\r\n\tThis book aims to expose the recent advances in the research and development of chemical and biochemical processes to obtain bio-based chemical compounds and fuels from glycerol.
\r\n
\r\n\tChapters dealing with the synthesis and characterization of catalysts (single and mixed hydroxides and oxides, supported catalysts, zeolites, heteropolyacids, pillared-clays, and metal-organic frameworks) and biocatalysts (novel microbial and fungi cultures, immobilized cells, immobilized enzymes, and nanobiocatalysts) to carry out the conversion of glycerol, as well as their testing in discontinuous and continuous stirred reactors, fixed-bed, fluidized-bed, trickle-bed, bubble column, airlift and membrane (bio)reactors are welcome.
\r\n
\r\n\tThe book will comprise, but will not be limited to, the homogeneous and heterogeneous chemical reactions of glycerol such as dehydration, hydrogenolysis, partial oxidation, steam- and dry-reforming, glycerol to hydrocarbon fuels and aromatics, (trans)esterification, etherification, halogenation, ammoxidation, as well as supercritical, and photocatalytic processes.
\r\n
\r\n\tAdditionally, we hope to cover the bioprocessing of glycerol, including microbial and fungal fermentation and enzymatic reactions to obtain C2-C4 alcohols, diols, hydrogen, methane, organic acids, dihydroxyacetone, biopolymers, and others. \r\n\tThe book will also deal with the engineering aspects of glycerol processing, such as chemical equilibrium of glycerol reactions, reaction kinetics, (bio)reactor modeling, as well as process simulation and optimization of process variables and reactors.
",isbn:"978-1-83969-849-1",printIsbn:"978-1-83969-848-4",pdfIsbn:"978-1-83969-850-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"f4b04aa4b82f5a8f2de916212b20da55",bookSignature:"Ph.D. Israel Pala-Rosas, Dr. Jose Salmones and Dr. Jose Luis Contreras",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11898.jpg",keywords:"Value-Added Compounds, Commodities, Fuels, Homogeneous Catalysis, Heterogeneous Catalysis, Supercritical Processing, Microbial Fermentation, Enzymatic Reactions, Biocatalysis, Chemical Equilibrium, Reaction Kinetics, (Bio)reactor Modeling",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 29th 2022",dateEndSecondStepPublish:"May 27th 2022",dateEndThirdStepPublish:"July 26th 2022",dateEndFourthStepPublish:"October 14th 2022",dateEndFifthStepPublish:"December 13th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"13 hours",secondStepPassed:!1,areRegistrationsClosed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Dr. Pala-Rosas has experience in the areas of production and quality in the canned food and beverage industry, and also in the processing of triglycerides for the production of soap and biodiesel. He focuses his work on the synthesis, characterization, and testing of catalysts, as well as the design and analysis of chemical and biochemical reactors. He has authored and co-authored numerous journal papers, book chapters, and other publications.",coeditorOneBiosketch:"Dr. José Salmones is the author of 65 international indexed articles, co-author of a published book, author of two book chapters, and author of 27 registered patents, out of which 20 have been granted by the Instituto Mexicano del Petróleo and 2 by the Instituto Politécnico Nacional. He has participated in national and international forums with 217 papers. Since 1986, he is a level II member of the National System of Researchers in Mexico.",coeditorTwoBiosketch:"Dr. Contreras has more than 40 years of industrial and research experience in topics related to heterogeneous catalysis. He is the author of several patents and international indexed articles. 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1. Introduction
Increase of environmental pollution and damage, together with the increase in public concern about the surrounding natural environment, has so far influenced the purchase and consumption behaviors, and thus, has led to emergence of a new group of consumers called green consumers [1, 2]. Even though there is no universally accepted definition of the green consumer [3], the most adopted is that green consumers or environmental friendly consumers are those who consider the impact of manufacturing process and consumption of environmental resources while making purchase or participating in other market related activities and make their decisions accordingly [4].
Due to the increase in the number of green consumers, green marketers have started to target green segments of market to exploit the opportunities existing in these segments. Green marketing and promoting environmental friendly consumption behavior are necessary in two ways: first, acceleration in the trend of exploitation and destruction of natural resources essential for human life such as water, oil, and forests is a serious threat for human being. The main causes of this destructive trend include population growth and detrimental consumption habits. This trend highlights the importance of encouraging people to promote environmental friendly or the so-called green behaviors [5, 2].
Second, public concern about environmental issues is arising; thus, everyday more consumers are willing to purchase and consume products introduced as environmental friendly. In order to respond to this growing need, it is essential to develop and implement green marketing strategies [6].
Consumers are not equally green and the levels of their willingness to purchase green products are not equal [7]. Therefore, to market green products effectively, it is essential to implement targeted marketing strategies rather than mass marketing. The initial step in implementation of targeted marketing strategy is market segmentation and determination of the unique characteristics of each segment. Profitability and performance of market segmentation depend on the accuracy of consumer profiling in each segment because imprecise prediction of market segment members reduces efficiency of marketing strategies [8].
Similarly, in Iran, according to evidence, the public concern about environmental issues has increased among consumers, and green segments have emerged in markets. Researchers have come to the conclusion that attitudes toward environmental issues and environmental friendly behaviors and factors that encourage consumers to purchase green products are not the same in all cultures [9]. Hence, due to the unique cultural norms and values of Iranian citizens, it is necessary to conduct appropriate studies to determine profile and characteristics of Iranian green consumers. However, despite the increasing number of green consumers in Iran and thus the importance of segmenting them and determining their profiles, no significant study has so far been conducted in this area. Focusing on the need for research in this field, the aim of this study is segmenting and profiling green consumers using data mining approach. Other objectives of this study are to determine the role of each demographic, behavioral, and psychographic variable in the behavior of Iranian green consumers.
2. Customer segmentation variables
The first step in market segmentation is to determine variables by which the segmentation should be performed. Researchers have used various demographic, psychographic, and behavioral variables for market segmentation and profiling of green consumers. Utilization of variables that have the greatest impact on consumers’ green behavior and thus can best determine the segments of green markets will lead to favorable results in segmentation [6]. The variables that have been used in previous studies to determine green segments of market and have been proven to affect environmental friendly behavior of consumers will be discussed next.
2.1 Demographic variables
Diamantopoulos et al. reviewed the results of previous researches about the impact of various demographic variables on knowledge, attitude, and behavior of green consumers. In previous researches, as their study results show, age, gender, education, and income are emphasized among demographic variables, and their effectiveness is confirmed [7].
Do Paco et al. segmented green consumers based on their environmental attitude and knowledge and then studied demographic characteristics of each segment. These researchers segmented green market into three segments named: the uncommitted, the greens activists, and the undefined. Their results show that the green activists are highly educated consumers with high income within the age groups 25–34 and 45–54, who have positive attitudes toward environmental friendly activities and practice green behaviors more than other consumers [6].
Abeliotis et al. in their research among Greek people came to the conclusion that women tend to have an environmental friendly behaviors more than men, also that income is negatively associated with the tendency of people toward green behaviors. The results of their study indicated no significant relationship between education level and green behavior of consumers [10].
A summary of the results of other studies conducted on the effects of demographic variables on behavior and attitude of green consumers is given in Table 1.
Role of demographic variables in green behaviors of consumers.
2.2 Psychographic variables
Psychographic variables are one of the most beneficial and widely used variables in segmentation of consumers, especially green consumers [6]. Based on previous studies, the psychological variables associated with the values and beliefs of consumers, which have the greatest impact on consumers’ attitudes and behaviors, have been identified. These variables will be discussed below:
Personal values: one of the most common theories in predicting green behavior of consumers is the value-belief-norm theory [5, 20, 21, 22, 23, 24]. Based on this theory, as Stern [4] describes, personal values including altruistic, biospheric, and egoistic are the most important variables influencing people’s environmental friendly behaviors. People with higher levels of altruism base their behavior on the perceived costs and benefits for other people in society. Biospheric people consider the effects of their actions on environment and living organisms and decide accordingly. Those, whose egoistic value is stronger, review the costs and benefits of environmental friendly behaviors only for themselves. Hence, if the earned benefits of such activities are more that their cost, they will act environmental friendly; otherwise, they will have negative attitude toward these activities. Researchers have found that the levels of consumers’ altruistic and biospheric values positively correlate with green behavior, but selfishness correlates negatively [5, 21].
Religiosity: another psychographic variable which, despite its less emphasis in previous researches, is expected to have a positive impact on the behavior of Iranian consumers is religiosity or religious values. Rice in a research on Egyptian Muslim consumers proved that the level of religiosity of consumers directly and significantly correlates with their intention to pro-environmental behavior [25]. Also, Biel and Nilsson concluded that people who believe nature is holy are more willing to behave pro-environmentally and green [26].
2.3 Behavioral variables
Segmenting green consumers by behavioral variables is based on their environmental knowledge and attitude as well as their tendency to demonstrate environmental friendly behaviors [6]. The most effective behavioral variables, which their effect on consumers’ environmental friendly behavior has been confirmed in previous studies, will be discussed below:
Environmental attitudes: social psychology researchers have addressed attitude as the most important variable in predicting behavior and behavioral intentions [27]. In respect with marketing and green consumers, numerous studies have been conducted on the impact of environmental attitudes on environmental friendly behaviors [28, 29]. For example, Mostafa concluded that consumers’ attitudes toward purchasing green products and also advertisements by manufacturers of green products have a positive effect on consumers’ purchase intention [8].
Environmental knowledge: environmental knowledge can be defined as a general knowledge of facts, concepts, and issues related to the environment [30]. Researchers have identified two different types of environmental knowledge: abstract knowledge about environmental problems, causes and solutions to these problems and concrete knowledge about the behaviors that can be done in those situations. Comparing people who actively participate in pro-environmental activities with those who show less intention in this regard, researchers concluded that the difference in level of their environmental knowledge is the main reason of the different behaviors among these two groups of people [1, 14, 18, 31].
Personal habits: another behavioral variable that is proven to play a role in consumers’ green behavior is personal habits [5, 21, 24, 29, 32, 33]. Typically, behavior change includes giving up an old habit and replacing it with a new one. Habits affect person’s willingness and intention to change their behavior and to convert attitudinal factors into actual behavior [5]. Previous researchers have concluded that personal habits affect people’s tendency to perform green behaviors such as recycling, reducing energy consumption and using green energy sources. In order to give up a habit and act oppositely, Thogersen and Moller believe it is necessary that the desired behavior is repeated and rewarded frequently [34]. Hence, it is expected that consumers’ habits related to environmental unfriendly behaviors will affect negatively on their willingness to perform green activities.
