge and rock fragments were produced due to the cutting friction between the cutter and the rock, and at the same time, a large amount of both floating and falling dust was produced in the cutter, as shown in Fig. 2. Therefore, the excavating face was the main dust-producing face in the tunnel. Since the TBM had a fully-enclosed structure, the dust produced in the tunnel face would not spread directly to the tunnel. To be specific, the dust particles were first concentrated around the anterior shield and then diffused to the tunnel through the space above the slag conveyor. In this study, using a pressure ventilation method, fresh air was first delivered to the shield tail by the air pressure cylinder and then mixed with the dust that

overflowed in the slag conveyor. The dirty wind produced within this range passed through the absorbed air cylinder, entered the dust removal system of the TBM for purification, and was finally discharged from the construction tunnel. Accordingly, both the dust diffusion distance and dust concentration in the tunnel were reduced in order to ensure a clean production environment. Fig. 3 displays the established computational grid in the model. On account of the great complexity and multiple factors in the support devices, tetrahedral meshes were generated (Wang et al., 2019). As shown in Fig. 4, finally, 2,131,537 meshes were generated, and the mesh quality was mainly concentrated in the range of 0.41–0.99 and 0.79 on average, with a poorest quality of 0.05 and an optimal quality of 1. The meshes within a quality range of 0.41–0.99 accounted for 99.839% of all the meshes in the entire model. Moreover, the mesh quality was favorable and

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exhibited a stable distribution. Next, the boundary conditions were set for numerical simulation (Wang et al., 2019).

An independence test is quite important since the mesh generation performance can affect the numerical simulation results. Before the numerical simulation, three different types of meshes

with different densities were generated using ICEM, which were denoted as Mesh 1, Mesh 2 and

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Mesh 3. The total numbers of points in Mesh 1, Mesh 2 and Mesh 3 were 1,856,829, 2,131,537 and 3,045,684, respectively. The related models were plotted. For each model, a line was set on

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the middle section (X=0, Y=1.5,10≤Z≤60), and several measuring points were arranged on the line at a spacing of 6 m for the measurement of airflow velocity. Using three different meshes, the

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airflow velocities at the same 10 measuring points were measured for comparison and analysis. Fig. 4 compares the dust concentrations using different meshes. Apparently, Mesh 2 and Mesh 3 produced approximate data, suggesting the independence of the simulation results of the mesh density when the number of meshes exceeded a certain value. The simulation results using Mesh 1

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differed greatly from the data using the other two meshes. By taking into account both the simulation accuracy and computing resources and time simultaneously, Mesh 2, with a total number of mesh points of 2,131,537, was selected for the numerical simulation. As shown in Fig.

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5, the mesh quality was mainly concentrated in the range of 0.41–0.99 and was 0.79 on average, with a poorest quality of 0.05 and an optimal quality of 1. The meshes within a quality range of

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0.41–0.99 accounted for 99.839% of all the meshes in the entire model. Moreover, the mesh quality was favorable and exhibited a stable distribution. Next, the boundary conditions were set for the numerical simulation.

4. A simulation of the field ventilation dust suppression performance The airflow and dust in the model were then simulated using FLUENT software. The simulation results were processed with the use of CFD-POST for a clear analysis. According to the field construction document: the ventilation quantity was 17.0 m³/s and the airflow velocity was 33.84 m/s at the pressure inlet; when the secondary pressure quantity =7 m³/s, the airflow velocity

was 18.67 m/s, which was 30 m away from the tunnel face; and at the exhaust inlet which was 20 m away from the tunnel face, the exhaust quantity was 8.2 m³/s and the airflow velocity was 21.32 m/s (Aminossadati et al., 2008). Next, the simulation results of the airflow trajectories and dust dispersion and distribution behaviors in the tunnel were analyzed. As shown in Fig. 6, the cutter was the main dust-producing equipment, and therefore, the dust particles produced were concentrated around the anterior shield, which fits well with the actual conditions at the construction site (Hu et al., 2017). Under the combined action of the positive pressed airflow produced during the rotation of the cutter and the ventilation dust removal system, the dust in the tunnel face spread to the tunnel via the slag conveyor (Xu et al., 2018). Since the secondary pressure quantity was lower than the absorbed

