SWOT and TOWS matrixes for EMS
\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n\r\n\tGenerally feed additives could be divided into five groups:
\r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics.
\r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour.
\r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production.
\r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production.
\r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). From 1988 to 1992, he worked in the Department of Animal Nutrition (Agricultural University in Wageningen). In 1992 he obtained a PhD degree in animal nutrition from the University of Wageningen.He has authored 297 publications (papers, book chapters). He edited 3 books and 14 international conference proceedings. His total number of citation is 407. \r\nHe is member of various committees e.g.: American Society of Animal Science (ASAS, USA); the editorial board of the Acta Agriculturae Scandinavica, Section A- Animal Science (Norway); KRMIVA, Journal of Animal Nutrition (Croatia), Austin Food Sciences (NJ, USA), E-Cronicon Nutrition (UK), SciTz Nutrition and Food Science (DE, USA), Journal of Medical Chemistry and Toxicology (NJ, USA), Current Research in Food Technology and Nutritional Sciences (USA). 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Environmental management has quickly emerged as an essential strategic factor in many industries. Environmental considerations are clearly becoming increasingly important and will be considered as one of the key factors in most companies’ success stories. For example, recently there are many firms in Asia that had already received ISO 14001 certification and adopted these Environmental Management Systems (EMS) standards as their state policy. No doubt that many firms have recognized the compatibility between environmental performance and profitability, as it witnessed by increasing interest in recycling programs and green marketing, in part due to realizing that the futility of running from such pressures.
Melnyk et al. (2003) apply a survey of North American managers to demonstrate that firms having gone through EMS certification experience a greater impact on performance than do firms that have not certified their EMS. Pan (2003) applies questionnaires to the organizations within Taiwan, Japan, Hong Kong and Korea on regards of ISO9000 and ISO14000 issues. He uses statistical analysis results of the survey data to gain eight common points for ISO9000 and ISO14000 certified firm within these four countries. Tan et al. (2003) develop an e-commerce structure for sorting, selecting and utilizing information for the effect of ISO9000 system. The related studies of environmental issues will be listed in Environment Management (Ahsen and Funck, 2001; Rao et al., 2006; Gernuks et al., 2007), Environmental Management Accounting (Jasch, 2003), ISO14001 Certification (Fryxell and Szteo, 2002; Mbohwa and Fukada, 2002; Rennings et al., 2006) and Life Cycle Assessment for EMS (Zobel, 2002).
In a country’s endeavor to implement EMS in both manufacturing and service sectors, the significance of Small and Medium Enterprises (SMEs) deserves special attention. In Taiwan, a SME is set under either two conditions. First, it is defined by the number of employees that they often refer to those with less than 200 employees involved in manufacturing, building and mining industries. Second, it is defined by its capital volume that is less than 80 million Taiwan dollars. The SMEs are typically much smaller in operation compared to the global and multinational enterprises, whereas most of the SEMs in Taiwan are positioned in the ending-role of the supply chain. Most EMSs in Taiwanese SMEs are implemented in accordance to specification in ISO 14001 or QC080000 standards, in which contain requirements that have to be fulfilled before third-party certification and /or registration can be achieved.
Strength, Weakness, Opportunity and Threat (SWOT) analysis is an important support tool for decision-making, and is commonly used as a means to systematically analyze an organization’s internal management capability and its external environment. The purpose of the analysis on internal strengths and weaknesses is to assess how an enterprise carries out its internal work, such as R&D, day to day business operation, etc. On the other hand, the purpose of the analysis on the external opportunities and threats is to assess whether or not an enterprise can seize opportunities and avoid threats, whilst facing an uncontrollable external environment, such as fluctuating prices, political destabilization, etc. SWOT analysis has been successfully applied in EMS fields, such as the environmental impact assessment in India (Paliwal, 2006), the development of an environmental management system (Lozano and Vallés, 2007) and regional energy planning for renewable development (Terrados et al., 2007).
For a quantitative SWOT, Kuttila et al. (2000) develop a hybrid method, the Analysis Hierarchy Process (AHP) in the SWOT analysis, to eliminate the weakness in the measurement and evaluation steps of the SWOT analysis. Examples in literature of studies that follow the method of Kuttila et al. include those by Kajanus et al. (2004), Leskinen et al. (2006) and Chang and Huang (2006). Yüksel and Dağdeviren (2007) demonstrate a process for quantitative SWOT analysis that can be performed even when there is dependence among strategic factors. They use the Analytic Network Process (ANP) that allows measurement of the dependency among the strategic factors as well as its AHP, which is based on independence between the factors. ANP is a more general form of its predecessor, the AHP, for ranking alternatives based on some set of criteria. Unlike AHP however, ANP is capable of handling feedbacks and interdependencies, which exist, in complex systems like a manufacturing system. ANP problem formulation starts by modeling the problem that depicts the dependence and influences of the factors involved to the goal or higher-level performance objectives. Dependence among the SWOT factors is observed to effect the strategic and sub-factor weights, as well as to change the strategy priorities. Dyson (2004) provides an SWOT and TOWS analysis to create strategy formation and its incorporation into the strategic development process at University of Warwick by scoring SWOT factors. A variation of SWOT analysis is the TOWS matrix. In the TOWS matrix the various factors are identified and these are then paired e.g. an opportunity with a strength, with the intention of stimulating a new strategic initiatives (Table 1).
A “top-down” way of thinking could be used to guide the formulation of decision hierarchy. In this paper, a new quantitative SWOT analysis is provided that allows measurement of the strategic factors as well as its vote-ranking method. The first task is to invite eighteen certificated ISO9000 and ISO14000 auditors (or lead auditors) to organize a “Task Force (TF)”. The TF will discuss SWOT of Taiwanese SMEs within their EMS issues and assess the competitive strategies. The second task is to apply the internal competitive strengths to find external market opportunities. As a result, the strategy combination for max {strengths, opportunities} and min {weaknesses, threats} will be provided. The third task is to regard these SWOT indexes and their sub-criteria as the candidates voted by the task force. In conclusion, the different results of ranking will expose different weights among the votes of the candidates.
This paper discusses the environmental issues of the SMEs not only by drawing insights from research conducted in different countries, but also look into the use of environmental factors of SWOT through their development, their context and adaptability to enhance the environment performance of SMEs. As for the medium, the vote-ranking method will be used to rank the different competitive strategies and priorities. This specific method provides a new quantitative SWOT methodology that will be extended to decision-making issues. The rest of this paper is organized as follows: Section 2 illustrates the vote-ranking method and the conceptual approach. Section 3 discloses the use of vote-ranking method to provide a quantitative SWOT method for assessing the SMEs’ competitive strategies in EMS by six-step procedure. Section 4 discusses the results of different strategies and suggestions. Section 5 highlights some conclusions and offers directions for further researches.