3. Methodology
This study is an applied research in terms of objectives and a survey-analytic research in terms of methodology. Also, since this study examines the data associated with a specific period of time, it is a cross-sectional study.
3.1 Sample and sampling method
The target population of this research is consumers in Yazd province. A convenience sampling method was used to select the respondents. So that, the interviewers randomly selected passers-by, asked them to take part in the study and to complete the standardized, self-administered questionnaire. A total of 300 initial responses have been received. After eliminating the confounding questionnaires, the number of final sample analyzed was 252, resulting in a response rate of 84%. Among the 252 respondents, 60% were male and 40% female. About 92% of the respondents have a bachelor’s degree or less and 76% aged from 18 to 35 years. Also, most respondents (43%) had a medium income of between 5,000,000 and 8,000,000 Rials.
3.2 Survey instrument
A questionnaire was used to collect the required data. All measures have been adopted from previous studies, and also, they were assessed by three marketing professors so that respondents would understand the questions correctly. Cronbach’s alpha was used to determine the reliability of the questionnaire. Table 2 shows the Cronbach’s alpha values and researches that the measures have taken from.
Reliability and sources of questionnaire measures.
3.3 A review on self-organizing maps
Self-organizing map (SOM) is a method based on neural networks, which provides a powerful and fascinating tool to display multidimensional data in spaces with lower dimensions (usually one or two). Research experiments show that utilizing new methods such as neural networks and self-organizing maps for segmenting consumers and predicting their behavior lead to better and more accurate results compared with conventional statistical methods [8, 37, 38].
Self-organizing maps are a kind of neural networks with unsupervised learning capability that are suitable for analyzing complex spaces. This model of neural networks was first introduced by Kohonen in 1981. SOM is effective at clustering and visualizing essential features of complex data and has a unique structure that allows multivariate data to be projected nonlinearly onto a rectangular grid layout with a rectangular or hexagonal lattice.
The structure of self-organizing maps is composed of two distinct layers: an input layer and an output layer which is called map layer as shown in Figure 1. The map layer is usually designed as a two-dimensional arrangement of neurons that maps n-dimensional input to two dimensions, preserving topological order. Each neuron in the map layer corresponds to an information node with dimensions equal to the dimensions of the analyzed space [39]. The two-dimensional map network consists of one layer of strongly interconnected neurons. Each neuron is connected to the n-dimensional input via a set of n weights.
Figure 1.
Basic structure of self-organizing map (SOM) (adapted from [40]).
After training the self-organizing network, a number of weight nodes are obtained, each of which represents a portion of the analyzed space. Also, the number of obtained weight nodes is the same as the number of neurons. Hence, if appropriate number of neurons is selected and the network is trained properly, the weighted display corresponding to neurons of each network can well represent the analyzed space. In the output of self-organizing maps, corresponding to the value of each attribute in the weight node, an RGB vector and thus a color will be considered in a way that all values can be visualized using color spectrum ranging from dark blue (the lowest value) to dark red (the highest value).
The SOM algorithm repeatedly repositions records in the map until a classification error function is minimized. Records that have similar characteristics are adjacent in the map, and dissimilar records are situated at a distance determined by degree of dissimilarity. In particular, SOM consists of a layer of input vectors and a two-dimensional grid of output nodes. Each output node is connected to all the input vectors through the link of weights. When an input vector is presented, the closest match (most similar) of the output node is identified as the winning node. The input vector is thus mapped to the location of the winning node. The weights of the winning node and its neighborhoods are then updated closer to the original input vector. This process repeats until weights are stabilized, and all input vectors are mapped onto the output array. In this way, input vectors with similar data patterns are located into adjacent region, while dissimilar vectors are situated at a distance in the output map. Therefore, it will be possible to identify clusters on the map visually. Various software packages are available to analyze data using self-organizing maps. In this study, Viscovery SOMine version 5.0 was used.
4. Data analysis and results
4.1 Network training and evaluation
Several experiments have been carried out with different combinations of parameters and selected the better neural architecture based on the following criteria: average quantization error, the meaningfulness of clusters, the visual interpretability, and the SOM-Ward cluster indicator. The resulting best segmentation is obtained through 551 neurons in the output layer (19 × 29 matrix). SOMine software, automatically selects the best dimensions for the map network based on the selected number of neurons in the output layer. After testing various dimensions and training the network, the software chose network dimensions equal to 19 × 29. Training schedule is set in a way that the software can autonomously provide maximum accuracy for the training of the network. Also, the tension level of the network training is set to 0.5. Training data of network constitute 252 (sample size) nine-dimensional vectors (psychographic and behavioral variables). Data associated with demographic factors was not used in training. After training the network and final segmentation, demographic characteristics of green consumers in each segment will be investigated.
Quantization error is used as a measure to evaluate the accuracy and validity the self-organizing maps. Quantization error, which is a value between 0 and 1, shows the level at which output maps are able to visualize input data on a two-dimensional space, where quantization error value close to (0) shows more accuracy of the network [41]. The final value of quantization error for the network used in this research was equal to 0.1898.
4.2 Analyzing output maps and final segmentation
Most researchers use U-Matrix which is one of the outputs of self-organizing maps for final clustering and determining the boundaries of each segment or cluster. Since, this method does not define exact and clear boundaries for each segment [8, 36], therefore in this study, a hierarchical cluster analysis method called SOM-Ward clusters is used to determine the boundaries of each segment and also to determine the optimal number of clusters. Final segmentation of green consumers in four clusters or segments is shown in Figure 2. This two-dimensional hexagonal grid shows clear division of the input pattern into four clusters. The clusters divide the input data into disjoint areas containing similar vectors. Since the order on the grid reflects the neighborhood within the data, features of the data distribution can be read off from the emerging landscape on the grid. The application of the SOM algorithm brought together samples by resemblance. The more similar the samples are, the closer they are positioned in the output space.
Figure 2.
Final segmentation map.
After defining boundaries of segments, the characteristics of consumers in each segment should be investigated. Table 3 shows the average of psychographic and behavioral variables in each of the segment.
Green behavior
Env. know.
Attitude
Intent
Habit
Egoistic
Altruistic
Biospheric
Religiosity
Seg. 1
4.25
3.92
4.49
4.26
1.66
3.01
4.39
4.77
4.21
Seg. 2
3.54
3.44
4.02
3.35
1.96
3.52
3.84
4.52
3.50
Seg. 3
3.29
3.25
4.25
3.48
3.10
2.98
4.37
4.88
3.73
Seg. 4
2.66
2.60
3.54
2.74
3.75
3.34
3.56
4.20
3.21
Table 3.
Average of psychographic and behavioral variables in each segment.
Another output of self-organizing maps is feature maps which show vector distribution of each of the segmentation variables in the whole analysis space. At the bottom of each of these maps, a color spectrum, ranging from blue to red, is shown that indicates various values of the variables. Via these maps, the situation of variable in each of the market segments can be studied, and also, the correlation between the different variables can be examined by visually comparing the pattern of shaded pixels for each map [36]. The feature maps reported for each segmentation variable are shown in Figure 3. On these maps, the nodes which share similar information are organized in close color proximity to each other. Figure 3 shows the feature maps for every cluster and for all input attributes.
Figure 3.
Feature maps for psychographic and behavioral variables.
As shown in Figure 3, the analysis space has nine dimensions. By comparing these maps, the following results can be obtained:
Variables of attitudes, knowledge, intent to pro-environmental behavior, and green behavior are positively correlated with each other, because wherever one variable is red (high value), the other variables have almost the same value. Level of correlation between the variables can be observed from the intensity of the color similarity.
Among psychographic variables, biospheric, altruistic, and religion values are positively correlated to each other and also to behavior variables including attitude, intent, knowledge, and green behavior, while egoistic is negatively correlated to other variables.
The variable habit is negatively correlated to other behavioral variables, especially green behaviors, and also to variables such as biospheric and altruistic.
Besides the feature maps reported for psychographic and behavioral variables, the status of demographic variables in each segment can also be studied through feature maps provided by the software. Figure 4 shows these feature maps.
Figure 4.
Feature maps for demographic variables.
Figure 4 shows that consumers with different gender, age, education, and income are scattered in all sectors. At first glance, it appears as no specific relationship exists between demographic variables and behavior of green consumers. However, according to Figure 5, which shows the importance of each demographic variable corresponding to other variables in that segment and also relative to importance of that variable in other segments, different results can be deduced.
Figure 5.
Importance of demographic variables in each market segment.
The following results can be obtained from Figure 5:
Consumers within the age group 18–24 are mostly scattered in segments 2 and 3 of the market, and most of the consumers in the segment 1 are within the age group 35–49. Consumers at other ages are scattered in different market segments.
In the segment 1 of the market, aggregation of women is more than men, while in segments 2 and 3, aggregation of men is more than women.
Most of the consumers with no graduate education are in the segment 1, and most of the consumers in segment 4 have postgraduate education.
Most of the consumers in segment 1 of the market have incomes less than 5,000,000 Rials, and high-income consumers are mostly in segments 4 and 5.
4.3 Naming and describing market segments
Once market segments are identified, they should be named and described based on their consumer profiles. Marketers and producers of environmental friendly products can use this information to identify their target market and then to utilize suitable marketing strategies and marketing mix based on characteristics of that segment. To provide a better description for each segment, SOMine draws a diagram for psychographic and behavioral variables in each segment like that drawn for demographics. The diagram is shown in Figure 6.
Figure 6.
Importance of psychographic and behavioral variables in each market segment.
According to Figures 5 and 6 and Table 3, which show the importance of psychographic, behavioral, and demographic variables in each market segment, market segments can be named as follows:
Intense greens (segment 1): this segment that includes 29.37% of total consumers can be assumed as the greenest segment of the market. In terms of demographic characteristics, most consumers in this segment are within the age group 35–49, and aggregation of women is more than men in this segment. In terms of education, most of the extreme greens are nongraduates. In terms of psychographic characteristics, extreme greens are biospheric, altruistic, and religiosity consumers with low level of egoistic value. In terms of behavioral characteristics, these consumers have high level of knowledge about environmental issues and positive attitude toward green behaviors. Also, their willingness to purchase green products and behave in an environmental friendly manner is higher than the rest of the consumers. Nongreen habits are very rare in this segment.
Egoistic browns (segment 2): this segment of the market includes 26.19% of total consumer. In terms of demographic characteristics, people of this segment are within the age group 18–24, and most of them are with an income level of above 10 million Rials. Consumers in this segment of the market mostly have undergraduate education. In terms of the psychographic characteristics, the level of altruistic, biospheric, and religiosity values is very low among this group of consumers, which is one of the main reasons for their negative attitudes toward green behaviors. In terms of behavioral characteristics, although these consumers claim that they have some knowledge about environmental issues and environmental friendly products, but their egoistic is the main reason for their low tendency toward green behavior.
Potential greens (segment 3): this segment includes 28.57% of total consumers. In terms of demographic characteristics, most of the consumers in this segment are within the age group 25–34, with an income above 10 million Rials. Most of the potential greens are men. No specific educational pattern can be found in this segment. In terms of behavioral characteristics, although these consumers have positive attitudes toward environmental friendly behaviors, frequency of green behaviors in their daily life is very low, and they show environmental unfriendly habits. In terms of the psychographic characteristics, due to the high level of altruistic and biospheric values, these consumers have the potential to become true greens.
Intense browns (segment 4): this segment of the market that is the smallest and the nongreen part of the market, includes 15.87% of total consumers. Most of the intense browns are within the age group 18–24 with graduate-level education, and their income is between 5 and 10 million Rials. In terms of behavioral characteristics, these consumers’ environmental knowledge and attitude and thus their tendency to green behavior and purchase of green products are very low. In contrast, they show nongreen behaviors. In terms of psychographic characteristics, altruistic, biospheric, and religious values in this group are very low, and they are egoistic consumers.
5. Conclusions and practical implications
In this research, Iranian green consumers where segmented based on their psychographic and behavioral characteristics with the use of a self-organizing map algorithms. Then the demographic characteristics of consumers in each segment were investigated. In the following sections, first the effects of each segmentation and demographic variables on behavior of green consumer are discussed. Then based on the results, practical suggestions on targeting each market segment are presented.
5.1 Role of demographic variables in pro-environmental behaviors
Gender: the results show that in the segment 1 which is occupied by extreme greens, most of the consumers are women, and in other segments of the market where tendency to green behaviors and frequency of environmental friendly activities are low, population of men is greater than women. Previous researchers including Memery et al. [12] and Abeliotis et al. [10] also concluded that women are generally greener than men.
Age: since most egoistic browns and intense browns are within the age group 18–24 years and also most intense greens are at older ages, 25–49, it can be concluded that age correlates positively with the greenness (environmental friendliness) of the consumers. Dsouza et al. [16] and Abeliotis et al. [10] also obtained a similar result.
Education: although no definite conclusion about the education can be deduced, it seems that level of education is negatively correlated with the greenness of consumers, because the abundance of uneducated people is the highest in the greenest segment of the market. Also, most of the intense browns have high education levels. Do Paco et al. [1, 6] also found similar results in their studies.
Income: based on the results of this study, it can be concluded that the income level has negative effect on greenness of consumers. The number of consumers with high income level in the brown segments of the market is higher than the other segments, which shows that consumers with higher income have lower intent to environmental friendly activities. Previous researchers, Banyte et al. [17] and Abeliotis et al. [10] also found the same result.
5.2 Effects of behavioral and psychographic variables on pro-environmental behaviors
Personal values: the importance of personal values in each of the market segments shows that biospheric and altruistic have positive effect and egoistic has negative effect on the level of greenness of consumers. The majority of consumers in the green segments of the market, such as intense greens and potential greens, are altruistic and biospheric consumers with low level of egoistic value. Previous researchers including Johnson et al. [9] and Cordano et al. [21] also obtained similar results.
Religiosity: as expected, due to the Islam’s advice about holiness of nature and importance of green behaviors such as avoiding damage to environment, dissipation, and protection of organisms, religiosity has positive effect on greenness of Iranian consumers. Rice [25] also achieved similar results in a study on the Egyptians.
Green knowledge and attitude: according to the results of this study, consumers’ knowledge about environmental issues and green products and also their attitude toward green purchase and behavior have positive effects on consumers’ greenness. Most of the previous researches achieved similar findings.
5.3 Practical recommendations on targeting each market segment
Intense green targeting: this segment includes the best group of consumers for the target market. Since most of the extreme greens are within the age group 35–49 and have low education levels, marketers can use marketing strategies appropriate to these consumers. It is noteworthy that extreme greens are not individuals with high income; thus, determining appropriate and reasonable prices to encourage these consumers to purchase green products is necessary. Also, because biospheric, altruistic, and religiosity are very high in this market segment, therefore marketers can emphasize on these values in their advertisements in order to encourage these consumers to purchase green products.
Egoistic brown targeting: at the first glance, egoistic browns do not seem to be suitable market segment for green products. However, if marketers can make these consumers aware of the dangers of environmental problems for themselves and also of the long-term benefits of environmental friendly products, they may be able to encourage this group of consumers to purchase green products and practice environmental friendly behaviors.
Potential green targeting: due to the strong values of altruistic and biospheric, potential greens can be a suitable target market for green products. The main cause for their low tendency to green behaviors is their low environmental knowledge. Consequently, marketers that target this group of consumers can encourage them to purchase green products by increasing their knowledge about the functions and benefits of green products and about threats of environmental issues.
Intense browns: this segment of the market that consist of consumers with higher level of income and from lower age groups relative to other segments is not a suitable target for marketing environmental friendly products.
\n',keywords:"green consumers, market segmentation, profiling, data mining, self-organizing maps",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65247.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65247.xml",downloadPdfUrl:"/chapter/pdf-download/65247",previewPdfUrl:"/chapter/pdf-preview/65247",totalDownloads:1056,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:1,totalAltmetricsMentions:0,impactScore:1,impactScorePercentile:65,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"September 19th 2018",dateReviewed:"December 7th 2018",datePrePublished:"April 30th 2019",datePublished:"March 4th 2020",dateFinished:"January 18th 2019",readingETA:"0",abstract:"Concern about the environment has led to a new segment of consumers called green consumers. Because not all the consumers are equally green, using target marketing for persuading them to buy green product is essential. The first step in target marketing strategy is to segment the market and then develop profiles of the resulting market segments. This study aims to identify distinct green market segments based on demographic, psychographic, and behavioral variables and also investigate the relationship between each variable and green consumer behavior. This study uses self-organizing maps (SOM) to segment and then develop profiles of Iranian green consumers. Based on the results, four market segments have been identified and were named intense greens, potential greens, egoist browns, and intense browns based on profiles of consumers in each segment. The results of this study also indicate that the level of education and income together with egoistic value and environmental unfriendly habits correlate negatively with the greenness (intent and intense of green behaviors) of Iranian consumers and the age of consumers together with environmental attitude and knowledge, biospheric and altruistic values, and religiosity correlate positively.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65247",risUrl:"/chapter/ris/65247",book:{id:"7830",slug:"consumer-behavior-and-marketing"},signatures:"Alireza Ziaei-Bideh and Mahsa Namakshenas-Jahromi",authors:[{id:"276052",title:"Ph.D.",name:"Alireza",middleName:null,surname:"Ziaei-Bideh",fullName:"Alireza Ziaei-Bideh",slug:"alireza-ziaei-bideh",email:"ess.ziaei@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"277331",title:"Dr.",name:"Mahsa",middleName:null,surname:"Namakshenas-Jahromi",fullName:"Mahsa Namakshenas-Jahromi",slug:"mahsa-namakshenas-jahromi",email:"ma.namakshenas@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Customer segmentation variables",level:"1"},{id:"sec_2_2",title:"2.1 Demographic variables",level:"2"},{id:"sec_3_2",title:"2.2 Psychographic variables",level:"2"},{id:"sec_4_2",title:"2.3 Behavioral variables",level:"2"},{id:"sec_6",title:"3. Methodology",level:"1"},{id:"sec_6_2",title:"3.1 Sample and sampling method",level:"2"},{id:"sec_7_2",title:"3.2 Survey instrument",level:"2"},{id:"sec_8_2",title:"3.3 A review on self-organizing maps",level:"2"},{id:"sec_10",title:"4. Data analysis and results",level:"1"},{id:"sec_10_2",title:"4.1 Network training and evaluation",level:"2"},{id:"sec_11_2",title:"4.2 Analyzing output maps and final segmentation",level:"2"},{id:"sec_12_2",title:"4.3 Naming and describing market segments",level:"2"},{id:"sec_14",title:"5. Conclusions and practical implications",level:"1"},{id:"sec_14_2",title:"5.1 Role of demographic variables in pro-environmental behaviors",level:"2"},{id:"sec_15_2",title:"5.2 Effects of behavioral and psychographic variables on pro-environmental behaviors",level:"2"},{id:"sec_16_2",title:"5.3 Practical recommendations on targeting each market segment",level:"2"}],chapterReferences:[{id:"B1",body:'Do Paco AMF, Raposo MLB. Green consumer market segmentation: Empirical findings from Portugal. International Journal of Consumer Studies. 2010;34:429-436'},{id:"B2",body:'Noonan KE, Coleman LJ. Marketing to green communities: How to successfully reach the green consumer. Journal of Marketing Analytics. 2013;1:18-31'},{id:"B3",body:'Connolly J, Shaw D. Identifying fair trade in consumption choice. Journal of Strategic Marketing. 2006;14:353-368'},{id:"B4",body:'Stern PC. Toward a coherent theory of environmentally significant behavior. 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Department of Business Management, Yazd University, Yazd, Iran
Department of Business Management, Yazd University, Yazd, Iran
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\n
1. Introduction
\n
Fires are one of the most serious accidents that can occur in underground mines due to the restricted ability to evacuate quickly from the confined excavations that can be filled quickly with smoke and noxious fumes [1]. The behavior of underground mine fires is difficult to predict due to their dependence on multiple factors that are closely related to the amount of flammable material, ignition location, ventilation system arrangement, time of occurrence, etc. [2]. These uncertainties associated with mine fire scenarios can have unexpected impacts on the evacuation process, firefighting, and rescue strategies and also further complicate the process of design and implementation of fire protection systems.
\n
Developing effective evacuation plans in case of fire in underground mine is the most important and sometimes the only option for safe evacuation of all involved in the fire scenario. The wide range of possibilities in the process of improving the evacuation plans in case of fire has motivated many researchers to make new or to modify the existing methodologies or procedures for developing effective and optimal evacuation plans.
\n
Ji et al. [3] developed a visual model to simulate the evacuation process of miners to determine the evacuation time, exit flow rate, and evacuation path and show that simulation is effective technology to establish safe evacuation system. Chen et al. [4] developed 3D CFD model to reconstruct the laneway conveyor belt fire scenes under two ventilating conditions to investigate the influence of smoke movement on miner evacuation behaviors. Wang et al. [5] through example demonstrated the use of their proposed framework for human error risk analysis of coal mine emergency evacuation and also the method to evaluate the reliability of human safety barriers. Wu et al. [6] conducted emergency evacuation simulation and visualized analysis of underground mine water bursting disaster scene, to achieve the simulation of the dynamic process of individual or group behavior and to provide platform for rational evacuation under the situation of mine disaster. Adjiski et al. [7, 8, 9] completed many different manuscripts and projects in the field of simulation and modeling of fire scenarios and evacuation plans in underground mines.
\n
To the authors’ best knowledge and the extensive search of literature, a lack of methodologies and systems that focus on developing evacuation plans in case of fire in underground mines is shown. Due to the large number of factors from which the effective evacuation process depends, this field of research requires continuous upgrading to address all challenges and also to provide optimal evacuation routes that sometimes represent the only option for preventing loss of human lives.
\n
This chapter is an extension and upgrade of the previously published works from the same author and hopefully will contribute to the process that will improve the methodologies and systems for optimal fire evacuations in underground mines.
\n
\n
\n
2. Methodology for developing underground mine fire scenarios
\n
In underground mines, a fire can occur wherever flammable material is found, but predicting it at all possible locations is practically impossible. So by analyzing this list of fire locations that have potential flammable materials, it is down to those places that have the highest risk of fire occurrence [10]. The process of conducting fire risk assessment is very straightforward and does not need to be considered in any further detail in this research.
\n
What is new in this study is the proposal of methodology for quickly and efficiently locating and generating fire scenarios ready for simulation on the basis of which optimal evacuation plans will be developed.
\n
To identify possible locations for fire scenarios in underground mines, different approaches can be used, such as [2, 9]:
Fire risk assessment
Historical records of fire incidents in the mine
Analysis of production plans
Analysis of work processes and mechanization, etc.
\n\n
The dynamics of mining activities to increase and fulfill production capacity generates a constant shift in production sites generally associated with mechanization that is likely to trigger a fire scenario. Due to this fact as a relevant indicator that realistically reflects and constantly updates, the list of possible fire locations would be a detailed analysis of daily or monthly production plans. This step involves a thorough analysis of the daily/monthly production plans that will detect any flammable materials mostly associated with the mechanization needed to achieve the required production capacity.
\n
A case study of the “SASA”-R.N. Macedonia mine was used in order to conduct the necessary steps presented on Figure 1.
\n
Figure 1.
Methodology for developing and locating fire scenarios in underground mines.
\n
The steps shown in Figure 1 are based on a simple analysis of the production plans that can detect all workplaces with the appropriate work cycle together with the related mechanization which is often associated with fire scenarios.
\n
To demonstrate the presented methodology, a 3D model of the underground ventilation network of the mine “SASA”-R.N. Macedonia is prepared on which all the necessary analysis and simulations will be performed (Figure 2). On the ventilation map, the possible fire locations along with the group of mineworkers identified using the proposed methodology on Figure 1 and also the possible exits from the underground mine are also marked.
\n
Figure 2.
Ventilation map of the “SASA” mine with marked possible fire locations, group of mineworkers, and exits.
\n
The process of modeling fire scenarios is closely related to the degree of uncertainty when it comes to the input data, which largely depends on the size of the fire itself [11, 12]. Examples of such input parameters that affect the fire models in underground mines are fire load, fire location, burn rate of materials, heat release rate, ventilation parameters, etc. Due to the stochastic nature of the input parameters related to the fire models, the appropriate results should be treated with caution.
\n
From the large list of stochastic input parameters, the authors decided to elaborate only on the process of obtaining fire load inputs, which largely depends on the severity of the fire scenario itself. The process of modeling fire load inputs that are closely related to the inability to accurately determine the type and quantity of flammable material covered by a fire scenario is done using the Monte Carlo simulation technique. The reason for selecting and analyzing the fire load parameter is because of its immense contribution in generating the amount of toxic gases from which the complexity of the evacuation process depends. The reason for choosing the Monte Carlo simulation technique is because of its speed and simplicity of implementation and also the ability to generate a large amount of input data sampled randomly from their respective distributions [13, 14, 15].
\n
The process of developing this model that incorporates the Monte Carlo simulation technique associated with the normal distribution defined by mean = 50, and standard deviation = 15, has been previously explained by the same author, and the entire methodology and reasons for selecting the highlighted parameters can be found here [16].
\n
What is new in this research is the development of a database that includes all fire scenarios in a predetermined location using the abovementioned methodology on Figure 1.
\n
All fire scenarios are analyzed in terms of impact from the fire load input parameters on the evacuation process, that is, how different distribution of combustible materials from the same mechanization (or other composition of combustible materials) will impact the evacuation process.
\n
The introduction of this database aims to select fire scenarios of the same type but with different fire load distribution, from which we can analyze the effects on the evacuation process. The results of this analysis can be used to improve the design of fire systems and evacuation plans and to test them for their effectiveness in different conditions.
\n
From the simple analysis of the monthly production plan of “SASA” mine, we have extracted all work sites for ore exploitation and development of mining facilities with the appropriate work cycle together with the related mechanization. To present the methodology, we will only analyze fire scenarios generated by only one mechanization and present the optimal evacuation route for only one group of workers.
\n
For the purposes of this analysis, we will present the results of the fire scenarios generated by the mechanization Scooptram ST7, located at the possible fire location 3, from where we will simulate the fire scenarios and calculate the optimal evacuation route for group 1 (Figure 2).
\n
The inputs in the next steps of the proposed methodology are the approximate values of the total fire load for the selected mechanization. To simplify the process of determining this data, we used the technical manual of the Scooptram ST7, from which we approximated the quantities for the tire, hydraulic fluid, and diesel fuel which will be threatened as total fire load (Table 1). Regardless of the fact that the amount of diesel fuel is stochastic in nature, and is dependent on a number of factors, to simplify the model, we will consider it a known value, and we will treat it in a further expansion of the research.
\n
\n
\n
\n
\n
\n\n
\n
\n
Tire [kg]
\n
Diesel fuel [L]
\n
Hydraulic fluid [L]
\n
\n\n\n
\n
Scooptram ST7
\n
238 * 4 (tires) = 952
\n
190
\n
111
\n
\n\n
Table 1.
Approximate fire load calculation for the fire scenario from Scooptram ST7.
\n
Following the analysis of the approximate amount of fire load, the next step is to model them using the previously mentioned Monte Carlo simulation technique, along with the necessary data for its normal distribution defined by mean and standard deviation [16].
\n
For the purpose of this study using the Monte Carlo simulation model, we have generated 20 scenarios with different fire load distribution, which will give variations in the results from the fire scenarios, and we will analyze their impact on the evacuation process (Figure 3).
\n
Figure 3.
Generated scenarios along with the corresponding fire load distribution obtained from the Monte Carlo simulation model.
\n
\n
\n
3. Modeling and simulation of fire scenarios in underground mines
\n
The purpose of fire models is to describe fire characteristics, such as heat release rate, the burning rate of material, smoke, generating toxic gases, etc., and the results of simulating these models will be as good as the inputs [9, 17]. In order to create a relevant fire model in underground mines, it must be based on an accurate ventilation model. This interconnection and accuracy of the fire and ventilation models will depend on the movement of smoke and toxic gases through the mine facilities from which the evacuation process is based.
\n
Various case studies previously published from the same author are based on the modeling of fire scenarios in a number of different mine ventilation layouts [7, 8, 9].
\n
For this study, i.e., for simulating fire models across the 3D ventilation network, we used the VentSim software along with VentFIRE™ module that are interconnected because they belong to the same software package. With the help of VentSim software a 3D ventilation network with all working parameters is developed, while the VentFIRE™ module is used for simulation and calculation of the fire scenarios previously generated with the Monte Carlo simulation model.
\n
The theoretical and the working principle of the VentSim software together with the VentFIRE™ module can be found here [18]. Fire models in some cases are analyzed by CFD software for the purpose of comparison between the results obtained from simpler computational methods. Due to the size and complexity of the underground mines, it should be emphasized that CFD analysis can only be used to represent a small section of the mine. The results of such CFD analyses that require a large number of computations which will generate only results related to the immediate proximity of the fire scenario cannot realistically represent the full image generated by the fire model [8, 19]. The functionality of the methodology presented in this chapter is based on the modeling and simulation of fire scenarios whose results can fully represent each time interval of the movement of smoke and fire gases through the whole ventilation network from which the evacuation process entirely depends.
\n
In the process of modeling fire scenarios in VentFIRE™ module in addition to the fire load data presented in Figure 3, which was generated with the Monte Carlo simulation model, specific data are also required for each material which is presented in Table 2. For the purpose of providing this data, laboratory tests or fire databases containing such information may be used [20, 21].
\n
\n
\n
\n
\n
\n\n
\n
\n
Tire
\n
Diesel fuel
\n
Hydraulic fluid
\n
\n\n\n
\n
Density [kg/m3]
\n
1150
\n
832
\n
760
\n
\n
\n
Simplified chemical hydrocarbon formula
\n
C4H6\n
\n
C12H23\n
\n
C36H74\n
\n
\n
\n
Heat of combustion [MJ/kg]
\n
44
\n
45
\n
48
\n
\n
\n
Burning rate of material [kg/m2\n\n\n∗\n\n s]
\n
0.062
\n
0.045
\n
0.039
\n
\n
\n
O2 consumed [kg/kg]
\n
3.62
\n
3.33
\n
3.57
\n
\n
\n
Yield CO2 [kg/kg]
\n
0.9
\n
3.2
\n
3.3
\n
\n
\n
Yield CO min [kg/kg]
\n
0.13
\n
0.019
\n
0.1
\n
\n
\n
Yield CO max [kg/kg]
\n
0.23
\n
0.21
\n
0.24
\n
\n
\n
Yield soot [kg/kg]
\n
0.1
\n
0.059
\n
0.1
\n
\n\n
Table 2.
Input fire characteristics data for the fire load.
\n
The results of the fire models obtained by the VentFIRE™ module are in the form of a dynamic representation of the real-time fire progression and utilize a graphic visualization of the spread and concentration of combustion products and all the fire-related data throughout the ventilation system (Figure 4).
\n
Figure 4.
Screenshot from the fire scenario S-1 at 30 minutes from the fire ignition.
\n
Monitoring points that are strategically placed throughout the ventilation network allow the extraction of data in the form of concentrations over time for all fire-related data. In this study, for the evaluation of the evacuation plans, only the CO concentration over time curve will be analyzed throughout the ventilation network. The results from the monitoring points will serve for realistic mapping of the CO inhalation throughout the evacuation route for anyone affected by the fire scenario. Figure 5 shows the CO concentration measured from the monitoring point at the location for the fire scenario S-1.
\n
Figure 5.
CO concentration over time curve at the fire scenario S-1 location.
\n
\nFigure 6 shows the average values of CO concentration vs. total duration time for all fire scenario variants generated by the Monte Carlo simulation model, measured from the fire location.
\n
Figure 6.
Average values of CO concentration at fire location and total time duration of the fire for all scenarios generated by the Monte Carlo simulation model.
\n
These results highlight the impact of different fire load distribution, thus providing additional data for analysis during the process for determining the optimal evacuation routes.
\n
\n
\n
4. Methodology for determining the optimal evacuation routes based on simulated fire scenarios
\n
\n
4.1 Life safety assessment during evacuation based on fractional effective dose (FED) from CO inhalation
\n
Statistical underground mine fire evidence shows that most injuries and deaths are not caused by direct contact with the fire but by way of smoke and toxic gases inhalation [22].
\n
While the fire scenario may be confined to a localized underground mine area, the smoke produced will rise and with the help of the ventilation system may spread rapidly through the mine.
\n
The spread of smoke and toxic gases through the underground mine network will cause difficulties in the evacuation process, and therefore, there is a need for an effective methodology for planning and developing of optimal evacuation routes.
\n
Purser [23] gives extensive review of smoke and toxic gases hazards, including exposure thresholds that can cause incapacitation and even death.
\n
In underground mine fires, the most common asphyxiate is CO, and its effects of incapacitation depend from the gas concentrations and the durations of exposure.
\n
The evacuation management system must be designed and evaluated against a set of criteria to ensure safe evacuation of the mineworkers, which can be achieved by analyzing the fire environments using modeling and simulation.
\n
The proposed method in this book chapter involves the determination of accumulating exposure effect at regular discrete time increments to get the cumulative dosage in terms of FED for the total period of exposure. The exposure doses are calculated as a fraction of incapacitation at every time increment, and the value of FED = 1.0 represents the state of incapacitation in which mineworkers are incapable of completing their own evacuation.
\n
Purser [24] suggests mathematical model for estimating toxic hazard from inhalation of CO from fire scenario in terms of time to incapacitation or death in form of FED and is given as follows:
where CO (carbon monoxide) is the average concentration (ppm) over the time increment Δt in minutes, K and D are constants which depend on the activity of the person (Table 3), %CO2 is the carbon dioxide concentration, and (20,9-%O2) is the oxygen vitiation over the time increment ∆t.
\n
\n
\n
\n
\n\n
\n
Activity
\n
K
\n
D
\n
\n\n\n
\n
At rest
\n
2,81945 \n\n∗\n\n 10−4\n
\n
40
\n
\n
\n
Light work
\n
8,2925 \n\n∗\n\n 10−4\n
\n
30
\n
\n
\n
Heavy work
\n
1,6585 \n\n∗\n\n 10−4\n
\n
20
\n
\n\n
Table 3.
Values for different activity levels for the constants K and D.
\n
One of the limitations of this model is the lack of a clear safety margin between the values of the FED in which the transition in the evacuation process from safe to unsafe zone begins. As previously stated, for the evacuation to be considered safe, the FED value should be <1. The question here is how much less than 1?
\n
To improve the methodology in this regard, additional model is introduced that will allow to link the entire evacuation timeline with another parameter in the form of COHb prediction in the blood as a result of the CO inhalation generated by the fire scenario.
\n
\n
\n
4.2 Model for predicting carboxyhemoglobin (COHb) concentration as a result of CO inhalation
\n
The overwhelming hazard in fires is the COHb buildup in the blood as a result of exposures to CO. Inhaled CO acts on the human body by competing with oxygen to combine with hemoglobin molecules in the blood, forming COHb rather than normal oxyhemoglobin (O2Hb) [25]. Exposure to a large concentration of CO is lethal, and the signs and symptoms produced are directly related to the percentage of COHb in the blood (Table 4).
\n
\n
\n
\n\n
\n
COHb (%)
\n
Clinical symptoms
\n
\n\n\n
\n
0,4–1
\n
Normal value for nonsmokers
\n
\n
\n
2,5–4
\n
Decreased exercise performance in patients with angina
\n
\n
\n
5–10
\n
Shortness of breath on vigorous exertion, possible tightness across forehead, statistically significant diminution of visual perception, manual dexterity, or ability to learn
\n
\n
\n
11–20
\n
Atypical dyspnea, throbbing headache, dizziness, nausea, confusion and decreased exercise tolerance, dilatation of skin vessels
\n
\n
\n
21–30
\n
Severe headache, pulsation in sides of head, impaired thinking, disturbed vision, fainting, easy fatigability, disturbed judgment
\n
\n
\n
31–40
\n
Severe headache, dizziness, respiratory failure, coma, intermittent convulsions
\n
\n
\n
41–50
\n
Brain damage, lethargy, seizures, syncope, death from severe cellular hypoxia if exposure is prolonged
\n
\n
\n
51–60
\n
Same as above, coma, convulsions, Cheyne-Stokes respiration
\n
\n
\n
>70
\n
Slowing and stopping of respiration and death within short period
\n
\n\n
Table 4.
Approximate clinical symptoms associated with the blood COHb (%) level [26].
\n
The most widely used mathematical model (Coburn-Forster-Kane (CFK)) was implemented in order to predict COHb (%) blood level from CO exposure on mineworkers during the underground mine fire scenario.
\n
Previous research by several authors validated both linear and nonlinear CFK model against observations made on subjects exposed to variable CO concentrations, and the consensus is that the model predictions works quite well. The CFK nonlinear model is given by the following Equation [27]:
\n\n\nM\n\n—Haldane constant, ratio of the affinity of Hb for CO to that of O2 = 240.
\n
\n\n\n\n\n\nO\n2\n\nHb\n\n\n\n—oxyhemoglobin concentration = 0,2 ml ml−1 blood.
\n
\n\n\n\n\nCOHb\n\nt\n\n\n—carboxyhemoglobin concentration at time t in ml CO per ml blood.
\n
\n\n\n\n\nCOHb\n\n0\n\n\n—initial concentration of carboxyhaemoglobin in blood (%COHb = 0,5% for nonsmokers; %COHb >2% for 80% of smokers; %COHb = 10% for heavy smokers).
\n
\n\n\n\nPO\n2\n\n\n—partial pressure of oxygen in lung capillaries = 13,3 kPa.
\n
\n\n\n\nV\nCO\n\n\n—endogenous CO production rate = 0,007 ml min−1.
\n
\n\n\nD\n\n—diffusion capacity of the lungs for CO = 225 ml min−1 kPa (in reality this is not a constant but is altered by a number of factors including exercise).
\n
\n\n\nP\n\n—Barometric pressure - saturated vapor pressure of water at 37°C = 95,1 kPa.
\n
\n\n\n\nV\nb\n\n\n—blood volume 5500 ml.
\n
\n\n\n\nP\n\n1\n,\nCO\n\n\n\n—partial pressure of CO in inspired air = 0,0101 kPa (adopted for the purposes of this model).
\n
\n\n\n\nV\na\n\n\n—alveolar ventilation rate = 6000 ml min−1.
\n
\n\n\nt\n\n—duration of exposure [min].
\n
The limitations in the CFK model are located with the physiological variables needed as input to the model which are difficult to measure, such as blood volume, endogenous production of CO, and the pulmonary diffusing capacity [28].
\n
For the purpose of this study, an Excel model based on the CFK equation is built to predict the individual’s COHb formation (%), as a result from CO inhalation. For simplification purposes the abovementioned physiological variables are set as default values (as defined in the equation).
\n
The proposed model for predicting COHb (%) with appropriate clinical symptoms (Table 4) connected with the FED model can better determine the threshold in which the evacuation will be considered safe.
\n
\n
\n
4.3 Model for the conversion of the factors that influence the speed of evacuation
\n
To be able to calculate the optimal evacuation routes in underground mines, details about the tunnels’ parameters should be provided. Each fire scenario generates factors that influence the complexity and the speed of the evacuation itself.
\n
Based on extensive literature review, two factors are located that have most influence on the evacuation speed, and these factors are generalized in the form of tunnel slope and smoke visibility [7, 29, 30, 31]. The model framework is shown in Figure 7.
\n
Figure 7.
Methodology for implementation of the evacuation speed influence model.
\n
These factors that influence mineworkers’ escape speed can increase the exposure time from the fire scenario and thus present very important factors to be considered in the process of determining optimal evacuation routes.
\n
We defined the mineworkers’ normal evacuation speed by \n\n\nv\n0\n\n\n, and under the influence of the above factors, the evacuation speed will be \n\n\nv\nf\n\n\n.
\n
The tunnel slope influences the mineworkers’ evacuation (and also walking) speed, and the greater the slope the more influence it will have on the process.
\n
The tunnel slope influence under climbing situation is given by the following Equation [31]:
where \n\nm\n\n is the standard human mass [kg], \n\ng\n\n is the gravity acceleration [m/s2], and \n\n\nθ\ns\n\n\n is the tunnel’s angle of slope in degrees.
\n
When mineworkers pass down slope tunnels, we will assume no influence on their speed, and the model will treat this as normal evacuation speed \n\n\nv\n0\n\n\n (i.e., \n\n\nk\nts\n\n=\n1\n)\n.\n\n\n
\n
The smoke generated by the fire scenario is a major factor in determining tunnel visibility. This visibility factor has important effects on the evacuation speed of the mineworkers who are escaping.
\n
Based on the reviewed literature, two threshold values hold a central function during an evacuation in a smoke-filled environment [30, 32]. The first threshold value is the visibility level at which evacuees in general can be expected to start reducing their evacuation speed. This value based on the reviewed experiments of the data presented from the literature was set to 3 meters as corresponding visibility threshold value [30, 33, 34].
\n
The second threshold value is the visibility level at which the mineworkers can be assumed to be evacuating with their slowest speed. Based on the reviewed literature, the slowest speed during an evacuation in a smoke-filled environment is similar to movement in complete darkness which can be expected to be about 0,2 m/s [30]. In this analysis, the value for the slowest speed of evacuation will also be applied when the mineworkers will move through the evacuation stairs in the ventilation raise.
