Property-Composition-Temperature Modeling of Waste Glass Melt Data Subject to a Randomization Restriction
Properties such as viscosity and electrical conductivity of glass melts are functions of melt temperature as well as glass composition. When measuring such a property for several compositions, the property is typically measured at several temperatures for one composition, then at several temperature...
Saved in:
| Published in | Journal of the American Ceramic Society Vol. 91; no. 10; pp. 3222 - 3228 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Malden, USA
Blackwell Publishing Inc
01.10.2008
Blackwell Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Properties such as viscosity and electrical conductivity of glass melts are functions of melt temperature as well as glass composition. When measuring such a property for several compositions, the property is typically measured at several temperatures for one composition, then at several temperatures for the next composition, and so on. This data collection process involves a restriction on randomization, which is referred to as a split‐plot experiment. The split‐plot data structure must be accounted for in developing property–composition–temperature models and the corresponding uncertainty equations for model predictions. Instead of ordinary least squares (OLS) regression methods, generalized least squares (GLS) regression methods using restricted maximum likelihood (REML) estimation must be used. This article summarizes the methodology for developing property–composition–temperature models and corresponding prediction uncertainty equations using the GLS/REML regression approach. Viscosity data collected on 197 simulated nuclear waste glasses are used to sequentially develop a viscosity‐composition‐temperature model. The final model has 29 terms in 15 components, reduced from the initial model of 44 terms in 22 components. For the initial model, the correct results using GLS/REML regression are compared with the incorrect results obtained using OLS regression. |
|---|---|
| AbstractList | Properties such as viscosity and electrical conductivity of glass melts are functions of melt temperature as well as glass composition. When measuring such a property for several compositions, the property is typically measured at several temperatures for one composition, then at several temperatures for the next composition, and so on. This data collection process involves a restriction on randomization, which is referred to as a split-plot experiment. The split-plot data structure must be accounted for in developing property-composition-temperature models and the corresponding uncertainty equations for model predictions. Instead of ordinary least squares (OLS) regression methods, generalized least squares (GLS) regression methods using restricted maximum likelihood (REML) estimation must be used. This article summarizes the methodology for developing property-composition-temperature models and corresponding prediction uncertainty equations using the GLS-REML regression approach. Viscosity data collected on 197 simulated nuclear waste glasses are used to sequentially develop a viscosity-composition-temperature model. The final model has 29 terms in 15 components, reduced from the initial model of 44 terms in 22 components. For the initial model, the correct results using GLS-REML regression are compared with the incorrect results obtained using OLS regression. The methodology for