The basics products in the shoe manufacturing (part welded) [11].
\r\n\tSolar radiation is the radiant energy that originated from the sun in the form of electromagnetic radiation at various wavelengths. Solar radiation is the source of renewable energy and can be captured and converted into various forms of energy (e.g. electricity and heat) using different technologies.
\r\n\tA very vast amount of solar energy reaches the atmosphere and surface of the earth and solar energy has been used for heating purposes for a very long-time and after solar cells’ invention in 1954, solar cells have also been used widely for electricity generation. Solar cells convert the sunlight into electricity by the creation of voltage and electric current through the so-called photovoltaic effect.
\r\n\tPhotovoltaic (PV) solar energy has attracted significant attention in the recent decade as a reliable source for power generation due to various merits such as the free source of energy, abundant materials resources, environmentally friendly and noise-free, longtime service life, requiring low maintenance, technological advancements, market potential, and very importantly, low cost. The growth of using photovoltaic (PV) solar energy as a promising renewable energy technology, is being increased more and more worldwide. Therefore, much further research is needed for possible future developments in the field of solar photovoltaic energy.
\r\n\tThe aim of this book is to provide detailed information about solar radiation as the source of photovoltaic (PV) solar energy for a broad range of readership including undergraduate and postgraduate students, young or experienced researchers and engineers.
\r\n\tThis should be accomplished by addressing the various technical and practical aspects of solar radiation fundamentals, modeling and the measurement for photovoltaic (PV) solar energy applications.
\r\n\tThe majority of this book should describe the basic, modern, and contemporary knowledge and technology of extraterrestrial and terrestrial solar irradiance for photovoltaic (PV) solar energy.
\r\n\tThe book covers the most recent developments, innovation and applications concerning the following topics:
\r\n\t• Fundamental of solar radiation and photovoltaic solar energy
\r\n\t• Solar radiation and photovoltaic solar energy potential
\r\n\t• Solar irradiance measurement: techniques, instrumentation and uncertainty analysis
\r\n\t• Solar radiation modeling for photovoltaic solar energy applications
\r\n\t• Solar monitoring and data quality assessment
\r\n\t• Solar resource assessment and photovoltaic system performance
\r\n\t• Solar energy and photovoltaic power forecasting
\r\n\tThese are accompanied with other useful research topics and material.
",isbn:"978-1-83968-859-1",printIsbn:"978-1-83968-858-4",pdfIsbn:"978-1-83968-860-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"4c3d1319d7286e81bfb15c1f4b20460a",bookSignature:"Dr. Mohammadreza Aghaei",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9862.jpg",keywords:"Solar Radiation Modeling, Solar Data Assessment, Solar Monitoring, Solar Radiation Forecasting, Solar Irradiance Measurements, Solar Instruments, Solar Spectral Distributions, Uncertainty Analysis, Solar Cell Technologies, Photovoltaics (PV), Solar Resource Assessment, Photovoltaics Power Forecasting",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 17th 2020",dateEndSecondStepPublish:"October 15th 2020",dateEndThirdStepPublish:"December 14th 2020",dateEndFourthStepPublish:"March 4th 2021",dateEndFifthStepPublish:"May 3rd 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"A senior researcher in the field of photovoltaic solar energy, a postdoctoral scientist at Eindhoven University of Technology (TU/e), Chair of the WG2: reliability and durability of PV in EU COST PEARL PV.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"317230",title:"Dr.",name:"Mohammadreza",middleName:null,surname:"Aghaei",slug:"mohammadreza-aghaei",fullName:"Mohammadreza Aghaei",profilePictureURL:"https://mts.intechopen.com/storage/users/317230/images/system/317230.jpg",biography:"Mohammadreza Aghaei is a senior researcher in the field of photovoltaic solar energy, Eindhoven University of Technology (TU/e), The Netherlands. He is chair of the Working Group 2: reliability and durability of PV in European Cooperation in Science and Technology, COST Action PEARL PV.\nHe received the M.S. degree in electrical engineering from the Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia, in 2013, and the Ph.D. degree in electrical engineering from the Politecnico di Milano, Milan, Italy, in 2016.\nHe was a Postdoctoral Scientist with Fraunhofer ISE and Helmholtz-Zentrum Berlin (HZB)-PVcomB, Germany, in 2017 and 2018, respectively. He is a Guest Scientist with the Department of Microsystems Engineering (IMTEK), Solar Energy Engineering, University of Freiburg since 2017. He is currently a Postdoctoral Scientist with the Design of Sustainable Energy Systems Group, Eindhoven University of Technology (TU/e), The Netherlands. He has authored numerous publications in international refereed journals, book chapters, and conference proceedings. The main his research interests include Solar Energy, Photovoltaic systems, PV monitoring, LSC PV, solar cells, machine learning, and UAVs.\nDr. Aghaei is a member of the International Energy Agency, PVPS program-Task 13 and International Solar Energy Society, and also an MC member in EU COST Action PEARL PV.",institutionString:"Eindhoven University of Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Eindhoven University of Technology",institutionURL:null,country:{name:"Netherlands"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"10",title:"Earth and Planetary Sciences",slug:"earth-and-planetary-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247865",firstName:"Jasna",lastName:"Bozic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247865/images/7225_n.jpg",email:"jasna.b@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Boots, shoes, sandals, slippers, clogs, and others are made entirely or in part with leather, rubber, synthetic and plastic, cloth, rope, and wood [1]. Our study deals with the shoe industry, that is to say one based on traditional manufacturing methods. The rubber boots factory (or equivalent synthetic materials), which is one of the branches of the rubber industry, is excluded from this study [1, 2].
\nShoes, boots and leather sandals, felt, or other materials have been for centurys hand. Quality shoes are still made entirely or partly in this way by craftsmen, but in all industrial countries, today the mass production predominates [2, 3].
\nOrganic solvents (OS) are components of various products such as paints, inks, pesticides, degreasers, solvents, and glues, which are used in many industries [1]. The shoe industry consumes large amounts of adhesives, diluents, and strippers OS bases. Consequently, the employees in this sector are likely to present particular nerve pathologies [2].
\nSfax is an industrial and agricultural pillar of the Tunisian economy [4]. Among its industries, shoemaking is highly developed sector, which employ about 10000 employees or 10 the workforce of the nonagricultural professional environment which 6000 artisans shoes located in the old city “the Medina.”
\nMap of (a) the center of Sfax city and (b) the old town of Sfax.
As in many cities, Sfax had a craft of the shoe. But in addition, Sfax was a shoe industry. The manufacture of the shoes to form Sfax “industrial” dates back to the nineteenth century. It was well structured in the Medina in the shoe area called “Souk Blaghgias.” It consists of the main street shoes exhibition: The current street Mongi Slim known under its former name “Street of Bay.” Until now, along this road for approximately 300 m shoe shops displaying their products. In the level one called “the Ally” in every store or in the neighboring streets were their own manufacturing facilities.