\n
Practically, in the process of calculating the reduction of evacuation speed based on the smoke visibility level, the model is set in the following way:
All individuals in the group are assumed to be evacuating with the same speed.
Visibility levels >3 m: mineworkers’ evacuation speed is represented by 1,2 m/s
Visibility levels ≤3 m: mineworkers’ evacuation speed is represented by a relative reduction of 0,34 m/s per meter visibility in a smoke-filled environment down to the previously defined minimum speed of 0,2 m/s.
\n\n
The correlation in this model is described by the following equation and by Figure 8 [30]:
\n
Figure 8.
Representation of relative reduction of speed in a smoke-filled environment according to the model.
where \n\nw\n\n is the evacuation speed [m/s] and \n\nV\n\n the visibility [m].
\n
\n
\n
\n
5. Results and discussion
\n
Determining the optimal routes for evacuation in the case of underground mine fire makes the difference between life and death. In this book chapter, we established a methodology for calculating the optimal routes for evacuation in case of underground mine fire based on simulated scenarios. The methodology shown in Figure 9 provides the necessary steps to assess the potential fire scenarios and to generate the necessary data on the basis of which all evacuation routes will be evaluated and the optimization process implemented.
\n
Figure 9.
Proposed methodology implementation framework.
\n
The methodology consists of three parts, i.e., developing underground mine fire scenarios, modeling and simulation of fire scenarios, and determining the optimal evacuation routes based on the generated results. The parts of the presented methodology and the procedures for their implementation are presented in detail above.
\n
For the purpose of this study, a case study of the “SASA”-R.N. Macedonia mine was used for determining the optimal routes for evacuations.
\n
To present all the steps that the methodology is consists of, we will present the results obtained from only one fire location from which we will calculate the optimal evacuation routes for only one group of workers for all of the 20 fire scenarios generated by the Monte Carlo simulation model.
\n
The results from the Monte Carlo simulation (Figure 3) are used as input fire load data for modeling and simulating fire scenarios in the VentFIRE™ module through the mine ventilation network (Figure 2). Following the simulation of all 20 fire scenarios from the same fire location, all possible evacuation routes for group 1 have been identified (Figure 10).
\n
Figure 10.
Identification of possible evacuation routes for group 1 for all generated fire scenarios.
\n
In the process of calculating all the parameters needed to determine the optimal evacuation routes, we will take into account the self-contained self-rescuer (SCSR). The use of SCSR in underground mining is a legal obligation in almost all countries around the world, so its introduction into the process of determining the optimal evacuation routes is a very important factor. The SCSR is a portable device that is used in underground mines to provide breathable air for the mineworkers when the surrounding atmosphere is filed with contaminants after emergency situation.
\n
Extensive research on fire reports provides the fact that sometimes this first line of defense from smoke inhalation in the form of SCSR fails to function properly due to technical problems or due to insufficient training of the mineworkers [35]. Because of this fact in this study, we will make two parallel analyses to calculate the optimal evacuation routes in which we will introduce the use of a SCSR with a capacity of 30 minutes and the possibility of its non-functionality. By introducing this parameter in the form of functionality and non-functionality of SCSR, we can provide a detailed analysis that can predict the evacuation routes under different conditions.
\n
To elaborate on the proposed methodology, we will present in details the results of scenario S-1.
\n
After the development of the underground mine fire scenarios and their modeling and simulation inside the VentFIRE™ module, all the necessary data for the optimization process is gathered.
\n
For the purpose of this analysis, an average evacuation speed of 1,2 m/s is assumed. The average evacuation speed will be affected by the tunnel slope and smoke visibility.
\n
To calculate the impact on the average speed inside the evacuation process, An Excel model was built based on Eqs. 9 and 10. The results from the simulated fire scenario S-1, which are required as inputs for the FED, COHb, and route calculation models, are shown in Tables 5–8.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Position
\n
Section length [m]
\n
Visibility [m]
\n
Slope [o]
\n
Reduction of evacuation speed (from visibility and slope) [m/s]
\n
Average CO (ppm)
\n
Evacuation time in section [s]
\n
Cumulative time [s]
\n
\n\n\n
\n
P1-P2
\n
667
\n
5
\n
0
\n
1,2
\n
448
\n
556
\n
556
\n
\n
\n
P2-P3
\n
232
\n
2
\n
6
\n
0,4
\n
881
\n
580
\n
1136
\n
\n
\n
P3-P4
\n
495
\n
4,6
\n
6,1
\n
0,3
\n
514
\n
1650
\n
2786
\n
\n
\n
P4-P5
\n
524
\n
12
\n
5,71
\n
0,29
\n
480
\n
1807
\n
4593
\n
\n
\n
P5-P6
\n
199
\n
25
\n
5,8
\n
0,55
\n
0
\n
362
\n
4955
\n
\n
\n
P6-P7
\n
792
\n
25
\n
1
\n
0,66
\n
0
\n
1200
\n
6155
\n
\n\n
Table 5.
Results for group 1, evacuated along route 1 for scenario S-1.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Position
\n
Section length [m]
\n
Visibility [m]
\n
Slope [o]
\n
Reduction of evacuation speed (from visibility and slope) [m/s]
\n
Average CO (ppm)
\n
Evacuation time in section [s]
\n
Cumulative time [s]
\n
\n\n\n
\n
P1-P2
\n
347
\n
5,1
\n
0
\n
1,2
\n
448
\n
289
\n
289
\n
\n
\n
P2-P3
\n
80
\n
5,3
\n
75
\n
0,2
\n
450
\n
400
\n
689
\n
\n
\n
P3-P4
\n
135
\n
4,7
\n
1,4
\n
1
\n
524
\n
135
\n
824
\n
\n
\n
P4-P5
\n
524
\n
12
\n
5,71
\n
0,29
\n
480
\n
1807
\n
2631
\n
\n
\n
P5-P6
\n
199
\n
25
\n
5,8
\n
0,55
\n
0
\n
362
\n
2993
\n
\n
\n
P6-P7
\n
797
\n
25
\n
1
\n
0,65
\n
0
\n
1226
\n
4219
\n
\n\n
Table 6.
Results for group 1, evacuated along route 2 for scenario S-1.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Position
\n
Section length [m]
\n
Visibility [m]
\n
Slope [o]
\n
Reduction of evacuation speed (from visibility and slope) [m/s]
\n
Average CO (ppm)
\n
Evacuation time in section [s]
\n
Cumulative time [s]
\n
\n\n\n
\n
P1-P2
\n
667
\n
5
\n
0
\n
1,2
\n
448
\n
556
\n
556
\n
\n
\n
P2-P3
\n
340
\n
2
\n
6
\n
0,34
\n
881
\n
1000
\n
1556
\n
\n
\n
P3-P4
\n
80
\n
25
\n
75
\n
0,2
\n
0
\n
400
\n
1956
\n
\n
\n
P4-P5
\n
462
\n
25
\n
0
\n
1,2
\n
0
\n
385
\n
2341
\n
\n
\n
P5-P6
\n
1689
\n
25
\n
0
\n
1,2
\n
0
\n
1408
\n
3748
\n
\n\n
Table 7.
Results for group 1, evacuated along route 3 for scenario S-1.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Position
\n
Section length [m]
\n
Visibility [m]
\n
Slope [o]
\n
Reduction of evacuation speed (from visibility and slope) [m/s]
\n
Average CO (ppm)
\n
Evacuation time in section [s]
\n
Cumulative time [s]
\n
\n\n\n
\n
P1-P2
\n
461
\n
5,1
\n
0
\n
1,2
\n
448
\n
384
\n
384
\n
\n
\n
P2-P3
\n
80
\n
14
\n
75
\n
0,2
\n
344
\n
400
\n
784
\n
\n
\n
P3-P4
\n
426
\n
22
\n
0
\n
1,2
\n
0
\n
355
\n
1139
\n
\n
\n
P4-P5
\n
671
\n
25
\n
0
\n
1,2
\n
0
\n
559
\n
1698
\n
\n
\n
P5-P6
\n
1689
\n
25
\n
0
\n
1,2
\n
0
\n
1408
\n
3106
\n
\n\n
Table 8.
Results for group 1, evacuated along route 4 for scenario S-1.
\n
In the calculation process for the CO exposure over the entire evacuation route, we will include the SCSR in its two previously mentioned forms. To calculate the exposure from CO for each of the possible evacuation routes, the results shown in Tables 5–8 are used as inputs to the FED and the COHb model. The results from the CO exposure based on FED and COHb models build inside Excel are shown in Figures 11–14.
\n
Figure 11.
Results from the FED and COHb models, for inhalation of CO during evacuation along the route 1.
\n
Figure 12.
Results from the FED and COHb models, for inhalation of CO during evacuation along the route 2.
\n
Figure 13.
Results from the FED and COHb models, for inhalation of CO during evacuation along the route 3.
\n
Figure 14.
Results from the FED and COHb models, for inhalation of CO during evacuation along the route 4.
\n
All of the gathered results from the models are stored and arranged in the database. The next step of the proposed methodology is to filter the results inside the database through a route calculation model that will sort out all the evacuation routes according to the level of CO exposure, i.e., the results obtained from the FED and COHb model.
\n
The purpose of the route calculation model is to generate a list of all evacuation routes, which will include the data for route length and cumulative CO exposure in the form of a FED through the evacuation process.
\n
The first step in the optimization model is to group the evacuation routes into five categories:
Group 1 of evacuation routes with a value of FED = 0
Group 2 of evacuation routes with a value of FED>0 ≤ 0,5
Group 3 of evacuation routes with a value of FED>0,5 ≤ 0,8
Group 4 of evacuation routes with a value of FED>0,8 ≤ 1
Group 5 of evacuation routes with a value of FED>1
\n\n
The values of the FED parameter on which the grouping is based are determined using the COHb model from which COHb (%) concentrations in the blood are predicted for the same CO exposure which in turn are related to the clinical symptoms presented in the Table 4.
\n
After grouping the routes into the abovementioned categories, they are filtered through a decision support process that applies the parameter optimization objectives. The optimization model is set so that there is no data in the first group to continue to the next one until the last group is reached.
\n
For the routes in the first group in which the level is set to FED = 0, the model will select the shortest route in length which will represent the optimal evacuation route.
\n
The same optimization process is also set for the second and the third group in which the level is set to FED>0 ≤ 0,5 and FED>0,5 ≤ 0,8 accordingly. The reason why this three groups are separated is to give an advantage in the optimization process to the routes with less CO exposure than on those with shorter lengths.