Data Envelopment Analysis (DEA) is an analytical procedure developed by Charnes et al. (1978) for measuring the relative efficiency of decision-making units (DMUs) that perform the same types of functions and have identical goals and objectives. The weights used for each DMU are those which maximize the ratio between the weighted output and weighted input. DEA is a mathematical programming technique that calculates the relative efficiencies of multiple DMUs, based on multiple inputs and outputs. A well-known method for ranking candidates in a ranked voting system is to compare the weighted sum of their votes after determining suitable weights. Cook and Kress (1990, 1992) present an approach to the problem of ranking candidates in a preferential election. They consider an alternative method which does not specify the sequence of weights by applying DEA. One would imagine that any reasonable person, voter, candidate or poll manager would agree that the first place votes should weigh at least as much as second place votes, and so on. They provide the following DEA model to obtain the total score for each candidate:
Where,
s: the number of places, s =1, …, S.
r: the number of candidates, r =1, …, R.
urs: the weights of the sth place with respect to the rth candidate.
xrs: the total votes of the rth candidate for the sth place.
d(s, ε): the given difference in weights between sth place with (s+1)th place; d(., ε), called the discrimination intensity function, is nonnegative and non-decreasing in ε. Parameter ε is nonnegative.
The Cook and Kress’s ranked voting model (1) is assumed that in a voting system, each voter selects R candidates and ranks them from the 1st to the Sth places, S≤ R. The d(s, ε) is to ensure that first place votes are valued at least as highly as second place votes which are valued at least as highly as third place votes etc. The Zrq is the cross-efficiency which can be thought of here as candidate r’s evaluation of candidate q’s desirability. The constraints Zrq are the usual DEA constraints i.e. that no candidate q should have a desirability greater than 1 under r’s weights. The Zrr has been used in the objective function to emphasize the candidate r’s evaluation of his/her own desirability. The rth candidate wishes to be assigned the weight urs so as to maximize the weighted sum of votes to candidate r, that is when the score Zrr becomes the largest. Notionally, each candidate was permitted to choose the most favorable weights to be applied to his/her standings in the normal DEA manner, with the additional ‘assurance region’ restriction, in which the weight for a sth place vote should be greater than the one for a (s+1)th place vote by some amount. Hashimoto and Ishikawa (1993) consider the candidates in ranked voting systems as the DMUs in DEA, and each is considered to have many outputs and only one input with unity. He also deems that it is fair to evaluate each candidate in terms of the weights optimal to himself/ herself.
Green et al. (1996) further develop this model by setting certain constraints to the weights. They point out that the form of d(s, ε) affects the ranking results and does not allow DMUs to choose their own weights unreservedly. Therefore, they present an alternative procedure that involved using each candidate’s rating of him/herself along with each candidate’s rating of all the other candidates. They utilize the cross-efficiency model to DEA to obtain the best candidate. On the other hand, Hashimoto (1997) proposes a method to determine a total ordering of candidates specifying nothing arbitrary, but only assuming the condition of decreasing and convex sequence of weights. They incorporate the condition of decreasing and convex sequence of weights into DEA as the assurance region. Green et al. and Hashimoto proposes these methods, whereabouts the existence of low preference candidates may change the ranks and DEA exclusion model, which seems to be unstable with respect to the inefficient candidates. Obata and Ishii (2003) consider that, the instability is caused by the fact mentioned above and that the inefficient candidates should not be used to discriminate efficient candidates. They also use this information only on efficient candidate while discriminating and realizing that the order of efficient candidates never changes even though the inefficient candidates are added or removed. Foroughi and Tamiz (2005) simplify the model of Obata and Ishii and extend it to rank the inefficient candidates as well as the efficient one.
Noguchi et al. (2002) revise the application of Green’s method and show that different weights among objects gave rise to different ranking results. If one wants to set particular constraints to a weight can be employed, which is characterized by the following constraints: (a) ur1 2 ur23 ur3 … S urS, (b) urs 1/[(1+2+…+S)*n] =2/(n* S(S+1)), where n is the number of voters. In this multiple criteria case, the vote-ranking model is defined as follows:
Where, these variables are the identical as model (1).
As for ranking of alternatives, one of the most popular methods compares the weighted sum of votes after determining suitable weights for each alternative. The different weights among objects resulted in different ranking results and propose a new method of ordering in order to solve the problem of weights ranking. As a final point, the module solver imbedded in EXCEL of Microsoft Office [2003] will be applied to solve the above linear programming problems (Liu and Hai, 2005).
This study proposes six-step procedure for selecting the competitive strategies of the Taiwanese SMEs. They are obtained from TF which will fall into four subjective criteria that discuss and analyze SWOT of Taiwan’s SMEs in the EMS. The first step is structuring the problem into a SWOT hierarchy. On the top level is the overall goal of selection competitive strategies. On the second level are the four SWOT criteria that contribute to the overall goal. The criteria (sub-criteria) for strengths (S1, S2, S3), opportunities (O1, O2, O3), weaknesses (W1, W2, W3) and threats (T1, T2, T3) are individually presented into Level 2 and 3. On the second level is that four criteria are decomposed into twelve sub-criteria under SWOT; additionally on the bottom (or fourth) level, there are five alternative competitive strategies that are to be evaluated in terms of the sub-criteria listed on the third level. These competitive strategies (OS-1, OS-2, OW-1, TS-1 and TW-1) are assessed in Level 4 and illustrated in Fig.1.
A SWOT hierarchy for selecting the competitive strategies
First of all, the author invites the eighteen certificated ISO9000 or ISO14000 auditors (or lead auditors), consists of 14 part-time and 4 full-time auditors, to organize a TF in this particular study. They are first briefed about the overall objective of the study, then specifically on the SWOT and vote-ranking methodologies. The questionnaires are used for interviewing purposes; however they mainly use a board or group decision method to determine the criteria and sub-criteria for selecting the competitive strategies. The study is to apply the internal competitive strengths to find external market opportunities. This is followed by the analysis on the organization’s external competitive environment and internal operating environment. Consequently, the internal analysis is followed by the selection and implementation of strategies. Due to highly global nature of the “Green House”, the requirements of EMS are also applicable for other countries in the European Union. For selecting the competitive strategies of SMEs for EMS, the TF has been mainly on the discussion of the SWOT method and problem defining after a series of revision. The strategy combination of EMS for max {strengths, opportunities} and min {weaknesses, threats}, OS-1, OS-2, OW-1, TS-1 and TW-1, is provided in Table 1.