\nIn 1960, after independence, observers were predicting the rapid decline and disappearance of the craft before the competition of the modern shoe industry. Now, after more than 30 years, this craft, far from having disappeared, has prospered and it feeds today, modern shoes, more than half of the Tunisian market. The artisan district of the boot has grown in the old town of Sfax; we hear all the noise of machines whose artisans are equipped to modernize the sensitive points of their manufacturing (Figure 1) [4].
\nThe realization of a shoe includes several operations. Each step has been mechanized, but the manual production is used as reference. The introduction of new materials has modified manufacturing processes without changing the outline [1, 2].
\nThe manufacture of rods requires sorting and preparation of leather or other materials. The stems are cut with cutters on stitching presses (or burning). The various parties, including the linings, are then “assembled,” that is to say, stitched or glued together. Perforation, eyeletting, or making buttonholes are the procedures carried out further.
\nIn the lower part, the outer and inner soles, heels and welts are cut using cutters to moving blades or molding presses. It manufactures heels compressing leather or wooden slats. The assembly is then cut, formed, cleaned, and labeled.
\nThe upper and lower parts of the shoe are then assembled and then stitched, glued, nailed, or screwed together. After that the shoes are shaped and smoothed by means of rollers. The finishing of the shoe includes waxing, staining, spraying, polishing, and packaging.
\nAmong the raw materials used in manufacturing, adhesives, including adhesive solids and liquids and natural adhesive solutions prepared from organic solvents, represent the largest occupational hazards [3].
\nIn the industry of the adhesive and its derivatives, products intended for the manufacture of the shoe are divided into three groups [2, 5–11]:\n
Four groups of adhesive,
Primers, hardeners, and additives,
Solvents, thinners, and strippers.
The polyurethane (PU) adhesive is used for assembling thermoplastic rubber soles (TR), rubber, polyvinyl chloride (PVC), leathers high in fat or synthetic fabrics requiring the use of high-performance adhesives.
The polychloroprene adhesives (neoprene) are used for making coatings soles, fast repair, and bonding of natural materials (leather, rubber). They are made from synthetic resins dissolved in ketone solvents.
The adhesives in aqueous emulsion (latex adhesives or white glues) may be natural or synthetic:\n
Natural latex adhesives are applied by machine gun or by hand. These are contact adhesives to attach soles, stitching, and lining.
The synthetic latex adhesives are used for laying the ends and bonding liners.
Products | \nComposition | \nUse | \n
---|---|---|
Polychloroprene adhesives (neoprene) Implemented by contact with the following: – Coating the two sides to stick, – Respect the drying time (in ways that the two adhesive films release their solvents) – Assembly of parts (pressure) after reactivation | \n– Solids based on polychloroprene (15–30%) depending on use – Organic solvents (mixture): the most used are, by decreasing importance order: * MEK * Gasoline C (up to 30% hexane) * Acetone * Ethyl acetate * Toluene * Cyclohexane * The alcohols and chlorinated hydrocarbons (little used) | \n– Welded shoe soles | \n
The polyurethane adhesives (PU) | \n\n | \n |
1. based polyurethane elastomers | \nPolyvinyl chloride Elements (PCV) – Solids of polyurethane elastomers – Organic solvents: mixture * Ketones and / or * Overheads | \n– Contact adhesive which is taken after solvent evaporation | \n
2. Two-component | \nPart A: – Polypol (polyester or polyester) – Organic solvents (mixture) : * Ketones and / or * Overheads Part B: – Isocyanate prepolymers (small amount of free isocyanate: Ex diphenylmethane diisocyanate ….) | \nVery flexible assemblies The mixture of A with B is carried out at the time of use – The adhesion is after solvent evaporation | \n
Adhesives in aqueous emulsion: latex adhesives or white glues | \n– Solids (40–60% may be: * Vinyl acetate * polyisoprene * Acrylic polymers * Copolymers: acrylic, isoprene, chlorobutadiene * polyurethane – Solvents: * In general: WATER * Optionally and low proportions: – Alcohol – Other solvents | \n– Installation of liners: manual application by simply coating and touch – Water evaporation Making contact | \n
Hot melt adhesives or “hot melt” Equipment cadence “very expensive” | \nCopolymer Ethylene-vinyl acetate ± possibly: plasticizers or antioxidants | \nApplying on one of the faces to be bonded by a polymer melt temperature rise | \n
The basics products in the shoe manufacturing (part welded) [11].
The hot melt adhesive is a new generation of glue developed in order to reduce the use of solvents. It consists in applying on one of the faces to be bonded by a polymer melt temperature rise. It requires clock equipment which is expensive.
\nThe halogenations operation of TR soles with new functions (rubber look and matt or transparent), must be strictly control any change of model. There are several formulas ready to use (one or two components) for receiving an additive to control the regular application on the soles.
Catalysts, slow or fast, are used in polyurethane adhesives, polychloroprene or dispersion, as well as direct injection. They provide good resistance to the migration of fat leathers.
Beside adhesives preparations, there are other necessary preparations and complementary type of strippers, thinners, and solvents (Table 2).
\nPreparations | \nApplications and examples | \n
---|---|
The cleaning agent | \n– PU soles: remove traces of mold release agent ; – PCV soles: remove surface plasticizer contained in PCV compounds | \n
The diluents | \n– Dilute polychloroprene adhesives ; – Clean the machine to glue and brushes, spray guns stained with paint, screens for screen printing ink | \n
The solvents | \n– Clean the rubber soles, spray guns stained with paint, screens for screen printing ink; – Dilute paints and glues. | \n
Other preparations used in the manufacture of shoes [13].
According to their properties, the solvents can be used in the manufacture of the shoe, of:\n
Degreasing (cleaning soles, textiles…)
Additives and solvents (paints, varnishes, inks, adhesives…)
Stripping (removal of paints, varnishes, glues…)
OS hydrocarbons are divided into eight main groups [12–14]. Six families are used in the manufacturing industry adhesives and footwear (Table 3).
\nThe intensive use of flammable liquids causes a high risk of fire, and the widespread use of presses and assembling machines has increased also the risk of accidents. The main danger to the health of workers is using toxic solvents, high concentrations of dust in the air, the risks due to ergonomic shortcomings, and noise from machinery.
\nOS can cause acute and chronic effects on the central nervous system. Benzene, which was once used in adhesives and solvents, has been replaced by toluene, xylene, hexane, methylethylketone (MEK), and methylbutylketone. The n-hexane and MEK can cause peripheral neuropathy and should be replaced by heptane or other solvents [2].
