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28Economics, Management and Sustainability
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Azizi, M. (2017). Strategic model for location selection of solar wood drying by applying TOPSIS. Economics, Management and Sustainability, 2(2), 15-23. doi:10.14254/jems.2017.2-2.2.
ISSN 2520-6303
Strategic model for location selection of solar wood drying by applying TOPSIS
Majid Azizi
College of Agriculture and Natural Resources, University of Tehran, 16th Azar St., Enghelab Sq., Tehran, Iran
Professor, Faculty of Natural Resources, Wood and Paper Sciences and Technology Department
Qaccess {g] dj
open
Article history:
Received: August 15, 2017 1st Revision: September 11, 2017
Accepted: October 11, 2017
JEL classification:
D4
L11
Q13
DOI:
10.14254/jems.2017.2-2.2
Abstract: The location of solar wood drying has not been selected in Iran yet. One of important applications of solar energy is to manufacture solar wood drying units. Effective indicators in location of solar wood drying were identified and a hierarchy was constructed based on five major groups of criteria. The weights of the indicators were then established by Analytical Hierarchy Process. The amounts of the indicators with regard to provinces were obtained from wood drying factories in public and private sectors. These weights were employed in TOPSIS to rank the provinces. Finally the potential provinces were identified according to the priorities obtained by this technique. The results showed that Qom Province, has the best priorities for establishment of solar wood drying.
Keywords: solar wood drying, TOPSIS, Analytical Hierarchy Process, location, priority.
1. Introduction
One of the possible and valuable applications of solar energy is in wood industry and manufacturing solar wood dryer. In solar dryers, solar energy is used for drying material indirectly or directly and air flow helps to moisture displacement naturally or in an under controlled way which accelerate wood drying process. The solar drying kiln is the most cost effective way for the craftsman to get quality boards for wood working from green lumber. Iran has been located between 25-40 degrees of northern latitude and regarding solar energy receiving has highest level in the world. The amount of sun radiation is between 1800-2200 (kWh)/m3 in a year which is higher than world average. In Iran more than 280 days are sunny which is very notable (www.suna.org.ir). Solar energy is one of the freest and cleanest sources of energy in the world which has no destructive effect on the environment. It has been used in various ways by the people for a long time. In the case of solar radiation for 40 days required energy for one century can be reserved. Thus by applying solar radiation concentrators along with the use of this free and clean and endless energy, the saving of fossil fuel consumption will also be possible. Today there are many band saw operators cutting boards from trees that grow in abundance in much of America. The solar kiln is the link between this resource and the shop. A wood kiln is any space used for controlling heat and humidity where lumber is dried. The solar drying kiln harnesses the free
Corresponding author: Majid Azizi
E-mail: mazizi@ut.ac.ir
This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license
energy of the sun. It operates on the regular cycle of day and night to prevent wood stress that can ruin lumber in other systems (Wilson, 2006). Solar drying is one of the important thermal applications, where solar energy can be utilized efficiently. Drying depends on the air ability to evaporate water (drying potential); hence its relative humidity is a key factor. The lower the relative humidity of the drying air, the more water of air evaporates from the product, resulting in lower final product moisture content. Drying potential is influenced by air temperature as well as relative humidity. Much work on solar energy has been concerned with the use of solar heated air (naturally or mechanically circulated) to remove the moisture from materials placed inside an enclosure where the heated air is blown past the material. Solar drying provides up to 50% reduction of final moisture content and drying time compared with air-drying (Helwa et al., 2004). Over the last few decades, much research and development has been conducted into the use of solar kilns for timber drying. This has led to the commercial use and availability of solar kilns in the timber industry over recent years (Desch & Dinwoodie, 1996). The present study aims to identify the effective criteria on best site selection to establish solar wood drying units in Iran via TOPSIS.
Studies on site selection for wood production by Michael et al (1998), identified a number of factors affecting the selection decision. They clustered the criteria into cost, market distribution, lower production cost and non-tangible factors. McCauley and Caulfield (1990) specified the effective criteria for selection of an OSB (Oriented strand board) factory and developed a mixed integer programming model to determine the optimal location of the OSB sites.
The AHP method is based on three steps: model structure; comparative judgment of the alternatives and criteria; and synthesis of the priorities. In the literature, the main developments in AHP have been widely used to solve many complicated decision-making problems (Ishizaka & Labib, 2011). For selecting the best wood panel, intensities of the criteria and sub criteria obtained. Then the wood panels have been ranked according to the AHP evaluation. The results indicate that the density of the product and its high intensity has the highest priority. The Ghazvin panel has the highest priority, and the moisture percentage criterion is very sensitive in comparison with other criteria (Azizi, 2012).