Strengths: +S1: Capability to execute and develop EMS certification +S2: Synergy with commerce, environmental protection and education units +S3: Possessing high level of environmental education | Weaknesses: -W1: SMEs respond slowly and difficultly for external customer requirements -W2: Some suppliers or manufacturers are unwilling to face higher environmental regulation required and seek other markets with lower quality consciousness -W3: The government’s regulations of environmental protection are too loose | |
Opportunities: +O1: Change in customers’ preferences (increase in market demand for EMS or QMS certification) +O2: Increase value-added of product +O3: Improvement in Green House and in environment | Maxi-maxi (O-S) Strategies OS-1: Extend EMS Certification effects to create high value-added markets OS-2: Involve in improving environment issues and promote company image and profits | Maxi-mini (O-W) Strategies OW-1:Change directly in manufacture preferences to create products of high environmental requirement standard |
Threats: -T1: Diminishing specific market demand and profitability -T2: Government or industry restrictive practices -T3: Negative corporation image if EMS certification is abandoned | Mini-maxi (T-S) Strategies TS-1: Increase strictly government or industry environmental regular | Mini-mini (T-W) Strategies TW-1: Government impel environmental education and assistance for SMEs |
SWOT and TOWS matrixes for EMS
The second task is to regard these SWOT indexes as candidates that are voted by TF. The four criteria are the strength, weakness, opportunity and threat indexes and the twelve sub-criteria are S1-3, W1-3, O1-3 and T1-3 alternatively, within the SWOT. They are regarded as the selected items and expected to receive votes with respect to the related elements within the model, as shown in Table 1.
The TF illustrate the order for the four criteria and the votes for each which are shown in Table 2. Every members will vote from 1 to S, (SR), where R is the number of criteria or sub-criteria. They are regarded as candidates whom are to be voted by different places. Afterward, TF will list its priority votes of sub-criteria in fixed first criterion within Table 3. They were only asked to determine the order of the criteria or sub-criteria, however not the weight of each criterion or sub-criterion.
Criteria | 1st | 2nd | 3rd | 4th | Weights |
Strengths | 4 | 8 | 4 | 2 | 0.264 |
Opportunities | 7 | 4 | 7 | 0 | 0.299 |
Weaknesses | 2 | 0 | 7 | 9 | 0.174 |
Threats | 5 | 6 | 0 | 7 | 0.263 |
Priority votes of four criteria. * The weights are normalized and totally equal to one.
The votes on Table 2 are used to calculate the weights of the four criteria by model (2), R=4, S=4, n=18 and the lowest weights of the fourth place are
Criteria | Votes | Weights | Votes | Weights | ||||||
1st | 2nd | 3rd | 1st | 2nd | 3rd | |||||
Strengths | Opportunities | |||||||||
S1 | 13 | 4 | 1 | 0.465 | O1 | 15 | 2 | 1 | 0.495 | |
S2 | 0 | 9 | 9 | 0.227 | O2 | 0 | 15 | 3 | 0.258 | |
S3 | 5 | 5 | 8 | 0.308 | O3 | 3 | 1 | 14 | 0.247 | |
Total | 18 | 18 | 18 | Total | 18 | 18 | 18 | |||
Weaknesses | Threats | |||||||||
W1 | 11 | 7 | 0 | 0.439 | T1 | 5 | 5 | 8 | 0.304 | |
W2 | 3 | 3 | 12 | 0.258 | T2 | 5 | 9 | 4 | 0.336 | |
W3 | 4 | 8 | 6 | 0.303 | T3 | 8 | 4 | 6 | 0.360 | |
Total | 18 | 18 | 18 | Total | 18 | 18 | 18 |
Priority votes and weights of twelve sub-criteria. * The weights are normalized and totally equal to one.
The competitive strategies, OS-1, OS-2, OW-1, TS-1 and TW-1 are subjective indices that could be translated into numerical ratings using different methods, such as questionnaire, AHP or vote-ranking and so much more. TF may ask their colleagues to answer these questionnaires in order to rate the competitive strategies of sub-criteria of each SWOT. A major problem was thus, to ensure the consistency between managers and to avoid any bias creeping in. A set of standard guidelines was placed after discussions with the TF (voters). It is mainly agreed that all performance scores would be based on a nine points grade scale. Each grade would have an adjective descriptor and an associated point score or range of point scores. The TF makes their judgment on the qualitative scale of adjectival descriptors.
Table 4 lists the example for rating the strength and opportunity indices, where the lower and upper scores are predetermined from 1-9. The strength and opportunity indices should be maximized, the least-favorable candidate is assigned the smallest value and the most-favorable candidate is assigned the largest value. On the other hand, the weakness and threat indices need to be minimized, where the least-favorable candidate is assigned the largest value and the most-favorable candidate is assigned the smallest value. The overcoming range of subjective indices is set between 1 and 9 illustrated in Table 5. Therefore, each of the competitive strategy can be awarded a ‘score’ from 1 to 9 on each sub-criterion.
Scores | Rules |
9 | Greatly conforming to market and sub-criteria of requirement, successful probability more than 90% |
7 | Better conforming to market and sub-criteria of requirement, successful probability about 70% |
5 | Conforming to market and sub-criteria of requirement, successful probability about 50% |
3 | Slightly conforming to market and sub-criteria of requirement, successful probability about 30% |
1 | Not conforming to market and sub-criteria of requirement, successful probability about 10% |
Grading different strategy scores in strength and opportunity indexes
Scores | Rules |
9 | Greatly overcoming sub-criteria requirement, successful probability more than 90% |
7 | Better overcoming sub-criteria requirement, successful probability about 70% |
5 | Exactly overcoming sub-criteria requirement, successful probability about 50% |
3 | Slightly overcoming sub-criteria requirement, successful probability about 30% |
1 | Not overcoming sub-criteria requirement, successful probability about 10% |
Grading different strategy scores in weakness and threat indexes
The five competitive strategies, OS-1, OS-2, OW-1, TS-1 and TW-1, by means of the highest rating were regarded as the best competitive strategies, with the rest being ranked accordingly. The competitive strategies will earn the average scores of questionnaires within Table 4 and Table 5 by TF. The average of collected scores is listed in the columns 5-9 of Table 6.
