Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques Article Swipe
Jasir Jawad
,
Alaa H. Hawari
,
Syed Javaid Zaidi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/membranes11010070
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/membranes11010070
The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.
Related Topics
Concepts
Response surface methodology
Artificial neural network
Fouling
Forward osmosis
Design of experiments
Desalination
Membrane fouling
Biological system
Sensitivity (control systems)
Process engineering
Engineering
Reverse osmosis
Materials science
Membrane
Computer science
Mathematics
Machine learning
Chemistry
Statistics
Biochemistry
Biology
Electronic engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/membranes11010070
- https://www.mdpi.com/2077-0375/11/1/70/pdf?version=1611202175
- OA Status
- gold
- Cited By
- 51
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3121144918
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3121144918Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/membranes11010070Digital Object Identifier
- Title
-
Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology TechniquesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-19Full publication date if available
- Authors
-
Jasir Jawad, Alaa H. Hawari, Syed Javaid ZaidiList of authors in order
- Landing page
-
https://doi.org/10.3390/membranes11010070Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-0375/11/1/70/pdf?version=1611202175Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2077-0375/11/1/70/pdf?version=1611202175Direct OA link when available
- Concepts
-
Response surface methodology, Artificial neural network, Fouling, Forward osmosis, Design of experiments, Desalination, Membrane fouling, Biological system, Sensitivity (control systems), Process engineering, Engineering, Reverse osmosis, Materials science, Membrane, Computer science, Mathematics, Machine learning, Chemistry, Statistics, Biochemistry, Biology, Electronic engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
51Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 11, 2024: 15, 2023: 13, 2022: 10, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3121144918 |
|---|---|
| doi | https://doi.org/10.3390/membranes11010070 |
| ids.doi | https://doi.org/10.3390/membranes11010070 |
| ids.mag | 3121144918 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/33478084 |
| ids.openalex | https://openalex.org/W3121144918 |
| fwci | 3.77101056 |
| type | article |
| title | Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques |
| awards[0].id | https://openalex.org/G4617468646 |
| awards[0].funder_id | https://openalex.org/F4320332753 |
| awards[0].display_name | |
| awards[0].funder_award_id | NPRP10-0117-170176 |
| awards[0].funder_display_name | Qatar National Research Fund |
| awards[1].id | https://openalex.org/G3646272135 |
| awards[1].funder_id | https://openalex.org/F4320322472 |
| awards[1].display_name | |
| awards[1].funder_award_id | IRCC-2019-004 |
| awards[1].funder_display_name | Qatar University |
| biblio.issue | 1 |
| biblio.volume | 11 |
| biblio.last_page | 70 |
| biblio.first_page | 70 |
| topics[0].id | https://openalex.org/T10197 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9987999796867371 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2312 |
| topics[0].subfield.display_name | Water Science and Technology |
| topics[0].display_name | Membrane Separation Technologies |
| topics[1].id | https://openalex.org/T12074 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9865000247955322 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2204 |
| topics[1].subfield.display_name | Biomedical Engineering |
| topics[1].display_name | Membrane-based Ion Separation Techniques |
| topics[2].id | https://openalex.org/T12697 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9714999794960022 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2312 |
| topics[2].subfield.display_name | Water Science and Technology |
| topics[2].display_name | Water Quality Monitoring Technologies |
| funders[0].id | https://openalex.org/F4320322472 |
| funders[0].ror | https://ror.org/00yhnba62 |
| funders[0].display_name | Qatar University |
| funders[1].id | https://openalex.org/F4320332753 |
| funders[1].ror | https://ror.org/01svaqq28 |
| funders[1].display_name | Qatar National Research Fund |
| is_xpac | False |
| apc_list.value | 2200 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2382 |
| apc_paid.value | 2200 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2382 |