\nIn many factories, there were outbreaks of a disease known as the “paralysis of the shoemaker” and characterized by clinical signs of a form of more or less serious paralysis. This is a flange type of paralysis, located in the upper and/or lower limbs resulting in deep tendon atrophy with areflexia, without altering the superficial or deep sensitivity. From the clinical point of view, this syndrome is due to an inhibition or a functional lesion of the lower motor neurons of the voluntary motor system (pyramidal system). Most often, it leads to a neurological regression with extensive proximodistal functional recovery [1, 2].
\nGroups of solvents | \nApplications and examples | \n
---|---|
Aromatic hydrocarbons | \nHomologues of benzene (toluene, xylenes, ethylbenzene …) are used as lacquer thinners, adhesives and inks. | \n
Petroleum solvents | \nUsed as solvents in paints, adhesives and coatings: – Alkanes such as hexane (C6H14); – Cycloalkanes: The most used is cyclohexane (C6H12) | \n
Alcohols | \nVery used as diluents inks, resins, varnishes, paints and glues. These are excellent dehydrating agents having good degreasing Action Example: isopropanol (or isopropyl alcohol). | \n
Ketones | \nMainly used as solvent paints, lacquers, varnishes, glues and adhesives. They are sometimes impregnated on wipes for small cleaning. They are good agents drying damp rooms. The most commonly encountered are: acetone or MEK. | \n
Acetates (esters) | \nOften mixed with other solvents, for example, in the curing products and halogenation | \n
Halogenated hydrocarbons | \nUsed as diluents glues, adhesives, paints. Examples: Trichloroethylene (degreaser leather, thinner glue) or dichloromethane (thinner adhesives and cleaner) | \n
In the manufacture of shoes and boots, handling and leather processing can cause diseases by exposure to certain chemical substances mentioned above and in tanning and finishing. In addition, various chemicals can produce other diseases. Exposure to toxic solvents contained in adhesives and cleaning products, and to leather dust in the air is of particular concern. The use of benzene can cause thrombocytopenia (decreased number of red blood cells, platelets, and white blood cells in the blood) or pancytopenia. It has been almost eliminated from the footwear industry [3]. It was also found in some shoe factories cases of peripheral neuropathy due to n-hexane content in adhesives. This substance has also been largely replaced by less toxic solvents. Cases of electroencephalographic changes, liver damage, and behavioral alterations due to exposure to solvents have been reported in workers in the shoe industry [3].
\nThe prevention of occupational risks, particularly chemicals, is based on the risk assessment (RA) according to the specified procedures. Thus, we realized an exposure assessment in making shoes in a sample of volunteer companies (Table 4).
\nThis chemical risk assessment (CRA) aims to validate the hypothesis of decreasing gradient of RC craft enterprises to industrial enterprises in passing by semi-industrial. The simplified assessment methodology for chemical risk (SAMCR) of National Institute of Research and Safety (INRS) [15] was applied only for strand “Health.” For each chemical, a hazard class (HC) is assigned based on the sign, and then the potential exposure class is determined based on the parameters “amount and frequency of use.”
\nProcess | \nIndustrial | \nSemi-industrial | \nArtisanal | \n
---|---|---|---|
Number of companies | \n4 | \n5 | \n9 | \n
Equipment used | \nMechanization (workflow) | \nMechanization and manual | \nManual | \n
Number of employees | \n>20 | \n[10–20] | \n<10 | \n
Installation location | \nIZ | \nIZ or Medina | \nMedina | \n
Business definition.
IZ: industrial zone.
The HC was determined only on the labeling. The allocation of HC to a preparation based on the analysis of the pictogram on the packaging (Table 5). For preparations of glues or solvents “unidentified” (no information on the composition and/or the manufacturing company), it was decided that the assigned pictogram would be one that represents the greatest health risk and consequently the HC.”
\nAtmospheric sampling (a) “individual” and (b) “stationary.”
\n | \n | la France (EU2) | \nUSA (ACGIH3) | \nGermany (MAK4) | \nOur Study (Sfax—Tunisia) | \n||||
---|---|---|---|---|---|---|---|---|---|
Solvent | \nCAS1 | \nVEA5 | \nTLV-TWA6 | \n\n | EAV adopted | \n||||
ppm7 | \nmg.m–3 | \nppm | \nmg.m−3 | \nppm | \nmg.m−3 | \nppm | \nmg.m−3 | \n||
Acetone | \n67-64-1 | \n500 | \n1210 | \n500 | \n– | \n500 | \n1200 | \n500 | \n1200 | \n
Cyclohexane | \n110-82-7 | \n200 | \n700 | \n300 | \n– | \n200 | \n700 | \n200 | \n700 | \n
n-Hexane | \n110-54-3 | \n20 | \n72 | \n50 | \n– | \n50 | \n180 | \n20 | \n72 | \n
Methylethylketone (MEK, 2-butanone) | \n78-93-3 | \n200 | \n600 | \n200 | \n– | \n200 | \n600 | \n200 | \n600 | \n
Toluene | \n108-88-3 | \n50 | \n192 | \n50 | \n– | \n50 | \n190 | \n50 | \n190 | \n
Values of atmospheric exposure limits [17].
1CAS: Chemical abstract service.
2EU: European Union.
3ACGIH: American Conference of Governmental Industrial Hygienists.
4MAK: Maximum Arbeitsplatz-konzentration.
5EAV: Exposure average value calculated over a reference period of 8 hours.
6TLV-TWA: Time-weighted average (weighted average values of 8 hours per day and 40 hours per week).
7ppm: Parts per million per volume of air.
Unidentified glue: The glue most dangerous to a symbol “Xn-Harmful” [5, 11] for the health and attributed HC is “3.” Thus, any unidentified glue the symbol “Xn-Harmful” and “3” class was assigned.
Unidentified solvent (solvents, thinners, and strippers): Two hypotheses “HC” were individualized [11, 13–15]:\n
Let the symbol “T-Toxic” and class “4,” the highest existing level;
Let the symbol “Xn-Harmful” and “3” class more realistic for the majority of solvents.
