Shedding Light on Data Reconciliation Techniques Applied to Analytical Chemistry

References

• de Oliveira, E. C.; Nogueira Frota, M.; de Oliveira Barreto, G. Use of Data Reconciliation: A Strategy for Improving the Accuracy in Gas Flow Measurements. J. Nat. Gas Sci. Eng. 2015, 22, 313–320. DOI: 10.1016/j.jngse.2014.12.008.
   Web of Science ®Google Scholar
• Narasimhan, S.; Jofache, C. Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data; Gulf Publishing Company Houston: Texas, 2000.
   Google Scholar
• Cochinwala, M.; Kurien, V.; Lalk, G.; Shasha, D. Efficient Data Reconciliation. Inf. Sci. 2001, 137, 1–15. DOI: 10.1016/S0020-0255(00)00070-0.
   Web of Science ®Google Scholar
• Arora, N.; Biegler, L. T. Redescending Estimators for Data Reconciliation and Parameter Estimation. Comput. Chem. Eng. 2001, 25, 1585–1599. DOI: 10.1016/S0098-1354(01)00721-9.
   Web of Science ®Google Scholar
• Llanos, C. E.; Sanchéz, M. C.; Maronna, R. A. Dynamic System State Estimation and Outlier Detection Using Robust Data Reconciliation. Chem. Eng. Trans. 2019, 74, 721–726. DOI: 10.3303/CET1974121.
   Google Scholar
• Alhaj-Dibo, M.; Maquin, D.; Ragot, J. Data Reconciliation: A Robust Approach Using a Contaminated Distribution. Control Eng. Pract. 2008, 16, 159–170. DOI: 10.1016/j.conengprac.2007.01.003.
   Web of Science ®Google Scholar
• Özyurt, D. B.; Pike, R. W. Theory and Practice of Simultaneous Data Reconciliation and Gross Error Detection for Chemical Processes. Comput. Chem. Eng. 2004, 28, 381–402. DOI: 10.1016/j.compchemeng.2003.07.001.
   Web of Science ®Google Scholar
• Abu-El-Zeet, Z. H.; Roberts, P. D.; Becerra, V. M. 2001 BIAS Detection and Identification in Dynamic Data Reconciliation. Presented at the 2001 European Control Conference, ECC, 2001, pp 2352–2357.
   Google Scholar
• Miao, Y.; Su, H.; Gang, R.; Chu, J. Industrial Processes: Data Reconciliation and Gross Error Detection. Meas. Control 2009, 42, 209–215. DOI: 10.1177/002029400904200704.
   Web of Science ®Google Scholar
• Llanos, C. E.; Sánchez, M. C.; Maronna, R. A. Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation. Ind. Eng. Chem. Res. 2017, 56, 9617–9628. DOI: 10.1021/acs.iecr.7b00726.
   Web of Science ®Google Scholar
• Llanos, C. E.; Sanchéz, M. C.; Maronna, R. A. Robust Estimators for Data Reconciliation. Ind. Eng. Chem. Res. 2015, 54, 5096–5105. DOI: 10.1021/ie504735a.
   Web of Science ®Google Scholar
• Farsang, B.; Sándor, N. J. Role of Steady State Data Reconciliation. Hung. J. Ind. Chem. 2013, 41, 65–75.
   Google Scholar
• Bagajewicz, M. A Review of Techniques for Instrumentation Design and Upgrade in Process Plants. Can. J. Chem. Eng. 2002, 80, 3–16. DOI: 10.1002/cjce.5450800101.
   Web of Science ®Google Scholar
• Koermer, S.; Noble, A. The Utility of Bayesian Data Reconciliation for Separations. Miner. Eng. 2021, 169, 106837. DOI: 10.1016/j.mineng.2021.106837.
   Web of Science ®Google Scholar
• Yellapu, V. S.; Zhang, W.; Vajpayee, V.; Xu, X. A Multiscale Data Reconciliation Approach for Sensor Fault Detection. Prog. Nucl. Energy 2021, 135, 103707. DOI: 10.1016/j.pnucene.2021.103707.
   Web of Science ®Google Scholar
• Hazra, S.; Prakash, A.; Samik, N.; Sam, M.; and Shankar, N. Application of Data Reconciliation and Gross Error Detection Techniques to Enhance Reliability and Consistency of the Blast Furnace Process Data. Asia-Pacific Journal of Chemical Engineering 2021, 16, e2628. https://doi.org/10.1002/apj.2628.
   Google Scholar
• Luan, Y.; Jiang, M.; Feng, Z.; Sun, B. Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation. Entropy 2021, 23, 473. DOI: 10.3390/e23040473.
   PubMed Web of Science ®Google Scholar
• de Menezes, D. Q. F.; Prata, D. M.; Secchi, A. R.; Pinto, J. C. A Review on Robust M-Estimators for Regression Analysis. Comput. Chem. Eng. 2021, 147, 107254. DOI: 10.1016/j.compchemeng.2021.107254.
   Web of Science ®Google Scholar
• Zhou, D.; Huang, D.; Zhang, L.; Hao, J.; Ma, S. A Global Thermodynamic Measurement Data Reconciliation Model considering Boundary Conditions and Parameter Correlations and Its Applications to Natural Gas Compressors. Measurement 2021, 172, 108972. DOI: 10.1016/j.measurement.2021.108972.
   Web of Science ®Google Scholar
• Ferrucci, A.; Vocciante, M. Improved Management of Water Resources in Process Industry by Accounting for Fluctuations of Water Content in Feed Streams and Products. J. Water Process Eng. 2021, 39, 101870. DOI: 10.1016/j.jwpe.2020.101870.
   Web of Science ®Google Scholar
• Ragot, J. Aberrant Measurements: Detection, Localization, Suppression, Acceptance and Robustness. Measurement 2021, 172, 108872. DOI: 10.1016/j.measurement.2020.108872.
   Web of Science ®Google Scholar
• Seman, L. O.; Giuliani, C. M.; Camponogara, E.; Müller, E. R.; Vieira, B. F.; Miyatake, L. K.; Gonçalves Medeiros, A. Tuning of Oil Well Models with Production Data Reconciliation. Comput. Chem. Eng. 2021, 145, 107179. DOI: 10.1016/j.compchemeng.2020.107179.
   Web of Science ®Google Scholar
• da Cunha, A. S.; Peixoto, F. C.; Prata, D. M. Robust Data Reconciliation in Chemical Reactors. Comput. Chem. Eng. 2021, 145, 107170. DOI: 10.1016/j.compchemeng.2020.107170.
   Web of Science ®Google Scholar
• Boryczko, B.; Hołda, A.; Dudek, M.; Kolenda, Z. Application of the Data Validation and Reconciliation Method in the Exergy Analysis of Technological Processes. International Journal of Exergy 2021, 34, 315–330. https://doi.org/10.1504/IJEX.2021.113847.
   Google Scholar
• Xie, S.; Wang, H.; Peng, J. Energy Efficiency Analysis and Optimization of Industrial Processes Based on a Novel Data Reconciliation. IEEE Access 2021, 9, 47436–47451. DOI: 10.1109/ACCESS.2021.3068374.
   Web of Science ®Google Scholar
• Ghosh-Dastidar, B.; Schafer, J. Outlier Detection and Editing Procedures for Continuous Multivariate Data. J. Off. Stat. 2006, 22, 487–506.
   Google Scholar
• Ragot, J.; Maquin, D. Reformulation of Data Reconciliation Problem with Unknown-but-Bounded Errors. Ind. Eng. Chem. Res. 2004, 43, 1530–1536. DOI: 10.1021/ie020213s.
   Web of Science ®Google Scholar
• Mermillod-Blondin, R.; Benzaazoua, M.; Bouzahzah, H.; Leroux, D. Development and Calibration of a Reconciliated Mineralogy Method Based on Multitechnique Analyses: Application to Acid Mine Drainage Prediction. Presented at the IMPC 2016—28th Int Miner Process Congr, Sep 2016.
   Google Scholar
• Tammishetti, V.; Subramanian, S.; Kumar, D.; et al. 2016 Quantitative Estimation of Minerals in Iron Ores Using Chemical Assays and Thermo-Gravimetry Analysis Data. Presented at the IMPC 2016—28th Int Miner Process Congr, Sep 2016.
   Google Scholar
• Tammishetti, V.; Rai, B.; Ravikumar, B.; et al. Quantitative Estimation of Mineral Phases from Chemical Assays and Powder X-Ray Diffraction Rietveld Analysis: A Case Study on Selective Flocculation of Iron Ore Slimes. Trans. Indian Inst. Met. 2015, 69, 125–133. DOI: 10.1007/s12666-015-0721-7.
   Web of Science ®Google Scholar
• Olavarria, K.; Quakkelaar, C.; van Renselaar, J.; Langerak, D.; van Loosdrecht, M. C. M.; Wahl, S. A. NADH-Driven Poly-3-Hydroxybutyrate Accumulation in Escherichia coli: Data from Enzymatic Assays and Oxygen-Limited Continuous Cultures. Data Brief. 2020, 33, 106588. DOI: 10.1016/j.dib.2020.106588.
   PubMed Web of Science ®Google Scholar
• Taymaz-Nikerel, H.; Borujeni, A. E.; Verheijen, P. J. T.; Heijnen, J. J.; van Gulik, W. M. Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry . Biotechnol. Bioeng. 2010, 107, 369–381. DOI: 10.1002/bit.22802.
   PubMed Web of Science ®Google Scholar
• Guiavarch, E.; Pons, A.; Christophe, G.; Creuly, C.; Dussap, C.-G. Analysis of a Continuous Culture of Fibrobacter Succinogenes S85 on a Standardized Glucose Medium. Bioprocess. Biosyst. Eng. 2010, 33, 417–425. DOI: 10.1007/S00449-009-0342-9.
   PubMed Web of Science ®Google Scholar
• Guiavarch, E.; Pons, A.; Creuly, C.; Dussap, C. G. Application of a Data Reconciliation Method to the Stoichiometric Analysis of Fibrobacter Succinogenes Growth. Appl. Biochem. Biotechnol. 2008, 151, 201–210. DOI: 10.1007/s12010-008-8172-9.
   PubMed Web of Science ®Google Scholar
• Heijnen, S. J.; Verheijen, P. J. T. How to Obtain True and Accurate Rate-Values. Methods in Enzymology, 2011, pp 457–508.DOI: 10.1016/B978-0-12-385118-5.00023-2.
   Google Scholar
• Maksimov, P.; Laari, A.; Ruuskanen, V.; Koiranen, T.; Ahola, J. Gas-Phase Methanol Synthesis with Raman Spectroscopy for Gas Composition Monitoring. RSC Adv. 2020, 10, 23690–23701. DOI: 10.1039/D0RA04455E.
   PubMed Web of Science ®Google Scholar
• Syed, M. S.; Dooley, K. M.; Madron, F.; Knopf, F. C. Enhanced Turbine Monitoring Using Emissions Measurements and Data Reconciliation. Appl. Energy 2016, 173, 355–365. DOI: 10.1016/j.apenergy.2016.04.059.
   Web of Science ®Google Scholar
• Marison, I.; Hennessy, S.; Foley, R.; Schuler, M.; Sivaprakasam, S.; Freeland, B. The Choice of Suitable Online Analytical Techniques and Data Processing for Monitoring of Bioprocesses. Adv. Biochem. Eng. Biotechnol. 2013, 132, 249–280. DOI: 10.1007/10_2012_175.
   PubMed Web of Science ®Google Scholar
• Srinivasan, S.; Billeter, J.; Narasimhan, S.; Bonvin, D. Data Reconciliation in Open Reaction Systems Using the Concept of Extents. Presented at Comput Syst Technol Div 2015 - Core Program Area 2015 AIChE Annu Meet, 2015; Vol 2, pp 848–850.
   Google Scholar
• Xie, S.; Yang, C.; Wang, X.; Xie, Y. Data Reconciliation Strategy with Time Registration for the Evaporation Process in Alumina Production. Can. J. Chem. Eng. 2018, 96, 189–204. DOI: 10.1002/cjce.22893.
   Web of Science ®Google Scholar
• Szega, M.; Rusinowski, H. Sensitivity Analysis of the Specific Consumption of Chemical Energy of Fuels for Assumed Uncertainty of Selected Measurements in a Steam Combined Heat and Power Unit. Presented at Proc 2015 16th Int Carpathian Control Conf ICCC, 2015, pp 526–531.
   Google Scholar
• de Oliveira, E. C.; Lourenço, F. R. Data Reconciliation Applied to the Conformity Assessment of Fuel Products. Fuel 2021, 300, 120936. DOI: 10.1016/j.fuel.2021.120936.
   Web of Science ®Google Scholar
• Courtonne, J.-Y.; Alapetite, J.; Longaretti, P.-Y.; Dupré, D.; Prados, E. Downscaling Material Flow Analysis: The Case of the Cereal Supply Chain in France. Ecol. Econ. 2015, 118, 67–80. DOI: 10.1016/j.ecolecon.2015.07.007.
   Web of Science ®Google Scholar
• Velis, C. A.; Wagland, S.; Longhurst, P.; Robson, B.; Sinfield, K.; Wise, S.; Pollard, S. Solid Recovered Fuel: Materials Flow Analysis and Fuel Property Development during the Mechanical Processing of Biodried Waste. Environ. Sci. Technol. 2013, 47, 2957–2965. DOI: 10.1021/es3021815.
   PubMed Web of Science ®Google Scholar
• Gleiss, A.; Matyus, T.; Bauer, G.; Deistler, M.; Glenck, E.; Lampert, C. Identification of Material Flow Systems: Extensions and Case Study. Environ. Sci. Pollut. Res. Int. 1998, 5, 245–258. DOI: 10.1007/BF02986408.
   PubMed Web of Science ®Google Scholar
• Klinglmair, M.; Zoboli, O.; Laner, D.; Rechberger, H.; Astrup, T. F.; Scheutz, C. The Effect of Data Structure and Model Choices on MFA Results: A Comparison of Phosphorus Balances for Denmark and Austria. Resour. Conserv. Recycl. 2016, 109, 166–175. DOI: 10.1016/j.resconrec.2016.03.009.
   Web of Science ®Google Scholar
• Bonnin, M.; Azzaro-Pantel, C.; Pibouleau, L.; Domenech, S.; Villeneuve, J. Development and Validation of a Dynamic Material Flow Analysis Model for French Copper Cycle. Chem. Eng. Res. Des. 2013, 91, 1390–1402. DOI: 10.1016/j.cherd.2013.03.016.
   Web of Science ®Google Scholar
• Kopec, G. M.; Allwood, J. M.; Cullen, J. M.; Ralph, D. A General Nonlinear Least Squares Data Reconciliation and Estimation Method for Material Flow Analysis. J. Ind. Ecol. 2016, 20, 1038–1049. DOI: 10.1111/jiec.12344.
   Web of Science ®Google Scholar
• Meylan, G.; Reck, B. K.; Rechberger, H.; Graedel, T. E.; Schwab, O. Assessing the Reliability of Material Flow Analysis Results: The Cases of Rhenium, Gallium, and Germanium in the United States Economy. Environ. Sci. Technol. 2017, 51, 11839–11847. DOI: 10.1021/ACS.EST.7B03086.
   PubMed Web of Science ®Google Scholar
• Oliveira, E. C.; Aguiar, P. F. Data Reconciliation in the Natural Gas Industry: Analytical Applications. Energy Fuels 2009, 23, 3658–3664. DOI: 10.1021/ef9001428.
   Web of Science ®Google Scholar
• Crowe, C. M. Reconciliation of Process Flow Rates by Matrix Projection. Part II: The Nonlinear Case. AIChE J. 1986, 32, 616–623. DOI: 10.1002/aic.690320410.
   Web of Science ®Google Scholar
• Crowe, C. M.; Campos, Y. A. G.; Hrymak, A. Reconciliation of Process Flow Rates by Matrix Projection. Part I: Linear Case. AIChE J. 1983, 29, 881–888. DOI: 10.1002/aic.690290602.
   Web of Science ®Google Scholar
• Crowe, C. M. Data Reconciliation—Progress and Challenges. J Process Control 1996, 6, 89–98. DOI: 10.1016/0959-1524(96)00012-1.
   Web of Science ®Google Scholar
• Separovic, L.; Simabukuro, R. S.; Couto, A. R.; Bertanha, M. L. G.; Dias, F. R. S.; Sano, A. Y.; Caffaro, A. M.; Lourenço, F. R. Measurement Uncertainty and Conformity Assessment Applied to Drug and Medicine Analyses—A Review. Crit. Rev. Anal. Chem. 2021, 1–16. DOI: 10.1080/10408347.2021.1940086.
   Web of Science ®Google Scholar
• Valentin, J. L. Ecologia Numérica : Uma Introdução à Análise Multivariada de Dados Ecológicos. Ed Interciência, Rio Janeiro, 2000.
   Google Scholar
• Narasimhan, S.; Bhatt, N. Deconstructing Principal Component Analysis Using a Data Reconciliation Perspective. Comput. Chem. Eng. 2015, 77, 74–84. DOI: 10.1016/j.compchemeng.2015.03.016.
   Web of Science ®Google Scholar
• Narasimhan, S.; Shah, S. L. Model-Identification and Error Covariance Matrix Estimation from Noisy Data Using PCA. Control Eng. Pract. 2008, 16, 146–155. DOI: 10.1016/j.conengprac.2007.04.006.
   Web of Science ®Google Scholar
• Radhakrishna, R. C. The Use and Interpretation of Principal Component Analysis in Applied Research. Sankhya A 1964, 26, 329–358.
   Google Scholar
• Yoon, S.; MacGregor, J. F. Fault Diagnosis with Multivariate Statistical Models Part I: Using Steady-State Fault Signatures. J. Process Control 2001, 11, 387–400. DOI: 10.1016/S0959-1524(00)00008-1.
   Web of Science ®Google Scholar
• Romagnoli, J. A.; Sánchez, M. C. Data Processing and Reconciliation for Chemical Process Operations, 2000.
   Google Scholar
• Wentzell, P. D.; Lohnes, M. T. Maximum Likelihood Principal Component Analysis with Correlated Measurement Errors: Theoretical and Practical Considerations. In Chemometrics and Intelligent Laboratory Systems; Elsevier, 1999; Vol. 45, pp 65–85. DOI: 10.1016/S0169-7439(98)00090-2.
   Google Scholar
• Wentzell, P. D.; Andrews, D. T.; Hamilton, D. C.; Faber, K.; Kowalski, B. R. Maximum Likelihood Principal Component Analysis. J. Chemom. 1997, 11, 339–366. DOI: 10.1002/(SICI)1099-128X(199707)11:4 < 339::AID-CEM476 > 3.0.CO;2-L.
   Web of Science ®Google Scholar
• Moore, B. C. Principal Component Analysis in Linear Systems: Controllability, Observability, and Model Reduction. IEEE Trans. Automat. Contr. 1981, 26, 17–32. DOI: 10.1109/TAC.1981.1102568.
   Web of Science ®Google Scholar
• Tong, H.; Crowe, C. M. Detection of Gross Errors in Data Reconciliation by Principal Component Analysis. AIChE J. 1995, 41, 1712–1722. DOI: 10.1002/aic.690410711.
   Web of Science ®Google Scholar
• Amand, T.; Heyen, G.; Kalitventzeff, B. Plant Monitoring and Fault Detection: Synergy between Data Reconciliation and Principal Component Analysis. Comput. Aided Chem. Eng. 2000, 8, 757–762. DOI: 10.1016/S1570-7946(00)80128-5.
   Google Scholar
• Tan, R. R.; Briones, L. M. A.; Culaba, A. B. Fuzzy Data Reconciliation in Reacting and Non-Reacting Process Data for Life Cycle Inventory Analysis. J. Clean. Prod. 2007, 15, 944–949. DOI: 10.1016/j.jclepro.2005.09.001.
   Web of Science ®Google Scholar
• Džubur, N.; Sunanta, O.; Laner, D. A Fuzzy Set-Based Approach to Data Reconciliation in Material Flow Modeling. Appl. Math. Model. 2017, 43, 464–480. DOI: 10.1016/j.apm.2016.11.020.
   Web of Science ®Google Scholar
• Dubois, D.; Fargier, H.; Ababou, M.; Guyonnet, D. A Fuzzy Constraint-Based Approach to Data Reconciliation in Material Flow Analysis. Int. J. Gen. Syst. 2014, 43, 787–809. DOI: 10.1080/03081079.2014.920840.
   Web of Science ®Google Scholar
• Laner, D.; Rechberger, H.; Astrup, T. Applying Fuzzy and Probabilistic Uncertainty Concepts to the Material Flow Analysis of Palladium in Austria. J. Ind. Ecol. 2015, 19, 1055–1069. DOI: 10.1111/jiec.12235.
   Web of Science ®Google Scholar