Modeling Drying-Energy Consumption in Automotive Painting Line Based on ANN and MLR for Real-Time Prediction View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Songhua Ma, Zhaoliang Jiang, Wenping Liu

ABSTRACT

By taking energy awareness and efficiency into consideration, this research applies multiple linear regression (MLR) models and artificial neural networks (ANNs) for predicting the real-time heating energy demand and natural gas consumption in the drying chamber of an automotive painting line. With the estimation of the correlation and randomness during the painting process, a total of six fundamental variables that integrate energy, product and process data are considered in order to cover the impact factors of the natural gas consumption. The independent variable value, used to calibrate and evaluate the model, is obtained from our constructed energy consumption monitoring system. In the prediction cases, the ANN-based model, which offers a well performance, provides great precision in the determination of the natural gas demand with an R2 coefficient and other error measurements of over 90%. Based on the MLR model, the process temperature and the corresponding variation are found to be two decisive factors for the natural gas consumption. It is foreseen that the ANN model, which can effectively perform input/output mapping, is reliable and powerful for predicting the natural gas consumption and estimating the energy demand. Being a general data-driven method, our method is convenient for application to other heating process lines and even other energy-consuming plants. More... »

PAGES

241-254

References to SciGraph publications

  • 2015-04. A methodology for customized prediction of energy consumption in manufacturing industries in INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY
  • 2014-08. Energy operation management for Smart city using 3D building energy information modeling in INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING
  • 2014-02. Simulation of CO2 emission reduction potential of the iron and steel industry using a system dynamics model in INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING
  • 2014-07. A comparison of energy consumption in bulk forming, subtractive, and additive processes: Review and case study in INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY
  • 2014-11. Energy consumption of the brushing process for PCB manufacturing based on a friction model in INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING
  • 2013-07. Analysis of energy efficiency and productivity in dry process in PCB manufacturing in INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s40684-019-00064-x

    DOI

    http://dx.doi.org/10.1007/s40684-019-00064-x

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112392453


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Shandong University", 
              "id": "https://www.grid.ac/institutes/grid.27255.37", 
              "name": [
                "Department of Mechanical Engineering, Shandong University, Building No. 8 414, 250061, Jinan, Shandong, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ma", 
            "givenName": "Songhua", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Shandong University", 
              "id": "https://www.grid.ac/institutes/grid.27255.37", 
              "name": [
                "Department of Mechanical Engineering, Shandong University, Building No. 8 414, 250061, Jinan, Shandong, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Jiang", 
            "givenName": "Zhaoliang", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Shandong University", 
              "id": "https://www.grid.ac/institutes/grid.27255.37", 
              "name": [
                "Department of Mechanical Engineering, Shandong University, Building No. 8 414, 250061, Jinan, Shandong, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Liu", 
            "givenName": "Wenping", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.apenergy.2015.12.066", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004420043"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/00207543.2013.813983", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004557530"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12541-014-0524-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005580955", 
              "https://doi.org/10.1007/s12541-014-0524-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jclepro.2015.12.098", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006060527"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jclepro.2016.11.019", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006655278"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/19397038.2015.1008599", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007281240"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jclepro.2015.05.093", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007473683"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jclepro.2015.05.049", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011540064"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s40684-014-0033-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012500994", 
              "https://doi.org/10.1007/s40684-014-0033-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12541-014-0346-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016540950", 
              "https://doi.org/10.1007/s12541-014-0346-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.energy.2015.03.084", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016881412"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.energy.2014.08.072", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019606045"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cirpj.2011.03.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022011833"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/19397038.2014.895067", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025832135"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijepes.2014.12.036", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027666005"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.eneco.2014.03.017", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028454277"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.trc.2014.03.014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030454058"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.compind.2014.01.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031394991"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jclepro.2014.10.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031468438"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12541-013-0165-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031628338", 