\n
For the routes in the fourth group in which the level is set to FED > 0,8 ≤ 1, the model will select the route with the minimum CO exposure presented in the form of FED. In this group, clinical symptoms of CO exposure predict conditions that can cause difficulties during the evacuation process, and because of this, the optimization is set based on the FED parameter with minimal value. The evacuation routes selected in this group should be treated with caution, and they should be thoroughly analyzed for opportunities to install additional evacuation support systems in certain critical locations.
\n
For the routes in the fifth group in which the level is set to FED > 1, the model will treat all routes as unsafe for evacuation. If the proposed methodology in this study does not generate data which will fall into the first four groups, then an additional analysis should be performed using the developed ventilation model that shows the movement of smoke and toxic gases through the underground mining facilities. These results could serve to plan the action strategy for the rescue teams or for a suggestion of additional systems that could help in the evacuation process for those affected by the fire scenario.
\n
\nTable 9 shows the results from the optimization methodology for scenario S-1 in which the routes are sorted by their ranking, taking into account the use of a SCSR.
\n
\n
\n
\n
\n\n
\n
\n
FED
\n
Route length [m]
\n
\n\n\n
\n
Route 3 (rank 1)
\n
0
\n
3282
\n
\n
\n
Route 4 (rank 2)
\n
0
\n
3327
\n
\n
\n
Route 2 (rank 3)
\n
0,24
\n
2082
\n
\n
\n
Route 1 (rank 4)
\n
0,84
\n
2912
\n
\n\n
Table 9.
Ranked evacuation routes from the optimization process for scenario S-1 with the use of a SCSR.
\n
Considering the use of a SCSR, the optimal evacuation route for scenario S-1 is route 3 which has the best rating according to the present methodology.
\n
\nTable 10 shows the results from the optimization methodology taking into account the possibility of malfunction of the SCSR for scenario S-1.
\n
\n
\n
\n
\n\n
\n
\n
FED
\n
Route length [m]
\n
\n\n\n
\n
Route 4 (rank 1)
\n
0,18
\n
3327
\n
\n
\n
Route 2 (rank 2)
\n
0,74
\n
2082
\n
\n
\n
Route 3 (rank 3)
\n
0,69
\n
3282
\n
\n
\n
Route 1 (rank 4)
\n
1,5
\n
2912
\n
\n\n
Table 10.
Ranked evacuation routes from the optimization process for scenario S-1 without the use of SCSR.
\n
The optimal evacuation route for scenario S-1 in which we assumed the malfunction of the SCSR is route 4 which according to the present methodology has the best rating.
\n
\nTable 11 Shows every optimal evacuation route for group 1 based on the fire scenarios generated by the Monte Carlo simulation model. As previously mentioned the simulation process in the VentFIRE™ module is done from the same fire location for each of the generated scenarios.
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
Optimal route with SCSR
\n
Optimal route without the use of SCSR
\n
\n\n\n
\n
Scenario S-2
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 2
\n
FED = 0,421
\n
Length = 2082 m
\n
\n
\n
Scenario S-3
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,115
\n
Length = 3327 m
\n
\n
\n
Scenario S-4
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,112
\n
Length = 3327 m
\n
\n
\n
Scenario S-5
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,175
\n
Length = 3327 m
\n
\n
\n
Scenario S-6
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,168
\n
Length = 3327 m
\n
\n
\n
Scenario S-7
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 2
\n
FED = 0,439
\n
Length = 2082 m
\n
\n
\n
Scenario S-8
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 2
\n
FED = 0,432
\n
Length = 2082 m
\n
\n
\n
Scenario S-9
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,165
\n
Length = 3327 m
\n
\n
\n
Scenario S-10
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,161
\n
Length = 3327 m
\n
\n
\n
Scenario S-10
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,159
\n
Length = 3327 m
\n
\n
\n
Scenario S-11
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,174
\n
Length = 3327 m
\n
\n
\n
Scenario S-12
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,163
\n
Length = 3327 m
\n
\n
\n
Scenario S-13
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 2
\n
FED = 0,448
\n
Length = 2082 m
\n
\n
\n
Scenario S-14
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,170
\n
Length = 3327 m
\n
\n
\n
Scenario S-15
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,169
\n
Length = 3327 m
\n
\n
\n
Scenario S-16
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,162
\n
Length = 3327 m
\n
\n
\n
Scenario S-17
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,156
\n
Length = 3327 m
\n
\n
\n
Scenario S-18
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,171
\n
Length = 3327 m
\n
\n
\n
Scenario S-19
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,144
\n
Length = 3327 m
\n
\n
\n
Scenario S-19
\n
Route 3
\n
FED = 0
\n
Length = 3282 m
\n
Route 4
\n
FED = 0,173
\n
Length = 3327 m
\n
\n\n
Table 11.
Optimal evacuation route for every fire scenario generated by the Monte Carlo simulation model.
\n
\n
\n
6. Conclusion and future aspects
\n
A methodology for determining optimal evacuation routes in case of underground mine fire has been developed based on the results from simulated fire scenarios. The presented methodology can be consistent with the actual situation of the mine because the development of the fire scenarios is based on the risk analysis generated from the current production plans, and the simulation of the developed scenarios are performed on the ventilation network from the mine.
\n
To address the stochastic nature of the fire scenarios, the methodology implements the Monte Carlo simulation technique to emphasize the fact related to the inability to accurately determine the input parameters for the fire modeling process. From the large list of stochastic input parameters that can have a noticeable effect on the fire scenarios itself, the authors decided to elaborate only on the process of obtaining fire load inputs, from which the size of the fire depends and thus the amount of generated toxic gases. The results of the proposed methodology point to the fact that by treating the stochastic input parameters presented in this chapter in the form of a fire load, the generated conditions influenced the process of determining the optimal evacuation routes.
\n
The Monte Carlo simulation model with the above-defined parameters which follows the normal distribution is implemented on a case study from “SASA”-R.N. Macedonia mine. After the analysis with the proposed methodology, a fire scenario generated by the mechanization Scooptram ST7 is located which represents the total fire load. The stochastic model is set to generate 20 variations from the fire load that are treated as separate scenarios in the process of determining the optimal evacuation routes.
\n
The process of modeling and simulation of the generated fire scenarios is done with the VentFIRE™ module which uses the ventilation network to calculate the movement of the smoke and toxic gases from which the evacuation process depends.
\n
The fire parameters obtained from the simulated scenarios are used to calculate the optimal evacuation routes for each of the generated scenarios.
\n
The proposed methodology as the main factors influencing the evacuation process treats the inhalation of CO through the evacuation route presented in the form of FED and COHb, factors in the form of tunnel slope, and smoke visibility that affect the speed of evacuation and also the SCSR.
\n
In the analysis presented in this chapter, differences in optimal routes for evacuation were located only in the conditions of SCSR malfunction. The results presented in Table 11 highlight the importance of this additional analysis that is possible only by creating multiple variants of one fire scenario which is actually the underlying purpose of the proposed methodology. In the conditions of using the SCSR, the proposed methodology has determined and confirmed route 3 as optimal for evacuation in all variants of the generated fire scenarios. The results obtained from the conditions of SCSR malfunction located the changes in the optimal evacuation between routes 2 and 4 depending on the variable conditions that determined all the fire scenarios. This approach of analyzing fire scenarios offers certainty in the process of confirming the optimal route as well as locating possibilities for its change depending on the variable fire conditions.
\n
In order to further improve the methodology, we need to expand our research by introducing the other stochastic variables that may have impact on the evacuation process such as the physical status of mineworkers that is related to age, gender, exercise ability, and response ability.
\n
This research provides a convenient methodology for improving the accuracy of determining the optimal evacuation routes which significantly can increase the safety in underground mines.
\n
\n
Acknowledgments
\n
This work was financially supported by the Faculty of Natural and Technical Sciences—Mining Engineering, “Goce Delchev” University, Shtip, R.N. Macedonia.