2. Modeling the selection problem
The modeling consists of three main stages, which are as follows:
2.1 First stage
For finding capable provinces of Iran to establish solar wood drying units 30 questionnaires were distributed among qualified people who were academic members (10%), Industries and mines organization; planning and budget organization (14%), members of furniture union (30%) and owners of industries (46%) and provinces which had no capability for establishing solar wood drying units were deleted. Capable Provinces which had appropriate site to establish solar wood drying units are as follow: Tehran, Qom, Khorasan Razavi, Markazi, Fars, Mazandaran, Isfahan, Ghazvin, Alborz. Climate changes is a limitation in this study. We studied the provinces in a stable situation regarding climate. The changes can be considered for future researches.
2.2 Second Stage
In order to analyze the candidate locations and identify the most preferred ones, the initial step is to identify the criteria. A comprehensive list of factors was prepared and a questionnaire was designed to evaluate their contribution in decision process in the case of Iran. This questionnaire was distributed among experts in Iran wood drying factories. The final set of the attributes was concluded via a Delphi method. A hierarchy of these factors was constructed to establish their weights, using Analytic Hierarchy Process (AHP). The pair-wise comparison matrices were completed by 20 experts from industry and academia. The individual judgments were directed towards consistency and the aggregated opinion was derived using TEAM- EC 2000. Figure 1 shows the hierarchy structure of the attributes influencing decision on selection of solar wood drying.
Figure 1: The hierarchy of criteria and sub-criteria
2.3 Third stage
In the third stage, the data for the attributes were collected from the alternative locations. For this, the questionnaires were presented to the managers of the neighboring factories. Then the Fuzzy Decision Making (FDM) (Memariani, 2000), software was used to rank the location because the data for certain attributes were either qualitative or imprecise. This software is base of on Fuzzy version of TOPSIS (Technique for order- preference by similarity to ideal solution). It incorporates besides quantitative information, the imprecise (Fuzzy numbers) and qualitative (linguistic) data. Figure 2 shows the description of the problem in FDM.
Figure 2: Description of the problem in FDM
Table 1 shows the weighing value of the attributes influencing decision on selection of provinces for solar wood drying.
Table 1: Factor table, criteria and sub criteria of solar wood drying location selection and their weighing values
Row Criteria Form of data Weighing value (Global) Kind of criteria Description
1 Raw material & product: raw material: quality linguistic 0.015 benefits Quality of raw material
2 Raw material & product: raw material: Confidence linguistic 0.009 benefits Confidence in supply
3 Raw material & product: raw material: distance: Now deterministic 0.006 costs Supply distance (present, Kilometer)
4 Raw material & product: raw material: distance: Future deterministic 0.002 costs Supply distance (future, Kilometer)
5 Raw material & product: final product: Market capacity linguistic 0.073 benefits Sale amount of product
6 Raw material & product: raw material: Quantity linguistic 0.015 benefits Quantity of raw material (inside the region, M3)
7 Raw material & product: final product: distance from market: outside deterministic 0.004 costs Distance from market (Kilometer)
8 Infrastructure Transportation network: Road linguistic 0.028 benefits Transportation network (road)
9 Infrastructure Transportation network: Rail linguistic 0.014 benefits Transportation network (rail)
10 Infrastructure Area profile Average rainfall linguistic 0.053 costs Average of rainfall in the province (mm)
11 Infrastructure Area profile Amount of absorbed solar energy linguistic 0.159 benefits Amount of absorbed solar energy In the province (cal/cm2) in the province
12 Infrastructure Area profile Relative humidity linguistic 0.053 costs Ralative humidity in the province (%)
13 Infrastructure Investment linguistic 0.033 benefits Capital absorption
14 Infrastructure Background of industry Energy linguistic 0.028 benefits Industry background
15 Infrastructure Background of industry Lateral industries linguistic 0.037 benefits Background of industry Lateral industries
16 Infrastructure: Background of industry: Services linguistic 0.013 benefits Background of industry: Services
17 Infrastructure: Background of industry: Competitors linguistic 0.018 costs Competitors in the province
18 Human & technical: Human: Training centers availability linguistic 0.01 benefits Training centers availability
19 Human & technical: Human: Skilled man force availability linguistic 0.071 benefits Skilled man force availability
20 Human & technical: Human: Welfare facilities linguistic 0.1 benefits Welfare facilities
21 Human & technical: Technical: Technological knowledge linguistic 0.021 benefits Technological knowledge
22 Economical: Costs: Man force cost linguistic 0.002 costs Man force costs (Monthly wage: Rial)
23 Economical Costs Price of land linguistic 0.02 costs Price of land (per m2: Rial)
24 Economical: Costs: Cost purchasing of raw material linguistic 0.071 costs Cost purchasing of raw material
25 Economical: linguistic 0.016 costs Storage cost
Costs: Storage cost (Daily: Rial)
26 Economical: Costs: Cost of transportation: Raw material Triangular Fuzzy 0.026 costs Cost of transportation: Raw material (Per10km:Rial)
27 Economical: Costs: Cost of transportation: Final product Triangular Fuzzy 0.009 costs Cost of transportation: Final product (Per 10km:Rial)
28 Economical: Granted facilities linguistic 0.142 benefits Granted facilities
29 Rules & regulations: Tax rate linguistic 0.02 costs regulations: Tax rate (Annual :%)
30 Rules & regulations: Limit of permissible distance linguistic 0.02 costs Rules & regulations: Limit of permissible distance (Km)
The software is also capable of generating detailed description and analysis of the decision problem in an intelligent report form. The weights are calculated as follows:
The questionnaires of the data for the attributes were distributed to the selected locations and then collected as the source of information. Some of the data were linguistic type while some of them were deterministic. Some kinds of attributes were divided into cost or benefits, depending on being considered as desirable or undesirable by the decision makers (Table 1). For applying FDM software, the linguistic data were converted to fuzzy data (Table 2). A sample of the attributes is shown in Fig. 3.