This step requires the TF to assess the performance of all the competitive strategies within the twelve sub-criteria of SWOT identified as important for competitive strategies rating. Simple score sheets were provided to assist the manager to record the scores for each strategy on each of the twelve sub-criteria. An example of this strategy is shown in Table 6. In the first row of Table 6, the number 0.123 is equal to the product of the “Strength” criterion score 0.264 multiply with the S1 given value of “0.465”. Moreover, the same method is applied to obtain other results. Once the weights for sub-criteria have been determined, it is relatively easy to calculate the resulting competitive strategies rating scores.
Mathematically, the rating is equivalent to the sum of the product of each sub-criterion weight and the competitive strategy performance score. The rating value of competitive strategies is obtained by summing the products of the respective elements. The competitive strategies rating value for strategy OS-1 is obtained by summing up the products of the respective elements in columns 4 and 5 for each row; given in the final column 10, the over all total weighted scores of the row is “6.859”. The rating method used in strategy OS-1, can be used to find the total scores of the other four strategies stated in columns 11-14 of Table 6.
The rating value for each competitive strategy is obtained by summing the products of the respective elements in the matrix; given in the final score, the values of over all competitive strategies of OS-1, OS-2, OW-1, TS-1 and TW-1 respectively is, 6.859, 8.357, 7.532, 7.298 and 8.274 stated within the last row of Table 6. This gave a rating score for each competitive strategy, whereas the higher the rating, the better the overall performance for competitive strategy.
In the last row of Table 6, the rating value for each strategy is obtained; the final score and the ranking of competitive strategies for OS-2, TW-1, OW-1, TS-1 and OS-1 is first, second, third, fourth and fifth respectively. Even though the score of OS-2 is only higher by 0.083 than TW-1 and the score of OW-1 is higher by 0.234 than TS-1, however for both of the competitive strategies, the difference of scores will definitely change the overall final rank. These results will be regarded as sensitivity analysis for five competitive strategies.
Criteria (A) | Sub-criteria (B) | Weights (C= A×B) | Grade Strategies Scores | Weighted Strategies Scores | ||||||||||
OS-1 | OS-2 | OW-1 | TS-1 | TW-1 | OS-1 | OS-2 | OW-1 | TS-1 | TW-1 | |||||
Strengths | S1 | 0.465 | 0.123 | 6.833 | 8.889 | 8.056 | 6.944 | 8.722 | 0.839 | 1.091 | 0.989 | 0.853 | 1.071 | |
0.264 | S2 | 0.227 | 0.060 | 6.944 | 8.944 | 7.611 | 7.278 | 8.500 | 0.416 | 0.536 | 0.456 | 0.436 | 0.509 | |
S3 | 0.308 | 0.081 | 7.056 | 8.833 | 7.556 | 8.611 | 8.611 | 0.574 | 0.718 | 0.614 | 0.700 | 0.700 | ||
Opportunities | O1 | 0.495 | 0.148 | 6.833 | 7.778 | 7.389 | 7.278 | 8.167 | 1.011 | 1.151 | 1.094 | 1.077 | 1.209 | |
0.299 | O2 | 0.258 | 0.077 | 6.944 | 8.000 | 7.611 | 7.389 | 8.000 | 0.536 | 0.617 | 0.587 | 0.570 | 0.617 | |
O3 | 0.247 | 0.074 | 6.778 | 8.111 | 7.778 | 7.500 | 7.944 | 0.501 | 0.599 | 0.574 | 0.554 | 0.587 | ||
Weaknesses | W1 | 0.439 | 0.076 | 6.611 | 7.778 | 7.222 | 6.944 | 7.833 | 0.505 | 0.594 | 0.552 | 0.530 | 0.598 | |
0.174 | W2 | 0.258 | 0.045 | 6.500 | 7.833 | 7.278 | 7.056 | 7.778 | 0.292 | 0.352 | 0.327 | 0.317 | 0.349 | |
W3 | 0.303 | 0.053 | 6.667 | 7.944 | 7.500 | 7.278 | 7.722 | 0.351 | 0.419 | 0.395 | 0.384 | 0.407 | ||
Threats | T1 | 0.304 | 0.080 | 7.056 | 8.611 | 7.278 | 7.000 | 8.444 | 0.564 | 0.688 | 0.582 | 0.560 | 0.675 | |
0.263 | T2 | 0.336 | 0.088 | 7.111 | 8.722 | 7.556 | 7.167 | 8.389 | 0.628 | 0.771 | 0.668 | 0.633 | 0.741 | |
T3 | 0.360 | 0.095 | 6.778 | 8.667 | 7.333 | 7.222 | 8.556 | 0.642 | 0.821 | 0.694 | 0.684 | 0.810 | ||
Total Weighted Scores | 6.859 | 8.357 | 7.532 | 7.298 | 8.274 |
The SWOT analysis of different strategies
First of all, considering that the strategy OS-1 has the lowest score within the strategy analysis, most of the SMEs supposed that this strategy is quite acceptable even though there are still have some doubts present, especially on whether or not by obtaining the EMS related international standard authentication, such as ISO14000, it will certainly create a high value-added market. From a present market condition which is quite unfeasible to reflect the practical demand, frequently as a final result it is invested in fund or modification. Even though most of the customers are quite optimistic and agreed to this way of doing, however when everything is fully involved within EMS in the future, it will certainly has some affect on its capital or product selling price. Which means that at the present moment, the demand on this particular product is lacking, furthermore, it might resulted in the incapability to agree on these certain analysis by some SMEs.
Moreover, from the strategy analysis OW-1and TS-1 point of view, direct changes in manufacture preferences to create products of high environmental requirement standard has a bigger risk toward the SMEs in term of direct investment. Generally, average companies do not have certain investment planning until it has reached a deal, order placement or customer’s promise in advance. Additionally, an increasingly strict government or industry environmental in carrying out this phase is facing difficulty, where presently the government mostly is using counseling method or fund assistance to encourage and urge the industry to increase its EMS ability in order to reach the low price product strategy and high level of product diversification.