developing property-composition-temperature models and corresponding prediction uncertainty equations using the generalised least squares (GLS)/restricted maximum likelihood (REML) regression approach is summarised. Viscosity data collected on 197 simulated nuclear waste glasses were used to sequentially develop a viscosity-composition-temperature model. The final model had 29 terms in 15 components, reduced from the initial model of 44 terms in 22 components. For the initial model, the correct results using GLS/REML regression were compared with the incorrect results obtained using ordinary least squares regression. 16 refs. Properties such as viscosity and electrical conductivity of glass melts are functions of melt temperature as well as glass composition. When measuring such a property for several glasses, the property is typically measured at several temperatures for one glass, then at several temperatures for the next glass, and so on. This data-collection process involves a restriction on randomization, which is referred to as split-plot experiment. The split-plot data structure must be accounted for in developing property-composition-temperature models and the corresponding uncertainty equations for model predictions. Instead of ordinary least squares (OLS) regression methods, generalized least squares (GLS) regression methods using restricted maximum likelihood (REML) estimation must be used. This article describes the methodology for developing property-composition-temperature models and corresponding prediction uncertainty equations using the GLS/REML regression approach. Viscosity data collected on 197 simulated nuclear waste glasses are used to illustrate the GLS/REML methods for developing a viscosity-composition-temperature model and corresponding equations for model prediction uncertainties. The correct results using GLS/REML regression are compared to the incorrect results obtained using OLS regression. Properties such as viscosity and electrical conductivity of glass melts are functions of melt temperature as well as glass composition. When measuring such a property for several compositions, the property is typically measured at several temperatures for one composition, then at several temperatures for the next composition, and so on. This data collection process involves a restriction on randomization, which is referred to as a split-plot experiment. The split-plot data structure must be accounted for in developing property-composition-temperature models and the corresponding uncertainty equations for model predictions. Instead of ordinary least squares (OLS) regression methods, generalized least squares (GLS) regression methods using restricted maximum likelihood (REML) estimation must be used. This article summarizes the methodology for developing property-composition-temperature models and corresponding prediction uncertainty equations using the GLS/REML regression approach. Viscosity data collected on 197 simulated nuclear waste glasses are used to sequentially develop a viscosity-composition-temperature model. The final model has 29 terms in 15 components, reduced from the initial model of 44 terms in 22 components. For the initial model, the correct results using GLS/REML regression are compared with the incorrect results obtained using OLS regression. [PUBLICATION ABSTRACT] |
| Author | Heredia-Langner, Alejandro Cooley, Scott K. Piepel, Greg F. |