\n
\n',keywords:"underground mines, fire, safety, evacuation, optimization, simulation, modeling",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/71079.pdf",chapterXML:"https://mts.intechopen.com/source/xml/71079.xml",downloadPdfUrl:"/chapter/pdf-download/71079",previewPdfUrl:"/chapter/pdf-preview/71079",totalDownloads:612,totalViews:0,totalCrossrefCites:0,dateSubmitted:"October 3rd 2019",dateReviewed:"January 15th 2020",datePrePublished:"February 14th 2020",datePublished:"November 4th 2020",dateFinished:"February 12th 2020",readingETA:"0",abstract:"The purpose of this chapter is to develop a methodology that will contribute in locating optimal evacuation routes in case of fire that are based on minimal carbon monoxide (CO) exposure during the evacuation procedures. The proposed methodology is tested using simulated fire scenarios from which CO concentration over time curve is extracted from all available evacuation routes and presented in a weighted form based on the accumulating effect of CO inhalation in the form of fractional effective dose (FED). The safety limits of the FED model on which the optimization process is based are determined using a model for the prediction of carboxyhemoglobin (COHb) levels in human blood. The COHb model is associated with predicted clinical symptoms that are the basis for determining the level of incapacitation at which the mineworkers are incapable of completing their evacuation. Also in the process of improving the fire risk analysis, the proposed methodology enables the development of evacuation plans that are based on the results of modeled fire scenarios combined together with the results of the anticipated hazards generated by CO inhalation. The results presented in this chapter indicate a more precise approach in the process of planning the evacuation system inside the underground mines.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/71079",risUrl:"/chapter/ris/71079",signatures:"Vancho Adjiski and Zoran Despodov",book:{id:"10032",type:"book",title:"Fire Safety and Management Awareness",subtitle:null,fullTitle:"Fire Safety and Management Awareness",slug:"fire-safety-and-management-awareness",publishedDate:"November 4th 2020",bookSignature:"Fahmina Zafar and Anujit Ghosal",coverURL:"https://cdn.intechopen.com/books/images_new/10032.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83962-426-1",printIsbn:"978-1-83962-425-4",pdfIsbn:"978-1-83962-427-8",isAvailableForWebshopOrdering:!0,editors:[{id:"89672",title:"Dr.",name:"Fahmina",middleName:null,surname:"Zafar",slug:"fahmina-zafar",fullName:"Fahmina Zafar"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"312936",title:"Ph.D.",name:"Vancho",middleName:null,surname:"Adjiski",fullName:"Vancho Adjiski",slug:"vancho-adjiski",email:"vanco.adziski@ugd.edu.mk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"317385",title:"Prof.",name:"Zoran",middleName:null,surname:"Despodov",fullName:"Zoran Despodov",slug:"zoran-despodov",email:"zoran.despodov@ugd.edu.mk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Goce Delcev University",institutionURL:null,country:{name:"Macedonia"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Methodology for developing underground mine fire scenarios",level:"1"},{id:"sec_3",title:"3. Modeling and simulation of fire scenarios in underground mines",level:"1"},{id:"sec_4",title:"4. Methodology for determining the optimal evacuation routes based on simulated fire scenarios",level:"1"},{id:"sec_4_2",title:"4.1 Life safety assessment during evacuation based on fractional effective dose (FED) from CO inhalation",level:"2"},{id:"sec_5_2",title:"4.2 Model for predicting carboxyhemoglobin (COHb) concentration as a result of CO inhalation",level:"2"},{id:"sec_6_2",title:"4.3 Model for the conversion of the factors that influence the speed of evacuation",level:"2"},{id:"sec_8",title:"5. Results and discussion",level:"1"},{id:"sec_9",title:"6. Conclusion and future aspects",level:"1"},{id:"sec_10",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'\nConti R, Chasko L, Wiehagen W. Fire Response Preparedness for Underground Mines. Pittsburgh, PA: National Institute for Occupational Safety and Health-NIOSH; 2005. pp. 1-19\n'},{id:"B2",body:'\nHansen R. Design fires in underground mines. In: Studies in Sustainable Technology 2010:02. Västerås: Mälardalen University; 2010. pp. 7-54\n'},{id:"B3",body:'\nJi J, Zhang J, Chen J, Wu S. Computer simulation of evacuation in underground coal mines. Mining Science and Technology (China). 2010;20(5):677-681. ISSN: 1674-5264. DOI: 10.1016/S1674-5264(09)60261-1\n'},{id:"B4",body:'\nChen P, Guo S, Wang Y. Human evacuation affected by smoke movement in mine fires. International Journal of Coal Science & Technology. 2016;3(1):28-34. DOI: 10.1007/s40789-015-0100-3\n'},{id:"B5",body:'\nWang L, Wang Y, Cao Q, Li X, Li J, Wu X. A framework for human error risk analysis of coal mine emergency evacuation in China. Journal of Loss Prevention in the Process Industries. 2014;30(2014):113-123. ISSN: 0950-4230. DOI: 10.1016/j.jlp.2014.05.007\n'},{id:"B6",body:'\nWu Q, Xu H, Du Y, Zhang X, Zhao Y. Emergency evacuation simulation system and engineering application for water bursting disaster in mine. Journal of China Coal Society. 2017;42(10):2491-2497\n'},{id:"B7",body:'\nAdjiski V, Mirakovski D, Despodov Z, Mijalkovski S. Simulation and optimization of evacuation routes in case of fire in underground mines. Journal of Sustainable Mining. 2015;14(3):133-143. DOI: 10.1016/j.jsm.2015.10.001\n'},{id:"B8",body:'\nAdjiski V. Possibilities for simulating the smoke rollback effect in underground mines using CFD software. GeoScience Engineering. 2014;2014(2):8-18. DOI: 10.2478/gse-2014-0008\n'},{id:"B9",body:'\nAdjiski V, Despodov Z, Mirakovski D, Mijalkovski S. Fire risk assessment and computer simulation of fire scenario in underground mines. Studies in Engineering and Technology. 2015;2(1):54-60. DOI: 10.11114/set.v2i1.825\n'},{id:"B10",body:'\nAdjiski V, Despodov Z, Serafimovski D. Prototype model for fire safety system in underground mining. American Journal of Mining and Metallurgy. 2017;4(1):62-67. DOI: 10.12691/ajmm-4-1-6\n'},{id:"B11",body:'\nLi X, Hadjisophocleous G, Sun X. Sensitivity and uncertainty analysis of a fire spread model with correlated inputs. Procedia Engineering. 2018;211(2018):403-414. DOI: 10.1016/j.proeng.2017.12.029\n'},{id:"B12",body:'\nGuanquan C, Jinhui W. Study on probability distribution of fire scenarios in risk assessment to emergency evacuation. Reliability Engineering and System Safety. 2012;99:24-32. DOI: 10.1016/j.ress.2011.10.014\n'},{id:"B13",body:'\nKong D, Johansson N, Hees P, Lu S, Lo S. A Monte Carlo analysis of the effect of heat release rate uncertainty on available safe egress time. Journal of Fire Protection Engineering. 2013;23(1):5-29. DOI: 10.1177/1042391512452676\n'},{id:"B14",body:'\nLindström T, Lund D. A Method of Quantifying User Uncertainty in FDS by Using Monte Carlo Analysis. Report 5309. Sweden: Department of Fire Safety Engineering and Systems Safety, Lund University; 2009. pp. 15-36\n'},{id:"B15",body:'\nSalem AM. Use of Monte Carlo simulation to assess uncertainties in fire consequence calculation. Ocean Engineering. 2016;117:411-430. DOI: 10.1016/j.oceaneng.2016.03.050\n'},{id:"B16",body:'\nAdjiski V, Zubicek V, Despodov Z. Monte Carlo simulation of uncertain parameters to evaluate the evacuation process in an underground mine fire emergency. The Southern African Institute of Mining and Metallurgy. 2019;119(11):907-917. DOI: 10.17159/2411-9717/701/2019\n'},{id:"B17",body:'\nGillies S, Wu HW. Case studies from simulating mine fires in coal mines and their effects on mine ventilation systems. In: Aziz N, editor. Coal 2004: Coal Operators’ Conference, University of Wollongong & the Australasian Institute of Mining and Metallurgy. 2004. pp. 111-125\n'},{id:"B18",body:'\nVentsim Visual™ User Guide. Ventsim Software by Chasm Consulting. Capalaba, QLD, Australia; 2014\n'},{id:"B19",body:'\nAdjiski V, Mirakovski D, Despodov Z, Mijalkovski S. CFD simulation of the brattice barrier method for approaching underground mine fires. Mining. Science. 2016;23:161-172. DOI: 10.5277/ msc162313\n'},{id:"B20",body:'\nHansen R, Ingason H. Heat release rate measurements of burning mining vehicles in an underground mine. Fire Safety Journal. 2013;61:12-25. DOI: 10.1016/j.firesaf.2013.08.009\n'},{id:"B21",body:'\nRoh JS, Ryou HS, Kim DH. Critical velocity and burning rate in pool fire during longitudinal ventilation. Tunneling Underground Space Technology. 2007;22(3):262-271\n'},{id:"B22",body:'\nHansen R. Literature survey-fire and smoke spread in underground mines. In: MdH SiST 2009:2. Västerås: Mälardalens Högskola; 2009. pp. 7-67\n'},{id:"B23",body:'\nPurser DA. Modelling toxic and physical hazard in fire. Fire Safety Science. 1989;2:391-400. DOI: 10.3801/IAFSS.FSS.2-391\n'},{id:"B24",body:'\nPurser DA. Toxicity assessment of combustion products. In: SFPE Handbook of Fire Protection Engineering. 3rd ed. Quincy, MA: National Fire Protection Association (NFPA); 2002. pp. 2-83\n'},{id:"B25",body:'\nDirks KN, Sturman A, Johns MD. Using health impacts to assess atmospheric carbon monoxide models. Meteorological Applications. 2006;13(1):83-87. DOI: 10.1017/S1350482705002057\n'},{id:"B26",body:'\nChaloulakou A, Fili N, Spyrelis N. Occupational exposure to CO concentrations in enclosed garages: Estimation of blood COHb levels. In: Environmental Pollution, Proceedings of the 5th International Conference, Thessaloniki, Greece. 2000. pp. 934-940\n'},{id:"B27",body:'\nCoburn RF, Forster RE, Kane PB. Considerations of the physiological variables that determine the blood carboxyhæmoglobin concentrations in man. The Journal of Clinical Investigation. 1965;44:1899-1910. DOI: 10.1172/JCI105296\n'},{id:"B28",body:'\nAdjiski V, Despodov Z, Serafimovski D. System for prediction of carboxyhemoglobin levels as an indicator for on-time installation of self-contained self-rescuers in case of fire in underground mines. GeoScience Engineering. 2019;65(4):23-37. ISSN: 1802-5420. DOI: 10.35180/gse-2019-0021\n'},{id:"B29",body:'\nRonchi E, Gwynne SMV, Purser DA. The impact of default settings on evacuation model results: A study of visibility conditions vs occupant walking speeds. In: Advanced Research Workshop - Evacuation and Human Behaviour in Emergency Situations-Santander, Spain. 2011. pp. 2-15\n'},{id:"B30",body:'\nFridolf K, Nilsson D, Frantzich H, Ronchi E, Arias S. Walking speed in smoke: Representation in life safety verifications. In: 12th International Performance-Based Codes and Fire Safety Design Methods Conference, Oahu, Hawaii. 2018. pp. 1-6\n'},{id:"B31",body:'\nGuangwei Y, Dandan F. Escape-route planning of underground coal mine based on improved ant algorithm. Mathematical Problems in Engineering. 2013;2013:32-46. DOI: 10.1155/2013/687969\n'},{id:"B32",body:'\nRuixin Z, Rongshan N, Hongze Z, Yanqiang F. Experimental study on the escape velocity of miners during mine fire periods. Mathematical Problems in Engineering. 2018;2018:1-12. DOI: 10.1155/2018/9458785. Article ID: 9458785\n'},{id:"B33",body:'\nFridolf K, Frantzich H, Ronchi E, Nilsson D. The relationship between obstructed and unobstructed walking speed: Results from an evacuation experiment in a smoke filled tunnel. In: 6th International Symposium on Human Behavior in Fire. Cambridge. 2015. pp. 537-548\n'},{id:"B34",body:'\nFridolf K, Ronchi E, Nilsson D, Frantzich H. Movement speed and exit choice in smokefilled rail tunnels. Fire Safety Journal. 2013;59:8-21. DOI: 10.1016/j.firesaf.2013.03.007\n'},{id:"B35",body:'\nMcAteer D. The Sago Mine Disaster. Buckhannon, West Virginia; 2016. p. 110. Available from: www.wvgov.org\n\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Vancho Adjiski",address:"vanco.adziski@ugd.edu.mk",affiliation:'
Faculty of Natural and Technical Sciences, Mining Engineering, Goce Delchev University, Shtip, R.N. Macedonia
Faculty of Natural and Technical Sciences, Mining Engineering, Goce Delchev University, Shtip, R.N. Macedonia
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