              "https://doi.org/10.1007/s12541-013-0165-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/1088198041269355", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035192811"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jclepro.2016.06.037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036329615"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.energy.2015.07.068", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038185711"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12541-014-0590-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045905547", 
              "https://doi.org/10.1007/s12541-014-0590-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s40684-015-0021-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051934524", 
              "https://doi.org/10.1007/s40684-015-0021-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.energy.2016.12.022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053188377"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.pecs.2016.11.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1054743535"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1061/(asce)ey.1943-7897.0000098", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1057630824"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tase.2013.2245120", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061515253"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/0954405413508280", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063882830"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/0954405413508280", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063882830"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/0954405415586711", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063883277"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/0954405415586711", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063883277"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/2041297511398541", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064084041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/2041297511398541", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064084041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1260/1748-3018.4.2.231", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064584857"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1260/1748-3018.4.2.231", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064584857"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-04", 
        "datePublishedReg": "2019-04-01", 
        "description": "By taking energy awareness and efficiency into consideration, this research applies multiple linear regression (MLR) models and artificial neural networks (ANNs) for predicting the real-time heating energy demand and natural gas consumption in the drying chamber of an automotive painting line. With the estimation of the correlation and randomness during the painting process, a total of six fundamental variables that integrate energy, product and process data are considered in order to cover the impact factors of the natural gas consumption. The independent variable value, used to calibrate and evaluate the model, is obtained from our constructed energy consumption monitoring system. In the prediction cases, the ANN-based model, which offers a well performance, provides great precision in the determination of the natural gas demand with an R2 coefficient and other error measurements of over 90%. Based on the MLR model, the process temperature and the corresponding variation are found to be two decisive factors for the natural gas consumption. It is foreseen that the ANN model, which can effectively perform input/output mapping, is reliable and powerful for predicting the natural gas consumption and estimating the energy demand. Being a general data-driven method, our method is convenient for application to other heating process lines and even other energy-consuming plants.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s40684-019-00064-x", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1136729", 
            "issn": [
              "2288-6206", 
              "2198-0810"
            ], 
            "name": "International Journal of Precision Engineering and Manufacturing-Green Technology", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "6"
          }
        ], 
        "name": "Modeling Drying-Energy Consumption in Automotive Painting Line Based on ANN and MLR for Real-Time Prediction", 
        "pagination": "241-254", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "6f38c01f42135acdd02d71b26042361b50a58dfba00132479696526537a2a33e"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s40684-019-00064-x"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1112392453"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s40684-019-00064-x", 
          "https://app.dimensions.ai/details/publication/pub.1112392453"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T13:55", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000371_0000000371/records_130811_00000006.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs40684-019-00064-x"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s40684-019-00064-x'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s40684-019-00064-x'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40684-019-00064-x'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40684-019-00064-x'