Solvents | \nIdentification | \nChemical substance | \nUse | \nNature | \nPictogram | \nHazard class | \n
---|---|---|---|---|---|---|
Acetone | \n\n | Acetone | \n\n | Ketones | \nXi-Irritant | \n2 | \n
Alcohol | \nIsopropyl alcohol (IPA) | \nIsopropyl alcohol | \nPaint solvent | \nAlcohol | \nXn-Harmful | \n3 | \n
Asouplex | \nStripper | \nAcetone MEK + | \nSoften the “strong against Calorex” | \nKetones | \nXi-Irritant | \n2 | \n
Stripper | \n\n | Methylene chloride (2%) in MEK | \nWashing, sole preparation (PU, PVC) | \n\n | Xn-Harmful | \n3 | \n
Desmodur | \nDESMODUR RC | \nTDI (5%) in Ethyl acetate | \nDiluent PU glue | \n\n | Xi-Irritant | \n2 | \n
Durcisseur | \nDESMODUR RC | \n\n | \n | \n | Xi-Irritant | \n2 | \n
Diluent | \nSolvent mixture | \nToluene (100%) | \nThinner neoprene glue | \nAromatic hydrocarbons | \nXn-Harmful | \n3 | \n
PA | \nIPA | \n\n | \n | Alcohol | \nXi-Irritant | \n2 | \n
Halogenation product | \n\n | 98% Ethyl acetate | \nSurface preparation sole | \nAcetates | \nXi-Irritant | \n2 | \n
Flexibility | \nASOUPLEX | \n\n | \n | Ketones | \nXi-Irritant | \n2 | \n
Supersouple | \nASOUPLEX | \n\n | \n | Ketones | \nXi-Irritant | \n2 | \n
Tucosolve | \nStripper | \nMEK, AE, Cyclohexane | \n\n | \n | Xn-Harmful | \n3 | \n
Bidonplaste | \nNot identified | \nSolvent mixture | \n\n | \n | Xn-Harmful or T-Toxic | \n3 or 4 | \n
Elaste | \n\n | \n | ||||
Special ink | \n\n | \n | ||||
Solvent not defined | \n\n | \n |
Inventory preparations “solvent” used in the footwear industry and hazard class.
Our approach to RA is complete, and ambient measurements [16] (Figure 2) for solvents (acetone, cyclohexane, n-hexane, MEK, toluene) to which employees are more frequently exposed are taken.
\nAdhesives being constituted as solvent mixture, an exposure index (I.exp) was used and compared with the limit values (Table 6) [17, 18]:\n
\nCn and EAVn are respectively the concentration and the average value of n pollutants exposure.
\nIf I.exp is greater than 1, the limit value is considered outdated. Calculated from stationary sampling, this index is called pollution index (I.pol).
\nHowever much the manufacturing process, the inventory of solvent borne formulations showed HC “2” and “3”. When the unidentified preparations, they were classified “3” or “4” (Table 7). For the 23 identified glue preparations, HC ranges from “1” to “3” (Table 8).
\nThe potential risk score (PRS) process “industrial” 2 in case of danger class puts 13 chemical preparations to examine with high priority, including five of solvent preparations. For the HC “3,” PRS do not exceed “10000,” while with the hazard class “4” three of five solvent borne preparations pass a PRS 100000.
\nThe situation of distinction for a process “semi-industrial” does not arise since we did not detect unidentified preparations. The preparations to consider with high priority are 2 solvents and 5 adhesives unidentified.
\nIn the “artisanal” process, we have identified eight chemical preparations to examine with high priority: two solvents borne preparations and six adhesives.
\nGlues | \nIdentification | \nChemical substances | \nUse | \nNature | \nPictogram | \nHazard class | \n
---|---|---|---|---|---|---|
110/Textile glue | \nRubber 110F | \nNatural rubber, hexane, petroleum resin | \nProvisional or final bonding of leather fabric (handcrafted process) | \nPolychloroprene | \nXn-Harmful | \n3 | \n
420 | \nCeylanprène 420 | \nPolycholoroprène, cyclohexane, hexane, MEK, Resin (phenol, phenol terpene), Toluene | \nBonding the leather | \nPolychloroprene | \nXn-Harmful | \n3 | \n
425S | \nCeylanprène 425 | \nPolycholoroprène, cyclohexane, hexane, MEK, Resin (phenol, phenol terpene), Toluene | \nBonding the leather | \nPolychloroprene | \nXn-Harmful | \n3 | \n
715 | \n\n | \n | Bonding the leather | \nPolychloroprene | \nXn-Harmful | \n3 | \n
945 | \n\n | \n | Bonding the leather | \nPolychloroprene | \nXn-Harmful | \n3 | \n
C3 | \n\n | \n | Bonding the leather | \nPolychloroprene | \nXn-Harmful | \n3 | \n
C4 | \n\n | \n | Bonding the leather | \nPolychloroprene | \nXn-Harmful | \n3 | \n
Calorex | \nCalorex | \nPolycholoroprène, cyclohexane, hexane, MEK, Resin (phenol, phenol terpene) on a ribbon | \nPolyester fabric coated with an adhesive | \nPolychloroprene | \nNo | \n1 | \n
CD11 | \n\n | \n | Gluing soles | \nPolyurethane | \nXi-Irritant | \n2 | \n
CN13 | \n\n | \n | Bonding the leather | \nPolychloroprene | \nXn-Harmful | \n3 | \n
White glue | \nGlue “Latex” | \nSynthetic or natural rubber in an aqueous phase | \nFor bonding wood heel | \nAqueous emulsion | \nNo | \n1 | \n
Yellow glue | \nNeoprene glue | \n\n | \n | Polychloroprene | \nXn-Harmful | \n3 | \n
Colle neoprene | \nNeoprene glue | \n\n | \n | Polychloroprene | \nXn-Harmful | \n3 | \n
Vinyl glue | \nEx: R10 | \nPolymer of vinyl acetate, acrylic polymer, water | \nManufacture of the shoe | \nAqueous emulsion | \nNo | \n1 | \n
D1 | \n\n | \n | Gluing soles | \nPolyurethane | \nXi-Irritant | \n2 | \n
Forestali/ICF | \n\n | \n | Gluing soles | \nPolyurethane | \nXi-Irritant | \n2 | \n
Plastipole | \nPolyurethane glue | \n\n | Gluing soles | \nPolyurethane | \nXi-Irritant | \n2 | \n
Polyurethane | \nPolyurethane glue | \n\n | Gluing soles | \nPolyurethane | \nXi-Irritant | \n2 | \n
Could | \nPolyurethane glue | \n\n | Gluing soles | \nPolyurethane | \nXi-Irritant | \n2 | \n
Tucoplast | \nPolyurethane glue | \n\n | Gluing soles | \nPolyurethane | \nXi-Irritant | \n2 | \n
UF43 | \nPlestiprene UF43 | \nPolyurethane, ethyl acetate, acetone, MEK, Toluene | \nGluing soles | \nPolyurethane | \nXi-Irritant | \n2 | \n
Colle geant | \nNOT IDENTIFIED | \nSolvent mixture | \n\n | \n | Xn-Harmful | \n3 | \n
Precision without glue | \n\n | \n |
Inventory preparations “glues” used in the footwear industry and hazard class.
Measurements of atmospheric concentrations (Table 9) show high levels especially in companies with semi-industrial process and type 1 craft.