The Triangular Fuzzy data is in the form of m, a and b, where 'm' means an approximate value, 'a' the positive tolerance of'm' and 'b' represents the negative tolerance of'm'. These results in a matrix of 31*9 has been presented in the attachment (Attachment 1).
In the next step, the fuzzy numbers are converted into real numbers by using de -fuzzification methods. Then, the matrices are normalized to do away with dimensions of indicators and their coefficients are multiplied by the related vector. We can obtain the radius value of any alternatives in an 'n' dimensional space (where n means number of indicators) by finding ideally positive and negative solutions. The final advantage of each alternative is because of its relative proximity to positive ideal response (Hwang & Yoon, 1981).
Figure 3: Description of the criteria in FDM (A sample)
ISSN 2520-6303 Economics, Management and Sustainability, 2 (2), 2017
Table 2: The conversion of linguistic data to fuzzy data
Linguistic data Fuzzy data(m1,m2, a, b)
Very low 0, 0.1,0,0.1
Low 0.2,0.2,0.1,0.1
Fairly low 0.3,0.4,0.1,0.1
Average 0.5,0.5,0.1,0.1
Fairly high 0.6,0.7,0.1,0.1
High 0.8,0.8,0.1,0.1
Very high 0.9,1,0.1,0
3. Results and conclusion
The 9 location candidates were ranked using FDM software and the ranking result is presented in Table 3.
Table 3: Final outcome
Rating Priority Province
2 85.71 Tehran
1 88.83 Qom
5 72.97 Alborz
9 47.66 Qazvin
7 63.29 Markazi
4 74.91 Mazandaran
8 61.44 Fars
6 70.86 Esfahan
3 76.48 Khorasan
3.1 Criteria
3.1.1 Amount of absorbed solar energy as the highest priority criteria
The result (Tablel) shows that below average temperature criteria (0.159), granted facilities, market capacity, labor force availability and price of raw material, have highest priority for site selection of solar wood drying units respectively.
Radiation is amount of energy of electromagnetic on area unit per unit of time which has been named as flux. Solar energy is an opportunity which there is extended programs for developing its application in the world. Programming for solar energy application is a capacity building for using a very large resource which is not comparable with other current energy resources because amount of solar energy is more than several times of energy consumption which man uses energy throughout the year, that is accessible (solar energy) on the earth per hour.
The application of enormous solar energy resources for electricity energy production, dynamic usage, heating generation for areas and buildings, drying agricultural products, chemical changes and so on, are the strategies which have been started in former years. The amount of solar energy obtained from sun radiation in one point of earth area throughout the year, depends on the intensity and duration of sun radiation in that region.
Results of the interview with the experts indicated that maximum radiation of sun throughout the year in the region is the most important criteria for site selection of solar wood drying units. Iran has various climates. Air temperatures, humidity, radiation of sun, rate of rain are different in the regions. Hence it will be logical that the average of air temperature or incoming energy from the sun in each region has the highest priority for site selection of solar wood drying units.
3.2 Alternatives
3.2.1 Prioritizing items using TOPSIS
Qom province (see Table3) is not only the closest province to the largest furniture consumption market of Iran but also has appropriate infrastructure similar ideal transportation network between Qom and Tehran, many equipped industrial towns with low distance to Tehran,
extended facilities and preferences for investment attractions. For these reasons Qom province actually has changed the largest regional industrial town near to Tehran. Permitted distance for establishing industrial units from Tehran as center of Iran is more than 120 km; in this regard Qom province obtains higher priority to establish industrial units. According to the existence of skillful man force criteria, Qom province has good background in wood industry and in this province access to skillful and knowledgeable man force has proper situation.
