Sulfur recovery units: Adaptive simulation and model validation on an industrial plant

Industrial and Engineering Chemistry Research

Volumn 49, Issue 12, 2010, Pages 5714-5724

  Signor, Stefano ^a   Manenti, Flavio ^b   Grottoli, Maria Grazia ^a   Fabbri, Paolo ^a   Pierucci, Sauro ^b  

^a Automation and Process Solutions   (United States)

^b POLITECNICO DI MILANO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE PARAMETERS; ADAPTIVE SIMULATION; COMMERCIAL PROCESS SIMULATORS; INDUSTRIAL EXPERIENCE; MODEL VALIDATION; PROCESS SIMULATIONS; ROBUST PARAMETER ESTIMATION; SIMSCI-ESSCOR; STANDARD LIBRARIES; SULFUR RECOVERY UNITS; TECHNIP; VISUAL C++;

CATALYTIC CONVERTERS; DESULFURIZATION; INDUSTRIAL PLANTS; NUMERICAL METHODS; PARAMETER ESTIMATION; SULFUR;

COMPUTER SIMULATION;

EID: 77953354943     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie901749t     Document Type: Article

References (44)

1. Albuquerque, J. S.; Biegler, L. T. Data reconciliation and gross-error detection for dynamic systems AIChE J. 1996, 42 (10) 2841-2856
2. Albuquerque, J. S.; Biegler, L. T.; Kass, R. E. Inference in dynamic error-in-variable-measurement problems AIChE J. 1997, 43 (4) 986-996
3. Binder, T.; Blank, L.; Dahmen, W.; Marquardt, W. On the regularization of dynamic data reconciliation problems J. Process Control 2002, 12 (4) 557-567
4. Manenti, F. From reacting to predicting technologies: A novel performance monitoring technique based on detailed dynamic models. Chem. Prod. Process Model. 2009, 4 (2).
5. White, D. C. The "Smart" Refinery: Economics and Technology. NPRA, Annual Meeting, San Antonio, TX, 2003.
6. Bagajewicz, M. J. Data Reconciliation and Instrumentation Upgrade. Overview and Challenges. FOCAPO 2003. 4th International Conference of Computer-Aided Process Operations, Coral Springs, FL 2003; pp 103-116.
7. Chiari, M.; Bussani, G.; Grottoli, M. G.; Pierucci, S. On-Line Data Reconciliation and Optimization: Refinery Applications. Proceedings of ESCAPE 7, Trondheim, Norway, 1997.
8. Chiari, M.; Grottoli, M. G.; Villa, S.; Pierucci, S. Refinery Sulphur Plant Optimization Sulphur 1997, 250, 53-59
9. Pierucci, S.; Ranzi, E.; Molinari, L. Modelling a Claus Reaction Furnace via a Radical Kinetic Scheme. Proceedings of ESCAPE-14, Lisbon, Portugal, 2004; pp 463-468.
10. Tjoa, I. B.; Biegler, L. T. Reduced Successive Quadratic-Programming Strategy for Errors-in-Variables Estimation Comput. Chem. Eng. 1992, 16 (6) 523-533
11. Esposito, W. R.; Floudas, C. A. Global optimization in parameter estimation of nonlinear algebraic models via the error-in-variables approach Ind. Eng. Chem. Res. 1998, 37 (5) 1841-1858
12. Simsci-Esscor, PRO/II, User Guide; Simsci-Esscor: Lake Forest, CA, 2002; www.simsci-esscor.com.
13. Arora, N.; Biegler, L. T. Parameter estimation for a polymerization reactor model with a composite-step trust-region NLP algorithm Ind. Eng. Chem. Res. 2004, 43 (14) 3616-3631
14. Arora, N.; Biegler, L. T. Redescending estimators for data reconciliation and parameter estimation Comput. Chem. Eng. 2001, 25 (11-12) 1585-1599
15. Bates, D. M.; Watts, D. G. Nonlinear Regression Analysis and Its Applications; John Wiley and Sons: Hoboken, NJ, 2007.
16. Weiss, G. H.; Romagnoli, J. A.; Islam, K. A. Data Reconciliation-An Industrial Case Study Comput. Chem. Eng. 1996, 20 (12) 1441-1449
17. Grottoli, M. G.; Signor, S.; Manenti, F. Strengthened Techniques For The Renewed Industrial Interest In Plant Data Reconciliation Benefits Chem. Eng. Trans. 2008, 265-268
18. Narasimhan, S.; Jordache, C. Data Reconciliation & Gross Error Detection, An Intelligent Use of Process Data; Gulf Publishing Co.: Houston, TX, 2000.