\nProcess | \nBusiness | \nNumber of workstation | \nPersonal sampling | \nAtmosphere sampling | \n|||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Exposure index* | \nPollution index** | \n||||||||||
Not | \nScope | \nAverage | \nNot | \nScope | \nAverage | \n||||||
Industrial | \nIndustrial 1 | \n5 | \n3 | \n0.3–1.1 | \n0.7 | \n2 | \n0.5–0.8 | \n0.6 | \n|||
Industrial 2 | \n2 | \n1 | \n– | \n1.8 | \n1 | \n– | \n0.3 | \n||||
Industrial 3 | \n5 | \n4 | \n0.9–9.4 | \n3.7 | \n1 | \n– | \n0.8 | \n||||
Industrial 4 | \n5 | \n4 | \n0.8–19.8 | \n6.8 | \n1 | \n– | \n0.0 | \n||||
Semi-industrial | \nSemi-industrial 1 | \n5 | \n3 | \n0.4–1.2 | \n0.7 | \n2 | \n0.0–0.3 | \n0.1 | \n|||
Semi-industrial 2 | \n5 | \n4 | \n0.5–2.0 | \n1.0 | \n1 | \n– | \n0.4 | \n||||
Semi-industrial 3 | \n4 | \n2 | \n0.2–2.5 | \n1.3 | \n2 | \n0.2–0.4 | \n0.3 | \n||||
Semi-industrial 4 | \n5 | \n4 | \n0.9–2.4 | \n1.7 | \n1 | \n– | \n0.1 | \n||||
Semi-industrial 5 | \n4 | \n1 | \n– | \n5.6 | \n3 | \n4.3–8.8 | \n6.8 | \n||||
Artisanal | \nType 1 | \nCraft 1 | \n2 | \n1 | \n– | \n0.1 | \n1 | \n– | \n0.4 | \n||
Craft 2 | \n1 | \n/ | \n\n | \n | 1 | \n– | \n0.7 | \n||||
Craft 3 | \n1 | \n/ | \n\n | \n | 1 | \n– | \n0.8 | \n||||
Craft 4 | \n3 | \n2 | \n0.4–1.7 | \n1.1 | \n1 | \n– | \n1.6 | \n||||
Craft 5 | \n2 | \n1 | \n– | \n1.5 | \n1 | \n– | \n0.9 | \n||||
Craft 6 | \n2 | \n2 | \n1.6–3.4 | \n2.5 | \n/ | \n\n | \n | ||||
Craft 7 | \n2 | \n1 | \n– | \n6.4 | \n1 | \n– | \n5.1 | \n||||
\n | Type 2 | \nCraft 8 | \n1 | \n/ | \n\n | \n | 1 | \n– | \n0.5 | \n||
Craft 9 | \n1 | \n/ | \n\n | \n | 1 | \n– | \n0.6 | \n||||
Total = 33 | \n\n | Total = 22 | \n\n | \n |
Atmospheric exposure of employees by industrial trial.
Stages of production | \nSources of pollution | \n
---|---|
1. Individual sizing Post “UPPER PART MAKER” | \nSolvent vapor: – Glue container in use – Piece glued – Materials used for coating (pre-sizing operation) | \n
2. The preparation of surfaces prior to bonding Post “SOLE MAKER” | \n– Clean and degrease the parts to be assembled (brush, cloth) – Washing of soles – Halogenation: Treatment of some soles (rubber) | \n
3. Drying | \nObjects are placed in general on fixed or movable shelves near collages positions | \n
4. Finish | \n– Cleaning after bonding to remove excess glue (solvent-soaked rag) – Coloring and application of polish (usually by spraying) | \n
5. Cleaning equipment | \nBrushes and accessories: With solvents | \n
6. Fractionation and storage “Decanting” preparations | \nAdhesives wholesale packaging: A transfer in small containers used in gluing station | \n
The steps of the shoe manufacturing (the welded Technique) and sources of pollution [11].
Exceedances of I.exp and/or I.pol are noted for exhibitors positions including the upper part makers, smelters and finishing (Table 10) within three manufacturing processes (Table 11).
\nAll measurements in different companies show that exposure to OS varied from one process to another and from one position to another (Table 12). The average atmospheric concentrations of hexane are particularly high, especially with the exposure average value (EAV) which overruns the position of the upper part maker, the sole maker, and finishing in all production processes except the process artisan type 2. The other solvents (acetone, cyclohexane, MEK, and toluene) are relatively high without exceeding the EAV.
\n\x3c!--Atmospheric exposure of employees based workstation.
1 | \n/ | \n\n | \n | 1 | \n– | \n0.0 | \n|||
1 | \n/ | \n\n | \n | 1 | \n– | \n0.8 | \n|||
4 | \n3 | \n0.9–19.8 | \n8.4 | \n1 | \n– | \n0.5 | \n|||
7 | \n5 | \n0.3–3.5 | \n1.5 | \n2 | \n0.3–0.8 | \n0.5 | \n|||
3 | \n3 | \n0.9–1.8 | \n1.2 | \n/ | \n\n | \n | |||
1 | \n1 | \n– | \n9.4 | \n/ | \n\n | \n | |||
8 | \n4 | \n0.5–2.5 | \n1.6 | \n4 | \n0.1–7.2 | \n2.5 | \n|||
12 | \n10 | \n0.2–5.6 | \n1.8 | \n2 | \n0.2–8.8 | \n4.5 | \n|||
3 | \n/ | \n\n | \n | 3 | \n0.3–4.3 | \n1.7 | \n|||
5 | \n2 | \n0.1–1.6 | \n0.9 | \n3 | \n0.9–5.1 | \n2.5 | \n|||
8 | \n5 | \n0.4–6.4 | \n2.7 | \n3 | \n0.4–0.7 | \n0.6 | \n|||
2 | \n/ | \n\n | \n | 2 | \n0.5–06 | \n0.6 | \n|||
\n |
Type 1: The positions of upper part maker and sole maker are separated (made by different people). Type 2: One confused post, in two upper part maker and sole maker.
\nDespite significant exposures to solvent-based preparations, an almost total lack of use of personal protective equipment such as gloves, masks, and goggles by the manipulators of the adhesives formulations and/or solvents was noted. The equipment of rooms by adequate ventilation is not rated very often, while the Tunisian regulations require companies to preserve human capital and implement a preventive political.
\nAverage atmospheric concentrations of solvents assayed.
Average atmospheric concentrations of solvents assayed.
The natural ventilation has been the main alternative ventilation: Some kind of artisans 2 working doors open because they have no other coming from vents (without windows), while others worked closed doors because of their irregular position (no statement to the National Case Social Security and/or cohabitation a group of craftsmen in a building intended for habitation and not commercial and industrial travailing each for its own account).
