# Fire monitoring in coal mines using wireless underground sensor network and interval type-2 fuzzy logic controller

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## Abstract

From the view of underground coal mining safety system, it is extremely important to continuous monitoring of coal mines for the prompt detection of fires or related problems inspite of its uncertainty and imprecise characteristics. Therefore, evaluation and inferring the data perfectly to prevent fire related accidental risk in underground coal mining (UMC) system are very necessary. In the present article, we have proposed a novel type-2 fuzzy logic system (T2FLS) for the prediction of fire intensity and its risk assessment for risk reduction in an underground coal mine. Recently, for the observation of underground coal mines, wireless underground sensor network (WUSN) are being concerned frequently. To implement this technique IT2FLS, main functional components are sensor nodes which are installed in coal mines to accumulate different imprecise environmental data like, temperature, relative humidity, different gas concentrations etc. and these are sent to a base station which is connected to the ground observation system through network. In the present context, a WUSN based fire monitoring system is developed using fuzzy logic approach to enhance the consistency in decision making system to improve the risk chances of fire during coal mining. We have taken Mamdani IT2FLS as fuzzy model on coal mine monitoring data to consider real-time decision making (DM). It is predicted from the simulated results that the recommended system is highly acceptable and amenable in the case of fire hazard safety with compared to the wired and off-line monitoring system for UMC. Legitimacy of the suggested model is prepared using statistical analysis and multiple linear regression analysis.

## Keywords

Type-2 fuzzy logic Underground coal mining system Mine environment Fire and risk monitoring of mine Wireless sensor networks Fire Intensity## 1 Introduction

The productivity, quality and safety performance of an UMC systems are highly influenced by the environmental states (uncertain or fuzzy) of it. Therefore, it is necessary to observe the complexity and perilousness of its environment in a continuous manner to ensure the safety and risk level of coal miming. At present, this particular discussed technology (WSN) is being utilized widely in any workplace imprecise environment of an industry or like UMC system in various uncertain environments. Banerjee (1985) has developed a spontaneous combustion process driven by auto-oxidation of coal with various carbonaceous matter which is basically a complex physico-chemical process used to generate heat energy and some polluting gases like CO, \({\text{CO}}_{2}\), higher degrees hydrocarbons etc. Bhattacharjee et al. (2012) has explained the enactment of WSN-based model which can be simulated for making a fire monitoring as well as alarm (FMA) system for the application in Bord-and-Piller coal mine panel. The proposed WSN-based coal mine consisted of Bord-and-Pillar system is skilled of not only providing real-time monitoring and alarm in case of a fire, but also providing the exact fire location and spreading track by continuously gathering, analyzing, and storing real time information.

Cheng et al. (2015) has developed a Web-based, lightweight remote monitoring and control platform using a wireless sensor network (WSN) with the REST style to collect temperature, humidity and methane concentration data in a coal mine using sensor nodes. The authors have implemented three different scenarios for Web-based, lightweight remote monitoring and control of coal mine safety and measured and analyze the system performance.

Muduli et al. (2018) has developed a WSN for environmental monitoring for UCM and this investigation represented a literature part in an organized manner based on the contemporary investigations in the application of WUSN technique. Hence, UCM driven by WSN i.e. online monitoring (Rawat et al. 2014; Mishra et al. 2013) has become the very common practice. NawrockiIzabela and Kowalska (2016) have developed a type-1 fuzzy logic model for combined internal and industrial risk assessment in a coal mining. It is important to predict conditions leading to the fire hazard by proper interpretation of the imprecise data from UCM system. The Table 1 describes a comparative study in this literature of UCM.

- (1)
The IT2FLC approach is applied to compute the system output in the construction of robust WSN based fire monitoring system for mines.

- (2)
In T2FLC assists to trace four inputs (temperature, CO, \({\text{CO}}_2, {\text{O}}_2\)) and one output fire intensity in a pre-planned way to build the inference train for which the system output can be forecast.

- (3)
The responsible qualitative factors which are involved highly for the improvement in system output, can be easily comprised in the T2F prediction model to achieve better accuracy.

- (4)
Legitimacy of the suggested model is prepared using statistical analysis and multiple linear regression.

- (5)
An autonomous system via supervised fuzzy learning under dynamic electricity prices.

- (6)
A robust T2FLC model is developed to evaluate the instant updates of mine fire risk chances from the UCM imprecise data.

In the present investigation, an IT2FLC has been developed to create global and local decisions at the sink and sensor nodes rather than ground observation centre where temperature, and other various gases mentioned before (like CO, \({\text{CO}}_{2}\) etc.) have been taken as input variables in this T2FLS to investigate the status of any UCM firing system. Mamdani T2FIS is considered to simulate the proposed model with the help of MATLAB 7.0 fuzzy logic tool box. As the expectation of the objective of this present investigation, the ultimate results after simulation verified that the T2F based monitoring system reduces the delay in decision making above the off-line system for monitoring UCM in adverse environments.

## 2 Notations and abbreviations

- (1)
\(UMC=\) underground coal mining

- (2)
\(WSN=\) wireless sensor network

- (3)
\(FIS=\) fuzzy inference system.

- (4)
\(MFIS=\) Mamdani fuzzy inference system.

- (5)
\(R^{2}=\) coefficient of determination.

- (6)
\(RMSE=\) root mean square error.

- (7)
\(MAE=\) mean absolute error.

- (8)
\(MAPE=\) mean absolute percentage error.

- (9)
\(MF=\) membership function.

- (10)
\(UCM=\) Underground coal mine.

- (11)
\(WUSN=\) Wireless underground sensor network.

### 2.1 Type-2 fuzzy sets and IT2-FLSs

In this section, we will present briefly and a lucid manner discussion on internal structure of IT2-FLSs and T2FS. The structure of the IT2-FLS is similar to T1- FLS counterpart. However, a type-2 fuzzy set has grades of membership that are type-1 fuzzy, so it could be called a ?fuzzy fuzzy set? and thus IT2-FLS has the extra type-reduction process. Therefore, IT2-FLS can able to tackle uncertainty in a much better way, which make T2-FS ideal for productivity forecast in fire intensity prediction.

*x*and

*u*. If

*A*is FT2 discrete, then it is denoted by Eq. (3)

*x*and

*u*. If \(f_x(u)=1, \forall u\in [\underline{J}_{x}^{u}, \overline{J}_{x}^{u}]\subseteq [0,1]\), the type-2 membership function \(\mu _{\tilde{\tilde{A}}}(x,u)\) is expressed by one type-1 inferior membership function, \(\underline{J}_{x}^{u}=\mu _{A}(x)\) and one type-1 superior, \(\overline{J}_{x}^{u}=\mu _{A}(x)\), then it is called an interval type-2 fuzzy set denoted by Eqs. (4) and (5).

### **Definition 1**

*X*is comprised of a domain \(D_{X}\) of real numbers (also called the universe of discourse of

*X*) together with a membership function (MF) \(\mu _{x}: D_{X} \rightarrow [0, 1]\), i.e.

### **Definition 2**

*x*called the primary variable, has domain \(D_{\tilde{X}}: u \in [0, 1]\), called the secondary variable, has domain \(J_{x}\subseteq [0, 1]\) at each \(x\in D_{\tilde{X}}\); \(J_x\) also can be termed as the support of secondary MF and the amplitude will be \(\mu _{\tilde{X}} (x, u)\), called a secondary grade of \(\tilde{X}\), equals 1 for \(\forall x\in D_{\tilde{X}}\) and \(\forall u\in J_{x}\subseteq [0, 1]\).

Generally, T2FSs \(\mu _{X} (x, u)\) may be any number in [0, 1], and it generally varies as x and/or u vary.

### **Definition 3**

### **Definition 4**

*Y*(

*x*) is an interval fuzzy set determined by two final outputs related with the left and the right part of primary membership functions which are called as \(Y_{L}(x)\) and \(Y_{R}(x)\) respectively. They can be expressed by the following Eqs. (16) and (17) as:

*le*is the rule that exchange the UMF to LMF,

*ri*is the rule that exchange the LMF to UMF. \(\overline{\nu }(x)\) and \(\underline{\nu }(x)\) are calculated as following Eqs. (18) and (19)

*UMF*) in the primary rule \(l = 1\) to

*le*. After that, we have exchanged

*UMF*by

*LMF*, considering now \(l = le\) to

*M*. To determine this specific rule \(l = le \) and to calculate the final output \(Y_{L}(x)\). In this procedure, the first calculus of \(Y_{L}(x)\)is given by Eq. (21)

*le*is exchanged by

*ri*, and (23) is evaluated instead of Eq. (20). Finally, the defuzzification of \(IT2-FLS\) is given by

*Y*(

*x*) is the defuzzified value of IT2F variables.

## 3 Motivation and model formulation

### 3.1 Motivation

Thus, in order to establish a safety measure for mines, The WSNs can prove to be useful and reliable prediction in an imprecise data. There are two types of WUSN based coal mine monitoring system has been deployed (a) Long wall mine in Fig. 1, which is formed of UMC where a long wall of coal is mined in a single slice and (b) Bord and pillar mine in Fig. 2, where the mined material is extracted across a horizontal plane, creating horizontal arrays of rooms and pillars.