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airflow quality at the exhaust inlet, some pressed airflow was inhaled directly into the absorbed air cylinder, which weakened the backflow velocity indirectly. In addition, since the airflow velocity at the secondary pressure inlet remained constant, the forced airflow velocity was always greater

than the backflow velocity. Under the forced airflow’s effect, the backflow continued pressing on the shield tail, thereby producing a vortex. Accordingly, the dust diffusion and distribution were

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far from ideal in the tunnel. Although part of the dust was discharged from the tunnel via the

absorbed air cylinder, as shown in Fig. 7, a large amount still spread towards the back of the

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tunnel, under the action of the hybrid airflow, and finally fell on the tunnel floor or covered the trolley (Wang et al., 2015). Through analysis, it was found that the ventilation dust removal

environment (Hu et.al., 2016).

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equipment employed had little effect and could barely achieve the goal of a clean production

Fig. 8 and Fig. 9 indicates that dust particles covered the operating region, where the lagging jack was being installed, the drilling and spraying system, and slag conveyor (Liu et al., 2017).

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This would not only accelerate the aging of the equipment and cause a lot of damage, but also lead to too high an environmental temperature during the operation of the equipment, which would lead to increased energy consumption and economic losses (Hu et al., 2015). More seriously,

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because of the accumulation of dust particles over a long period, the mechanical equipment would not operate properly. Therefore, the dust suppression effect in the tunnel would not be ideal.

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According to the numerical simulation, the mean dust concentration in the tunnel was 7.94E-04 kg/m³ and the dust-collecting efficiency was 30% (Geng et al., 2019). Moreover, the concentration was 397 times greater than the standard specified in China’s tunnel ventilation construction and design (2E-06 kg/m³), suggesting there was great room for improvement (Wang et al., 2019). For further analysis, the researchers in this study made field measurements at the construction site. After the TBM operated normally for five minutes, as shown in Fig. 10, the related data were measured. In order to ensure the accuracy of the data and avoid any errors, the measurements were taken at each point 2–3 times for averaging. To be specific, the airflow velocity was measured by a

TSI-9545 anemometer and the dust concentration by a TSL9306 dust concentration tester. The field measured data were compared with the simulated results for validating the accuracy of the model using a numerical simulation. As shown in Fig. 11 and Fig. 12, through comparison, the mean relative error between the two sets of data was found to be 4.36%, which validated the effectiveness of the present dust simulation. The above simulation results also suggest that the dust suppression effect was far from ideal. The large amount of dust in the tunnel continuously endangered the operators’ health and accelerated the damage to the support devices. Accordingly, the demand for a clean production environment was not satisfied. Therefore, with the aim of improving the working face’s environment, prolonging the equipment’s service life and enhancing the overall operating

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efficiency, this study conducted a comprehensive investigation involving multiple parameters. This is described in the next section (Wang et al., 2015).

5. The effect of the secondary pressure quantity on the dust distribution in the tunnel

In a TBM system, the ventilation dust removal device is responsible for the exchange with

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the external gas in order to discharge the dirty gas and inhale fresh air. The first air pressure

cylinder forces the external fresh air into the tunnel, whilst the secondary air pressure cylinder

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delivers the fresh air to the shield tail in order to ensure there is sufficient fresh air in the entire tunnel. In comparison with the first air cylinder, the secondary air cylinder can release more

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airflow in order to change the air flow performance and dust distribution in the tunnel under construction. Therefore, by changing the secondary pressure quantity and fixing the other parameters, the effect of the secondary pressure quantity on the dust distribution in the tunnel was examined (Geng et al., 2017). The dust diffusion distances and mean dust concentrations under

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different conditions were fully compared to achieve the optimal results. Based on the findings from previous research, the secondary pressure quantity was set as 2 m³/s, 4 m³/s, 6 m³/s, 8 m³/s, and 10 m³/s.