| concepts[0].id | https://openalex.org/C150077022 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9103195667266846 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q3136137 |
| concepts[0].display_name | Response surface methodology |
| concepts[1].id | https://openalex.org/C50644808 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6522833704948425 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[1].display_name | Artificial neural network |
| concepts[2].id | https://openalex.org/C115792997 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5753177404403687 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1439803 |
| concepts[2].display_name | Fouling |
| concepts[3].id | https://openalex.org/C81842627 |
| concepts[3].level | 4 |
| concepts[3].score | 0.5016474723815918 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2357011 |
| concepts[3].display_name | Forward osmosis |
| concepts[4].id | https://openalex.org/C34559072 |
| concepts[4].level | 2 |
| concepts[4].score | 0.47646141052246094 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2334061 |
| concepts[4].display_name | Design of experiments |
| concepts[5].id | https://openalex.org/C2776870568 |
| concepts[5].level | 3 |
| concepts[5].score | 0.47038212418556213 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q190873 |
| concepts[5].display_name | Desalination |
| concepts[6].id | https://openalex.org/C180461467 |
| concepts[6].level | 4 |
| concepts[6].score | 0.4541539251804352 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2623659 |
| concepts[6].display_name | Membrane fouling |
| concepts[7].id | https://openalex.org/C186060115 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4422645568847656 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q30336093 |
| concepts[7].display_name | Biological system |
| concepts[8].id | https://openalex.org/C21200559 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4383179247379303 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7451068 |
| concepts[8].display_name | Sensitivity (control systems) |
| concepts[9].id | https://openalex.org/C21880701 |
| concepts[9].level | 1 |
| concepts[9].score | 0.41178545355796814 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2144042 |
| concepts[9].display_name | Process engineering |
| concepts[10].id | https://openalex.org/C127413603 |
| concepts[10].level | 0 |
| concepts[10].score | 0.3818262815475464 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[10].display_name | Engineering |
| concepts[11].id | https://openalex.org/C130797344 |
| concepts[11].level | 3 |
| concepts[11].score | 0.3583105206489563 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q272175 |
| concepts[11].display_name | Reverse osmosis |
| concepts[12].id | https://openalex.org/C192562407 |
| concepts[12].level | 0 |
| concepts[12].score | 0.3253710865974426 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q228736 |
| concepts[12].display_name | Materials science |
| concepts[13].id | https://openalex.org/C41625074 |
| concepts[13].level | 2 |
| concepts[13].score | 0.3218335211277008 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q176088 |
| concepts[13].display_name | Membrane |
| concepts[14].id | https://openalex.org/C41008148 |
| concepts[14].level | 0 |
| concepts[14].score | 0.2818845510482788 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[14].display_name | Computer science |
| concepts[15].id | https://openalex.org/C33923547 |
| concepts[15].level | 0 |
| concepts[15].score | 0.25421833992004395 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[15].display_name | Mathematics |
| concepts[16].id | https://openalex.org/C119857082 |
| concepts[16].level | 1 |
| concepts[16].score | 0.20895060896873474 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[16].display_name | Machine learning |
| concepts[17].id | https://openalex.org/C185592680 |
| concepts[17].level | 0 |
| concepts[17].score | 0.1570928692817688 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[17].display_name | Chemistry |
| concepts[18].id | https://openalex.org/C105795698 |
| concepts[18].level | 1 |
| concepts[18].score | 0.07890951633453369 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[18].display_name | Statistics |
| concepts[19].id | https://openalex.org/C55493867 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[19].display_name | Biochemistry |
| concepts[20].id | https://openalex.org/C86803240 |
| concepts[20].level | 0 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[20].display_name | Biology |