### 3.2 Model formulation

## 4 Solution procedure

In the first stage, we have collected source data and calculated input criteria for the proposed inference model. According to previously adopted assumptions, the basic criteria for assessing operational risk assessment in coal mining enterprisers are based on practical data, which may be obtained by analyzing the content of interim reports and financial statements by the mining companies.

## 5 Results and discussion

### 5.1 Statistical data analysis

*n*is the number of data patterns in the data set, \(x_{pred_i}\) defines the predicted value of one data point

*i*and \(x_{obs_i}\) is the observed value of one data point

*i*. In Table 2, a statistical data analysis of output has shown.

Statistical data analysis for T2 and T1FLC

FIS | RMSE | \( R^2 \) | MAPE | MAE |
---|---|---|---|---|

T2FLC | 0.104 | 0.991 | 4.703 | 0.072 |

T1FLC | 0.056 | 0.953 | 3.965 | 0.039 |

### 5.2 Multiple linear regression for error analysis

Statistical analysis of output data using ANFIS and MLR

Model | Approach | Statistics | |||
---|---|---|---|---|---|

RMSE | MAE | MAPE | \(R^2\) | ||

1 | MLR | 0.05313 | 0.0752 | 0.968 | 0.9714 |

ANFIS | 0.05387 | 0.0763 | 0.978 | 0.9811 |

## 6 Simulation results and discussion

Data collected from UCM (temperature, carbon mono-oxide and oxygen) are simulated using Mamdani IT2FIS. From the collected data, it can be witnessed on the increment in temperature, CO and \({\text{CO}}_2\) concentrations, while the concentration of oxygen decreases w.r.t. time. This occurs due to the spontaneity of the combustion process of coal. The Mamdani IT2FS receives these data and generates the fire intensity model on the basis of data characteristics as output which are shown in Figs. 12, 13, 14, 15. The steps involved for simulation in resulting the fire intensity of UCM from collected data using fuzzy logic are shown in Fig. 9. from which should be modified as the logical approach is of four steps. It converts the collected data into T2F input mfs. Then these inputs were evaluated using the Mamdani fuzzy rules given in Fig. 11. Then the T2F outputs were combined into a single output like fire intensity. At last, fire intensity is displayed as a crisp output using COG defuzzification method (Figs. 16, 17). Out of 35 sets of data collected, 10 sets of randomly selected data and the indices including *R*^{2}, *RMSE*, were used to evaluate the performance of model. The results show that for 10 sets of randomly selected data, the type-2 fuzzy logic model, with \(R^2 = 0.99\), \(RMSE = 2.10\), performs better than the multiple regression models and may be used for the estimation in the longwall panels. This paper provides a new way to support fire hazard assessment in a preliminary concept in type-2 fuzzy environment and this problem requires further research followed by verification in industry. The regression lines have been drawn for T1FIS in Fig. 18 and T2FIS in Fig. 19 respectively. From these result T2FIS has provided better performance which is expect by coal mine manager.

## 7 Conclusions and future research work

In this paper, we have developed interval type-2 fuzzy logic control approach for evaluation of prompt sensing capability of any fire risk situation and to handle the safety assessment in UCM through the implementation of WUSN system. The T2FLC helped by assisting to trace the inputs and outputs in a well-secured as well as comprehensive manner for developing the inferences train so that various types of underground environmental conditions could be predicted during coal mining from UCM system. The help of this particular T2FLC is taken for the implementation of WUSN because the difficulty and complexity both are higher to take decisions from ground monitoring system through wired connection including the high uncertainty and rough nature of underground. Prediction of various types of environmental conditions helps to optimize the sensing parameters during the underground monitoring system of UCM. The authors argue that the suggested T2FLC method is a completely new and unique approach for construction and controlling the WUSN of UCM during coal mining proposed in this paper needs to be developed, regarding coal mining from UCM that must be satisfied to get an effective output in the workplace. Moreover, in this paper, the financial effects of the discussed process are not taken into account while constructing an interval type-2 fuzzy rule base. The study is mainly focused on the development of WUSN system in UCM for the safety assessment to minimize the fire risk hazard in adverse condition. Therefore, quantitative factors responsible to get positive output can be easily included in the fuzzy prediction model for improving accuracy. The major contribution of this research work is proposing a systematic integrated approach for modelling process condition and their prediction so that accuracy of sensor and its performance efficiency during coal mining in UCM can be improved. For further research work, we will utilize and integrate other intelligent methods, such as interpretative structural modelling method, simulink, fuzzy artificial neural network, to evaluate the scale of efficiency of our current work.

## Notes

### Compliance with ethical standards

### Conflict of interest

The authors have no conflict of interest for the publication of this paper.

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