Data processing and reconciliation for chemical process operations /:
Computer techniques have made online measurements available at every sampling period in a chemical process. However, measurement errors are introduced that require suitable techniques for data reconciliation and improvements in accuracy. Reconciliation of process data and reliable monitoring are ess...
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
San Diego :
Academic Press,
©2000.
|
| Schriftenreihe: | Process systems engineering ;
v. 2. |
| Schlagworte: | |
| Online-Zugang: | Volltext Volltext |
| Zusammenfassung: | Computer techniques have made online measurements available at every sampling period in a chemical process. However, measurement errors are introduced that require suitable techniques for data reconciliation and improvements in accuracy. Reconciliation of process data and reliable monitoring are essential to decisions about possible system modifications (optimization and control procedures), analysis of equipment performance, design of the monitoring system itself, and general management planning. While the reconciliation of the process data has been studied for more than 20 years, there is no single source providing a unified approach to the area with instructions on implementation. Data Processing and Reconciliation for Chemical Process Operations is that source. Competitiveness on the world market as well as increasingly stringent environmental and product safety regulations have increased the need for the chemical industry to introduce such fast and low cost improvements in process operations. Key Features * Introduces the first unified approach to this important field * Bridges theory and practice through numerous worked examples and industrial case studies * Provides a highly readable account of all aspects of data classification and reconciliation * Presents the reader with material, problems, and directions for further study. |
| Beschreibung: | 1 online resource (xv, 270 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and indexes. |
| ISBN: | 9780080530277 0080530273 1281049867 9781281049865 9786611049867 661104986X |
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| 245 | 1 | 0 | |a Data processing and reconciliation for chemical process operations / |c José A. Romagnoli, Mabel Cristina Sánchez. |
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| 504 | |a Includes bibliographical references and indexes. | ||
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| 505 | 0 | |a General Introduction. Reliable and Complete Knowledge. Some Issues Associated with a General Data Reconciliation Problem. About This Book. References of Chapter 1. Estimability and Redundancy Within the Framework of the General Estimation Theory. Introduction. Basic Concepts and Definitions. Decomposition of the General Estimation Problem. Structural Analysis. Conclusions. Notation. References of Chapter 2. Appendix 2 -- A. Classification of the Process Variables for Chemical Plants. Introduction. Modeling Aspects. Classification of Process Variables. Analysis of the Process Topology. Different Approaches for Solving the Classification Problem. Use of Output Set Assignments for Variable Classification. The Solution of Special Problems. A Complete Classification Example. Formulation of a Reduced Reconciliation Problem. Conclusions. Notation. References of Chapter 3. Appendix 3 -- A. Appendix 3 -- B. Decomposition Using Orthogonal Transformations. Introduction. Linear Mass Balances. Bilinear Multicomponent and Energy Balances. Conclusions. Notation. References of Chapter 4. Steady State Data Reconciliation. Introduction. Problem Formulation. Linear Data Reconciliation. Non-Linear Data Reconciliation. Conclusions. Notation. References of Chapter 5. Appendix 5 -- A. Sequential Processing of the Information. Introduction. Sequential Processing of the Constraints. Sequential Processing of the Measurements. Alternative Formulation from Estimation Theory. Conclusions. Notation. References of Chapter 6. Appendix 6 -- A. Treatment of Gross Errors. Introduction. Gross Error detection. Identification of the Measurements with Gross Error. Estimation of the Magnitude of Bias and Leaks. A Recursive Scheme for Gross Error Identification and Estimation. Conclusions. Notation. References of Chapter 7. Appendix 7 -- A. Appendix 7 -- B. Rectification of Process Measurement Data in Dynamic Situations. Introduction. Dynamic Data Reconciliation: A Filtering Approach. Dynamic Data Reconciliation: Using Non-linear Programming Techniques. Conclusions. Notation. References of Chapter 8. Joint Parameter Estimation Data Reconciliation. Introduction. The Parameter Estimation Problem. Joint Parameter Estimation-Data Reconciliation Problem. Dynamic Joint State-Parameter Estimation: A Filtering Approach. Dynamic Joint State-Parameter Estimation: A Non-linear Programming Approach. Conclusions. Notation. References of Chapter 9. Estimation of Measurement Error Variances from Process Data. Introduction. Direct Method. Indirect Method. Robust Covariance Estimator. Conclusions. Notation. References of Chapter 10. New Trends. Introduction. The Bayesian Approach. Robust Estimation Approaches. Principal Component Analysis in Data Reconciliation. Conclusions. Notation. References of Chapter 11. Case Studies. Introduction. Decomposition/Reconciliation in a Section of an Olefine Plant. Data Reconciliation of a Pyrolysis Reactor. Data Reconciliation of an Experimental Distillation Column. Conclusions. Notation. References of Chapter 12. Statistical Concepts. A1 -- Frequency Distributions. A2 -- Measures of Central Tendency and Spread. A3 -- Estimation. A4 -- Confidende Intervals. A5 -- Testing of Statistical Hypotheses. References of Appendix. | |
| 520 | |a Computer techniques have made online measurements available at every sampling period in a chemical process. However, measurement errors are introduced that require suitable techniques for data reconciliation and improvements in accuracy. Reconciliation of process data and reliable monitoring are essential to decisions about possible system modifications (optimization and control procedures), analysis of equipment performance, design of the monitoring system itself, and general management planning. While the reconciliation of the process data has been studied for more than 20 years, there is no single source providing a unified approach to the area with instructions on implementation. Data Processing and Reconciliation for Chemical Process Operations is that source. Competitiveness on the world market as well as increasingly stringent environmental and product safety regulations have increased the need for the chemical industry to introduce such fast and low cost improvements in process operations. Key Features * Introduces the first unified approach to this important field * Bridges theory and practice through numerous worked examples and industrial case studies * Provides a highly readable account of all aspects of data classification and reconciliation * Presents the reader with material, problems, and directions for further study. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Chemical process control |x Automation. | |
| 650 | 0 | |a Automatic data collection systems. |0 http://id.loc.gov/authorities/subjects/sh85010095 | |
| 650 | 0 | |a Error analysis (Mathematics) |0 http://id.loc.gov/authorities/subjects/sh85044724 | |
| 650 | 0 | |a Chemical processes. |0 http://id.loc.gov/authorities/subjects/sh85022948 | |
| 650 | 0 | |a Chemical process control. |0 http://id.loc.gov/authorities/subjects/sh85022947 | |
| 650 | 6 | |a Procédés chimiques |x Contrôle |x Automatisation. | |
| 650 | 6 | |a Collecte automatique des données. | |
| 650 | 6 | |a Théorie des erreurs. | |
| 650 | 6 | |a Procédés chimiques. | |
| 650 | 6 | |a Procédés chimiques |x Contrôle. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Chemical & Biochemical. |2 bisacsh | |
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| 700 | 1 | |a Sánchez, Mabel Cristina. |0 http://id.loc.gov/authorities/names/no99087706 | |
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| contents | General Introduction. Reliable and Complete Knowledge. Some Issues Associated with a General Data Reconciliation Problem. About This Book. References of Chapter 1. Estimability and Redundancy Within the Framework of the General Estimation Theory. Introduction. Basic Concepts and Definitions. Decomposition of the General Estimation Problem. Structural Analysis. Conclusions. Notation. References of Chapter 2. Appendix 2 -- A. Classification of the Process Variables for Chemical Plants. Introduction. Modeling Aspects. Classification of Process Variables. Analysis of the Process Topology. Different Approaches for Solving the Classification Problem. Use of Output Set Assignments for Variable Classification. The Solution of Special Problems. A Complete Classification Example. Formulation of a Reduced Reconciliation Problem. Conclusions. Notation. References of Chapter 3. Appendix 3 -- A. Appendix 3 -- B. Decomposition Using Orthogonal Transformations. Introduction. Linear Mass Balances. Bilinear Multicomponent and Energy Balances. Conclusions. Notation. References of Chapter 4. Steady State Data Reconciliation. Introduction. Problem Formulation. Linear Data Reconciliation. Non-Linear Data Reconciliation. Conclusions. Notation. References of Chapter 5. Appendix 5 -- A. Sequential Processing of the Information. Introduction. Sequential Processing of the Constraints. Sequential Processing of the Measurements. Alternative Formulation from Estimation Theory. Conclusions. Notation. References of Chapter 6. Appendix 6 -- A. Treatment of Gross Errors. Introduction. Gross Error detection. Identification of the Measurements with Gross Error. Estimation of the Magnitude of Bias and Leaks. A Recursive Scheme for Gross Error Identification and Estimation. Conclusions. Notation. References of Chapter 7. Appendix 7 -- A. Appendix 7 -- B. Rectification of Process Measurement Data in Dynamic Situations. Introduction. Dynamic Data Reconciliation: A Filtering Approach. Dynamic Data Reconciliation: Using Non-linear Programming Techniques. Conclusions. Notation. References of Chapter 8. Joint Parameter Estimation Data Reconciliation. Introduction. The Parameter Estimation Problem. Joint Parameter Estimation-Data Reconciliation Problem. Dynamic Joint State-Parameter Estimation: A Filtering Approach. Dynamic Joint State-Parameter Estimation: A Non-linear Programming Approach. Conclusions. Notation. References of Chapter 9. Estimation of Measurement Error Variances from Process Data. Introduction. Direct Method. Indirect Method. Robust Covariance Estimator. Conclusions. Notation. References of Chapter 10. New Trends. Introduction. The Bayesian Approach. Robust Estimation Approaches. Principal Component Analysis in Data Reconciliation. Conclusions. Notation. References of Chapter 11. Case Studies. Introduction. Decomposition/Reconciliation in a Section of an Olefine Plant. Data Reconciliation of a Pyrolysis Reactor. Data Reconciliation of an Experimental Distillation Column. Conclusions. Notation. References of Chapter 12. Statistical Concepts. A1 -- Frequency Distributions. A2 -- Measures of Central Tendency and Spread. A3 -- Estimation. A4 -- Confidende Intervals. A5 -- Testing of Statistical Hypotheses. References of Appendix. |
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Reliable and Complete Knowledge. Some Issues Associated with a General Data Reconciliation Problem. About This Book. References of Chapter 1. Estimability and Redundancy Within the Framework of the General Estimation Theory. Introduction. Basic Concepts and Definitions. Decomposition of the General Estimation Problem. Structural Analysis. Conclusions. Notation. References of Chapter 2. Appendix 2 -- A. Classification of the Process Variables for Chemical Plants. Introduction. Modeling Aspects. Classification of Process Variables. Analysis of the Process Topology. Different Approaches for Solving the Classification Problem. Use of Output Set Assignments for Variable Classification. The Solution of Special Problems. A Complete Classification Example. Formulation of a Reduced Reconciliation Problem. Conclusions. Notation. References of Chapter 3. Appendix 3 -- A. Appendix 3 -- B. Decomposition Using Orthogonal Transformations. Introduction. Linear Mass Balances. Bilinear Multicomponent and Energy Balances. Conclusions. Notation. References of Chapter 4. Steady State Data Reconciliation. Introduction. Problem Formulation. Linear Data Reconciliation. Non-Linear Data Reconciliation. Conclusions. Notation. References of Chapter 5. Appendix 5 -- A. Sequential Processing of the Information. Introduction. Sequential Processing of the Constraints. Sequential Processing of the Measurements. Alternative Formulation from Estimation Theory. Conclusions. Notation. References of Chapter 6. Appendix 6 -- A. Treatment of Gross Errors. Introduction. Gross Error detection. Identification of the Measurements with Gross Error. Estimation of the Magnitude of Bias and Leaks. A Recursive Scheme for Gross Error Identification and Estimation. Conclusions. Notation. References of Chapter 7. Appendix 7 -- A. Appendix 7 -- B. Rectification of Process Measurement Data in Dynamic Situations. Introduction. Dynamic Data Reconciliation: A Filtering Approach. Dynamic Data Reconciliation: Using Non-linear Programming Techniques. Conclusions. Notation. References of Chapter 8. Joint Parameter Estimation Data Reconciliation. Introduction. The Parameter Estimation Problem. Joint Parameter Estimation-Data Reconciliation Problem. Dynamic Joint State-Parameter Estimation: A Filtering Approach. Dynamic Joint State-Parameter Estimation: A Non-linear Programming Approach. Conclusions. Notation. References of Chapter 9. Estimation of Measurement Error Variances from Process Data. Introduction. Direct Method. Indirect Method. Robust Covariance Estimator. Conclusions. Notation. References of Chapter 10. New Trends. Introduction. The Bayesian Approach. Robust Estimation Approaches. Principal Component Analysis in Data Reconciliation. Conclusions. Notation. References of Chapter 11. Case Studies. Introduction. Decomposition/Reconciliation in a Section of an Olefine Plant. Data Reconciliation of a Pyrolysis Reactor. Data Reconciliation of an Experimental Distillation Column. Conclusions. Notation. References of Chapter 12. Statistical Concepts. A1 -- Frequency Distributions. A2 -- Measures of Central Tendency and Spread. A3 -- Estimation. A4 -- Confidende Intervals. A5 -- Testing of Statistical Hypotheses. References of Appendix.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Computer techniques have made online measurements available at every sampling period in a chemical process. However, measurement errors are introduced that require suitable techniques for data reconciliation and improvements in accuracy. Reconciliation of process data and reliable monitoring are essential to decisions about possible system modifications (optimization and control procedures), analysis of equipment performance, design of the monitoring system itself, and general management planning. 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| id | ZDB-4-EBA-ocn182553351 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:16:12Z |
| institution | BVB |
| isbn | 9780080530277 0080530273 1281049867 9781281049865 9786611049867 661104986X |
| language | English |
| oclc_num | 182553351 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xv, 270 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2000 |
| publishDateSearch | 2000 |
| publishDateSort | 2000 |
| publisher | Academic Press, |
| record_format | marc |
| series | Process systems engineering ; |
| series2 | Process systems engineering ; |
| spelling | Romagnoli, José A. (José Alberto) https://id.oclc.org/worldcat/entity/E39PCjtMM9JrDjJq36Y7QHmPQq http://id.loc.gov/authorities/names/no99088029 Data processing and reconciliation for chemical process operations / José A. Romagnoli, Mabel Cristina Sánchez. San Diego : Academic Press, ©2000. 1 online resource (xv, 270 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file rdaft http://rdaregistry.info/termList/fileType/1002. Process systems engineering ; v. 2 Includes bibliographical references and indexes. Print version record. General Introduction. Reliable and Complete Knowledge. Some Issues Associated with a General Data Reconciliation Problem. About This Book. References of Chapter 1. Estimability and Redundancy Within the Framework of the General Estimation Theory. Introduction. Basic Concepts and Definitions. Decomposition of the General Estimation Problem. Structural Analysis. Conclusions. Notation. References of Chapter 2. Appendix 2 -- A. Classification of the Process Variables for Chemical Plants. Introduction. Modeling Aspects. Classification of Process Variables. Analysis of the Process Topology. Different Approaches for Solving the Classification Problem. Use of Output Set Assignments for Variable Classification. The Solution of Special Problems. A Complete Classification Example. Formulation of a Reduced Reconciliation Problem. Conclusions. Notation. References of Chapter 3. Appendix 3 -- A. Appendix 3 -- B. Decomposition Using Orthogonal Transformations. Introduction. Linear Mass Balances. Bilinear Multicomponent and Energy Balances. Conclusions. Notation. References of Chapter 4. Steady State Data Reconciliation. Introduction. Problem Formulation. Linear Data Reconciliation. Non-Linear Data Reconciliation. Conclusions. Notation. References of Chapter 5. Appendix 5 -- A. Sequential Processing of the Information. Introduction. Sequential Processing of the Constraints. Sequential Processing of the Measurements. Alternative Formulation from Estimation Theory. Conclusions. Notation. References of Chapter 6. Appendix 6 -- A. Treatment of Gross Errors. Introduction. Gross Error detection. Identification of the Measurements with Gross Error. Estimation of the Magnitude of Bias and Leaks. A Recursive Scheme for Gross Error Identification and Estimation. Conclusions. Notation. References of Chapter 7. Appendix 7 -- A. Appendix 7 -- B. Rectification of Process Measurement Data in Dynamic Situations. Introduction. Dynamic Data Reconciliation: A Filtering Approach. Dynamic Data Reconciliation: Using Non-linear Programming Techniques. Conclusions. Notation. References of Chapter 8. Joint Parameter Estimation Data Reconciliation. Introduction. The Parameter Estimation Problem. Joint Parameter Estimation-Data Reconciliation Problem. Dynamic Joint State-Parameter Estimation: A Filtering Approach. Dynamic Joint State-Parameter Estimation: A Non-linear Programming Approach. Conclusions. Notation. References of Chapter 9. Estimation of Measurement Error Variances from Process Data. Introduction. Direct Method. Indirect Method. Robust Covariance Estimator. Conclusions. Notation. References of Chapter 10. New Trends. Introduction. The Bayesian Approach. Robust Estimation Approaches. Principal Component Analysis in Data Reconciliation. Conclusions. Notation. References of Chapter 11. Case Studies. Introduction. Decomposition/Reconciliation in a Section of an Olefine Plant. Data Reconciliation of a Pyrolysis Reactor. Data Reconciliation of an Experimental Distillation Column. Conclusions. Notation. References of Chapter 12. Statistical Concepts. A1 -- Frequency Distributions. A2 -- Measures of Central Tendency and Spread. A3 -- Estimation. A4 -- Confidende Intervals. A5 -- Testing of Statistical Hypotheses. References of Appendix. Computer techniques have made online measurements available at every sampling period in a chemical process. However, measurement errors are introduced that require suitable techniques for data reconciliation and improvements in accuracy. Reconciliation of process data and reliable monitoring are essential to decisions about possible system modifications (optimization and control procedures), analysis of equipment performance, design of the monitoring system itself, and general management planning. While the reconciliation of the process data has been studied for more than 20 years, there is no single source providing a unified approach to the area with instructions on implementation. Data Processing and Reconciliation for Chemical Process Operations is that source. Competitiveness on the world market as well as increasingly stringent environmental and product safety regulations have increased the need for the chemical industry to introduce such fast and low cost improvements in process operations. Key Features * Introduces the first unified approach to this important field * Bridges theory and practice through numerous worked examples and industrial case studies * Provides a highly readable account of all aspects of data classification and reconciliation * Presents the reader with material, problems, and directions for further study. English. Chemical process control Automation. Automatic data collection systems. http://id.loc.gov/authorities/subjects/sh85010095 Error analysis (Mathematics) http://id.loc.gov/authorities/subjects/sh85044724 Chemical processes. http://id.loc.gov/authorities/subjects/sh85022948 Chemical process control. http://id.loc.gov/authorities/subjects/sh85022947 Procédés chimiques Contrôle Automatisation. Collecte automatique des données. Théorie des erreurs. Procédés chimiques. Procédés chimiques Contrôle. TECHNOLOGY & ENGINEERING Chemical & Biochemical. bisacsh SCIENCE Chemistry Industrial & Technical. bisacsh Automatic data collection systems fast Chemical process control fast Chemical process control Automation fast Chemical processes fast Error analysis (Mathematics) fast Sánchez, Mabel Cristina. http://id.loc.gov/authorities/names/no99087706 has work: Data processing and reconciliation for chemical process operations (Text) https://id.oclc.org/worldcat/entity/E39PCGdThWYCrV7Mpv3JbdvrMd https://id.oclc.org/worldcat/ontology/hasWork Print version: Romagnoli, José A. (José Alberto). Data processing and reconciliation for chemical process operations. San Diego : Academic Press, ©2000 0125944608 9780125944601 (DLC) 99060407 (OCoLC)42954362 Process systems engineering ; v. 2. http://id.loc.gov/authorities/names/no99055720 FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/bookseries/18745970/2 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=210406 Volltext |
| spellingShingle | Romagnoli, José A. (José Alberto) Data processing and reconciliation for chemical process operations / Process systems engineering ; General Introduction. Reliable and Complete Knowledge. Some Issues Associated with a General Data Reconciliation Problem. About This Book. References of Chapter 1. Estimability and Redundancy Within the Framework of the General Estimation Theory. Introduction. Basic Concepts and Definitions. Decomposition of the General Estimation Problem. Structural Analysis. Conclusions. Notation. References of Chapter 2. Appendix 2 -- A. Classification of the Process Variables for Chemical Plants. Introduction. Modeling Aspects. Classification of Process Variables. Analysis of the Process Topology. Different Approaches for Solving the Classification Problem. Use of Output Set Assignments for Variable Classification. The Solution of Special Problems. A Complete Classification Example. Formulation of a Reduced Reconciliation Problem. Conclusions. Notation. References of Chapter 3. Appendix 3 -- A. Appendix 3 -- B. Decomposition Using Orthogonal Transformations. Introduction. Linear Mass Balances. Bilinear Multicomponent and Energy Balances. Conclusions. Notation. References of Chapter 4. Steady State Data Reconciliation. Introduction. Problem Formulation. Linear Data Reconciliation. Non-Linear Data Reconciliation. Conclusions. Notation. References of Chapter 5. Appendix 5 -- A. Sequential Processing of the Information. Introduction. Sequential Processing of the Constraints. Sequential Processing of the Measurements. Alternative Formulation from Estimation Theory. Conclusions. Notation. References of Chapter 6. Appendix 6 -- A. Treatment of Gross Errors. Introduction. Gross Error detection. Identification of the Measurements with Gross Error. Estimation of the Magnitude of Bias and Leaks. A Recursive Scheme for Gross Error Identification and Estimation. Conclusions. Notation. References of Chapter 7. Appendix 7 -- A. Appendix 7 -- B. Rectification of Process Measurement Data in Dynamic Situations. Introduction. Dynamic Data Reconciliation: A Filtering Approach. Dynamic Data Reconciliation: Using Non-linear Programming Techniques. Conclusions. Notation. References of Chapter 8. Joint Parameter Estimation Data Reconciliation. Introduction. The Parameter Estimation Problem. Joint Parameter Estimation-Data Reconciliation Problem. Dynamic Joint State-Parameter Estimation: A Filtering Approach. Dynamic Joint State-Parameter Estimation: A Non-linear Programming Approach. Conclusions. Notation. References of Chapter 9. Estimation of Measurement Error Variances from Process Data. Introduction. Direct Method. Indirect Method. Robust Covariance Estimator. Conclusions. Notation. References of Chapter 10. New Trends. Introduction. The Bayesian Approach. Robust Estimation Approaches. Principal Component Analysis in Data Reconciliation. Conclusions. Notation. References of Chapter 11. Case Studies. Introduction. Decomposition/Reconciliation in a Section of an Olefine Plant. Data Reconciliation of a Pyrolysis Reactor. Data Reconciliation of an Experimental Distillation Column. Conclusions. Notation. References of Chapter 12. Statistical Concepts. A1 -- Frequency Distributions. A2 -- Measures of Central Tendency and Spread. A3 -- Estimation. A4 -- Confidende Intervals. A5 -- Testing of Statistical Hypotheses. References of Appendix. Chemical process control Automation. Automatic data collection systems. http://id.loc.gov/authorities/subjects/sh85010095 Error analysis (Mathematics) http://id.loc.gov/authorities/subjects/sh85044724 Chemical processes. http://id.loc.gov/authorities/subjects/sh85022948 Chemical process control. http://id.loc.gov/authorities/subjects/sh85022947 Procédés chimiques Contrôle Automatisation. Collecte automatique des données. Théorie des erreurs. Procédés chimiques. Procédés chimiques Contrôle. TECHNOLOGY & ENGINEERING Chemical & Biochemical. bisacsh SCIENCE Chemistry Industrial & Technical. bisacsh Automatic data collection systems fast Chemical process control fast Chemical process control Automation fast Chemical processes fast Error analysis (Mathematics) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85010095 http://id.loc.gov/authorities/subjects/sh85044724 http://id.loc.gov/authorities/subjects/sh85022948 http://id.loc.gov/authorities/subjects/sh85022947 |
| title | Data processing and reconciliation for chemical process operations / |
| title_auth | Data processing and reconciliation for chemical process operations / |
| title_exact_search | Data processing and reconciliation for chemical process operations / |
| title_full | Data processing and reconciliation for chemical process operations / José A. Romagnoli, Mabel Cristina Sánchez. |
| title_fullStr | Data processing and reconciliation for chemical process operations / José A. Romagnoli, Mabel Cristina Sánchez. |
| title_full_unstemmed | Data processing and reconciliation for chemical process operations / José A. Romagnoli, Mabel Cristina Sánchez. |
| title_short | Data processing and reconciliation for chemical process operations / |
| title_sort | data processing and reconciliation for chemical process operations |
| topic | Chemical process control Automation. Automatic data collection systems. http://id.loc.gov/authorities/subjects/sh85010095 Error analysis (Mathematics) http://id.loc.gov/authorities/subjects/sh85044724 Chemical processes. http://id.loc.gov/authorities/subjects/sh85022948 Chemical process control. http://id.loc.gov/authorities/subjects/sh85022947 Procédés chimiques Contrôle Automatisation. Collecte automatique des données. Théorie des erreurs. Procédés chimiques. Procédés chimiques Contrôle. TECHNOLOGY & ENGINEERING Chemical & Biochemical. bisacsh SCIENCE Chemistry Industrial & Technical. bisacsh Automatic data collection systems fast Chemical process control fast Chemical process control Automation fast Chemical processes fast Error analysis (Mathematics) fast |
| topic_facet | Chemical process control Automation. Automatic data collection systems. Error analysis (Mathematics) Chemical processes. Chemical process control. Procédés chimiques Contrôle Automatisation. Collecte automatique des données. Théorie des erreurs. Procédés chimiques. Procédés chimiques Contrôle. TECHNOLOGY & ENGINEERING Chemical & Biochemical. SCIENCE Chemistry Industrial & Technical. Automatic data collection systems Chemical process control Chemical process control Automation Chemical processes |
| url | https://www.sciencedirect.com/science/bookseries/18745970/2 https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=210406 |
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