| concepts[21].id | https://openalex.org/C24326235 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q126095 |
| concepts[21].display_name | Electronic engineering |
| keywords[0].id | https://openalex.org/keywords/response-surface-methodology |
| keywords[0].score | 0.9103195667266846 |
| keywords[0].display_name | Response surface methodology |
| keywords[1].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[1].score | 0.6522833704948425 |
| keywords[1].display_name | Artificial neural network |
| keywords[2].id | https://openalex.org/keywords/fouling |
| keywords[2].score | 0.5753177404403687 |
| keywords[2].display_name | Fouling |
| keywords[3].id | https://openalex.org/keywords/forward-osmosis |
| keywords[3].score | 0.5016474723815918 |
| keywords[3].display_name | Forward osmosis |
| keywords[4].id | https://openalex.org/keywords/design-of-experiments |
| keywords[4].score | 0.47646141052246094 |
| keywords[4].display_name | Design of experiments |
| keywords[5].id | https://openalex.org/keywords/desalination |
| keywords[5].score | 0.47038212418556213 |
| keywords[5].display_name | Desalination |
| keywords[6].id | https://openalex.org/keywords/membrane-fouling |
| keywords[6].score | 0.4541539251804352 |
| keywords[6].display_name | Membrane fouling |
| keywords[7].id | https://openalex.org/keywords/biological-system |
| keywords[7].score | 0.4422645568847656 |
| keywords[7].display_name | Biological system |
| keywords[8].id | https://openalex.org/keywords/sensitivity |
| keywords[8].score | 0.4383179247379303 |
| keywords[8].display_name | Sensitivity (control systems) |
| keywords[9].id | https://openalex.org/keywords/process-engineering |
| keywords[9].score | 0.41178545355796814 |
| keywords[9].display_name | Process engineering |
| keywords[10].id | https://openalex.org/keywords/engineering |
| keywords[10].score | 0.3818262815475464 |
| keywords[10].display_name | Engineering |
| keywords[11].id | https://openalex.org/keywords/reverse-osmosis |
| keywords[11].score | 0.3583105206489563 |
| keywords[11].display_name | Reverse osmosis |
| keywords[12].id | https://openalex.org/keywords/materials-science |
| keywords[12].score | 0.3253710865974426 |
| keywords[12].display_name | Materials science |
| keywords[13].id | https://openalex.org/keywords/membrane |
| keywords[13].score | 0.3218335211277008 |
| keywords[13].display_name | Membrane |
| keywords[14].id | https://openalex.org/keywords/computer-science |
| keywords[14].score | 0.2818845510482788 |
| keywords[14].display_name | Computer science |
| keywords[15].id | https://openalex.org/keywords/mathematics |
| keywords[15].score | 0.25421833992004395 |
| keywords[15].display_name | Mathematics |
| keywords[16].id | https://openalex.org/keywords/machine-learning |
| keywords[16].score | 0.20895060896873474 |
| keywords[16].display_name | Machine learning |
| keywords[17].id | https://openalex.org/keywords/chemistry |
| keywords[17].score | 0.1570928692817688 |
| keywords[17].display_name | Chemistry |
| keywords[18].id | https://openalex.org/keywords/statistics |
| keywords[18].score | 0.07890951633453369 |
| keywords[18].display_name | Statistics |
| language | en |
| locations[0].id | doi:10.3390/membranes11010070 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210225467 |
| locations[0].source.issn | 2077-0375 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2077-0375 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Membranes |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2077-0375/11/1/70/pdf?version=1611202175 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Membranes |
| locations[0].landing_page_url | https://doi.org/10.3390/membranes11010070 |
| locations[1].id | pmid:33478084 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Membranes |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/33478084 |
| locations[2].id | pmh:oai:doaj.org/article:14e5cfe161c242248bf7d3be1630d583 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Membranes, Vol 11, Iss 1, p 70 (2021) |
| locations[2].landing_page_url | https://doaj.org/article/14e5cfe161c242248bf7d3be1630d583 |
| locations[3].id | pmh:oai:mdpi.com:/2077-0375/11/1/70/ |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400947 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | True |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | MDPI (MDPI AG) |
| locations[3].source.host_organization | https://openalex.org/I4210097602 |