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As shown in Fig. 13 and Table 1, when the secondary pressure quantity = 2 m³/s, since the pressed airflow was low and the pressure inlet was far away from the tunnel face, the airflow was

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not diffused towards the shield tail; meanwhile, the rock slags on the main engine’s slag conveyor fell down to the trolley’s slag conveyor and produced a re-entrainment of dust. According to the fluid inertia, the dust particles covering the rock slags ran through to the tunnel exit. Some dust particles were affected by the absorbed air cylinder and were thus discharged from the tunnel, while the other dust particles dropped to the support device or accumulated on the tunnel floor (Nie et al., 2018). As the secondary pressure quantity increased to 4 m³/s and 6 m³/s, in spite of the significant reduction of the diffusion distance of the re-entrained dust, the overall diffusion distance of the dust was twice greater than that when the secondary pressure quantity =2 m³/s. A

large amount of dust was densely distributed in the working face, which is not conducive for a clean production environment. More seriously, the workers can suffer from occupational diseases under such conditions (Liu et al., 2018). As shown in Fig. 14, by comparing the situations when the secondary pressure quantity = 8 and 10 m³/s, it can be clearly observed that the negative pressure effect of the absorbed air cylinder greatly weakened under a large pressed airflow velocity in the latter situation; therefore, a large amount of dust was accumulated (Parra et al., 2006, Zhang et al., 2008). When the secondary pressure quantity =8 m³/s, the absorbed air cylinder exhibited a very good dust collection performance. Although some dust particles were blown towards the shield tail, only a small amount of dust accumulated and the mean dust concentration in the tunnel reached the lowest

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level. Overall, a favorable ventilation dust removal performance can be achieved when the secondary pressure quantity = 8 m³/s.

However, the dust diffusion and distribution performance was still unsatisfactory, and the

mean dust concentration in the tunnel still exceeded the industrial standard; i.e., the goal of a clean production environment could not be satisfied (Geng et al., 2018). Next, by means of an

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orthogonal experiment, the variable factors and evaluation indexes were selected in order to

6. Orthogonal experiment

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explore the optimal dust suppression scheme (Nath et al., 2019).

The present experiment aimed to seek the optimal combination of levels and thus employed

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an orthogonal table for the experimental design. By referring to previous research findings and conclusions, this study examined the effects of three variables (namely, the distance between the pressure inlet and the tunnel face (in this project, the distance between the pressure inlet and the working face should be no greater than 50 m), the distance between the exhaust inlet and the

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tunnel face, and the forced-to-absorbed airflow quantity ratio) on the dust suppression performance in the tunnel. In order to ensure experimental accuracy, three indexes, namely, the dust diffusion distance, mean dust concentration in the tunnel and dust-collecting efficiency, were

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selected for evaluation purposes, as shown in Table 2. As described above, the secondary pressure quantity was set at 8 m³/s, and the orthogonal experimental scheme was designed in order to

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explore the optimal combination, as shown in Table 3 (Zhang., 2007). In order to ensure the comparability of the simulation results using different models, the

mesh sizes at the same positions were identical when using ICEM, thus the mesh number in the present experiment was maintained within a range of 2,100,000–2,200,000. (The distance between the secondary pressure inlet and the tunnel face, the distance between the exhaust inlet and the tunnel face and the forced-to-absorbed airflow quantity ratio are hereinafter referred to as Factor A, Factor B, and Factor C, respectively. K1, K2, K3, K4 and K5 represent the extracted sums of each factor at each level, while k1, k2, k3, k4 and k5 represent the mean sum of each factor at each

level.) According to the results of the No. 1–No. 5 experiments in Fig. 15, the position of the pressure inlet was near the shield tail and coincident with the unloading point of the slag conveyor. Since the positive pressure at the pressure inlet exceeded the negative pressure at the exhaust inlet, the dust particles that were diffused towards the posterior shield in the space above the conveyor, and the secondary raised dust, accumulated near the shield tail under the airflow. As the airflow velocity dropped, dust was scattered gradually and fell on the tunnel floor. As the distance between the absorbed outlet and the tunnel face increased gradually, the dust diffusion results became more prominent. In spite of the increasing airflow quantity at the exhaust inlet, the effect of the exhaust inlet’s position exceeded the effect of the forced-to-absorbed airflow quantity ratio.

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Therefore, dust was not controlled adequately and the mean dust concentration exhibited no remarkable decline, which was not conducive for realizing the goal of a clean production environment.

By comparing the results in the No. 6–No. 9 experiments in Fig. 16, the dust diffusion distance dropped steadily. This can be partly attributed to the gradual increase of the ventilation

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quantity at the exhaust inlet. Meanwhile, since the position of the pressure inlet was greatly improved, the highly concentrated dust regions around the devices at the bottom of tunnel were

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significantly reduced when compared with the conditions in the No. 1–No. 5 experiments. In the No. 10 experiment, since the exhaust inlet was too far away and the ventilation too small, dust

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particles accumulated in the tunnel and diffused outwardly, rather than being discharged adequately. Accordingly, the dust could not be controlled effectively and the mean dust concentration also exhibited no significant decrease, which was not conducive for realizing the goal of a clean production environment.

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As shown in Fig. 17, compared with the first 10 sets of experiments, the No. 11–No. 13 experiments presented significantly enhanced results. On the one hand, owing to a good matching between the positions of the exhaust inlet and absorbed cylinder, dust was discharged effectively

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out of the tunnel. On the other hand, since the pressure inlet was far away from the shield tail, the effect of the vortex weakened. It was possible to discharge the dust directly from the tunnel via the

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exhaust inlet due to the dust concentration in the front segment of the tunnel. According to the results of the No. 14 and No. 15 experiments, the exhaust inlet was arranged too far away and the absorbed quantity was too low. Therefore, the dust could not be discharged adequately but accumulated in the tunnel and diffused outwardly. In addition, the dust was controlled poorly and the mean dust concentration was only reduced slightly; i.e., the goal of a clean production environment could not be achieved. As shown in Fig. 18, according to the No. 16–No. 25 experimental results, the dust suppression effect was favorable in the No. 16 and No. 21 experiments. Since the position of the

pressure inlet was nearest to the tunnel face and the ventilation quantity around the exhaust inlet was also high, the dust removal performance was favorable in these two sets of experiments. In the other sets of experiments, because of a poor matching between the position of the exhaust inlet and the related exhaust quantity, dust accumulated, the dust diffusion distance increased, and the dust suppression efficiency was low. All of these outcomes were not conducive to a clean production environment. Table 3 and Fig. 19 were then established for analyzing the effects of each factor on different indexes. The change of the level of each factor imposed different effects on the evaluation index. A greater range suggested a greater effect on the evaluation index when the factor level was changed.

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(1) The effect of Factor A on the evaluation indexes. As listed in Table 3, out of the three indexes, the range of Factor A was not optimal. As shown in Fig. 19, A3 was the most favorable in terms of the dust diffusion distance and dust-collecting efficiency, while A5 was the most favorable in terms of mean dust concentration in the tunnel.

(2) The effect of Factor B on the evaluation indexes. It can be observed from Table 3 that the

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ranges of Factor B were optimal for two indexes, suggesting that Factor B had the most significant

effect in the present experiment. As shown in Fig. 19, in terms of the dust diffusion distance, B2

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was best; in terms of the mean dust concentration in the tunnel, B5 achieved the best results; and in terms of dust-collecting efficiency, B1 was favorable.

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(3) The effect of Factor C on the evaluation indexes. As listed in Table 3, Factor C had an optimal range for the three indexes, suggesting that C is a secondary influencing factor. In combination with Fig. 19, C = 1 was moderate by comparison, and the optimal parameter needed further analysis and selection.

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Based on the numerical calculation results, the optimal combination was determined as A3B2C5 in terms of the dust diffusion distance, the optimal combination corresponding to the best mean dust concentration in the tunnel was determined as A5B5C3, and the optimal combination

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was A3B1C1 in terms of dust-collecting efficiency. In order to determine the optimal combination, a numerical simulation experiment was performed on these three sets of data for determining the

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direction for a further experimental optimization.