Lastly, the strategy OS-2 imposes a similar way of thinking with strategy TW-1. Taiwanese SMEs apperceive the significance of EMS and also recognize the importance to survive within the diversified competing market environment, whereas they need to build up its environmental management that has to suit the EMS specification and attention. However, the investment within environmental protection for its resources and facilities requires a great amount of expenditures. Under this major investment, if the expected outcomes are unpredictable, therefore the willingness on investing within the environmental management will suffer an enormous drawback. These SMEs certainly would hope that government will work together with country resources, providing some assistance in procuring EMS needed facilities and equipments or even any related training within the environmental management scope, moreover guidance or counselling in obtaining different kinds of ISO authentic certificate will also be valuable resource.
Obviously, most people are familiar with the conflicts between environmental protection and economic development. Those who are convinced of the consequences of global warming will remain convinced, while those suspicious will remain suspicious. After all, economic development means bread, while the mankind cannot immediately appreciate the deep implications of its damage to the great nature. Therefore, politicians should be aware of the environmental implications of legal provisions and regulations. Likewise, the industry authorities, when developing new products, should consider the intangible social cost of pollution as a part of the overall cost and deal with the issue of pollution as a part of life cycle management, so that such considerations and practices will benefit our earth. In EMS, this will further our understanding of the potential poisonous substances to be produced in production, deployment and replacement stages, and will help us minimize pollution and thus contribute to environmental protection.
With the continuing development of human civilization and technology, the life cycle of any products, from production, consumption to final waste, it is involving more and more external adverse factors which bring about direct or indirect impact on the environment. Economists said that we should stop aggravation of global warming now; and there is only one earth; therefore, be environmentally friendly.
Lastly, the competitive strategies OS-2 and TW-1 will be provided to Taiwanese SME department and industry union. The main contributions of this study are as follows:
The selection procedure of competitive strategies in SWOT can assist the audience to think in a very comprehensive and detailed manner, while allowing them to categorize various issues.
In this field, many researchers have sought to improve the different capabilities of quantitative SWOT, such as AHP, ANP or fully rank decision-making units. In this case, the vote-ranking methodology incorporated with SWOT is applied and as a result, it became the easiest and most convenient method compared to others.
The vote-ranking is presented as an approach to the problem of ranking candidates in a preferential election. The future researches had suggested that the cross-evaluation method is better off to be applied to assess candidates through peer-group, whereas one can attain a more balanced view of the weight-setting. The cross evaluation can be used to overcome the problem of maverick decision-makers. The proposed methodology can be utilized to issues of SWOT, such as AHP or ANP within this study.
In the last years, the problem with crude oil depletion has arisen. Intensive research has been carried out to find out alternative to fossil fuels. Alternative fuels are derived from resources different from petroleum. When used in internal combustion engines (ICE), these fuels generate lower air pollutants compared to petrol fuel, and a majority of them are more economically beneficial compared to fossils fuels. They are also renewable. The most common fuels that are used as alternative fuels are natural gas, propane, methanol, ethanol, and hydrogen. Regarding engine operating with blended fuels, a lot of papers have been written about these blended fuels; but a small number of works have compared some of these fuels together in the same engine [1, 2, 3, 4]. Low contents of ethanol or methanol have been added to gasoline since at least the 1970s, when there was a reduction in oil supplies and scientists began searching for alternative energy carriers in order to replace petrol fuels. In the beginning, ethanol and methanol were thought to be the most attractive alcohols to be added to gasoline. Ethanol and methanol can be manufactured from natural products or waste materials, whereas gasoline fuel which is a nonrenewable energy resource cannot be manufactured [5, 6]. An important feature is that methanol and ethanol can be used without requiring any significant changes in the structure of the engine. Being part of the various alcohols, ethanol and methanol are known as the most suitable fuels for spark-ignition (SI) engines.
The use of blended fuels is crucial since many of these blends can be used in engines with the aim to improve its performance, efficiency, and emissions. The oxygenates are one of the most important fuel additives to improve fuel efficiency (organic oxygen-containing compounds). A few oxygenates have been used as fuel additives, such as ethanol, methanol, methyl tertiary butyl alcohol, and tertiary butyl ether [7]. The process of using oxygenates makes more oxygen available in the combustion process and has a great potential to reduce SI engine exhaust emissions.
Regarding the combustion process, the flash point and autoignition temperature of methanol and ethanol are higher than pure gasoline, which makes it safer for storage and transportation. The latent heat of ethanol of evaporation is three to five times higher than pure gasoline; this leads to increase the volumetric efficiency because temperature of the intake manifold is lower. The heating value of ethanol is lower than gasoline. Consequently, 1.6 times more alcohol fuel is needed to achieve the exact same energy output. The stoichiometric air-fuel ratio of ethanol is around two-third of the pure gasoline; therefore, for complete combustion, the needed amount of air is lesser for ethanol [8]. Ethanol has several advantages compared to gasoline, e.g., lowering of unburned HC emissions, CO, and much better antiknock characteristics [9]. Ethanol and methanol have a lot higher octane number compared to pure gasoline fuel [10]. This enables higher compression ratios of engines and, as a result, increases its thermal efficiency [11]. The production of methanol can be from natural gas at no great cost and is easy to blend with gasoline fuel. These properties of methanol make it as an attractive additive. Methanol is aggressive to some materials, like plastic components and some of the metals in the fuel system. When using methanol it is necessary that precautions had to be taken when handling it [12].
There are many publications with different blends of alcohols and gasoline fuel. For example, Palmer [13] examined the influence of blends of ethanol and gasoline in spark-ignition engine. The obtained results pointed out that ethanol addition (10%) leads to 5% increase in the engine power and 5% octane number increase for each 10% ethanol added. The result showed that 10% of ethanol addition to gasoline fuel lead to reduction the emissions of CO up to 30%. In another study, Bata et al. [9] examined different blends of ethanol and gasoline and discovered that ethanol reduced the UHC and CO emissions. The lowered CO emissions are caused by the oxygenated characteristic and wide flammability of ethanol. Other researchers [14] studied that the potentialities for ethanol production are equivalent to about 32% of the total gasoline consumption worldwide, when used in 85% ethanol in gasoline for a passenger vehicle. In another study, Shenghua et al. [15] examined a gasoline engine with various percentages of methanol blends (from 10 to 30%) in gasoline. The results showed that engine torque and power decreased, whereas the brake thermal efficiency improved with the increase of methanol percentage in the fuel blend. Other authors [16] have studied the influence of methanol-gasoline blends on the gasoline engine performance. The results showed that the highest brake mean effective pressure (BMEP) was obtained from 5% methanol-gasoline blend. In another study, Altun et al. [17] researched the influence of 5 and 10% methanol and ethanol blending in gasoline fuel on engine performance and emissions. The best result in emissions showed blended fuels. The HC emissions of E10 and M10 are reduced by 13 and 15% and the CO emissions by 10.6 and 9.8%, respectively. An increased CO2 emission for E10 and M10 was observed. The methanol and ethanol addition to gasoline showed an increase in the brake-specific fuel consumption (BSFC) and a decrease in break thermal efficiency compared to gasoline.
It can be seen in the literature survey that the exhaust emissions for ethanol-gasoline and methanol-gasoline blends are lower than that of pure gasoline fuel [9, 13, 14, 17]. The engine performance and exhaust emissions with ethanol-gasoline blends resemble those with methanol-gasoline blends.
From the reviewed literature, a conclusion was made that the exhaust emission and engine performance of various blends of methanol and ethanol in gasoline engines have not been investigated sufficiently. Therefore, the objective of this work is to investigate the effects of methanol-gasoline and ethanol-gasoline fuel blends on the performance and exhaust emissions of a gasoline engine under various engine speeds, comparing them with those of pure gasoline.
The simulation tools are the most used in recent years owing to the continuous increase in computational power. The use of engine simulations enables optimization of engine combustion, geometry, and operating characteristics toward improving specific fuel consumption and exhaust emissions and reducing engine development time and costs. Consequently, it can be expected that the use of engine simulations during engine construction will continue to increase. Engine modeling is a fruitful research area, and therefore many laboratories have their own engine thermodynamic models with varying degrees of complexity, scope, and ease to use [18].
Computer simulation is becoming an important tool for time and cost efficiency in engine’s development. The simulation results are challenging to be obtained experimentally. Using computational fluid dynamics (CFD) has allowed researchers to understand the flow behavior and quantify important flow parameters such as mass flow rates or pressure drops, under the condition that the CFD tools have been properly validated against experimental results. Many processes in the engine are three-dimensional; however, it requires greater knowledge and large computational time. Thus, simplified one-dimensional simulation is occasionally used. Hence, simulating the complex components by means of a three-dimensional code and modeling the rest of the system with a one-dimensional code are the right choice to save computational time, i.e., the ducts. This way, a coupling methodology between the one-dimensional and the three-dimensional codes in the respective interfaces is necessary and has become the aim of numerous authors [19, 20, 21].
The aim of the present chapter is to develop the one-dimensional model of four-stroke port fuel injection (PFI) gasoline engine for predicting the effect of methanol-gasoline (M0–M50) and ethanol-gasoline (E0–E50) addition to gasoline on the exhaust emissions and performance of gasoline engine. For this, simulation of gasoline SI engine (calibrated) as the basic operating condition and the laminar burning velocity cor relations of methanol-gasoline and ethanol-gasoline blends for calculating the changed combustion duration was used. The engine power, specific fuel consumption, and exhaust emissions were compared and discussed [22, 23].
The one-dimensional SI engine model is created by using the AVL BOOST software and has been employed to examine the performance and emissions working on gasoline, ethanol-gasoline, and methanol-gasoline blends.
In Figure 1, PFIE symbolizes the engine, while C1–C4 is the number of cylinders of the SI engine. The cylinders are the main element in this model, because they have many very important parameters to settle: the internal geometry, bore, stroke, connecting rod, length and compression ratio, as well as the piston pin offset and the mean crankcase pressure. The measuring points are marked with MP1–MP18. PL1–PL4 symbolizes the plenum. System boundary stands for SB1 and SB2. CL1 represents the cleaner. R1–R10 stands for flow restrictions. CAT1 symbolizes catalyst and fuel injectors—I1–I4. The flow pipes are numbered 1–34.
Schematic of the gasoline PFI engine model.
The calibrated gasoline engine model was described by Iliev [23], and its layout is shown in Figure 1 with engine specification shown in Table 1.
Engine parameters | Value |
---|---|
Bore | 86 (mm) |
Stroke | 86 (mm) |
Compression ratio | 10.5 |
Connection rod length | 143.5 (mm) |
Number of cylinder | 4 |
Piston pin offset | 0 (mm) |
Displacement | 2000 (cc) |
Intake valve open | 20 BTDC (deg) |
Intake valve close | 70 ABDC (deg) |
Exhaust valve open | 50 BBDC (deg) |
Exhaust valve close | 30 ATDC (deg) |
Piston surface area | 5809 (mm2) |
Cylinder surface area | 7550 (mm2) |
Number of stroke | 4 |
Engine specification.
Table 2 presents a comparison between the properties of gasoline, ethanol, and methanol. As shown in Table 2, compared with gasoline and ethanol, methanol has a higher elemental oxygen content and a lower heating value, molecular weight, elemental carbon, hydrogen content, and stoichiometric air/fuel ratio (AFR).
Properties | Gasoline | Methanol | Ethanol |
---|---|---|---|
Chemical formula | C8H15 | CH3OH | C2H5OH |
Molecular weight | 111.21 | 32.04 | 46.07 |
Oxygen content (wt%) | — | 49.93 | 34.73 |
Carbon content (wt%) | 86.3 | 37.5 | 52.2 |
Hydrogen content (wt%) | 24.8 | 12.5 | 13.1 |
Stoichiometric AFR | 14.5 | 6.43 | 8.94 |
Lower heating value (MJ/kg) | 44.3 | 20 | 27 |
Heat of evaporation (kJ/kg) | 305 | 1.178 | 840 |
Research octane number | 96.5 | 112 | 111 |
Motor octane number | 87.2 | 91 | 92 |
Vapor pressure (psi at 37.7 OC) | 4.5 | 4.6 | 2 |
Destiny (g/cm3) | 0.737 | 0.792 | 0.785 |
Normal boiling point (OC) | 38–204 | 64 | 78 |
Autoignition temperature (OC) | 246–280 | 470 | 365 |
Comparison of fuel properties.
In this research, two-zone model of Vibe was chosen for the combustion simulation and analysis. The combustion chamber was divided into two regions: unburned gas region and burned gas regions [17]. For the burned charge and unburned charge, the first law of thermodynamics is applied:
where
Moreover, the sum of the zone volumes must be equal to the cylinder volume, and the sum of the volume changes must be equal to the cylinder volume change:
The amount of burned mixture at each time setup is obtained from the Vibe function. For all other terms, for instance, wall heat losses, etc., models similar to the single-zone models with an appropriate distribution on the two zones are used [24].