\nSemi-industrial footwear companies are craft enterprises working on a large scale with some mechanization, which paid their employees the number of parts produced per day (upper part makers and sole makers). They correspond to industrial companies that should be in IZ [19]. Another feature in this type of business is the presence of women in age mount positions rods and finishing [20]. Shoes manufacturing companies are increasing in the region with an extension to the new IZ south of the city of Sfax. The semi-industrial companies continue to invade Medina and especially the houses which are in most cases protected historical monuments.
\nThe SAMCR of INRS was chosen because it is one of the reference methods in RA established from the Kinney model (mathematical model) [21].
\nIf these atmospheric samples and toxicology tests were made for the first time in the shoe industry in Sfax, they concerned a sample of companies of the three shoes manufacturing processes (industrial, semi-industrial, and artisanal) preceded by a preliminary RA with a job and a study inventory of products handled.
\nInterest carcinogenic characteristics of certain preparations and especially their potential toxicity for reproduction must be taken, as a female presence in the shoe manufacturing sector increasingly important and cases of couple infertility [22] have been highlighted.
\nIdentification and CRA are important steps in this process of prevention in the shoe industry. This detection was to (i) highlight the chemical preparations that are really harmful. This could be achieved by the inventory and prioritization of chemical preparations; and (ii) identify the nature of the manipulated chemical preparations. This identification was made by labeling of preparations. After this step, the semi-industrial process CRA is most at risk followed the industrial process and finally that of the artisanal process. When the study of exposure to workplace needs to know not only the average exposure over the EAV, but also to identify the polluting phases to determine the short-term exposure over the TLV.
\nApart from the strengthening of health and safety measures in the shoe industry, two actions are to be undertaken in parallel: (i) the substitution of certain solvents by others in the manufacture of glues and derivatives seems necessary while maintaining the preparations use properties and (ii) a change in the Tunisian regulations on hygiene and safety, in particular, is necessary to mandate the RA in business.
\nThis study was partly funded and supported by Sfax University. We are grateful to the director and staff of the adhesive company SIFCOL for their cooperation. We are also indebted to the workers who participated voluntarily in the study.
\nQatar’s rapid development over the past decade led to a remarkable growth on its economy and population. Hence, increasing the demands on food, water, electronics and services. All of which relies on electricity to power the industries such as desalinization plants, farms, commercial infrastructures, semiconductor factories and more. According to the Qatar Water and Electricity Corporation or QWEC, a foremost power generation plant in the country stated that the electricity demand in the country is increasing at an estimated yearly average growth rate of 6–7% in the coming years [1]. In order to address the increasing electricity demand, the state is considering a new energy strategy that would foster sustainability, but also contribute to the reduction of the greenhouse gas emission levels. Fortunately, the gulf region where the country resides, experiences 6 kWh/m2/day amounting to 4449 h/year where 70% comes from sunshine, thus, focusing on optimization of energy extraction from sunlight is a viable solution [2]. In fact, renewable energy sources such as those from photovoltaic cell (PV) plants are estimated to contribute 11% to the global demand by 2050 according to the International Energy Agency (IEA) [3].
\nAnother possible source of renewable energy in Qatar can be harnessed from wind turbines. An assessment on wind energy potential in Qatar conducted by Qatar Petroleum [4] revealed that Qatar may employ use of small and medium wind turbines since 80% of the time wind speed over the country exceeds the critical speed of 3 m/s with annual mean speed over land and offshore of 4.3 and 5.7 m/s, respectively. It was estimated that 150 W/m2 may be harnessed from a 5 m/s wind speed but the power generated from wind turbines may be 8% less compared to the gas fired electricity. The cost projected for an offshore wind turbine is 10% less than the gas-based counterpart. Although wind turbines sound promising as a potential source of renewable energy, it does present several disadvantages compared to PV plants such as: annual maintenance on the turbine’s gear box in contrast to minimal maintenance for the PV, loud noise during operation for nearby inhabitants, and smaller life span of 20–25 years compared to 30 year life span of PV [5]. Qatar does not have immediate plans for installing wind turbines yet, instead it has been focusing on solar energy by allocating US $1 billion investment for the project which includes desalinization plants and a 200 MW power plant by Kahramaa [4]. With the upcoming 2022 FIFA cup, the country aims to be the first carbon neutral world cup utilizing solar energy to power air conditioning and fan zones. Since the state is leaning towards utilizing mostly solar energy to help power its industry, this study was conducted to primarily focus on PV alternative that was designed specifically for Qatar’s environment to test and understand its performance through measurment, prediction and analysis that should provide possible references for its solar industry.
\nLarge-scale PV farms are usually situated where maximum solar energy conversion can be generated which are either semi-arid lands or a desert. However, soaring temperatures reaching 50°C or more, high humidity and heavy sandstorms are some examples of environmental factors that may significantly reduce the efficiency in power generation of the PV systems. These issues are region-specific and may differ from one place to another even within the Gulf region, Hence, it is significant to investigate the modern PV technology under these harsh conditions that are specifically present in Qatar so that performance could be strongly correlated to it [6]. One apparent benefit from this is that the uncertainty of PV performance will be greatly reduced leading to a more predictable and profitable solar megaprojects that are planned to be constructed in the area [7, 8, 9]. The results could also cater to the interests of the manufacturers, researchers and technology enthusiasts in order to develop or innovate solutions.
\nEfficient energy management is among the benefits from understanding PV performance since some modern communities now use hybrid systems where they integrate renewable sources of energy such as solar PV to determine how it behaves in such systems. In [10], the authors discussed modeling and optimization of urban integrated energy systems to provide an energy plan or policy for a better energy efficiency aiming to mitigate energy crisis experienced in urban communities. In addition, Menetti et al. [11] proposed an efficient energy management that effectively use energy storage systems for renewable energy sources and the electric grid to reduce energy exchanged and power peaks on the grid. The data from the monitoring system becomes a necessary tool for conducting important analysis on the system for a region such as [12] to determine its costs and profit throughout its operation to assess its financial sustenance and feasibility for its possible application to other regions. In addition, it would also aid in contributing to the continuing development of efficient operations in industries to yield better results through exergy and energy analysis such as in [13, 14] and techno-economic analysis in [15, 16]. With increasing amount of studies being conducted centered on renewable energy especially on solar energy and PV, this study will prove useful to the scientific community and may serve as a significant reference to the ones conducted similarly in Qatar.
\nSeveral similar investigations in Qatar with same line of inquiries [17, 18, 19, 20, 21, 22, 23, 24, 25, 26] were conducted but none has been able to provide a cost-effective yet reliable system that satisfies the requirement for accessing, monitoring and predicting PV yield. Another major concern is the data acquisition system (DAS); most available commercial DAS tend to be costly when implemented for large solar PV plants. In addition, commercial DAS are inflexible for reconfigurations and modifications for various scenarios, thus, limiting its use. Furthermore, numerous efforts have been conducted in designing and implementing PV monitoring systems that utilize several sensors and data acquisition [27]. The system in [28] included an off-shelf component of Agilent 24902A, wherein the data were transmitted to the wired general purpose instrumentation bus to a computer that is running a LabVIEW™ program to determine the impact of solar irradiance and ambient temperature. Haba [29] developed a designated monitoring system for several PV panels that utilizes three gateways intended for weather station, current and voltage readings and storm detection which were then sent and hosted to online cloud specifically freeboard.io. A readily available commercial DAS was used for investigating the impact of module temperature and solar irradiance on PV efficiency and transmits to a server through the use of GPIB bus and cloud service [30]. Study [31] used a system consisting of LM35 temperature sensor and LDRs (light dependent resistors) for measuring ambient temperature and solar irradiance of PV module, respectively. The data is then transmitted to the computer wirelessly via Wi-Fi by connecting the microcontroller with EGSR7150 modem through its serial interface.
\nForecasting of PV performance were recently introduced to improve the quality of the systems such as providing dispatch management, control operations, power ramp and flicker prediction on hourly basis; and load consumption and production monitoring on daily basis [32]. Parametric models were also utilized for forecasting which are mostly affected by the execution of the component models and factors that are not readily available, thus, affects the accuracy of the system [33]. Recently, ML was introduced to overcome the above drawbacks; which is driven by the interactions between the input and output variables according to the data. Several studies were already conducted like in [34] were they determined the solar potential from rooftops in Switzerland by utilizing ML. Li et al. [35] used ML to predict solar irradiance to precisely determine the PV output utilizing Markov model and regression. Most of these forecasts were conducted on a specific environment, hence it would not be able to provide the same accruacy when used in another locations that exhibits different environmetal parameters like in Doha were it experiences unique intense heat and heavy dust storms that lasts year long. Therefore, we planned to deliberately harness ML for predicting the performance of PV systems from the various environmetal parameters that are present in Doha along the year for viability and bankability of PV energy source.
\nThis study describes the development of an in-house customized DAS system that is viable for monitoring PV systems under Qatar’s climate and which comprises of two parts: hardware and software. Also, the study is enhanced by describing the calibration tools that are necessary in such studies. The remainder of the study is as follows: Section 2 describes the hardware and signal acquisition. Section 3 depicts the ML used for the data gathered throughout the duration of the study. Section 4 discusses the results from the developed system and the ML results. Finally, the conclusion and future work is provided in Section 5.
\nThe hardware and signal acquisition system were installed in the Solar Lab facility under the College of Engineering, Qatar University. The ground floor of the solar lab facility houses computer workstation and wireless access point while its rooftop emulates the PV panel remote site where PV panels and data acquisition hardware system are mounted along with all environmental sensors and transducers. Qatar, having an arid environment with extreme ambient temperature easily surpassing 38°C during summer and often approaches 50°C with a humidity of 90% [36].
\nThe authors developed an in-house and customized DAS that acquires six environmental parameters and two electrical parameters enhanced by analog filters with gain and offset adjustments for calibration purposes. The in-house DAS was designed to allow flexibility in order to construct a customized signal conditioning circuit suitable for each sensor that are deemed appropropriate for the range of parameter values in an arid environment. The selected sensors along with the signal conditioning circuit and topology were chosen in order to implement a robust DAS that is appropriate to Doha’s harsh weather condition.
\n\nFigure 1 depicts the overall data acquisition framework. Data acquisition starts from the PV panel remote site where the PV panels are installed to ensure maximum exposure to sun’s irradiance, free from shadows due to obstructions. Selection of azimuth and tilt angle of PV panels are also important mounting details that needs to be considered. Two polycrystalline PV panels connected in series were installed in the remote site where the electrical and environmental parameters are needed to be monitored periodically in a specified sequence of steps as shown in the generalized flowchart in Figure 2. Periodic acquisition are normally spaced 15 minutes apart to ensure seamless wireless transmission between the PV panel remote site to the research lab site due to the considering the response time of the hardware. Information collected in the research lab site are stored locally and to the file hosting service of Dropbox™ along with the visualization facility of ThingSpeak™ through and iCloud™ server.
\nOverall data acquisition system.
Generalized flowchart of the PV monitoring system.
A detailed illustration of the connection diagram exhibiting important components of the PV panel remote site is shown in Figure 3. Six environmental and two electrical parameters, namely; (1) ambient temperature, (2) irradiance level, (3) wind speed, (4) surface temperature, (5) relative humidity, (6) dust levels, along with PV voltage and current are carefully studied and chosen by the authors in [37, 38] in order to provide highest probable impact contributing to the correlation to PV panel performance and efficiency, thus, allowing higher reliability when applying ML algorithms in [37, 38, 39]. The system specifications of each sensor are enumerated in Table 1 that includes actual part number of the off-the-shelf sensors along with the manufacturer and range of operation. The details of DAS design and operation were presented by the authors in [37, 38, 39, 40].
\nSystem set-up of the PV panel remote site.
Parameter to be measured | \nDiscrete sensor | \nManufacturer | \nMeasurement range | \n
---|---|---|---|
Ambient temperature | \nLM35 | \nNational Instruments | \n0–70°C | \n
Irradiance | \nPyranometer SP-110 | \nApogee | \n0 \n | \n
Wind speed | \nType 485 Wind sensor | \nQS-FS | \n0 \n | \n
Surface temperature | \nPlatinum RTD PT100 | \nFarnell | \n0–100°C | \n
Humidity | \nHIH-4000-003 | \nHoneywell | \n0–100% | \n
Dust level | \nGP2Y1010AU0F Optical Sensor | \nSharp | \n0 \n | \n
Voltage | \nVoltage transducer LV 25-P | \nLEM | \n0 V to 40 V | \n
Current | \nHall effect current transducer LA 100-P | \nLEM | \n0A to 5A | \n
PV system specifications of each sensor.
\nFigure 4 exhibits the simplified connection of various elements to process the required signal for redundant storage and visualization in the research lab set-up. The computer workstation uses LabVIEW™ program to process data that allows visualization of recently acquired data as depicted in Figure 5.
\nSystem set-up of the research lab site.
Sample visualization of collected data using LabVIEW™ in the computer workstation.