In this province man force cost and price of land for establishing a factory is lower than Tehran. Average of air temperature or incoming energy from the sun in Qom province is favorite situation so that division of different regions of Iran regarding average of air temperature shows this province is located in the region with high radiation of sun. Accordingly the selection of Qom province as an appropriate alternative for establishing solar wood drying units is logical and justified.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, in the online version, at https://dx.doi.org/10.14254/jems.2017.2-2.2
Funding
The authors received no direct funding for this research. Citation information
Azizi, M. (2017). Strategic model for location selection of solar wood drying by applying TOPSIS. Economics, Management and Sustainability, 2(2), 15-23. doi:10.14254/jems.2017.2-2.2.
References
Azizi, M. (2012). A decision making model regarding wood panel selection. International Journal of Analytic Hierarchy Process (IJAHPJ, 4(2).
Desch, H. E., & Dinwoodie, J. M. (1996). Timber: Structure, Properties, Conversion and use. London: Macmillan Press Ltd.
Helwa, N. H., Khater, H. A., Enayet, M. M., & Hashish, M. I. (2004). Experimental evaluation of solar kiln for drying wood. Drying technology, 22(4), 703-717.
Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making. Springer Verlag.
Ishizaka, A., & Labib, A. (2011). Review of the main developments in the analytic hierarchy process. Expert systems with applications, 38(11), 14336-14345.
McCauley, C. K., & Caulfield, J. P. (1990). Using mixed-integer programming to determine the optimal location for an oriented strandboard plant in Alabama. Forest products journal, 40(2), 39-44.
Memariani, A. (2000). FDM Manual. Tarbiat Modarres University, 11-24.
Michael, J. H., Teitel, J., & Granskog, J. E. (1998). Production facility site selection factors for Texas value-added wood producers. Forest products journal, 48(7/8), 27-32.
Scientific, technical and statistical information provided by SANA (renewable energy organization of Iran). Retrieved from http://www.suna.org.ir/fa/home.
Wilson, J. (2006). Solar drying kilns: A cost effective and efficient way to take green board to usable lumber. Popular wood working, 74-79. Retrieved from
http://washtenawcd.org/downloads/solarkiln.
Attachment 1
Quality of raw material Confidence of raw material Distance of raw material km Distance raw material future km
Alborz very high very high 150 50
Fars low medium 250 150
Isfahan medium high 250 200
Khorasan high high 150 100
Markazi medium medium 250 50
Mazandaran very high very high 50 50
Qazvin medium medium 250 150
Qom very high very high 50 50
Tehran very high very high 50 50
Market capacity Quality of raw material Distance from market km Transportation road Transportation rail
Alborz high high 50 high medium
Fars low low 350 high medium
Isfahan high low 50 very high very high
Khorasan very high low 50 very high very high
Markazi medium medium 350 high high
Mazandaran medium very high 350 high high
Qazvin low low 250 medium low
Qom very high very high 70 very high very high
Tehran very high very high 50 very high very high
Background lateral industries Background services Background competitors Training center availability
Alborz high high medium high
Fars low low low low
Isfahan medium medium medium high
Khorasan high high low very high
Markazi medium low low high
Mazandaran medium medium high high
Qazvin low medium low low
Qom very high very high low very high
Tehran very high very high medium very high
Skill man force Human welfare Technical technological Man force cost
availability facility knowledge
Alborz high medium medium medium
Fars medium high medium medium
Isfahan medium medium medium medium
Khorasan high very high high medium
Markazi medium high high medium
Mazandaran high high medium medium
Qazvin low high medium medium
Qom very high very high very high medium
Tehran very high very high very high Relatively high
Price of land Purchase cost raw material Storage cost Transportation raw material cost
Alborz low medium low m:450000 a:50000 b 50000
Fars very low medium very low m:450000 a:50000 b 50000
Isfahan medium medium low m:450000 a:50000 b 50000
Khorasan medium medium low m:450000 a:50000 b 50000
Markazi very low medium very low m:550000 a:10000 b 20000
Mazandaran low low very low m:150000 a:50000 b:50000
Qazvin very low high very low m:450000 a:50000 b 50000
Qom very low medium medium m:250000 a:50000 b:50000
Tehran medium medium medium m:350000 a:50000 b:50000
Transportation final product cost Granted facility Regulation tax rate Regulation permissible distance
Alborz m:450000 a:50000 b:50000 high medium high
Fars m:250000 a:50000 b:50000 high medium high
Isfahan m:450000 a:50000 b:50000 high medium high
Khorasan m:450000 a:50000 b:50000 medium medium high
Markazi m:450000 a:50000 b:50000 high medium high
Mazandaran m:350000 a:50000 b:50000 high medium high
Qazvin m:450000 a:50000 b:50000 medium medium high
Qom m:350000 a:50000 b:50000 high medium high
Tehran m:350000 a:50000 b:50000 high relatively high very high
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