Monitoring data-based real-time prediction method for gas emission quantity of driving face

Abstract

The invention relates to a real-time prediction method for the gas emission amount of a driving face based on monitoring data, and belongs to the field of coal mine safety engineering. According to the method, key feature information such as working face gas concentration, return air gas concentration, tunneling speed, roadway length, coal seam gas content and the like is collected, the quantitative relation between the gas emission amount and key factors is established according to the principle of a split-source prediction method, and then the working face gas emission amount is calculated in real time through monitoring data. And further obtaining the coal falling gas emission amount and the coal wall gas emission amount. Then, data such as the tunneling speed, the roadway length and the gas content at the corresponding time are set and collected according to the advanced prediction time, a tunneling working face gas emission quantity prediction model is established, and a gas emission quantity prediction value at the set advanced prediction time is calculated; the method can predict and forecast the gas emission amount of the driving face in real time, provides a basis for mine gas disaster control, and guarantees safe and efficient production of a mine.

Description

Technical Field

[0001] The invention belongs to the field of coal mine safety engineering and relates to a real-time prediction method for gas emission of an excavation working face based on monitoring data. Background Art

[0002] In the field of mining engineering, especially in the process of coal mining, gas outburst is an important safety issue. Gas refers to the methane gas that gushes out of the coal seam during coal mining. If the gas concentration is too high, it is easy to cause explosions, suffocation and poisoning. Therefore, accurately predicting the amount of gas outburst at the excavation face is of great significance to ensure the safe production of mines. In the field of safety engineering, the prediction of gas outburst is a complex technical problem involving knowledge of multiple disciplines such as geology, mining engineering, and safety engineering. The existing prediction methods are mainly divided into several categories, such as source classification method, mining statistics method, machine learning and big data analysis. In the field of data analysis and prediction, methods such as machine learning and big data analysis have been widely used in prediction problems in various fields, including financial market prediction, weather forecast, disease prediction, etc. These methods usually require a large amount of historical data for training in order to establish an accurate prediction model.

[0003] The existing gas emission prediction methods mainly include the following:

[0004] Source prediction method: Based on factors such as geological conditions and mining technology, the gas outburst volume is divided into different sources, such as coal seam gas outburst, mining-affected gas outburst, etc., and then predicted separately.

[0005] Mine statistics method: Use the historical production data of the mine to establish a statistical model to predict future gas emission.

[0006] Machine learning and big data analysis: Use machine learning algorithms, such as neural networks and support vector machines, to collect and analyze large amounts of production data, discover patterns in the data, establish prediction models, and predict future gas outbursts.

[0007] Although the existing gas emission prediction methods can meet production needs to a certain extent, there are still some problems and limitations:

[0008] Source prediction method: The source prediction method needs to determine relevant parameters when predicting gas emission, and these parameters are generally derived from statistical values. The gas emission prediction result obtained is the average gas emission in the prediction area. The source prediction method is generally used in the early stage of mine construction or the design stage of the working face to evaluate the gas emission level of the entire mine or working face. Obviously, this method is difficult to be directly used for daily gas emission prediction during mining.

[0009] Mine statistics method: The mine statistics method predicts the gas emission volume based on the approximately linear relationship between the gas emission volume and the burial depth or elevation. This method is generally used for the macro prediction of gas emission volume in the working face or mining area. It is difficult to predict the gas emission volume for each shift.

[0010] Machine learning and big data analysis: Although the prediction is highly intelligent, it is easy to ignore the changes in factors affecting gas outburst volume, such as geological conditions, mining technology, working face environment, etc., resulting in large errors in actual application.

