Advanced control of chemical processes (ADCHEM '94) : IFAC by D Bonvin; International Federation of Automatic Control

By D Bonvin; International Federation of Automatic Control

This booklet brings jointly the most recent examine findings within the key region of chemical method keep watch over; together with dynamic modelling and simulation - modelling and version validation for program in linear and nonlinear model-based keep watch over: nonlinear model-based predictive keep watch over and optimization - to facilitate restricted real-time optimization of chemical tactics; statistical regulate techniques - significant advancements within the statistical interpretation of measured information to lead destiny learn; knowledge-based v model-based control - the mixing of theoretical points of keep watch over and optimization concept with newer advancements in man made intelligence and laptop technological know-how.

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Additional info for Advanced control of chemical processes (ADCHEM '94) : IFAC symposium, Kyoto, Japan, 25-27 May 1994

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Ogunnaike et al. 1 994, for further details). where A = ju0j/uc. Note that for A = 1 , y• = 2u0, so that one effect of the nonlinearity is to double the system steady-state gain. e. when 42 Direct Integration. By direct integration, the model form in (3) becomes: 11(t;) = 11(to) + + jt; (tl>o + t/>111 + t/>2112 ) dt to jt; ("Yo + "Y1Y + "Y2112 ) u(t)dt lo and, without loss of generality, setting reduces to 11(t ;) Equation (20) is a "velocity" (or differential) form of (17) in which, instead of integrals over the time in­ terval (0, t ; ), of length t ; , and fixed origin t = 0, the integrals are taken over the intervals of length 6(T;), and variable origin tL; .

Define the relationships be­ tween the Poisson transform and Poisson wavelet transform. l ( 1 2) Note that for r � 0 , the PWT of the exponential function is itself, the same exponential with t re­ p laced by r , weighted by a function of only the ratio of the scaling variable /3 to the time constant rr. This "eigenfunction-like" behavior for positive time shifts suggests its applicability to analysis of signals composed of decaying exponentials. For r < 0 , negative translations, the PWT has inter­ esting behavior that may be utilized for model validation .

2 0 ������ 15 0 � '-' � .... O: -10 -1 5 Figure 8: (W1 /W2) vs T for T < t d at f3 = 1 0. 5 . Solid line = three-tank system, dashed line 5: ( W1 /W2) vs f3 at T = 1 0. Solid line = three-tank system, dashed line = P WT domain least-squares fi t with n o noise. Figure = PWT domain least-squares fit with n o noise, dots = three-tank system with noise. 40 Copyright © IFAC Advanced Control of Chemical Processes, Kyoto, Japan, 1994 Low Order Empirical Modeling for Nonlinear Systems Babatunde A. Ogunnaike: Ronald K.

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