     

    This table displays all metadata directly associated to this object as RDF triples.

    177 TRIPLES      21 PREDICATES      60 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s40684-019-00064-x schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Nd1424a273a8b4e29ac4e2fca3a8f40d0
    4 schema:citation sg:pub.10.1007/s12541-013-0165-0
    5 sg:pub.10.1007/s12541-014-0346-5
    6 sg:pub.10.1007/s12541-014-0524-5
    7 sg:pub.10.1007/s12541-014-0590-8
    8 sg:pub.10.1007/s40684-014-0033-0
    9 sg:pub.10.1007/s40684-015-0021-z
    10 https://doi.org/10.1016/j.apenergy.2015.12.066
    11 https://doi.org/10.1016/j.cirpj.2011.03.007
    12 https://doi.org/10.1016/j.compind.2014.01.007
    13 https://doi.org/10.1016/j.eneco.2014.03.017
    14 https://doi.org/10.1016/j.energy.2014.08.072
    15 https://doi.org/10.1016/j.energy.2015.03.084
    16 https://doi.org/10.1016/j.energy.2015.07.068
    17 https://doi.org/10.1016/j.energy.2016.12.022
    18 https://doi.org/10.1016/j.ijepes.2014.12.036
    19 https://doi.org/10.1016/j.jclepro.2014.10.006
    20 https://doi.org/10.1016/j.jclepro.2015.05.049
    21 https://doi.org/10.1016/j.jclepro.2015.05.093
    22 https://doi.org/10.1016/j.jclepro.2015.12.098
    23 https://doi.org/10.1016/j.jclepro.2016.06.037
    24 https://doi.org/10.1016/j.jclepro.2016.11.019
    25 https://doi.org/10.1016/j.pecs.2016.11.001
    26 https://doi.org/10.1016/j.trc.2014.03.014
    27 https://doi.org/10.1061/(asce)ey.1943-7897.0000098
    28 https://doi.org/10.1080/00207543.2013.813983
    29 https://doi.org/10.1080/19397038.2014.895067
    30 https://doi.org/10.1080/19397038.2015.1008599
    31 https://doi.org/10.1109/tase.2013.2245120
    32 https://doi.org/10.1162/1088198041269355
    33 https://doi.org/10.1177/0954405413508280
    34 https://doi.org/10.1177/0954405415586711
    35 https://doi.org/10.1177/2041297511398541
    36 https://doi.org/10.1260/1748-3018.4.2.231
    37 schema:datePublished 2019-04
    38 schema:datePublishedReg 2019-04-01
    39 schema:description By taking energy awareness and efficiency into consideration, this research applies multiple linear regression (MLR) models and artificial neural networks (ANNs) for predicting the real-time heating energy demand and natural gas consumption in the drying chamber of an automotive painting line. With the estimation of the correlation and randomness during the painting process, a total of six fundamental variables that integrate energy, product and process data are considered in order to cover the impact factors of the natural gas consumption. The independent variable value, used to calibrate and evaluate the model, is obtained from our constructed energy consumption monitoring system. In the prediction cases, the ANN-based model, which offers a well performance, provides great precision in the determination of the natural gas demand with an R2 coefficient and other error measurements of over 90%. Based on the MLR model, the process temperature and the corresponding variation are found to be two decisive factors for the natural gas consumption. It is foreseen that the ANN model, which can effectively perform input/output mapping, is reliable and powerful for predicting the natural gas consumption and estimating the energy demand. Being a general data-driven method, our method is convenient for application to other heating process lines and even other energy-consuming plants.
    40 schema:genre research_article
    41 schema:inLanguage en
    42 schema:isAccessibleForFree false
    43 schema:isPartOf N3f3ddf06857c47e887932204d9e68ed0
    44 Nbb24cc1d0ba74b728cb614525dd3123c
    45 sg:journal.1136729
    46 schema:name Modeling Drying-Energy Consumption in Automotive Painting Line Based on ANN and MLR for Real-Time Prediction
    47 schema:pagination 241-254
    48 schema:productId N3bb6e2ac20fb4d148687018b9edb2882
    49 Nbc947ce779a84fb38731d6e487342bfa
    50 Ncbb74f7b31c3434e84d2dc4776523d9b
    51 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112392453
    52 https://doi.org/10.1007/s40684-019-00064-x
    53 schema:sdDatePublished 2019-04-11T13:55
    54 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    55 schema:sdPublisher Ncee7b30d510e446d91c10172ad1cf700
    56 schema:url https://link.springer.com/10.1007%2Fs40684-019-00064-x
    57 sgo:license sg:explorer/license/
    58 sgo:sdDataset articles
    59 rdf:type schema:ScholarlyArticle
    60 N2426845bd209481fab05da2846792ede rdf:first Nc4ca5f32457441fc97779879c365f376
    61 rdf:rest rdf:nil
    62 N2b037e0a770b43168935224bc63a9606 schema:affiliation https://www.grid.ac/institutes/grid.27255.37
    63 schema:familyName Jiang
    64 schema:givenName Zhaoliang
    65 rdf:type schema:Person
    66 N37a2734303a048e1ad72df63793fe137 schema:affiliation https://www.grid.ac/institutes/grid.27255.37
    67 schema:familyName Ma
    68 schema:givenName Songhua
    69 rdf:type schema:Person
    70 N3bb6e2ac20fb4d148687018b9edb2882 schema:name readcube_id
    71 schema:value 6f38c01f42135acdd02d71b26042361b50a58dfba00132479696526537a2a33e
    72 rdf:type schema:PropertyValue
    73 N3f3ddf06857c47e887932204d9e68ed0 schema:issueNumber 2
    74 rdf:type schema:PublicationIssue
    75 N5d50a34f0a1a4ec5929f08789f370dd0 rdf:first N2b037e0a770b43168935224bc63a9606
    76 rdf:rest N2426845bd209481fab05da2846792ede
    77 Nbb24cc1d0ba74b728cb614525dd3123c schema:volumeNumber 6
    78 rdf:type schema:PublicationVolume
    79 Nbc947ce779a84fb38731d6e487342bfa schema:name doi
    80 schema:value 10.1007/s40684-019-00064-x
    81 rdf:type schema:PropertyValue
    82 Nc4ca5f32457441fc97779879c365f376 schema:affiliation https://www.grid.ac/institutes/grid.27255.37
    83 schema:familyName Liu
    84 schema:givenName Wenping
    85 rdf:type schema:Person
    86 Ncbb74f7b31c3434e84d2dc4776523d9b schema:name dimensions_id
    87 schema:value pub.1112392453
    88 rdf:type schema:PropertyValue
    89 Ncee7b30d510e446d91c10172ad1cf700 schema:name Springer Nature - SN SciGraph project