| Author_xml | – sequence: 1 givenname: Greg F. surname: Piepel fullname: Piepel, Greg F. email: greg.piepel@pnl.gov organization: Statistics and Sensor Analytics, Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352 – sequence: 2 givenname: Alejandro surname: Heredia-Langner fullname: Heredia-Langner, Alejandro organization: Statistics and Sensor Analytics, Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352 – sequence: 3 givenname: Scott K. surname: Cooley fullname: Cooley, Scott K. organization: Statistics and Sensor Analytics, Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352 |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20759709$$DView record in Pascal Francis https://www.osti.gov/biblio/946647$$D View this record in Osti.gov |
| BookMark | eNqNkkFz1CAUxzNOnXFb_Q7ojI6XRCBAwkWnxnbVadVZqz0yhCVKmoQVyHTXTy_ZdPbQg1MuvAe_9-fBn-PkaLCDThKAYIbieNNmiFKUYo5YhiEsM4gph9n2UbI4bBwlCwghTosSwyfJsfdtTBEvySIx35zdaBd2aWX7jfUmGDukV7qPizKMToNLu9adGX4B24Br6YMGy056Dy51F8AHGST4PtatVgEECyRYyWFte_NXTkJgpX1wRk3x0-RxIzuvn93NJ8mP87Or6mN68XX5qTq9SBXlCKaSN0SxWmlKyjwGXDHIdQ2LQrEGU9VAwsockVojLBuyVoTWrJZE1XW9ZjXMT5Lns671wQivTNDqt7LDEFsUnDBGisi8mpmNs3_G2KPojVe66-Sg7ehFziDhrCwj-Pq_IIIlxhAhxiL64h7a2tEN8aoCo4ITgjGN0Ms7SHolu8bJQRkvNs700u0EhgXlBeSRK2dOOeu9080BQVBMvotWTPaKyV4x-S72vottLH17rzQ-wd6N4KTpHiLwbha4NZ3ePfhg8fm0OtvHUSGdFUz8LtuDgnQ3ghV5QcX1l6WgFeJ4-XMl3uf_AD8u2GI |
| CODEN | JACTAW |
| CitedBy_id | crossref_primary_10_1016_j_jnoncrysol_2022_121832 crossref_primary_10_1016_j_jnoncrysol_2016_01_007 crossref_primary_10_1016_j_jnoncrysol_2024_123119 crossref_primary_10_1111_jace_19333 crossref_primary_10_1016_j_jhazmat_2023_132437 crossref_primary_10_1080_21870764_2021_2012903 |
| Cites_doi | 10.1080/16843703.2007.11673155 10.1080/00224065.2002.11980160 10.1002/9781118204221 10.1137/1.9780898718393 10.1198/004017002753398344 10.1080/00224065.2006.11918603 10.1080/00224065.2004.11980249 10.1198/00401700152404291 10.1198/004017002188618725 |
| ContentType | Journal Article |
| Copyright | 2008 Battelle Memorial Institute 2008 INIST-CNRS Copyright American Ceramic Society Oct 2008 |
| Copyright_xml | – notice: 2008 Battelle Memorial Institute – notice: 2008 INIST-CNRS – notice: Copyright American Ceramic Society Oct 2008 |
| CorporateAuthor | Pacific Northwest National Lab. (PNNL), Richland, WA (United States) |
| CorporateAuthor_xml | – name: Pacific Northwest National Lab. (PNNL), Richland, WA (United States) |
| DBID | BSCLL AAYXX CITATION IQODW 7QQ 7SR 8FD JG9 OTOTI |
| DOI | 10.1111/j.1551-2916.2008.02590.x |
| DatabaseName | Istex CrossRef Pascal-Francis Ceramic Abstracts Engineered Materials Abstracts Technology Research Database Materials Research Database OSTI.GOV |
| DatabaseTitle | CrossRef Materials Research Database Engineered Materials Abstracts Ceramic Abstracts Technology Research Database |
| DatabaseTitleList | Materials Research Database Materials Research Database CrossRef Materials Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Visual Arts Engineering Applied Sciences |
| EISSN | 1551-2916 |
| EndPage | 3228 |
| ExternalDocumentID | 946647 1579195171 20759709 10_1111_j_1551_2916_2008_02590_x JACE02590 ark_67375_WNG_5C192GVR_B |
| Genre | article |
| GroupedDBID | .3N .4S .DC .GA .Y3 05W 0R~ 10A 1OB 1OC 29L 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5HH 5LA 5VS 66C 6TJ 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 8WZ 930 A03 A6W AAESR AAEVG AAHHS AANLZ AAONW AASGY AAXRX AAZKR ABCQN ABCUV ABDBF ABDPE ABEFU ABEML ABJNI ABPVW ABTAH ACAHQ ACBEA ACBWZ ACCFJ ACCZN ACGFO ACGFS ACGOD ACIWK ACKIV ACNCT ACPOU ACSCC ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADMHG ADOZA ADXAS ADZMN AEEZP AEGXH AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFEBI AFFNX AFFPM AFGKR AFPWT AFZJQ AHBTC AHEFC AI. AIAGR AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN AMBMR AMYDB ARCSS ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BSCLL BY8 CAG CO8 COF CS3 D-E D-F DC6 DCZOG DPXWK DR2 DRFUL DRSTM DU5 EAD EAP EBO EBS EDO EJD EMK ESX F00 F01 F04 FEDTE FOJGT FZ0 G-S G.N GODZA H.T H.X HF~ HGLYW HVGLF HZI HZ~ H~9 I-F IRD ITF ITG ITH IX1 J0M K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 NDZJH NF~ O66 O9- OIG P2P P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 QF4 QM1 QN7 QO4 R.K RAX RIWAO RJQFR ROL RX1 SAMSI SJN SUPJJ TAE TH9 TN5 TUS UB1 UPT V8K VH1 W8V W99 WBKPD WFSAM WH7 WIH WIK WOHZO WQJ WRC WTY WXSBR WYISQ XG1 YQT ZCG ZE2 ZY4 ZZTAW ~02 ~IA ~WT AAHQN AAMNL AANHP AAYCA ACRPL ACUHS ACYXJ ADNMO AFWVQ ALVPJ AAYXX ADMLS ADXHL AEYWJ AGHNM AGQPQ AGYGG CITATION AAMMB AEFGJ AGXDD AIDQK AIDYY IQODW 7QQ 7SR 8FD JG9 08R AAJUZ ABCVL ABHUG ABPTK ACSMX ACXME ADAWD ADDAD AFVGU AGJLS G8K OTOTI PK8 PQEST UMP |
| ID | FETCH-LOGICAL-c5910-a9f4c6bce5483c6b9c609eb077c6f25cf0468314be12af4dc45b6ba4cbbbd6b03 |
| IEDL.DBID | DR2 |
| ISSN | 0002-7820 |
| IngestDate | Thu May 18 18:37:33 EDT 2023 Thu Jul 10 17:51:57 EDT 2025 Fri Jul 11 15:50:33 EDT 2025 Fri Jul 25 19:14:23 EDT 2025 Mon Jul 21 09:11:58 EDT 2025 Thu Apr 24 22:54:45 EDT 2025 Tue Jul 01 00:21:44 EDT 2025 Wed Jan 22 16:29:50 EST 2025 Wed Oct 30 09:56:06 EDT 2024 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Keywords | Randomization Property composition relationship Waste treatment Glass waste Glass Temperature effect Theoretical study Composition effect Manufacturing Glass melt Fabrication property relation Modeling |
| Language | English |
| License | http://onlinelibrary.wiley.com/termsAndConditions#vor CC BY 4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c5910-a9f4c6bce5483c6b9c609eb077c6f25cf0468314be12af4dc45b6ba4cbbbd6b03 |
| Notes | istex:B2BB2DA04E993E571441A75AECC7B9C43E851933 ArticleID:JACE02590 ark:/67375/WNG-5C192GVR-B I. Tanaka—contributing editor This work was conducted in the Waste Treatment Plant Support Program at the Pacific Northwest National Laboratory. The work was performed under Contract DE‐AC05‐76RL01830 with the U. S. Department of Energy and MOA #24590‐QL‐HC9‐WA49‐00001 with Bechtel National, Inc., the lead contractor on the Waste Treatment and Immobilization Plant at the Hanford Site near Richland, WA. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 PNNL-SA-58808 USDOE AC05-76RL01830 |
| OpenAccessLink | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/j.1551-2916.2008.02590.x |
| PQID | 217944225 |
| PQPubID | 23500 |
| PageCount | 7 |
| ParticipantIDs | osti_scitechconnect_946647 proquest_miscellaneous_36049688 proquest_miscellaneous_1082201166 proquest_journals_217944225 pascalfrancis_primary_20759709 crossref_primary_10_1111_j_1551_2916_2008_02590_x crossref_citationtrail_10_1111_j_1551_2916_2008_02590_x wiley_primary_10_1111_j_1551_2916_2008_02590_x_JACE02590 istex_primary_ark_67375_WNG_5C192GVR_B |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | October 2008 |
| PublicationDateYYYYMMDD | 2008-10-01 |
| PublicationDate_xml | – month: 10 year: 2008 text: October 2008 |
| PublicationDecade | 2000 |
| PublicationPlace | Malden, USA |
| PublicationPlace_xml | – name: Malden, USA – name: Malden,MA – name: Columbus – name: United States |
| PublicationTitle | Journal of the American Ceramic Society |
| PublicationYear | 2008 |
| Publisher | Blackwell Publishing Inc Blackwell Wiley Subscription Services, Inc |
| Publisher_xml | – name: Blackwell Publishing Inc – name: Blackwell – name: Wiley Subscription Services, Inc |
| References | G. F. Piepel, S. K. Cooley, A. Heredia-Langner, S. M. Landmesser, W. K. Kot, H. Gan, and I. L. Pegg, IHLW PCT, Spinel T1%, Electrical Conductivity, and Viscosity Model Development, VSL-07R1240-4, Rev. 0, Vitreous State Laboratory. The Catholic University of America, Washington, DC. JMP, JMP Release 7. SAS Institute, Inc, Cary, NC, 2007. D. R. Bingham and R. R. Sitter, "Fractional Factorial Split-Plot Designs for Robust Parameter Experiments," Technometrics, 45 [1] 80-9 (2003). G. F. Piepel, J. M. Szychowski, and J. L. Loeppky, "Augmenting Scheffé Linear Mixture Models with Squared and/or Crossproduct Terms," J. Qual. Tech., 34 [3] 297-314 (2002). P. Goos and M. Vanderbroek, "Outperforming Completely Randomized Designs," J. Qual. Tech., 36 [1] 12-26 (2004). D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis, 3rd edition, John Wiley and Sons, New York, 2001. J. A. Cornell, Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data, 3rd edition, John Wiley, New York, 2002. S. M. Kowalski, J. A. Cornell, and G. G. Vining, "Split-Plot Designs and Estimation Methods for Mixture Experiments With Process Variables," Technometrics, 44 [1] 72-9 (2002). H. Gan and I. L. Pegg, Development of Property-Composition Models for RPP-WTP HLW Glasses, VSL-01R3600-1, Rev. 0, Vitreous State Laboratory. The Catholic University of America, Washington, DC, 2001. R. Ihaka and R. Gentleman, "R: A Language for Data Analysis and Graphics," J. Comp. Graph. Stat., 5 [3] 299-314 (1996). R Development Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2006 URL http://www.R-project.org. P. Goos, I. Langhans, and M. Vandebroek, "Practical Inference from Industrial Split-Plot Designs," J. Qual. Tech., 38 [2] 162-79 (2006). L. A. Trinca and S. G. Gilmour, "Multistratum Response Surface Designs," Technometrics, 43 [1] 25-33 (2001). SAS, SAS Release 9.1.3. SAS Institute, Inc, Cary, NC, (2005). W. F. Smith, Experimental Design for Formulation. Society for Industrial and Applied Mathematics\American Statistical Association, Philadelphia, PA\Alexanderia, VA, 2005. G. F. Piepel, "A Component Slope Linear Model for Mixture Experiments," Qual. Tech. Quant. Mgmt., 4 [3] 331-43 (2007). 2001 2004; 36 2006; 38 2002; 34 2002; 44 2007 2006 2005 2007; 4 2002 1996; 5 2003; 45 2001; 43 Piepel G. F. (e_1_2_9_12_2) Trinca L. A. (e_1_2_9_5_2) 2001; 43 SAS (e_1_2_9_8_2) 2005 Goos P. (e_1_2_9_3_2) 2004; 36 Kowalski S. M. (e_1_2_9_6_2) 2002; 44 Montgomery D. C. (e_1_2_9_4_2) 2001 Ihaka R. (e_1_2_9_10_2) 1996; 5 R Development Core Team (e_1_2_9_11_2) 2006 JMP (e_1_2_9_9_2) 2007 Piepel G. F. (e_1_2_9_17_2) 2007; 4 e_1_2_9_14_2 Gan H. (e_1_2_9_13_2) 2001 Piepel G. F. (e_1_2_9_16_2) 2002; 34 e_1_2_9_15_2 Goos P. (e_1_2_9_7_2) 2006; 38 Bingham D. R. (e_1_2_9_2_2) 2003; 45 |
| References_xml | – reference: S. M. Kowalski, J. A. Cornell, and G. G. Vining, "Split-Plot Designs and Estimation Methods for Mixture Experiments With Process Variables," Technometrics, 44 [1] 72-9 (2002). – reference: SAS, SAS Release 9.1.3. SAS Institute, Inc, Cary, NC, (2005). – reference: P. Goos, I. Langhans, and M. Vandebroek, "Practical Inference from Industrial Split-Plot Designs," J. Qual. Tech., 38 [2] 162-79 (2006). – reference: H. Gan and I. L. Pegg, Development of Property-Composition Models for RPP-WTP HLW Glasses, VSL-01R3600-1, Rev. 0, Vitreous State Laboratory. The Catholic University of America, Washington, DC, 2001. – reference: JMP, JMP Release 7. SAS Institute, Inc, Cary, NC, 2007. – reference: G. F. Piepel, J. M. Szychowski, and J. L. Loeppky, "Augmenting Scheffé Linear Mixture Models with Squared and/or Crossproduct Terms," J. Qual. Tech., 34 [3] 297-314 (2002). – reference: J. A. Cornell, Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data, 3rd edition, John Wiley, New York, 2002. – reference: G. F. Piepel, "A Component Slope Linear Model for Mixture Experiments," Qual. Tech. Quant. Mgmt., 4 [3] 331-43 (2007). – reference: W. F. Smith, Experimental Design for Formulation. Society for Industrial and Applied Mathematics\American Statistical Association, Philadelphia, PA\Alexanderia, VA, 2005. – reference: D. R. Bingham and R. R. Sitter, "Fractional Factorial Split-Plot Designs for Robust Parameter Experiments," Technometrics, 45 [1] 80-9 (2003). – reference: R Development Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2006 URL http://www.R-project.org. – reference: P. Goos and M. Vanderbroek, "Outperforming Completely Randomized Designs," J. Qual. Tech., 36 [1] 12-26 (2004). – reference: L. A. Trinca and S. G. Gilmour, "Multistratum Response Surface Designs," Technometrics, 43 [1] 25-33 (2001). – reference: G. F. Piepel, S. K. Cooley, A. Heredia-Langner, S. M. Landmesser, W. K. Kot, H. Gan, and I. L. Pegg, IHLW PCT, Spinel T1%, Electrical Conductivity, and Viscosity Model Development, VSL-07R1240-4, Rev. 0, Vitreous State Laboratory. The Catholic University of America, Washington, DC. – reference: D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis, 3rd edition, John Wiley and Sons, New York, 2001. – reference: R. Ihaka and R. Gentleman, "R: A Language for Data Analysis and Graphics," J. Comp. Graph. Stat., 5 [3] 299-314 (1996). – volume: 38 start-page: 162 issue: [2] year: 2006 end-page: 79 article-title: Practical Inference from Industrial Split‐Plot Designs publication-title: J. Qual. Tech. – volume: 5 start-page: 299 issue: [3] year: 1996 end-page: 314 article-title: R publication-title: A Language for Data Analysis and Graphics – year: 2005 – year: 2002 – volume: 45 start-page: 80 issue: [1] year: 2003 end-page: 9 article-title: Fractional Factorial Split‐Plot Designs for Robust Parameter Experiments publication-title: Technometrics – year: 2001 – year: 2007 – year: 2006 – volume: 43 start-page: 25 issue: [1] year: 2001 end-page: 33 article-title: Multistratum Response Surface Designs publication-title: Technometrics – volume: 4 start-page: 331 issue: [3] year: 2007 end-page: 43 article-title: A Component Slope Linear Model for Mixture Experiments publication-title: Qual. Tech. Quant. Mgmt. – volume: 44 start-page: 72 issue: [1] year: 2002 end-page: 9 article-title: Split‐Plot Designs and Estimation Methods for Mixture Experiments With Process Variables publication-title: Technometrics – volume: 34 start-page: 297 issue: [3] year: 2002 end-page: 314 article-title: Augmenting Scheffé Linear Mixture Models with Squared and/or Crossproduct Terms publication-title: J. Qual. Tech. – volume: 36 start-page: 12 issue: [1] year: 2004 end-page: 26 article-title: Outperforming Completely Randomized Designs publication-title: J. Qual. Tech. – volume: 4 start-page: 331 issue: 3 year: 2007 ident: e_1_2_9_17_2 article-title: A Component Slope Linear Model for Mixture Experiments publication-title: Qual. Tech. Quant. Mgmt. doi: 10.1080/16843703.2007.11673155 – volume: 34 start-page: 297 issue: 3 year: 2002 ident: e_1_2_9_16_2 article-title: Augmenting Scheffé Linear Mixture Models with Squared and/or Crossproduct Terms publication-title: J. Qual. Tech. doi: 10.1080/00224065.2002.11980160 – volume-title: R: A Language and Environment for Statistical Computing year: 2006 ident: e_1_2_9_11_2 – ident: e_1_2_9_14_2 doi: 10.1002/9781118204221 – ident: e_1_2_9_15_2 doi: 10.1137/1.9780898718393 – volume-title: SAS Release 9.1.3 year: 2005 ident: e_1_2_9_8_2 – volume-title: IHLW PCT, Spinel T1%, Electrical Conductivity, and Viscosity Model Development, VSL‐07R1240‐4, Rev. 0, Vitreous State Laboratory ident: e_1_2_9_12_2 – volume: 44 start-page: 72 issue: 1 year: 2002 ident: e_1_2_9_6_2 article-title: Split‐Plot Designs and Estimation Methods for Mixture Experiments With Process Variables publication-title: Technometrics doi: 10.1198/004017002753398344 – volume-title: Development of Property‐Composition Models for RPP‐WTP HLW Glasses, VSL‐01R3600‐1, Rev. 0, Vitreous State Laboratory year: 2001 ident: e_1_2_9_13_2 – volume: 38 start-page: 162 issue: 2 year: 2006 ident: e_1_2_9_7_2 article-title: Practical Inference from Industrial Split‐Plot Designs publication-title: J. Qual. Tech. doi: 10.1080/00224065.2006.11918603 – volume: 36 start-page: 12 issue: 1 year: 2004 ident: e_1_2_9_3_2 article-title: Outperforming Completely Randomized Designs publication-title: J. Qual. Tech. doi: 10.1080/00224065.2004.11980249 – volume: 43 start-page: 25 issue: 1 year: 2001 ident: e_1_2_9_5_2 article-title: Multistratum Response Surface Designs publication-title: Technometrics doi: 10.1198/00401700152404291 – volume: 5 start-page: 299 issue: 3 year: 1996 ident: e_1_2_9_10_2 article-title: R publication-title: A Language for Data Analysis and Graphics – volume-title: JMP Release 7 year: 2007 ident: e_1_2_9_9_2 – volume: 45 start-page: 80 issue: 1 year: 2003 ident: e_1_2_9_2_2 article-title: Fractional Factorial Split‐Plot Designs for Robust Parameter Experiments publication-title: Technometrics doi: 10.1198/004017002188618725 – volume-title: Introduction to Linear Regression Analysis year: 2001 ident: e_1_2_9_4_2 |
| SSID | ssj0001984 |
| Score | 1.9371586 |
| Snippet | Properties such as viscosity and electrical conductivity of glass melts are functions of melt temperature as well as glass composition. When measuring such a... The methodology for developing property-composition-temperature models and corresponding prediction uncertainty equations using the generalised least squares... |
| SourceID | osti proquest pascalfrancis crossref wiley istex |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 3222 |
| SubjectTerms | Applied sciences Building materials. Ceramics. Glasses Chemical industry and chemicals Conductivity ELECTRIC CONDUCTIVITY Exact sciences and technology FORECASTING GLASS Glasses Least squares method MANAGEMENT OF RADIOACTIVE WASTES, AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES Manufacture Mathematical analysis Mathematical models Melting Melts Physical properties RADIOACTIVE WASTES Randomization Regression SIMULATION Temperature effects VISCOSITY WASTES |
| Title | Property-Composition-Temperature Modeling of Waste Glass Melt Data Subject to a Randomization Restriction |