In AVL BOOST, the model of formation on NOx is based on AVL List Gmbh [24], which incorporates the Zeldovich mechanism [25]. The rate of NOx production was obtained using Eq. (5):
where
In the above equation,
The NOx formation model in AVL Boost is based on Onorati et al. [26]:
In Eq. (6),
The unburned HC has different sources. A complete description of HC formation still cannot be given, and the achievement of a reliable model within a thermodynamic approach is definitely prevented by the fundamental assumptions and the requirement of reduced computational times. Still, a phenomenological model which accounts for the main formation mechanisms and is able to capture the HC trends as function of the engine operating parameter may be proposed. The following important sources of unburned HC can be identified in SI engines [21]:
During the intake and compression stroke, fuel vapor is absorbed into the oil layer and deposits on the cylinder walls. The following desorption occurs when the cylinder pressure decreases during the expansion stroke and complete combustion cannot take place anymore.
A fraction of the charge enters the crevice volumes and is not burned since the flame quenches at the entrance.
Occasional complete misfire or partial burning takes place when combustion quality is poor.
Quench layers on the combustion chamber wall which are left as the flame extinguishes prior to reaching the walls.
The flow of fuel vapor into the exhaust system during valve overlap in gasoline engines.
The first two mechanisms and in particular the crevice formation are considered to be the most important and need to be accounted for in a thermodynamic model. Partial burn and quench layer effect cannot be physically described in a quasi-dimensional approach, but may be included by adopting tunable semiempirical correlations.
The formation of unburned HC in the crevices is described by assuming that the pressure in the cylinder and in the crevices is the same and that the temperature of the mass in the crevice volumes is equal to the piston temperature.
The mass in the crevices at any time is described by Eq. (7):
In Eq. (7),
The second important source of HC is the presence of lubricating oil in the fuel or on the walls of the combustion chamber. During the compression stroke, the fuel vapor pressure increases so, by Henry’s law, absorption occurs even if the oil was saturated during the intake. During combustion the concentration of fuel vapor in the burned gases goes to zero so the absorbed fuel vapor will desorb from the liquid oil into the burned gases. Fuel solubility is a positive function of the molecular weight, so the oil layer contributed to HC emissions depending on the different solubility of individual hydrocarbons in the lubricating oil.
The assumptions made in the development of the HC absorption/desorption are the following:
Fuel is constituted by a single hydrocarbon species, completely vaporized in the fresh mixture.
The oil film temperature is at the same as the cylinder wall.
Traverse flow across the oil film is negligible.
Oil is represented by squalane (C30H62), whose characteristics resemble those of the SAE5W20 lubricant.
Diffusion of the fuel in the oil film is the limiting factor, for the diffusion constant in the liquid phase which is 104 times smaller than the corresponding value in the gas phase.
The radial distribution of the fuel mass fraction in the oil film can be determined by solving the diffusion Eq. (8):
In Eq. (8),
The present research focused on the performance and emission characteristics of the methanol and ethanol-gasoline blends. Various concentrations of the blends 0% methanol (ethanol) M0 (E0), 5% methanol (ethanol) M5 (E5), 10% methanol (ethanol) M10 (E10), 20% methanol (ethanol) M20 (E20), 30% methanol (ethanol) M30 (E30), 50% methanol (ethanol) M50 (E50), and 85% methanol (ethanol) M85 (E85) by volume were analyzed.
The results of the brake power and specific fuel consumption for ethanol-gasoline blended fuels at different engine speeds are shown on Figures 2 and 3.
Influence of ethanol-gasoline blended fuels on brake power.
Influence of ethanol-gasoline blended fuels on brake-specific fuel consumption.
The brake power is one of the important factors that determine the performance of an engine. The variation of brake power with speed was obtained at full load conditions for E5, E10, E20, E30, E50, and pure gasoline E0. The ethanol content in the blended fuel increased, and the brake power decreased for all engine speeds. The gasoline brake power was higher than E5–E50 for all engine speeds. The ethanol’s heat of evaporation is higher in comparison to gasoline fuel, providing air-fuel charge cooling and increasing the density of the charge. The blended fuel causes the equivalence ratio of blend approaches to stoichiometric condition which can lead to a better combustion. However, the ethanol heating value is lower compared to gasoline, and it can neutralize the previous positive effects. Consequently, a lower power output is obtained.
Figure 3 shows the changes of the BSFC for ethanol-gasoline blends under various engine speeds. The figure shows that the BSFC increased as the ethanol percentage increased. Heating value and stoichiometric air-fuel ratio are the smallest for these two fuels, which means that for specific air-fuel equivalence ratio, more fuel is needed. The highest specific fuel consumption is obtained at E50 ethanol-gasoline blend.
Moreover, there is a slight difference between the BSFC when using pure gasoline and when using blends (E5, E10, and E20). The lower energy content of blended fuels causes some increment in BSFC of the engine.
Figure 4 shows the influence of methanol-gasoline blended fuels on engine brake power. The variation of brake power with speed was obtained at full load conditions for M5, M10, M20, M30, M50, and pure gasoline M0. When the methanol content in the blended fuel was increased (M10, M20, and M30), there was not a significant increase in engine brake power.
Influence of methanol-gasoline blended fuels on brake power.
The engine brake power may be due to the increase of the indicated mean effective pressure for higher methanol content blends. The methanol’s heat of evaporation is higher compared to that of gasoline, thus providing air-fuel charge cooling and increasing the density of the charge. Therefore, a higher power output is obtained. The engine brake power was higher in operation with gasoline in comparison to M50 for all engine speeds.
Figure 5 shows the variations of the BSFC for methanol-gasoline blended fuels under various engine speeds. As shown in this figure, the BSFC increased as the methanol percentage increased. This can be described with heating value, and stoichiometric air-fuel ratio is the smallest for these two fuels, which means that for specific air-fuel equivalence ratio, more fuel is needed. The specific fuel consumption of M50 methanol-gasoline blend was highest compared to those of gasoline for all engine speeds.
Influence of methanol-gasoline blended fuels on engine brake power.
Furthermore, there is a small difference between the BSFC when using gasoline and when using methanol-gasoline blended fuels (M5–M30). As engine speed increased reaching 2000 rpm, the BSFC decreased reaching its minimum value.
The results of the brake power and specific fuel consumption for ethanol- and methanol-gasoline blended fuels at different engine speeds are presented in Figures 6 and 7.
Effect of blended fuels on engine brake power.
Influence of blended fuels on engine fuel consumption.