[0011] Therefore, how to develop a new gas emission prediction method that can dynamically reflect the changes in the gas emission of the working face in real time and take into account the changes in the factors affecting the gas emission is an urgent problem to be solved in the current fields of mining engineering and safety engineering. Summary of the invention

[0012] In view of this, the purpose of the present invention is to provide a real-time prediction method for the gas emission of an excavation working face based on monitoring data. The relationship between the gas emission and the excavation speed, tunnel length, gas content and other parameters reflected by the gas emission prediction formula of the excavation working face by the source separation method, combined with the gas emission monitored in real time by the mine safety monitoring system, realizes the dynamic real-time prediction of the gas emission of the working face, and solves the problem that it is difficult to accurately predict the gas emission in the daily excavation process in the source separation prediction method. This technical solution collects key feature information data such as the excavation speed, gas content, tunnel length and other key feature information that affect the gas emission in real time, without collecting other non-key feature data information, and establishes a gas emission prediction model with an advanced prediction time, which not only ensures the field applicability of the prediction model, but also ensures the accuracy of the prediction model, solves the problem that machine learning and big data analysis methods easily ignore the changes in the factors affecting the gas emission. This technical solution collects key feature information data that affect the gas emission in real time, can consider the changes in the factors affecting the gas emission, improve the accuracy of the prediction, and provide more timely warnings for the safe production of the mine.

[0013] In order to achieve the above object, the present invention provides the following technical solutions:

[0014] A method for real-time prediction of gas emission from a tunneling working face based on monitoring data comprises the following steps:

[0015] S1: Through the mine safety monitoring, mining progress, intelligent ventilation and other systems, the key characteristic information data affecting the gas emission volume of the target excavation working face during the shift are collected and processed to obtain a set of characteristic index values ​​for predicting the current gas emission volume of the working face;

[0016] S2: Calculate the coal falling gas emission and coal wall gas emission based on the key characteristic data of the gas emission during the shift;

[0017] S3: Set the advance prediction time t of the gas outflow of the working face;

[0018] S4: Collect key characteristic information data that affect the gas outflow volume of the working face at the advanced prediction time t;

[0019] S5: Establish a prediction model for the gas emission of the working face with an advance prediction time of t;

[0020] S6: Calculate the predicted value of the gas emission at the advanced prediction time t according to the gas emission prediction model;

[0021] S7: As the working face advances, repeat S1 to S6 to continuously predict the gas outburst volume of the working face ahead of the predicted time t.

[0022] Furthermore, in S1, the key characteristic information data that affect the gas outburst volume are the working face gas concentration T1, the return air gas concentration T2, the average excavation speed of the shift, the distance between the working face and the opening, and the gas content of the coal seam where the working face is located.

[0023] Furthermore, in S1, the key feature information data is processed as follows:

[0024] S11: Collect the working face gas concentration T1 of the working face sensor and the return air gas concentration T2 of the return air sensor and sort them out to form a gas concentration sequence set A = (c1, c2, ..., c i , …, c n ) and B=(b1,b2,…,b i , …, b n ), where c1, c2, c i, c n are the gas concentration values ​​of the 1st, 2nd, i and nth sample working faces respectively, and n is the number of gas concentration samples collected during the shift; b1, b2, b i ,b n are the return air gas concentration values ​​of the 1st, 2nd, i and nth samples respectively;

[0025] S12: Sort the gas concentration sequence matrix A to obtain a new ordered set A1, denoted as A1 = (c'1, c'2, ...,

[0026] c' i , …, c' n ), where c'1≤c'2≤…≤c' i ≤…≤c' n ;

[0027] S13: Calculate the average gas concentration data C of the working face using the gas concentration sequence set A p1; Calculate the average gas concentration C of the return air at the working face based on the gas concentration sequence set B p2 ; Calculate the average value C of the minimum gas concentration of 10 samples in the shift according to the ordered set of gas concentration sequence A1 m :

[0028]

[0029] Furthermore, in S2, the calculation method of the coal falling gas emission Q1 and the coal wall gas emission Q2 is:

[0030]

[0031] Where F is the air volume at the excavation face, in m 3 / min.