| URI | https://api.istex.fr/ark:/67375/WNG-5C192GVR-B/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fj.1551-2916.2008.02590.x https://www.proquest.com/docview/217944225 https://www.proquest.com/docview/1082201166 https://www.proquest.com/docview/36049688 https://www.osti.gov/biblio/946647 |
| Volume | 91 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELbQcoEDb0RaKEZC3LLKw3acY1narSq1Qqu-bpbtOFK1bYI2WQl66n_gH_JLmMmLDQKpQtzysC15MjP-7Mx8Q8h7mWdZlCTC5xnTPkuF9LXQ0ncB07GUscliTHA-OhYHp-zwgl908U-YC9PyQwwHbmgZjb9GA9emGhs5rPZ-BPimC4kEJB9MEU9i6Bbio8UvJinYW7MeCSNF3Dio548DjVaq-yh0eDQpwfIwgFJXIMO8LX4xQqebGLdZpPYfk2U_vTY2ZTld12Zqb35jfvw_839CHnVYlu62yveU3HPFM_Jwg-EQ7s4uq3XbpnpOrj_j0f-q_vbj9js6oi5gDO5OHMD3lt6ZYnk2TJKnZU7PNQiEzhHi0yN3VdNPutYUvB0eH9G6pJoudJGV1106KV04LETSJGu8IKf7eyezA7-r9-BbDqjF12nOrDDWwS4qhovUiiB1JkgSK_KI2xz28jIOmXFhpHOWWcaNMJpZY0wmTBC_JJOiLNwrQnmah6nh3IHLYamMtQ1tGkgYJ8sjJkOPJP23VbYjQ8eaHFdqY1ME4lUo3q5UJ4pXffVIOPT80hKC3KHPh0Z9hg56tcSAuoSr8-O54jOA2fOzhfrokS3ULwXoByl8LcY62Vo1NQASj-yMtG4YLQLYlyZB6pHtXg1V54kqFaHHZeC1PfJueAsuBP8L6cKV6wo5YiPEgUJ45O1f2sQiQEOWHpGNUt557upwd7bXXG_9e9dt8iDqeYjD12RSr9buDYDB2uw0Zv4TgldQmg |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEB6h9AAceCPcQrtIiJsjP9br9bGENqE0EYrSx221u15LqGmMEkcCTvwH_iG_hBnbCQkCqULc_NqVPJ6Z_WY98w3AK1nkeZSmwk9yrn2eCelroaXvAq5jKWOTx1TgPByJwRk_uUwu23ZAVAvT8EOsN9zIMmp_TQZOG9LbVo7LvR8hwGlzIhHKB10ElDvU4LuOr8a_uKQwuuYrLEwkcdtpPX-caWut2iGx46VOibZHKZR6gVIsmvYXW_h0E-XWy9TxfZiuXrDJTrnqLivTtV9_4378TxJ4APdaOMsOG_17CLfc7BHc3SA5xLPzj4tl88ziMVx_oN3_efXlx7fv5IvanDE8mzhE8A3DM6MObVQnz8qCXWiUCOsTymdDN63YW11phg6PdpBYVTLNxnqWl9dtRSkbO-pFUtdrPIGz46NJb-C3LR98myBw8XVWcCuMdRhIxXiQWRFkzgRpakURJbbAcF7GITcujHTBc8sTI4zm1hiTCxPET6EzK2fuGbAkK8LMJIlDr8MzGWsb2iyQOE9eRFyGHqSrj6tsy4dObTmmaiMuQvEqEm_brZPEqz57EK5Hfmo4QW4w5nWtP-sBen5FOXVpoi5GfZX0EGn3z8fqjQe7pGAKARCx-FpKd7KVqtsApB7sb6nderYIkV-WBpkHeys9VK0zWqiInC5Hx-3By_Vd9CL0a0jPXLlcEE1sRFBQCA8O_vJMLAKyZemBrLXyxu-uTg57R_Xx7r8PPYDbg8nwVJ2-G73fgzvRipY4fA6dar50LxAbVma_tvmfdQ1UtQ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3db9MwED-hTkLwwDciG2xGQrylyofjOI-jXTsGq6ZqX2-W7TgS6tZMbSoBT_wP_If8JdwlaWkRSBPizU5iS7ncnX927n4H8EYWeR6lqfCTnGufZ0L6Wmjpu4DrWMrY5DElOB-PxOEZP7pMLtv4J8qFafghVgduZBm1vyYDv8mLTSPH1d6PEN-0IZGI5IMu4sktLgJJGt4f_6KSws01X0Jh4ojbjOr540wbS9UWSR0vdUo0PYqg1HMUYtFUv9iAp-sgt16lBg9hsny_Jjhl0l1Upmu__kb9-H8E8AgetGCW7Tfa9xjuuOkTuL9GcYi980_zRfPM_Clcn9DZ_6z68uPbd_JEbcQY9k4d4veG35lRfTbKkmdlwS40CoQNCeOzY3dVsb6uNEN3R-dHrCqZZmM9zcvrNp-UjR1VIqmzNZ7B2eDgtHfotwUffJsgbPF1VnArjHW4jYqxkVkRZM4EaWpFESW2wM28jENuXBjpgueWJ0YYza0xJhcmiJ9DZ1pO3QtgSVaEmUkShz6HZzLWNrRZIHGevIi4DD1Il99W2ZYNnYpyXKm1XRGKV5F421qdJF712YNwNfKmYQS5xZi3tfqsBujZhCLq0kRdjIYq6SHOHp6P1TsPtkm_FMIf4vC1FOxkK1UXAUg92N3QutVsEeK-LA0yD3aWaqhaVzRXEblcjm7bg9eru-hD6MeQnrpyMSeS2IiAoBAe7P3lmVgEZMnSA1kr5a3fXR3t9w7q9va_D92Duyf9gfr4fvRhB-5FS07i8CV0qtnCvUJgWJnd2uJ_AkZKU20 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Property-Composition-Temperature+Modeling+of+Waste+Glass+Melt+Data+Subject+to+a+Randomization+Restriction&rft.jtitle=Journal+of+the+American+Ceramic+Society&rft.au=Piepel%2C+Greg+F&rft.au=Heredia-Langner%2C+Alejandro&rft.au=Cooley%2C+Scott+K&rft.date=2008-10-01&rft.issn=0002-7820&rft.eissn=1551-2916&rft.volume=91&rft.issue=10&rft.spage=3222&rft.epage=3228&rft_id=info:doi/10.1111%2Fj.1551-2916.2008.02590.x&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0002-7820&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0002-7820&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0002-7820&client=summon |