19. Romagnoli, J. A.; Sanchez, M. C. Data Processing and Reconciliation for Chemical Process Operations; Process Systems Engineering; Academic: San Diego, 1999; Vol. 2.
20. Crowe, C. M. Reconciliation of Process Flow Rates by Matrix Projection. II: Nonlinear Case AIChE J. 1989, 32, 616-623
21. Crowe, C. M.; Campos, Y. A. G.; Hrymak, A. Reconciliation of Process Flow Rates by Matrix Projection. I: Linear Case AIChE J. 1983, 29, 881-888
22. Lid, T.; Skogestad, S. Scaled steady state models for effective on-line applications Comput. Chem. Eng. 2008, 32 (4-5) 990-999
23. Bagajewicz, M. J.; Jiang, Q. Gross Error Modeling and Detection in Plant Linear Dynamic Reconciliation Comput. Chem. Eng. 1998, 22 (12) 1789-1810
24. Buzzi-Ferraris, G.; Manenti, F. Outlier Detection. Comput. Chem. Eng. 2010.
25. Rousseeuw, P. J. Least median of squares regressions J. Am. Stat. Assoc. 1984, 79, 871-880
26. Buzzi-Ferraris, G.; Manenti, F. Interpolation and Regression Models for the Chemical Engineer: Solving Numerical Problems; Wiley-VCH: Weinheim, Germany, 2010.
27. Buzzi-Ferraris, G.; Manenti, F. Kinetic models analysis Chem. Eng. Sci. 2009, 64 (5) 1061-1074
28. Draper, N. R.; Smith, H. Applied Regression Analysis, 3 rd ed.; Wiley: New York, 1998.
29. Rousseeuw, P. J.; Leroy, A. M. Robust Regression and Outlier Detection; John Wiley & Sons: New York, 1987.
30. Ryan, T. P. Modern Regression Methods, 2 nd ed.; Wiley: New Jersey, 2009.
31. Seber, G. A. F.; Wild, C. J. Nonlinear Regression; John Wiley and Sons: Hoboken, NJ, 2003.
32. Hampel, F. R.; Ronchetti, E. M.; Rousseeuw, P. J.; Stahel, W. A. Robust Statistics: The Approach Based on Influence Functions; Wiley: New York, 1986.
33. Hampel, F. R.; Ronchetti, E. M.; Rousseeuw, P. J.; Stahel, W. A. Robust Statistics: The Approach Based on Influence Functions, 2 nd ed.; Wiley: New York, 2005.
34. Ali, Y.; Narasimhan, S. Sensor Network Design for Maximizing Reliability of Bilinear Processes AIChE J. 1996, 42 (9) 2563-2575
35. Ali, Y.; Narasimhan, S. Sensor Network Design for Maximizing Reliability of Linear Processes AIChE J. 1993, 39, 820
36. Ali, Y.; Narasimhan, S. Sensor Network Design for Maximizing Reliability of Linear Processes with Redundant Sensors AIChE J. 1995, 41, 2237
37. Raghunathan, A. U.; Perez-Correa, J. R.; Biegler, L. T. Data reconciliation and parameter estimation in flux-balance analysis Biotechnol. Bioeng. 2003, 84 (6) 700-709
38. Buzzi-Ferraris, G. Ottimizzazione di funzioni a più variabili. Nota I. Variabili non vincolate Ing. Chim. It. 1967, 3, 101
39. Buzzi-Ferraris, G.; Manenti, F. A Combination of Parallel Computing and Object-Oriented Programming to Improve Optimizer Robustness. Comput.-Aided Chem. Eng. 2009, 28, in press.
40. Buzzi-Ferraris, G. BzzMath: Numerical library in C++; Politecnico di Milano: Milano, 2009; http://chem.polimi.it/homes/gbuzzi.
41. Buzzi-Ferraris, G.; Manenti, F. Fundamentals and Linear Algebra for the Chemical Engineer: Solving Numerical Problems; Wiley-VCH: Weinheim, Germany, 2010.
42. Manenti, F.; Dones, I.; Buzzi-Ferraris, G.; Preisig, H. A. Efficient Numerical Solver of Partially Structured Differential and Algebraic Equation Systems Ind. Eng. Chem. Res. 2009, 48, 9979-9984
43. Manenti, F.; Buzzi-Ferraris, G. Criteria for Outliers Detection in Nonlinear Regression Problems. In Computer Aided Chemical Engineering; Jezowski, J.; Thullie, J. Eds.; Elsevier: Oxford, 2009; Vol. 26, pp 913-917.
44. Manenti, F.; Buzzi-Ferraris, G. A New Strategy to Improve Parameters Estimation Chem. Eng. Trans. 2009, 17 (3) 1335-1340