[0032] Furthermore, in S4, the key characteristic information data are the average planned excavation speed of the shift at the advance time t, the estimated distance between the working face and the opening at the advance time t, and the gas content of the coal seam at the estimated location of the working face.

[0033] Furthermore, in S5, the method for establishing the working face gas emission prediction model with the advance prediction time t is as follows:

[0034] S51: collecting key characteristic information data that affect the gas emission volume of the working face at the advanced prediction time t: the planned average excavation speed v of the shift at the advanced time t, the estimated distance l between the working face and the opening at the advanced time t, and the gas content w of the coal seam at the estimated location of the working face;

[0035] S52: The gas outburst amount of the working face at the advanced prediction time t is expressed as:

[0036]

[0037] Where: v0 is the average excavation speed of the current shift, in m / min; v is the average excavation speed planned for the overdue forecast time t, in m / min; l0 is the distance between the working face and the opening at the current time, in m; l is the distance between the working face and the opening at the overdue forecast time t, in m; w0 is the gas content of the coal seam at the location of the working face of the current shift, in m 3 / t; w is the coal seam gas content at the location of the working face at the advanced prediction time t, in m 3 / t.

[0038] The beneficial effects of the present invention are:

[0039] (1) Real-time dynamic prediction: The present invention collects key characteristic information data that affects the gas emission volume of the target excavation face in real time through mine safety monitoring, progress management, intelligent ventilation and other systems, and processes them to obtain a set of characteristic index values ​​for predicting the current gas emission volume of the face. This real-time data collection and processing method enables the present invention to dynamically reflect the changes in the gas emission volume of the face, and has higher timeliness and accuracy than the source prediction method and mine statistics method in the prior art that can only provide macro-overall prediction results.

[0040] (2) Considering the changes in influencing factors: The present invention utilizes the mathematical relationship between the gas emission volume and the gas content, mining speed and tunnel length reflected in the source separation method, and combines the monitoring data to monitor the current gas emission volume in real time, and organically combines the two to obtain a method for predicting the gas emission volume of the excavation working face that can reflect the dynamic changes in the gas emission volume of the working face. This combination allows the present invention to consider the changes in the main influencing factors of the gas emission volume, such as gas content, footage or output, tunnel length, etc., and has higher prediction accuracy and reliability compared to the machine learning or big data analysis methods in the prior art that easily ignore the changes in these factors.

[0041] (3) Wide scope of application: Since the present invention is developed based on the principle of source separation and combined with real-time data collection and processing technology, the present invention is applicable to coal mines with various geological conditions and mining processes and has a wide range of applications. Existing technologies, such as big data analysis methods, often require a large amount of relevant data for training. However, under the actual conditions of limited sensor deployment in mines, it is difficult to obtain a lot of data online in real time, which greatly affects the prediction effect of these methods.

[0042] (4) Resource saving: The present invention does not require a large amount of computing resources and data storage capabilities, and is suitable for mining enterprises of various sizes, especially for small mining enterprises. The implementation cost of the present invention is low and it is easy to promote and apply.

[0043] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0045] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0046] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0047] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0048] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0049] The present invention provides a real-time prediction method for gas emission in a tunneling working face based on monitoring data, such as Figure 1 As shown, the following steps are included:

[0050] S1: Through the mine safety monitoring, progress management, intelligent ventilation and other systems, the key characteristic information data affecting the gas emission volume of the target excavation working face during the shift are collected and processed to obtain a set of characteristic index values ​​for predicting the current gas emission volume of the working face;

[0051] S2: Based on the collected key characteristic data of gas emission during the shift, calculate the coal falling gas emission and coal wall gas emission.

[0052] S3: Set the advance prediction time t of the gas outflow of the working face;

[0053] S4: Collect key characteristic information data that affects the gas outburst volume of the working face at the advance prediction time t.

[0054] S5: Establish a prediction model for the gas emission of the working face with an advance prediction time of t;

[0055] S6: According to the gas emission prediction model, the predicted value of the gas emission with the advance prediction time being t is calculated.

[0056] S7: As the working face advances, repeat S1 to S6 to continuously predict the gas outburst volume of the working face ahead of the predicted time t.

[0057] The present invention utilizes the principle of predicting the gas emission of the excavation working face in the source prediction method, organically combines the source prediction method with real-time gas monitoring data, obtains the quantitative relationship between the gas emission and gas content, mining footage, excavation working face length and other parameters through the source prediction method, uses the monitoring system to obtain the gas emission of the working face in real time, and then obtains the gas emission of coal falling and coal wall, and obtains the dynamic prediction model of the gas emission of the excavation working face according to the relationship between the gas emission of coal falling and coal wall and the mining footage, tunnel length and gas content, respectively, to achieve the online real-time prediction of the gas emission of the excavation working face. It effectively improves the value of the gas monitoring data and the gas emission influencing characteristic information such as gas content, mining speed, and tunnel length, realizes the early prediction of gas disasters such as gas explosion and gas suffocation, and also provides an important basis for formulating gas disaster prevention and control measures.

[0058] The embodiments of the present invention are described below by means of specific examples.

[0059] A real-time prediction method for gas emission from an excavation face based on monitoring data, wherein in S1, the key characteristic information data affecting the gas emission are the gas concentration T1 of the working face, the gas concentration T2 of the return air, the average excavation speed v0 of the shift, the distance l0 between the working face and the opening, and the gas content w0 of the coal seam at the working face.

[0060] In order to more clearly show the key characteristic information data affecting the gas emission in S1 in this embodiment: the average excavation speed of the shift, the distance between the working face and the opening, and the gas content of the coal seam at the working face, an example is given: the key characteristic information data affecting the gas emission in a shift of the 13041 excavation working face of a certain mine are collected as follows: the average excavation speed v0 is 0.003m / min, the distance between the working face and the opening l0 is 115m, and the gas content of the coal seam at the working face w0 is 7.8m 3 / t.

[0061] In S1, the key feature information data processing method is as follows:

[0062] S11: Collect the working face gas concentration T1 of the working face sensor and the return air gas concentration T2 of the return air sensor and sort them out to form a gas concentration sequence set A = (c1, c2, ..., c i , …, c n ) and B=(b1,b2,…,b i , …, b n ), where c1, c2, c i, c n are the gas concentration values ​​of the 1st, 2nd, i, and nth sample working faces, respectively, and n is the number of gas concentration samples collected during the shift; b1, b2, b i ,b n They are the return air gas concentration values ​​of the 1st, 2nd, i, and nth samples respectively.

[0063] S12: Sort the gas concentration sequence matrix A to obtain a new ordered set A1, denoted as A1 = (c'1, c'2, ...,

[0064] c' i , …, c' n ), where c'1≤c'2≤…≤c' i ≤…≤c' n ;

[0065] S13: Calculate the average gas concentration data C of the working face using the gas concentration sequence set A p1 ; Calculate the average gas concentration C of the return air at the working face based on the gas concentration sequence set B p2 ; Calculate the average value C of the minimum gas concentration of 10 samples in the shift according to the ordered set of gas concentration sequence A1 m :

[0066]

[0067] In order to more clearly demonstrate the key characteristic information data processing method described in S1 of this embodiment, the following example is cited for illustration: For example, the key characteristic information data of the gas emission volume of a certain shift of the 13041 excavation working face of a certain mine is collected and organized as follows:

[0068] Gas concentration sequence set A = (0.12, 0.13, 0.23, 0.23, 0.16, 0.18, 0.32, 0.25, 0.17, 0.32, 0.16, 0.15, 0.3, 0.23, 0.13, 0.26, 0.4, 0.18, 0.28, 0.23, 0.37, 0.4, 0.44, 0.24, 0.2, 0.2, 0.16, 0.15, 0.13);

[0069] Return air gas concentration sequence set B = (0.12, 0.14, 0.25, 0.24, 0.16, 0.18, 0.32, 0.23, 0.19, 0.33, 0.19, 0.18, 0.33, 0.23, 0.18, 0.27, 0.44, 0.19, 0.29, 0.25, 0.37, 0.42, 0.43, 0.26, 0.26, 0.22, 0.17, 0.16, 0.14).

[0070] Sort the gas concentration sequence matrix A to obtain a new ordered set A1:

[0071] A1=(0.12, 0.13, 0.13, 0.13, 0.15, 0.15, 0.16, 0.16, 0.16, 0.17, 0.18, 0.18, 0.2,

[0072] 0.2, 0.23, 0.23, 0.23, 0.23, 0.24, 0.25, 0.26, 0.28, 0.3, 0.32, 0.32, 0.37, 0.4, 0.4, 0.44)

[0073] Calculate the average gas concentration data C of the working face using the gas concentration series set A p1 ; Calculate the average gas concentration C of the return air at the working face based on the gas concentration sequence set B p2 ; Calculate the average value C of the minimum gas concentration of 10 samples in the shift according to the ordered set of gas concentration sequence A1 m :

[0074]

[0075] In S2, the calculation method of the coal falling gas emission Q1 and the coal wall gas emission Q2 is:

[0076]

[0077] Where F is the air volume at the excavation face, in m 3 / min.

[0078] In order to more clearly demonstrate the calculation method of coal falling gas emission Q1 and coal wall gas emission Q2 described in Example S2 of this embodiment, the following examples are given for illustration: Using the key characteristic information data of gas emission of a certain shift of 13041 excavation working face obtained in the case S1 of this embodiment, the air volume F of 13041 excavation working face is 360m 3 / min, the coal falling gas emission Q1 and coal wall gas emission Q2 can be obtained:

[0079]

[0080] Q2=C p2 F / 100-Q1=0.25×360 / 100-0.288=0.612m 3 / min

[0081] In S4, the key characteristic information data are the average planned excavation speed of the shift at the advance time t, the estimated distance between the working face and the opening at the advance time t, and the coal seam gas content at the estimated location of the working face.

[0082] In S5, the method for establishing the working face gas emission prediction model with the advance prediction time t is as follows:

[0083] S51 collects key characteristic information data that affect the gas outburst volume of the working face at the advance prediction time t: the planned average excavation speed v of the shift at the advance time t, the estimated distance l from the working face to the opening at the advance time t, and the gas content w of the coal seam at the estimated location of the working face.

[0084] S52: The gas emission of the working face at the advanced prediction time t can be expressed as:

[0085]

[0086] Where: v0 is the average excavation speed of the current shift, in m / min; v is the average excavation speed planned for the overdue forecast time t, in m / min; l0 is the distance between the working face and the opening at the current time, in m; l is the distance between the working face and the opening at the overdue forecast time t, in m; w0 is the gas content of the coal seam at the location of the working face of the current shift, in m 3 / t; w is the coal seam gas content at the location of the working face at the advanced prediction time t, in m 3 / t. This formula reflects the influence of gas content, excavation speed and tunnel length on the gas emission of coal falling and coal wall respectively.

[0087] In order to more clearly show the method of establishing the gas emission prediction model of the working face with the advance prediction time t in S5 of this embodiment, an example is given for illustration: in the S1 case, the planned average excavation speed v of the 13041 excavation working face ahead of this shift t=1d is 0.0041m / min, the distance l between the working face and the opening is expected to be 120m after 1d, and the gas content w at the location of the working face is predicted to be 7.2m after 1d. 3 / t.