| locations[3].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[3].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Membranes; Volume 11; Issue 1; Pages: 70 |
| locations[3].landing_page_url | https://dx.doi.org/10.3390/membranes11010070 |
| locations[4].id | pmh:oai:pubmedcentral.nih.gov:7835737 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S2764455111 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | PubMed Central |
| locations[4].source.host_organization | https://openalex.org/I1299303238 |
| locations[4].source.host_organization_name | National Institutes of Health |
| locations[4].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[4].license | other-oa |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/other-oa |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Membranes (Basel) |
| locations[4].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7835737 |
| locations[5].id | pmh:oai:qspace.qu.edu.qa:10576/43383 |
| locations[5].is_oa | False |
| locations[5].source.id | https://openalex.org/S4306400014 |
| locations[5].source.issn | |
| locations[5].source.type | repository |
| locations[5].source.is_oa | False |
| locations[5].source.issn_l | |
| locations[5].source.is_core | False |
| locations[5].source.is_in_doaj | False |
| locations[5].source.display_name | Qatar University QSpace (Qatar University) |
| locations[5].source.host_organization | https://openalex.org/I60342839 |
| locations[5].source.host_organization_name | Qatar University |
| locations[5].source.host_organization_lineage | https://openalex.org/I60342839 |
| locations[5].license | |
| locations[5].pdf_url | |
| locations[5].version | submittedVersion |
| locations[5].raw_type | Article |
| locations[5].license_id | |
| locations[5].is_accepted | False |
| locations[5].is_published | False |
| locations[5].raw_source_name | |
| locations[5].landing_page_url | http://hdl.handle.net/10576/43383 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5077115642 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9815-7679 |
| authorships[0].author.display_name | Jasir Jawad |
| authorships[0].countries | QA |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I60342839 |
| authorships[0].affiliations[0].raw_affiliation_string | Centre for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar |
| authorships[0].institutions[0].id | https://openalex.org/I60342839 |
| authorships[0].institutions[0].ror | https://ror.org/00yhnba62 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I60342839 |
| authorships[0].institutions[0].country_code | QA |
| authorships[0].institutions[0].display_name | Qatar University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jasir Jawad |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Centre for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar |
| authorships[1].author.id | https://openalex.org/A5083246810 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Alaa H. Hawari |
| authorships[1].countries | QA |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I60342839 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Civil and Architectural Engineering, Qatar University, P.O. Box 2713, Doha, Qatar |
| authorships[1].institutions[0].id | https://openalex.org/I60342839 |
| authorships[1].institutions[0].ror | https://ror.org/00yhnba62 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I60342839 |
| authorships[1].institutions[0].country_code | QA |
| authorships[1].institutions[0].display_name | Qatar University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Alaa Hawari |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Department of Civil and Architectural Engineering, Qatar University, P.O. Box 2713, Doha, Qatar |
| authorships[2].author.id | https://openalex.org/A5108652065 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Syed Javaid Zaidi |
| authorships[2].countries | QA |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I60342839 |
| authorships[2].affiliations[0].raw_affiliation_string | Centre for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar |
| authorships[2].institutions[0].id | https://openalex.org/I60342839 |
| authorships[2].institutions[0].ror | https://ror.org/00yhnba62 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I60342839 |
| authorships[2].institutions[0].country_code | QA |
| authorships[2].institutions[0].display_name | Qatar University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Syed Zaidi |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Centre for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2077-0375/11/1/70/pdf?version=1611202175 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10197 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9987999796867371 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2312 |