7. The selection of the optimal scheme through a comparison As shown in Fig. 20, when using scheme ②, a large amount of highly concentrated dust was

distributed near the shield tail. In combination with Fig. 21, it can be seen that since the exhaust inlet and pressure inlet were arranged far away, the airflow velocity of the pressure cylinder weakened gradually with the increase of the dust diffusion distance, and some airflow was inhaled directly into the absorbed air cylinder and discharged from the tunnel. Accordingly, when the cutter was in operation, the pressed airflow was comparable with the airflow produced from the

main engine in order to form a vortex in the front of the tunnel. As a consequence, the overflowing dust particles from the main engine accumulated in a dense manner (Sasmito et al., 2013). Using scheme ③, since the pressure inlet was at the appropriate position, the airflow from the air pressure cylinder moved to the shield tail. However, since the exhaust inlet was arranged too closely and the generated negative pressure was weak, some dust was inhaled into the absorbed air cylinder under the airflow’s action. A large proportion of dust was diffused towards the tunnel end under the combined action of the airflow and finally dropped on the trolley’s support device and the bottom of the tunnel. Apparently, as shown in Fig. 21 and listed in Table 4, scheme ① was superior to schemes ② and ③. On the one hand, as the pressure inlet and the exhaust inlet were at the appropriate positions, i.e., the tunnel face was at a moderate value, an insufficient pressure

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distance or vortex field did not appear. It can be observed from Fig. 20-① that a high concentration of dust on the main engine’s conveyor belt was suppressed effectively. In other

words, the high concentration of dust neither accumulated at the shield tail nor diffused to the working face, and most of the dust was inhaled into the absorbed cylinder under the airflow’s action. At that time, the dust concentration and dust diffusion distance clearly improved and the

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high concentration of dust was mainly controlled. Although some low concentrations of dust were present at the working face, the conditions in the tunnel fully satisfied China’s ventilation design

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standard and specifications. It can thus be concluded that dust suppression effect in scheme ① was clearly better than that in schemes ② and ③.

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8. Conclusions

This study focused on the section from the Guizhou-Road Station and the Guipaijing Station on the No. 1 line of the Qingdao Metro, China. Through simulation, the dust diffusion and distribution and airflow dynamics during the TBM’s operating process were investigated in depth.

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The ventilation dust removal system that was designed was optimized by using the dust diffusion distance, mean dust concentration in the tunnel and dust-collecting efficiency as the evaluation indexes.

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An orthogonal experiment was performed on the position of the pressure inlet, the position of the exhaust inlet and the forced-to-absorbed airflow quantity ratio for examining the effect of

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different combinations on the dust suppression effect in the tunnel. By selecting the dust diffusion distance, mean dust concentration in the tunnel and dust-collecting efficiency as the evaluation indexes, three favorable schemes were determined, from which the optimal scheme was selected. According to the parameters in Scheme ①, the optimal scheme, the TBM in the construction site was improved. To be specific, the secondary pressure air quantity was raised to 8 m³/s, the forced-to-absorbed airflow quantity ratio of TBM was set as 1.6, and the distances between the pressure inlet and the tunnel face and between the secondary pressure inlet and the tunnel face were modified to 18 m and 28 m, respectively. Finally, by comparing the field measurement

results, it was found that the dust diffusion distance was shortened by 53%, the mean dust concentration in the tunnel was reduced to 1.52E-06 kg/ m³ and the dust-collecting efficiency was enhanced by 13.19% after the improvements. Conclusively, the field environment was significantly improved and the dust diffusion distance was adequately controlled, thereby enhancing the operating efficiency of the TBM-matched ventilation dedusting equipment. The main conclusions are now outlined. (1) Using the present scheme (when the secondary pressure quantity =7 m³/s), dust particles accumulated at the front of the tunnel, and the ventilation dust removal equipment exhibited a poor performance. By performing a numerical simulation analysis on different values of the secondary pressure quantity, the dust suppression effect reached the optimal level when the

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secondary pressure quantity = 8m³/s; in addition, the mean dust concentration in the tunnel was reduced by 6% and the dust diffusion distance was greatly reduced.