ML is the process of training a system to automatically predict output from given inputs. The system is trained using available set of inputs and their respective outputs. The concept of ML is useful in biomedical applications [41, 42], power prediction [43] and in general for any data processing and analysis studies. ML will be used to learn from the large amount of monitoring data collected from the setup discussed in the previous section and this phase is the training phase. During the training phase a part of the input data used for training is kept for validation purposes of the trained network. The validation accuracy is a metric used to determine how good or bad a trained ML network is. This ML trained network is then used for testing some data, which was unknown to the ML network, and is used to check if the ML trained network can actually predict the output correctly. The best performing ML network can later be used to predict the PV performance in the future based on the environmental and electrical inputs. The various stages that are involved in the ML are shown in Figure 6 and will also be discussed in details in the sub sections below.
\nStages involved in ML training and testing phase.
It is always important to make sure that the data given to the ML network for training is correctly formatted, making sure all outliers in the data or data which are incorrect and not trustable are removed. The data should be made in a format which is acceptable to the ML network in whichever platform it is being operated on. The ML Toolbox in Matlab 2019a version was used in the study. There are many other popular ML platforms available such as TensorFlow, Keras, Shogun, and RapidMiner.
\nOnce the data (input and output) for the training and testing purpose is ready, it is important to select the inputs that can help in predicting the output better. Sometimes giving more input or options to help in prediction can lead to overfitting problem. Overfitting is an issue where a ML network is trained to work the best for only the trained dataset and predicts mostly wrong outputs in the testing phase. This process of selecting the input data that can increase the testing accuracy is called feature selection. Selection of features is the process of selecting a subset of relevant, high-quality and non-redundant features to create learning models with better accuracy [44, 45]. Well known feature selection techniques – Correlation feature selection (CFS) and Relief feature selection (ReliefF) was used in this study. CFS technique selects feature sub-sets based on correlation-based heuristic evaluation function and ReliefF is an instance-based algorithm that assigns a relevance weight to each feature that reflects its ability to differentiate class values [43].
\nOnce the data that will be given as input to the ML training phase is selected, then there are several ML techniques that can be used to see which techniques help in reaching better performance. The techniques used in this study can be broadly classified into two categories: Classical ML Technique and Artificial Neural Network. These techniques are compared in the performance in prediction during the testing phase and the best performing technique is archived for future use.
\nSeveral simple and popular regression and prediction models are stated in this work to estimate the PV output power. These are namely Simple Linear Regression [46], Gaussian Process Regression (GPR) [47] from the regression learner, and M5P regression tree [37, 48]. Simple linear regression model has a linear relationship between the output response and the input parameters. GPR involves a Gaussian process using lazy learning and a measure of the point similarity (kernel function) to predict the value from the training data for an unseen point. The M5P regression tree uses algorithm which contains if and else statements [48, 49] . In other words, predicted power will be the result of “if… then…else…” statements.
\nArtificial Neural Network (ANN) (Figure 7) can be thought of a replication of how the human nervous system works, but as it is artificial thus it gets its name [50]. ANN has three major layers: (1) Input Layer, Output Layer and the Hidden Layer. The input layer are the artificial neurons where the actual learning happens and is also the layer where the input is fed. Each neuron in this layer has specific weights, which are details used to solve a specific problem. These weighted summed inputs are used in the hidden layers or in the transfer functions. Transfer functions are then inputs to activation function which tries to predict the output or provides the error back to the network as a feedback. This feedback acts as learning for the input layers again to try providing inputs to the activation function to help in better prediction.
\nANN architecture and its main components.
There are several Training Algorithms (TA) available in the Matlab implementation of ANN and each of them have their advantages and disadvantages and each application can have a specific TA giving better results than the others due to the nature of the data. It is always important to explore various combinations of number of hidden layers and training functions to find the best combination that predicts the PV power most accurately, as shown in Figure 8. The algorithm first varies the training algorithms, then the number of hidden layers and then does many tries using the combination. During each trial the algorithm stores the network with best performance for testing purpose. The final best network is used for predicting the PV power using the input variables.
\nMethod to find the best ANN to predict PV power.
\nFigure 9 summarizes the network settings for the ANN based PV power prediction. The optimum number of hidden layers providing the best model were different for all features (60), CFS technique (260) and ReliefF technique (180) and were found using the algorithm stated in Figure 8.
\nDetails of the ANN.
In order to compare between the various categories, techniques of ML and also the various feature selection techniques the below statistical parameters were used as performance metrics [51].
\n\n\n
The prototype system (setup shown in Figures 1 and 3) was used for collecting the PV and environmental parameters and PV power output data from the period November 2014 until October 2016. Summary of the PV and environmental parameters and the data used for deriving the predictive model of the PV power is shown in Table 2.
\nDetails of the environment parameters used for the predictive model.
Selected features vector.
\nTable 3 summarizes the parameters selected based on the feature selection techniques CFS and relief F.
\n\nTable 4 summarizes the performance of the different classical ML techniques with the different feature selection techniques. It shows both the Training and Testing Phase performance metrics. It can be clearly seen the best performance is the CFS feature selection technique using the GPR algorithm with RMSE of 12.7144 watts compared to the maximum power of 114.2017 watts generated from the setup, as shown in Table 2.
\nPerformance comparison between the various regression techniques.
\nTable 5 summarizes the performance of the ANN best trained network found using the algorithm in Figure 8 and with the different feature selection techniques. It can be clearly seen that the ANN trained network outperforms the techniques in the classical ML techniques. In ANN, without feature selection techniques provides the best testing performance with RMSE of 5.48 watts compared to the maximum power of 114.20 watts generated from the setup, as shown in Table 2.
\nPerformance comparison between the various ANN techniques.
A customized PV system was developed at Qatar University to monitor, analyze and evaluate the performance of PV using various weather factors. The study also showed details of how the data collected could be used for training different ML algorithms which were compared using different statistical analytical tools. Several feature selection techniques were also used to avoid the problem of overfitting. Comparison between the different ML techniques and different feature selection techniques helped in concluding an ANN model to be used for predicting PV performance using different environment and electrical parameters. The paper also showed the opportunity of tuning the ANN by varying the number of hidden layers and changing the training algorithm. This study describes the development of an in-house customized DAS system that is viable for monitoring PV systems under Qatar’s climate and which comprises of two parts: hardware and software. Also, the study is enhanced by describing the calibration tools that are necessary in such studies. The remainder of the study is as follows: Section 2 describes the hardware and signal acquisition. Section 3 depicts the ML used for the data gathered throughout the duration of the study. Section 4 discusses the results from the developed system and the ML results. Finally, the conclusion and future work is provided in Section 5.
\nThe authors would like to thank Qatar University for the financial, technical, and administrative support, without which this work would have not been achieved.
\nThe authors declare no conflict of interest.
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\\n\\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
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\\n\\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
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\\n\\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
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\\n\\nDigital Archiving Policy
\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
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\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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