| primary_topic.subfield.display_name | Water Science and Technology |
| primary_topic.display_name | Membrane Separation Technologies |
| related_works | https://openalex.org/W1999362653, https://openalex.org/W3109340276, https://openalex.org/W3015742614, https://openalex.org/W3114553067, https://openalex.org/W2899462221, https://openalex.org/W2351387726, https://openalex.org/W1982300567, https://openalex.org/W2344970400, https://openalex.org/W2477396796, https://openalex.org/W2160163390 |
| cited_by_count | 51 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 11 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 15 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 13 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 10 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 2 |
| locations_count | 6 |
| best_oa_location.id | doi:10.3390/membranes11010070 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210225467 |
| best_oa_location.source.issn | 2077-0375 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2077-0375 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Membranes |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2077-0375/11/1/70/pdf?version=1611202175 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Membranes |
| best_oa_location.landing_page_url | https://doi.org/10.3390/membranes11010070 |
| primary_location.id | doi:10.3390/membranes11010070 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210225467 |
| primary_location.source.issn | 2077-0375 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2077-0375 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Membranes |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2077-0375/11/1/70/pdf?version=1611202175 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Membranes |
| primary_location.landing_page_url | https://doi.org/10.3390/membranes11010070 |
| publication_date | 2021-01-19 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W1872089867, https://openalex.org/W2058126399, https://openalex.org/W2082619287, https://openalex.org/W2164121299, https://openalex.org/W2016055197, https://openalex.org/W2018198710, https://openalex.org/W2897192721, https://openalex.org/W2071975690, https://openalex.org/W2469267700, https://openalex.org/W1972315390, https://openalex.org/W2005085549, https://openalex.org/W2011579218, https://openalex.org/W2014571742, https://openalex.org/W2066718448, https://openalex.org/W2004847971, https://openalex.org/W2982257420, https://openalex.org/W1968827550, https://openalex.org/W2612927211, https://openalex.org/W2254168642, https://openalex.org/W3013623430, https://openalex.org/W2072274571, https://openalex.org/W2959179739, https://openalex.org/W2028703231, https://openalex.org/W1983834805, https://openalex.org/W1967581779, https://openalex.org/W2513504879, https://openalex.org/W2354029907, https://openalex.org/W2735206546, https://openalex.org/W2989757888, https://openalex.org/W2499330297, https://openalex.org/W1982977312, https://openalex.org/W2013888518, https://openalex.org/W2915572135, https://openalex.org/W2137983211, https://openalex.org/W2002096058, https://openalex.org/W2167694888, https://openalex.org/W2073001825, https://openalex.org/W2131740321, https://openalex.org/W2788180525, https://openalex.org/W6638646117, https://openalex.org/W2060391650, https://openalex.org/W2088376206, https://openalex.org/W3016624250, https://openalex.org/W2022279884, https://openalex.org/W2236623899, https://openalex.org/W1833005471 |
| referenced_works_count | 46 |
| abstract_inverted_index.A | 111 |
| abstract_inverted_index.a | 54, 65, 119 |
| abstract_inverted_index.DS | 148 |
| abstract_inverted_index.FO | 80 |
| abstract_inverted_index.FS | 145 |
| abstract_inverted_index.In | 62 |
| abstract_inverted_index.R2 | 151 |
| abstract_inverted_index.an | 6, 14, 88 |
| abstract_inverted_index.as | 13 |
| abstract_inverted_index.in | 102 |
| abstract_inverted_index.is | 5, 69, 91, 115 |
| abstract_inverted_index.of | 46, 167, 184 |
| abstract_inverted_index.on | 53, 87, 188 |
| abstract_inverted_index.to | 16, 19, 71, 93, 104, 117, 178 |
| abstract_inverted_index.ANN | 59, 83, 125, 156, 169 |
| abstract_inverted_index.RSM | 49, 107, 158 |
| abstract_inverted_index.The | 0, 82, 130, 150, 165 |
| abstract_inverted_index.and | 24, 33, 44, 73, 147, 157, 162, 171, 180 |
