Download PDF by José A. Romagnoli, Mabel Cristina Sanchez: Data Processing and Reconciliation for Chemical Process

By José A. Romagnoli, Mabel Cristina Sanchez

ISBN-10: 0125944608

ISBN-13: 9780125944601

Discussing the most concerns within the remedy and reconciliation of plant information, this article covers the thoughts of estimability and redundancy in steady-state tactics; approach variable category for linear and nonlinear plant types; the adjustment of measurements for other kinds of plant versions; benefits of sequential processing of measurements and constraints; definition and research of the knowledge reconciliation challenge; research of dynamic and quasi-steady-state approaches; the matter of joint parameter estimation-data reconciliation; and traits within the box. Case reports are provided within the ultimate bankruptcy. compatible for graduate scholars, complex undergraduates in chemical engineering, and practitioners engaged within the commercial software of reconciliation strategies.

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A new approach to the identification of gross errors in chemical engineering measurements. Chem. Eng. Sci. 40, 1855-1860. Noble, B. (1969). " Prentice-Hall, Englewood Cliffs, NJ. Rao, C. R. (1973). " Wiley, New York, 1973. , and Pearson, J. B. (1976). Structural controllability of multiinput linear systems. IEEE Trans. Autom. Control AC-21,203-212. , and Mah, R. S. H. (198 l a). Observability and redundancy in process data estimation. Chem. Eng. Sci. 36, 259-272. , and Mah, R. S. H. (1981 b). Observability and redundancy classification in process networksm Theorems and algorithms.

CEF'87: Use Comput. Chem. , Italy, pp. 41--46. , and Mah, R. S. H. (1987). Observability and redundancy classification in multicomponent process networks. AIChE J. 33, 70-82. , and Mah, R. S. H. (1988a). Observability and redunancy classification in generalised process networks. I: Theorems. Comput. Chem. Eng. 12, 671-687. , and Mah, R. S. H. (1988b). Observability and redundancy classification in generalised process networks. II. Algorithms. Comput. Chem. Eng. 12, 689-703. Madron, E (1992). "Process Plant Performance.

10) E D]~1 , where ~p 6 ~)~m,m being the number of additional constraint equations. The functional relationships that characterize the real process behavior are never known exactly. A conventional way to account for the inaccuracies generated by approximations is to introduce additive noise, which in some sense reflects the expected degree of modeling errors, that is, 0 --- (/9(x) -qt- w , x E ~)~g y- Y ~ 9~t. 12) Cx+e, where A and C are the (m • g) and (1 • g) matrices of the Jacobian of ~0 and q~.

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Data Processing and Reconciliation for Chemical Process Operations by José A. Romagnoli, Mabel Cristina Sanchez


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