    90 rdf:type schema:Organization
    91 Nd1424a273a8b4e29ac4e2fca3a8f40d0 rdf:first N37a2734303a048e1ad72df63793fe137
    92 rdf:rest N5d50a34f0a1a4ec5929f08789f370dd0
    93 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    94 schema:name Information and Computing Sciences
    95 rdf:type schema:DefinedTerm
    96 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    97 schema:name Artificial Intelligence and Image Processing
    98 rdf:type schema:DefinedTerm
    99 sg:journal.1136729 schema:issn 2198-0810
    100 2288-6206
    101 schema:name International Journal of Precision Engineering and Manufacturing-Green Technology
    102 rdf:type schema:Periodical
    103 sg:pub.10.1007/s12541-013-0165-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031628338
    104 https://doi.org/10.1007/s12541-013-0165-0
    105 rdf:type schema:CreativeWork
    106 sg:pub.10.1007/s12541-014-0346-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016540950
    107 https://doi.org/10.1007/s12541-014-0346-5
    108 rdf:type schema:CreativeWork
    109 sg:pub.10.1007/s12541-014-0524-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005580955
    110 https://doi.org/10.1007/s12541-014-0524-5
    111 rdf:type schema:CreativeWork
    112 sg:pub.10.1007/s12541-014-0590-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045905547
    113 https://doi.org/10.1007/s12541-014-0590-8
    114 rdf:type schema:CreativeWork
    115 sg:pub.10.1007/s40684-014-0033-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012500994
    116 https://doi.org/10.1007/s40684-014-0033-0
    117 rdf:type schema:CreativeWork
    118 sg:pub.10.1007/s40684-015-0021-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1051934524
    119 https://doi.org/10.1007/s40684-015-0021-z
    120 rdf:type schema:CreativeWork
    121 https://doi.org/10.1016/j.apenergy.2015.12.066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004420043
    122 rdf:type schema:CreativeWork
    123 https://doi.org/10.1016/j.cirpj.2011.03.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022011833
    124 rdf:type schema:CreativeWork
    125 https://doi.org/10.1016/j.compind.2014.01.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031394991
    126 rdf:type schema:CreativeWork
    127 https://doi.org/10.1016/j.eneco.2014.03.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028454277
    128 rdf:type schema:CreativeWork
    129 https://doi.org/10.1016/j.energy.2014.08.072 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019606045
    130 rdf:type schema:CreativeWork
    131 https://doi.org/10.1016/j.energy.2015.03.084 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016881412
    132 rdf:type schema:CreativeWork
    133 https://doi.org/10.1016/j.energy.2015.07.068 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038185711
    134 rdf:type schema:CreativeWork
    135 https://doi.org/10.1016/j.energy.2016.12.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053188377
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.1016/j.ijepes.2014.12.036 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027666005
    138 rdf:type schema:CreativeWork
    139 https://doi.org/10.1016/j.jclepro.2014.10.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031468438
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1016/j.jclepro.2015.05.049 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011540064
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.1016/j.jclepro.2015.05.093 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007473683
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1016/j.jclepro.2015.12.098 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006060527
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1016/j.jclepro.2016.06.037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036329615
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1016/j.jclepro.2016.11.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006655278
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1016/j.pecs.2016.11.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054743535
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1016/j.trc.2014.03.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030454058
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1061/(asce)ey.1943-7897.0000098 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057630824
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1080/00207543.2013.813983 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004557530
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1080/19397038.2014.895067 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025832135
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1080/19397038.2015.1008599 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007281240
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1109/tase.2013.2245120 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061515253
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1162/1088198041269355 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035192811
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1177/0954405413508280 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063882830
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1177/0954405415586711 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063883277
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1177/2041297511398541 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064084041
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1260/1748-3018.4.2.231 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064584857
    174 rdf:type schema:CreativeWork
    175 https://www.grid.ac/institutes/grid.27255.37 schema:alternateName Shandong University
    176 schema:name Department of Mechanical Engineering, Shandong University, Building No. 8 414, 250061, Jinan, Shandong, China
    177 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...