(2) The ventilation dust removal system was also optimized via an orthogonal experiment

and numerical simulation analysis. The position of the secondary pressure inlet, the position of the exhaust inlet and the forced-to-absorbed airflow quantity ratio were selected as the evaluation

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variables for further optimizing the design scheme. By comparing the ranges of different factors, it

was seen that the position of the exhaust inlet affected the clean production during the tunneling

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process most significantly, followed by the forced-to-absorbed airflow quantity ratio, while the position of the secondary pressure inlet imposed the least effect. Accordingly, if the aim is to

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optimize the production environment in a tunneling operation, the consideration of the position of the exhaust inlet should be given priority.

(3) If the dust diffusion distance, mean dust concentration in the tunnel and dust-collecting efficiency were selected as the evaluation indexes, the optimal scheme for the TBM ventilation

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dust removal can be determined. The present study can provide a robust theoretical basis and foundation for further research on dust control, suppression, and collection. Acknowledgements

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This work has been funded by the National Natural Science Foundation of China (NO. 51874191and 51404147) , the Focus on Research and Development Plan in Shandong Province

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(NO. 2017GSF20111), the National Key R&D Program of China (2017YFC0805201), the China Post-doctoral Science Foundation (NO. 2015M570601 and 2017T100503). References

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10.1016/j.enconman.2007.10.010

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Fig.1. An illustration of the lines in the construction section.

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Figures

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Fig. 2 The full-scale physical model established using Solidworks.

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Fig. 3 Displays the established computational grid in the model.

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Fig. 4 A Mesh independence study.

Fig. 5 The optimization results of ICEM mesh.

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Fig. 6 The field airflow-dust coupling simulation results.

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Fig. 7 The simulation results of the dust diffusion and distribution at the construction site.

Fig.8 A great amount of dust was concentrated at the working face after the TBM stopped.

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Fig.9 The dust-filled field device.

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Fig. 10 Labeling the selected measurement point position.

Fig. 11. A comparison of the wind speed measured on site with the numerically simulated wind speed.

Fig. 12. A comparison of the dust concentration on site with the numerically simulated dust

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concentration.

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Fig. 13 The dust diffusion distances when the secondary pressure quantity = 2 m³/s, 4 m³/s, 6 m³/s,

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8 m³/s and 10 m³/s, respectively.

Fig. 14 Comparing the secondary pressure quantity at 8 and 10 m³/s.

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Fig. 15 The dust diffusion patterns on the axial surface using the No. 1–No. 5 experiments in the

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orthogonal table.

Fig. 16 The dust diffusion patterns on the axial surface using the No. 6–No.10 experiments in the orthogonal table.

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Fig. 17 The dust diffusion patterns on the axial surface using the No. 11–No. 15 experiments in

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the orthogonal table.

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Fig. 18 The dust diffusion patterns on the axial surface using the No. 16–No. 25 experiments in

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the orthogonal table.

(A) The dust diffusion distance (m).

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(B) The mean dust concentration in the tunnel (E-06 kg/m³).

(C) The dust-collecting efficiency.

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Fig. 19 A line chart of the orthogonal experimental analysis.

Fig. 20 The dust concentrations at different cross-sections and airflow trajectories using the

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optimal schemes.

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Fig. 21 The airflow-dust coupling behaviors using the optimal schemes.

Tables Table 1 An analysis of the simulation results when the secondary pressure quantity was set at different values. The secondary pressure quantity

Highly concentrated dust diffusion distance

Mean dust concentration in the tunnel

2 m³/s

33.205 m

1.92E-04 kg/ m³

4 m³/s

120 m

9.95E-05 kg/ m³

6 m³/s

89 m

7.85E-05 kg/ m³

8 m³/s

44 m

4.88E-05 kg/ m³

10 m³/s

34.685 m

6.83E-05 kg/ m³

Number

The distance

The distance

between the secondary

between the exhaust

forced-to-absorbed

pressure inlet and the

inlet and the tunnel

airflow quantity ratio

tunnel face (A)

face (B)

(C)

14 m

11 m

2

21 m

18 m

3

28 m

25 m

4

35 m

5

42 m

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1

0.4 0.7 1

32 m

1.3

39 m

1.6

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The

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Factors

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Table 2 The determination of the horizontal factors in the orthogonal experimental scheme.