When there was an increase in the ethanol content in the blended fuel, the brake power decreased for all engine speeds. The brake power of gasoline fuel was higher than those of E5–E50. The heating value of ethanol is lower than pure gasoline fuel, and the heating value of the blends decreases with the increase of the ethanol percentage. Consequently, a lower power output is obtained [22, 23].
By increasing the percentage of methanol in the blends (M5 and M10), the brake power slightly increased, which can be explained by better combustion efficiency of oxygenated fuels. By increasing the methanol content in the blends (M30 and M50), the engine brake power decreased for all engine speeds. The blended fuel heating value decreases with the increase of the percentage of methanol. This results in a lower power output. The gasoline brake power was higher compared to blend M50.
Figure 7 shows the changes of the BSFC for blended fuels under different engine speeds. The BSFC increased as the ethanol and methanol percentage increased. The reason has been known—the heating value and stoichiometric air-fuel ratio are the smallest for this fuel, which means that more fuel is needed for specific air-fuel equivalence ratio. The highest specific fuel consumption is obtained at E50 (M50) blended fuel.
What is more, there is small difference between the BSFC when using pure gasoline and blended fuels (E5 (M5), E10 (M10), and E20 (M20)). The lower energy content of ethanol blended fuels makes some increment in BSFC.
The result of the ethanol-blended fuels on CO emissions is shown in Figure 8.
Influence of ethanol-gasoline blended fuels on CO emissions.
A conclusion, which can be made by Figure 8, is that when ethanol content increases, the CO emission decreases. The reason for this could be explained with the enrichment of oxygen owing to the ethanol, in which an increase in the proportion of oxygen will promote the further oxidation of CO during the engine exhaust process. One of the other significant reasons for this reduction is that ethanol (C2H5OH) has less carbon than gasoline (C8H18).
The result of the ethanol gasoline blends on HC emissions is shown in Figure 9. The figure shows that when ethanol percentage increases, the HC concentration decreases. The HC emission decreases with the increase of the relative air-fuel ratio. The decrease of HC can be explained similarly to that of CO concentration described above.
Influence of ethanol-gasoline blended fuels on HC emissions.
The effect of the ethanol gasoline blends on NOx emissions for various engine speeds is shown in Figure 10. When the combustion process is closer to stoichiometric, flame temperature increases. As a result, the NOx emissions are increased.
Influence of ethanol-gasoline blended fuels on NOx emissions.
The effect of the methanol-gasoline blends on CO emissions for various engine speeds can be seen in Figure 11. When methanol percentage increases, the CO concentration decreases. This can be explained with the enrichment of oxygen because of the methanol and less carbon of methanol than gasoline.
Influence of methanol-gasoline blended fuels on CO emissions.
The effect of the methanol-gasoline blends on HC emissions is visible in Figure 12. When methanol percentage increases, the HC concentration decreases. The concentration of HC emissions decreases with the increase of the relative air-fuel ratio. The reason for the decrease of HC concentration resembles that of ethanol.
Influence of methanol-gasoline blended fuels on HC emissions.
The effect of the methanol-gasoline blends on NOx emissions can be seen in Figure 13. When methanol percentage increases, the NOx concentration increases. When combustion process is closer to stoichiometric, flame temperature increases and the NOx emissions increase as well.
Influence of methanol-gasoline blended fuels on NOx emissions.
The effect of the ethanol- and methanol-gasoline blends on CO emissions can be viewed in Figure 14. By increasing the methanol and ethanol content in the blended fuel, the CO emission decreases. The reason can be the enrichment of oxygen because of the ethanol and methanol, in which an increase in the proportion of oxygen will promote the further oxidation of CO during the engine exhaust process. Another major reason for this reduction is that ethanol (C2H5OH) and methanol (CH3OH) have less carbon than gasoline (C8H18). The lowest CO emissions are obtained with blended fuel containing methanol (M50).
Influence of ethanol- and methanol–gasoline blended fuels on CO emissions.
The effect of the ethanol- and methanol-gasoline blends on HC emissions is visible in Figure 15. When there is an increase in the ethanol and methanol percentage, the HC concentration decreases.
Influence of blended fuels on HC and NOx emissions.
When the relative air-fuel ratio increases, the concentration of HC emissions decreases. The reason for the decrease in HC emissions is similar to that of CO described above. The comparison between the decrease in HC emissions and the blended fuels indicates that methanol is more effective than ethanol. The lowest HC emissions are obtained with methanol-blended fuel (M50). When more combustion is complete, it will result in lower HC emissions.
Figure 15 shows the influence of the blended fuels on NOx emissions. It is noticeable that when methanol and ethanol percentage increases up to 30% E30 (M30), the NOx emission increases, after which it decreases with increasing the percentage of the methanol (ethanol).
The reason is that the improved combustion results in increased temperature in combustion chamber. The higher methanol (ethanol) content in the blends lowers the temperature in combustion chamber. The lower temperature is due to:
Latent heat of evaporation of alcohols, which decreases the temperature in combustion chamber during the vaporization.
The more triatomic molecules are produced: the higher the gas heat capacity and the lower the combustion gas temperature will be. However, the low temperature in combustion chamber can also lead to an increment in the unburned combustion product.
The purpose of the present chapter is to demonstrate the influence of ethanol and methanol addition to gasoline on spark-ignition engine performance and emission characteristics. The summarized results from this study are the following:
With the increase of the percentage of ethanol in the blended fuel, the engine brake power decreased for various engine speeds.
With the increase of the percentage of methanol in the blends M5 and M10, the brake power slightly increased, and with the increase of the percentage of methanol in the blends M30 and M50, the brake power decreased.
As the ethanol (methanol) percentage increased, the BSFC increased. The blended fuels show higher BSFC and lower engine brake power than pure gasoline. Furthermore, there is a slight difference between the BSFC in comparison of gasoline and gasoline blended fuels (E5, E10, and E20 and M5, M10, and M20).
When there is an increase in ethanol and methanol percentage, the CO and HC concentration decreases. The lowest CO and HC emissions are obtained with blended fuel containing methanol (M50).
Increasing the percentage of ethanol and methanol leads to a significant increase in NOx emissions.
When there is an increase in the ethanol and methanol percentage up to 30% E30 (M30), there is an increase in the NOx concentration, followed by a decrease, after which it decreases with increasing ethanol (methanol) percentage. The lowest NOx emissions are obtained with gasoline.
The present chapter has been written with the Project No 2018-RU-07’s financial assistance. We are also eternally grateful to AVL-AST, Graz, Austria, for granting the use of AVL BOOST under the university partnership program.
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