[0088] The gas outburst volume of the working face at the advance prediction time t = 1d is:

[0089]

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A real-time prediction method for gas emission in a tunneling working face based on monitoring data, characterized in that: The following steps are involved: S1: Through the mine safety monitoring, mining progress, intelligent ventilation and other systems, the key characteristic information data affecting the gas emission volume of the target excavation working face during the shift are collected and processed to obtain a set of characteristic index values ​​for predicting the current gas emission volume of the working face; S2: Calculate the coal falling gas emission and coal wall gas emission based on the key characteristic data of the gas emission during the shift; S3: Set the advance prediction time t of the gas outflow of the working face; S4: Collect key characteristic information data that affect the gas outflow volume of the working face at the advanced prediction time t; S5: Establish a prediction model for the gas emission of the working face with an advance prediction time of t; S6: Calculate the predicted value of gas emission with the advance prediction time t according to the gas emission prediction model; S7: As the working face advances, repeat S1 to S6 to continuously predict the gas outburst volume of the working face ahead of the predicted time t.

2. The method for real-time prediction of gas emission in a tunneling working face based on monitoring data according to claim 1 is characterized in that: In S1, the key characteristic information data that affect the gas outburst volume are the working face gas concentration T1, the return air gas concentration T2, the average excavation speed of the shift, the distance between the working face and the opening, and the gas content of the coal seam where the working face is located.

3. The method for real-time prediction of gas emission in a tunneling working face based on monitoring data according to claim 1 is characterized in that: In S1, the key feature information data is processed as follows: S11: Collect the working face gas concentration T1 of the working face sensor and the return air gas concentration T2 of the return air sensor and sort them out to form a gas concentration sequence set A = (c1, c2, ..., c i , …, c n ) and B=(b1,b2,…,b i , …, b n ), where c1, c2, c i, c n are the gas concentration values ​​of the 1st, 2nd, i and nth sample working faces respectively, and n is the number of gas concentration samples collected during the shift; b1, b2, b i ,b n are the return air gas concentration values ​​of the 1st, 2nd, i and nth samples respectively; S12: Sort the gas concentration sequence matrix A to obtain a new ordered set A1, denoted as A1 = (c'1, c'2, ..., c' i , …, c' n ), where c'1≤c'2≤…≤c' i ≤…≤c' n ; S13: Calculate the average gas concentration data C of the working face using the gas concentration sequence set A p1 ; Calculate the average gas concentration C of the return air at the working face based on the gas concentration sequence set B p2 ; Calculate the average value C of the minimum gas concentration of 10 samples in the shift according to the ordered set of gas concentration sequence A1 m :

4. The method for real-time prediction of gas emission in a tunneling working face based on monitoring data according to claim 1 is characterized in that: In S2, the calculation method of coal falling gas emission Q1 and coal wall gas emission Q2 is: Where F is the air volume at the excavation face, in m 3 / min.

5. The method for real-time prediction of gas emission in a tunneling working face based on monitoring data according to claim 1 is characterized in that: In S4, the key characteristic information data are the average planned excavation speed of the shift at the advance time t, the estimated distance between the working face and the opening at the advance time t, and the coal seam gas content at the estimated location of the working face.

6. The method for real-time prediction of gas emission in a tunneling working face based on monitoring data according to claim 1 is characterized in that: In S5, the method for establishing the working face gas emission prediction model with the advance prediction time t is as follows: S51: collecting key characteristic information data that affect the gas emission volume of the working face at the advanced prediction time t: the planned average excavation speed v of the shift at the advanced time t, the estimated distance l between the working face and the opening at the advanced time t, and the gas content w of the coal seam at the estimated location of the working face; S52: The gas outburst amount of the working face at the advanced prediction time t is expressed as: Where: v0 is the average excavation speed of the current shift, in m / min; v is the average excavation speed planned for the advance prediction time t, in m / min; l0 is the distance between the working face and the opening at the current time, in m; l is the distance between the working face and the opening at the advance prediction time t, in m; w0 is the gas content of the coal seam at the location of the working face of the current shift, in m 3 / t; w is the coal seam gas content at the location of the working face at the advanced prediction time t, in m 3 / t.

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