| abstract_inverted_index.are | 160 |
| abstract_inverted_index.due | 18 |
| abstract_inverted_index.for | 41, 78, 98, 109, 153 |
| abstract_inverted_index.has | 10 |
| abstract_inverted_index.its | 20 |
| abstract_inverted_index.low | 21 |
| abstract_inverted_index.the | 42, 51, 75, 79, 95, 99, 106, 124, 128, 154, 168, 172, 182, 185, 189 |
| abstract_inverted_index.(DS) | 143 |
| abstract_inverted_index.(FO) | 3 |
| abstract_inverted_index.(FS) | 139 |
| abstract_inverted_index.been | 11 |
| abstract_inverted_index.data | 52 |
| abstract_inverted_index.does | 60 |
| abstract_inverted_index.draw | 141 |
| abstract_inverted_index.feed | 137 |
| abstract_inverted_index.flux | 77, 97 |
| abstract_inverted_index.have | 38 |
| abstract_inverted_index.less | 25 |
| abstract_inverted_index.not. | 61 |
| abstract_inverted_index.that | 9 |
| abstract_inverted_index.this | 63 |
| abstract_inverted_index.used | 92, 116, 177 |
| abstract_inverted_index.were | 133, 176 |
| abstract_inverted_index.(BBD) | 114 |
| abstract_inverted_index.(RSM) | 37 |
| abstract_inverted_index.based | 86 |
| abstract_inverted_index.flux. | 191 |
| abstract_inverted_index.input | 131 |
| abstract_inverted_index.model | 108, 126, 159, 170 |
| abstract_inverted_index.order | 103 |
| abstract_inverted_index.plots | 175 |
| abstract_inverted_index.study | 181 |
| abstract_inverted_index.where | 123 |
| abstract_inverted_index.work, | 64 |
| abstract_inverted_index.(ANNs) | 32 |
| abstract_inverted_index.become | 39 |
| abstract_inverted_index.create | 105 |
| abstract_inverted_index.design | 57, 101, 113, 122 |
| abstract_inverted_index.energy | 22 |
| abstract_inverted_index.model, | 84 |
| abstract_inverted_index.neural | 30 |
| abstract_inverted_index.severe | 26 |
| abstract_inverted_index.study, | 90 |
| abstract_inverted_index.0.9408, | 163 |
| abstract_inverted_index.0.98036 | 161 |
| abstract_inverted_index.ANN-RSM | 67 |
| abstract_inverted_index.develop | 118 |
| abstract_inverted_index.forward | 1 |
| abstract_inverted_index.osmosis | 2 |
| abstract_inverted_index.osmotic | 134 |
| abstract_inverted_index.popular | 40 |
| abstract_inverted_index.predict | 72, 94 |
| abstract_inverted_index.process | 4 |
| abstract_inverted_index.surface | 35, 121, 174 |
| abstract_inverted_index.weights | 166 |
| abstract_inverted_index.whereas | 58 |
| abstract_inverted_index.approach | 68 |
| abstract_inverted_index.combined | 66 |
| abstract_inverted_index.emerging | 7 |
| abstract_inverted_index.fouling. | 28 |
| abstract_inverted_index.membrane | 47, 76, 96, 190 |
| abstract_inverted_index.modeling | 43 |
| abstract_inverted_index.networks | 31 |
| abstract_inverted_index.obtained | 152 |
| abstract_inverted_index.optimize | 74, 179 |
| abstract_inverted_index.pressure | 135 |
| abstract_inverted_index.process. | 81 |
| abstract_inverted_index.requires | 50 |
| abstract_inverted_index.response | 34, 120, 173 |
| abstract_inverted_index.solution | 138, 142 |
| abstract_inverted_index.specific | 55 |
| abstract_inverted_index.developed | 85, 155 |
| abstract_inverted_index.evaluates | 127 |
| abstract_inverted_index.influence | 183 |
| abstract_inverted_index.operating | 186 |
| abstract_inverted_index.presented | 70 |
| abstract_inverted_index.variables | 132 |
| abstract_inverted_index.velocity, | 140, 144 |
| abstract_inverted_index.Artificial | 29 |
| abstract_inverted_index.conditions | 187 |
| abstract_inverted_index.considered | 12 |
| abstract_inverted_index.processes. | 48 |
| abstract_inverted_index.responses. | 129 |
| abstract_inverted_index.reversible | 27 |
| abstract_inverted_index.technology | 8 |
| abstract_inverted_index.alternative | 15 |
| abstract_inverted_index.consumption | 23 |
| abstract_inverted_index.difference, | 136 |
| abstract_inverted_index.methodology | 36 |
| abstract_inverted_index.desalination | 17 |
| abstract_inverted_index.experimental | 56, 89, 100 |
| abstract_inverted_index.optimization | 45 |
| abstract_inverted_index.temperature, | 146 |
| abstract_inverted_index.temperature. | 149 |
| abstract_inverted_index.Box–Behnken | 112 |
| abstract_inverted_index.optimization. | 110 |
| abstract_inverted_index.respectively. | 164 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 93 |
| corresponding_author_ids | https://openalex.org/A5083246810 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I60342839 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.9100000262260437 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.92379836 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |