Computational and experimental study for the desalination of petrochemical industrial effluents using direct contact membrane distillation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Muhammad S. Osman, Vhahangwele Masindi, Adnan M. Abu-Mahfouz

ABSTRACT

The petrochemical, mining and power industries have reacted to the recent South African water crisis by focussing on improved brine treatment for water and salt recovery with the aim of achieving zero liquid effluent discharge. The purpose of this novel study was to compare experimentally obtained results from the treatment of synthetic NaCl solutions and petrochemical industrial brines such as spent ion exchange regenerant brines and reverse osmosis (RO) brines to the classical well-known Knudsen diffusion, molecular diffusion and transition predictive models. The predictive models were numerically solved using a developed mathematical algorithm that was coded using MATLAB® software. The impact of experimentally varying the inlet feed temperature on process performance of the system is presented here and compared to simulated results. It was found that there was good agreement between the experimentally obtained results, for both the synthetic NaCl solution and the industrial brines. The mean average percentage error (MAPE) was found to be 7.9% for the synthetic NaCl solutions when compared to the Knudsen model. The Knudsen/molecular diffusion transition theoretical model best predicted the performance of the membrane for the industrial spent ion exchange regenerant brine with a mean absolute percentage error (MAPE) of 13.3%. The Knudsen model best predicted the performance of the membrane (MAPE of 10.5%) for the industrial RO brine. Overall, the models were able to successfully predict the water flux and can be used as potential process design tools. More... »

PAGES

29

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13201-019-0910-3

DOI

http://dx.doi.org/10.1007/s13201-019-0910-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112305835


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/2008/0904", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Chemical Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Council for Scientific and Industrial Research", 
          "id": "https://www.grid.ac/institutes/grid.7327.1", 
          "name": [
            "Council for Scientific and Industrial Research (CSIR), P.O. Box 395, Pretoria, 0001, South Africa"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Osman", 
        "givenName": "Muhammad S.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of South Africa", 
          "id": "https://www.grid.ac/institutes/grid.412801.e", 
          "name": [
            "Council for Scientific and Industrial Research (CSIR), P.O. Box 395, Pretoria, 0001, South Africa", 
            "Department of Environmental Sciences, School of Agriculture and Environmental Sciences, University of South Africa (UNISA), P.O. Box 392, 1710, Florida, South Africa"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Masindi", 
        "givenName": "Vhahangwele", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tshwane University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.412810.e", 
          "name": [
            "Council for Scientific and Industrial Research (CSIR), P.O. Box 395, Pretoria, 0001, South Africa", 
            "Department of Electrical Engineering, Tshwane University of Technology, Private Bag X680, 0001, Pretoria, South Africa"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Abu-Mahfouz", 
        "givenName": "Adnan M.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.seppur.2016.06.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000258914"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.seppur.2016.06.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000258914"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5004/dwt.2010.1657", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002626895"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1252/kakoronbunshu.17.1168", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002976880"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2011.11.057", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003072645"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.seppur.2009.11.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004535363"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.memsci.2006.05.040", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012059712"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11356-016-7961-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012367785", 
          "https://doi.org/10.1007/s11356-016-7961-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11356-016-7961-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012367785", 
          "https://doi.org/10.1007/s11356-016-7961-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/ceat.201100586", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014141106"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.seppur.2013.07.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015721926"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2006.09.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019635729"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0956-9618(94)00107-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020222599"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2016.07.043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020270599"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2013.01.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021460105"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijheatmasstransfer.2013.07.051", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021681800"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5004/dwt.2011.2513", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022016744"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/10408398.2012.685116", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024812805"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.memsci.2006.03.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025392551"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2014.10.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026369679"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/19443994.2016.1152637", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026755275"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.memsci.2013.11.022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028853693"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/19443994.2015.1040271", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028861283"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jclepro.2016.05.127", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030543525"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0376-7388(96)00236-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031297240"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.seppur.2013.05.052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031637961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1081/ss-100100224", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031779233"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jece.2015.07.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033481400"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2014.10.033", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035103985"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/19443994.2013.808422", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035899360"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.apenergy.2016.09.090", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040089956"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0376-7388(02)00603-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040608237"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0376-7388(02)00603-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040608237"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0376-7388(00)80287-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041706434"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ifset.2011.02.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045528267"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2015.01.043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047637667"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2015.01.043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047637667"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1871-2711(09)00204-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048684883"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.memsci.2014.09.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048696944"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.energy.2015.02.099", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048899829"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2011.08.027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049050180"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2016.11.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049614753"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2016.11.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049614753"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.desal.2016.11.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049614753"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0011-9164(02)01070-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050066668"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.memsci.2006.08.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051627772"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0011-9164(91)85048-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052550925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0011-9164(00)82074-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053091067"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/es404056e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055506999"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/es505439p", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055508777"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ie060138b", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055599835"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ie060138b", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055599835"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1504/ijnd.2003.003441", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067480451"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1504/ijnd.2005.007020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067480489"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1504/ijw.2013.056674", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067503493"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2166/wst.2014.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069146728"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5004/dwt.2017.20175", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083999214"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.seppur.2017.03.035", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084108329"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5004/dwt.2017.20236", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085867974"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5004/dwt.2017.20249", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086017471"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.seppur.2017.07.069", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090940325"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.memsci.2017.08.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091047098"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.envpol.2017.08.070", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091381556"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.memsci.2017.08.059", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091399044"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/imece2015-50171", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092825581"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/irsec.2013.6529673", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093655161"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2166/wst.1982.0124", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104121269"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-03", 
    "datePublishedReg": "2019-03-01", 
    "description": "The petrochemical, mining and power industries have reacted to the recent South African water crisis by focussing on improved brine treatment for water and salt recovery with the aim of achieving zero liquid effluent discharge. The purpose of this novel study was to compare experimentally obtained results from the treatment of synthetic NaCl solutions and petrochemical industrial brines such as spent ion exchange regenerant brines and reverse osmosis (RO) brines to the classical well-known Knudsen diffusion, molecular diffusion and transition predictive models. The predictive models were numerically solved using a developed mathematical algorithm that was coded using MATLAB\u00ae software. The impact of experimentally varying the inlet feed temperature on process performance of the system is presented here and compared to simulated results. It was found that there was good agreement between the experimentally obtained results, for both the synthetic NaCl solution and the industrial brines. The mean average percentage error (MAPE) was found to be 7.9% for the synthetic NaCl solutions when compared to the Knudsen model. The Knudsen/molecular diffusion transition theoretical model best predicted the performance of the membrane for the industrial spent ion exchange regenerant brine with a mean absolute percentage error (MAPE) of 13.3%. The Knudsen model best predicted the performance of the membrane (MAPE of 10.5%) for the industrial RO brine. Overall, the models were able to successfully predict the water flux and can be used as potential process design tools.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s13201-019-0910-3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1051421", 
        "issn": [
          "2190-5487", 
          "2190-5495"
        ], 
        "name": "Applied Water Science", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "9"
      }
    ], 
    "name": "Computational and experimental study for the desalination of petrochemical industrial effluents using direct contact membrane distillation", 
    "pagination": "29", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "aaf8013f7901796fe020352c0a7d440af4aa3495e11690f5acef3d56327d52a5"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s13201-019-0910-3"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112305835"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s13201-019-0910-3", 
      "https://app.dimensions.ai/details/publication/pub.1112305835"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:04", 
    "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/v90/0000000366_0000000366/records_112042_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs13201-019-0910-3"
  }
]
 

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/s13201-019-0910-3'

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/s13201-019-0910-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13201-019-0910-3'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13201-019-0910-3'


 

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

261 TRIPLES      21 PREDICATES      87 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s13201-019-0910-3 schema:about anzsrc-for:09
2 anzsrc-for:0904
3 schema:author Nce36ba752390445da069885f970bdd29
4 schema:citation sg:pub.10.1007/s11356-016-7961-x
5 https://doi.org/10.1002/ceat.201100586
6 https://doi.org/10.1016/0011-9164(91)85048-y
7 https://doi.org/10.1016/0956-9618(94)00107-4
8 https://doi.org/10.1016/j.apenergy.2016.09.090
9 https://doi.org/10.1016/j.desal.2006.09.010
10 https://doi.org/10.1016/j.desal.2011.08.027
11 https://doi.org/10.1016/j.desal.2011.11.057
12 https://doi.org/10.1016/j.desal.2013.01.003
13 https://doi.org/10.1016/j.desal.2014.10.028
14 https://doi.org/10.1016/j.desal.2014.10.033
15 https://doi.org/10.1016/j.desal.2015.01.043
16 https://doi.org/10.1016/j.desal.2016.07.043
17 https://doi.org/10.1016/j.desal.2016.11.018
18 https://doi.org/10.1016/j.energy.2015.02.099
19 https://doi.org/10.1016/j.envpol.2017.08.070
20 https://doi.org/10.1016/j.ifset.2011.02.005
21 https://doi.org/10.1016/j.ijheatmasstransfer.2013.07.051
22 https://doi.org/10.1016/j.jclepro.2016.05.127
23 https://doi.org/10.1016/j.jece.2015.07.008
24 https://doi.org/10.1016/j.memsci.2006.03.014
25 https://doi.org/10.1016/j.memsci.2006.05.040
26 https://doi.org/10.1016/j.memsci.2006.08.002
27 https://doi.org/10.1016/j.memsci.2013.11.022
28 https://doi.org/10.1016/j.memsci.2014.09.016
29 https://doi.org/10.1016/j.memsci.2017.08.001
30 https://doi.org/10.1016/j.memsci.2017.08.059
31 https://doi.org/10.1016/j.seppur.2009.11.004
32 https://doi.org/10.1016/j.seppur.2013.05.052
33 https://doi.org/10.1016/j.seppur.2013.07.021
34 https://doi.org/10.1016/j.seppur.2016.06.028
35 https://doi.org/10.1016/j.seppur.2017.03.035
36 https://doi.org/10.1016/j.seppur.2017.07.069
37 https://doi.org/10.1016/s0011-9164(00)82074-3
38 https://doi.org/10.1016/s0011-9164(02)01070-6
39 https://doi.org/10.1016/s0376-7388(00)80287-2
40 https://doi.org/10.1016/s0376-7388(02)00603-8
41 https://doi.org/10.1016/s0376-7388(96)00236-0
42 https://doi.org/10.1016/s1871-2711(09)00204-9
43 https://doi.org/10.1021/es404056e
44 https://doi.org/10.1021/es505439p
45 https://doi.org/10.1021/ie060138b
46 https://doi.org/10.1080/10408398.2012.685116
47 https://doi.org/10.1080/19443994.2013.808422
48 https://doi.org/10.1080/19443994.2015.1040271
49 https://doi.org/10.1080/19443994.2016.1152637
50 https://doi.org/10.1081/ss-100100224
51 https://doi.org/10.1109/irsec.2013.6529673
52 https://doi.org/10.1115/imece2015-50171
53 https://doi.org/10.1252/kakoronbunshu.17.1168
54 https://doi.org/10.1504/ijnd.2003.003441
55 https://doi.org/10.1504/ijnd.2005.007020
56 https://doi.org/10.1504/ijw.2013.056674
57 https://doi.org/10.2166/wst.1982.0124
58 https://doi.org/10.2166/wst.2014.003
59 https://doi.org/10.5004/dwt.2010.1657
60 https://doi.org/10.5004/dwt.2011.2513
61 https://doi.org/10.5004/dwt.2017.20175
62 https://doi.org/10.5004/dwt.2017.20236
63 https://doi.org/10.5004/dwt.2017.20249
64 schema:datePublished 2019-03
65 schema:datePublishedReg 2019-03-01
66 schema:description The petrochemical, mining and power industries have reacted to the recent South African water crisis by focussing on improved brine treatment for water and salt recovery with the aim of achieving zero liquid effluent discharge. The purpose of this novel study was to compare experimentally obtained results from the treatment of synthetic NaCl solutions and petrochemical industrial brines such as spent ion exchange regenerant brines and reverse osmosis (RO) brines to the classical well-known Knudsen diffusion, molecular diffusion and transition predictive models. The predictive models were numerically solved using a developed mathematical algorithm that was coded using MATLAB® software. The impact of experimentally varying the inlet feed temperature on process performance of the system is presented here and compared to simulated results. It was found that there was good agreement between the experimentally obtained results, for both the synthetic NaCl solution and the industrial brines. The mean average percentage error (MAPE) was found to be 7.9% for the synthetic NaCl solutions when compared to the Knudsen model. The Knudsen/molecular diffusion transition theoretical model best predicted the performance of the membrane for the industrial spent ion exchange regenerant brine with a mean absolute percentage error (MAPE) of 13.3%. The Knudsen model best predicted the performance of the membrane (MAPE of 10.5%) for the industrial RO brine. Overall, the models were able to successfully predict the water flux and can be used as potential process design tools.
67 schema:genre research_article
68 schema:inLanguage en
69 schema:isAccessibleForFree false
70 schema:isPartOf N935a789c91fc4033878428fd151a1cc6
71 Ne18bcf8bb69d4c54b7e041dd6df83bbc
72 sg:journal.1051421
73 schema:name Computational and experimental study for the desalination of petrochemical industrial effluents using direct contact membrane distillation
74 schema:pagination 29
75 schema:productId N2c14b45a7ff74188ae2ced8cd8f5a50c
76 N4e70c73e9fab46bba9bc5165653cccc7
77 Ne4441743ee7c4b768937c7cbfea02de9
78 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112305835
79 https://doi.org/10.1007/s13201-019-0910-3
80 schema:sdDatePublished 2019-04-11T13:04
81 schema:sdLicense https://scigraph.springernature.com/explorer/license/
82 schema:sdPublisher Nc14588b1cf584082a4911d7dc5460cf8
83 schema:url https://link.springer.com/10.1007%2Fs13201-019-0910-3
84 sgo:license sg:explorer/license/
85 sgo:sdDataset articles
86 rdf:type schema:ScholarlyArticle
87 N0af0b7dc028e413695548534061747ed schema:affiliation https://www.grid.ac/institutes/grid.412810.e
88 schema:familyName Abu-Mahfouz
89 schema:givenName Adnan M.
90 rdf:type schema:Person
91 N2c14b45a7ff74188ae2ced8cd8f5a50c schema:name readcube_id
92 schema:value aaf8013f7901796fe020352c0a7d440af4aa3495e11690f5acef3d56327d52a5
93 rdf:type schema:PropertyValue
94 N4e70c73e9fab46bba9bc5165653cccc7 schema:name doi
95 schema:value 10.1007/s13201-019-0910-3
96 rdf:type schema:PropertyValue
97 N58f5af80896d4b4a931356f64f0c7fd3 rdf:first N59ec64908ede408aa57bc19ec331f2f7
98 rdf:rest N825bc7c8351a4edea0efc38e231d4d11
99 N59ec64908ede408aa57bc19ec331f2f7 schema:affiliation https://www.grid.ac/institutes/grid.412801.e
100 schema:familyName Masindi
101 schema:givenName Vhahangwele
102 rdf:type schema:Person
103 N5a294860e32f4e568ab006b036d51fbd schema:affiliation https://www.grid.ac/institutes/grid.7327.1
104 schema:familyName Osman
105 schema:givenName Muhammad S.
106 rdf:type schema:Person
107 N825bc7c8351a4edea0efc38e231d4d11 rdf:first N0af0b7dc028e413695548534061747ed
108 rdf:rest rdf:nil
109 N935a789c91fc4033878428fd151a1cc6 schema:issueNumber 2
110 rdf:type schema:PublicationIssue
111 Nc14588b1cf584082a4911d7dc5460cf8 schema:name Springer Nature - SN SciGraph project
112 rdf:type schema:Organization
113 Nce36ba752390445da069885f970bdd29 rdf:first N5a294860e32f4e568ab006b036d51fbd
114 rdf:rest N58f5af80896d4b4a931356f64f0c7fd3
115 Ne18bcf8bb69d4c54b7e041dd6df83bbc schema:volumeNumber 9
116 rdf:type schema:PublicationVolume
117 Ne4441743ee7c4b768937c7cbfea02de9 schema:name dimensions_id
118 schema:value pub.1112305835
119 rdf:type schema:PropertyValue
120 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
121 schema:name Engineering
122 rdf:type schema:DefinedTerm
123 anzsrc-for:0904 schema:inDefinedTermSet anzsrc-for:
124 schema:name Chemical Engineering
125 rdf:type schema:DefinedTerm
126 sg:journal.1051421 schema:issn 2190-5487
127 2190-5495
128 schema:name Applied Water Science
129 rdf:type schema:Periodical
130 sg:pub.10.1007/s11356-016-7961-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1012367785
131 https://doi.org/10.1007/s11356-016-7961-x
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1002/ceat.201100586 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014141106
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/0011-9164(91)85048-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1052550925
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/0956-9618(94)00107-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020222599
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1016/j.apenergy.2016.09.090 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040089956
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1016/j.desal.2006.09.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019635729
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1016/j.desal.2011.08.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049050180
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1016/j.desal.2011.11.057 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003072645
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1016/j.desal.2013.01.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021460105
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1016/j.desal.2014.10.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026369679
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1016/j.desal.2014.10.033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035103985
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1016/j.desal.2015.01.043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047637667
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1016/j.desal.2016.07.043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020270599
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1016/j.desal.2016.11.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049614753
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1016/j.energy.2015.02.099 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048899829
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1016/j.envpol.2017.08.070 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091381556
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.ifset.2011.02.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045528267
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1016/j.ijheatmasstransfer.2013.07.051 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021681800
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/j.jclepro.2016.05.127 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030543525
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1016/j.jece.2015.07.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033481400
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/j.memsci.2006.03.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025392551
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/j.memsci.2006.05.040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012059712
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/j.memsci.2006.08.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051627772
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/j.memsci.2013.11.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028853693
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1016/j.memsci.2014.09.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048696944
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1016/j.memsci.2017.08.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091047098
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1016/j.memsci.2017.08.059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091399044
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1016/j.seppur.2009.11.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004535363
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1016/j.seppur.2013.05.052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031637961
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1016/j.seppur.2013.07.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015721926
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1016/j.seppur.2016.06.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000258914
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1016/j.seppur.2017.03.035 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084108329
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1016/j.seppur.2017.07.069 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090940325
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1016/s0011-9164(00)82074-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053091067
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1016/s0011-9164(02)01070-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050066668
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1016/s0376-7388(00)80287-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041706434
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1016/s0376-7388(02)00603-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040608237
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1016/s0376-7388(96)00236-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031297240
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1016/s1871-2711(09)00204-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048684883
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1021/es404056e schema:sameAs https://app.dimensions.ai/details/publication/pub.1055506999
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1021/es505439p schema:sameAs https://app.dimensions.ai/details/publication/pub.1055508777
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1021/ie060138b schema:sameAs https://app.dimensions.ai/details/publication/pub.1055599835
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1080/10408398.2012.685116 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024812805
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1080/19443994.2013.808422 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035899360
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1080/19443994.2015.1040271 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028861283
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1080/19443994.2016.1152637 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026755275
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1081/ss-100100224 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031779233
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1109/irsec.2013.6529673 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093655161
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1115/imece2015-50171 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092825581
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1252/kakoronbunshu.17.1168 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002976880
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1504/ijnd.2003.003441 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067480451
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1504/ijnd.2005.007020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067480489
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1504/ijw.2013.056674 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067503493
236 rdf:type schema:CreativeWork
237 https://doi.org/10.2166/wst.1982.0124 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104121269
238 rdf:type schema:CreativeWork
239 https://doi.org/10.2166/wst.2014.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069146728
240 rdf:type schema:CreativeWork
241 https://doi.org/10.5004/dwt.2010.1657 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002626895
242 rdf:type schema:CreativeWork
243 https://doi.org/10.5004/dwt.2011.2513 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022016744
244 rdf:type schema:CreativeWork
245 https://doi.org/10.5004/dwt.2017.20175 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083999214
246 rdf:type schema:CreativeWork
247 https://doi.org/10.5004/dwt.2017.20236 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085867974
248 rdf:type schema:CreativeWork
249 https://doi.org/10.5004/dwt.2017.20249 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086017471
250 rdf:type schema:CreativeWork
251 https://www.grid.ac/institutes/grid.412801.e schema:alternateName University of South Africa
252 schema:name Council for Scientific and Industrial Research (CSIR), P.O. Box 395, Pretoria, 0001, South Africa
253 Department of Environmental Sciences, School of Agriculture and Environmental Sciences, University of South Africa (UNISA), P.O. Box 392, 1710, Florida, South Africa
254 rdf:type schema:Organization
255 https://www.grid.ac/institutes/grid.412810.e schema:alternateName Tshwane University of Technology
256 schema:name Council for Scientific and Industrial Research (CSIR), P.O. Box 395, Pretoria, 0001, South Africa
257 Department of Electrical Engineering, Tshwane University of Technology, Private Bag X680, 0001, Pretoria, South Africa
258 rdf:type schema:Organization
259 https://www.grid.ac/institutes/grid.7327.1 schema:alternateName Council for Scientific and Industrial Research
260 schema:name Council for Scientific and Industrial Research (CSIR), P.O. Box 395, Pretoria, 0001, South Africa
261 rdf:type schema:Organization
 




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


...