Table 3 The orthogonal experimental analysis table. Factors

Experimental results of each indicator 1

2

A

3

B

C

Number

The dust

concentration in

diffusion

the tunnel

distance (m)

1

1

1

Mean dust

1

(E-06

The absorbed air cylinder dust-collecting efficiency

kg/m³)

111.

5.88

52.89%

69.

6.60

47.12%

6 2

1

2

2

1

3

3

75

5.30

42.08%

4

1

4

4

59

5.19

31.98%

5

1

5

5

93

4.52

18.55%

6

2

1

2

70

6.12

45.26%

7

2

2

3

69

6.09

47.95%

8

2

3

4

54

7.96

37.56%

9

2

4

5

46

8.20

30.98%

10

2

5

1

126

3.92

36.48%

11

3

1

3

60

5.25

47.43%

12

3

2

41.

9.37

41.84%

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3

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3

4

1

3

3

5

40

8.65

28.90%

14

3

4

1

111.

5.60

42.32%

3

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15

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13

6

5

2

92

5.63

42.10%

4

1

4

66

4.27

45.90%

17

4

2

5

47

8.97

35.45%

18

4

3

1

126

6.03

35.49%

19

4

4

2

126

3.59

31.20%

20

4

5

3

95

3.43

23.21%

21

5

1

5

46.

4.48

33.93%

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16

6 22

5

2

1

126

5.62

39.50%

23

5

3

2

126

5.33

37.90%

24

5

4

3

126

3.71

28.30%

25

5

The dust

5

4

K 407.9

354.2

601.2

365.0

352.4

483.3

K 344.7

421.0

425.0

460.0

468.6

346.1

K 550.6

532.0

272.6

1

diffusion distance(

2

m)

126

5.88

18.93%

3 K 4 K

k1

81.

70.8

k2 6 73.

70.5

k4 0 84.2

0 93.7

0

2

.1 R

ation in

K4

the

K5

tunnel

35.

9

A

B2

3

7.5

26

3

5

2.3

7

3 4.5

kg/m³)

23 .8

26.

2

30 .8

21. 5

5.

27

33.

3

3.2

.1

.3

3

6.3

27

36.

2

k1

C

2

(E-

06

65

.7

na

1.2

ur K3

Jo

concentr

54.

5

4

B

K2

106.4

lP

110

an dust

69.

re

92.

K1

85.

34 .8

5.2

-p

68. 9

Me

96. 7

k5

est

120 .2

k3

ange

ro of

5

5.

6.

k4

46

k5

7.3 4

6. 90

The absorbed

dust-coll ecting

y (%)

98.23

3

02.58

4 K

1

6.68

.86

20 3.58

181 .93

18

8.97 164

17

.78

6.21

139

.26

14

7.81

na

58.56

20

211

1 71.25

5

.41

2

K

C

225

1

K

efficienc

20

3

92.62

2

2.

B5

1

K

cylinder

4

5

1

air

96 3.0

A

K

6.

lP

est

16

0

26

6.

4.3

2.

B

76

6

64

4.

5.2

4.

ange

46

6

26

5.

6.6

5.

R

42

.34

k4

.72

k1 k2

3

8.52

3

ur

k3

9.65

k5

37

39 32.

3

R ange B

37 .79

96

1.71

40

36.

3 4.25

41

42.

4

0.52

Jo

45.

08

35 .24

27. 85

8.

-p

k3

0

ro of

50

re

k2

29 .56

17.

11

81

23

.78

A3

B1

C1

est Table 4 A comparison of the simulation results using the optimal schemes. Number

The dust diffusion distance

Mean dust

The absorbed air

concentration in the

cylinder dust-collecting

tunnel(kg/m³)

efficiency

41.3 m

1.52 E-06 kg/ m³

43.5%

②A5B5C3

79 m

1.68 E-06 kg/ m³

22.0%

③A3B1C1

65 m

1.84 E-06 kg/ m³

45.2%

Jo

ur

na

lP

re

-p

ro of

①A3B2C5