Continuous parameter estimation

Global fit procedure on all selected temperature ranges for continuous parameter estimation... 

In passing we remark that there are well-known statistical methods of hypothesis testing and parameter estimation used in decisionmaking. Sequential analysis is a method of sampling used to decide whether to accept or reject a lot with defective items, or whether to continue sampling. Also, there are various statistical methods used in quality control of a manufacturing process, to decide on how much the quality should be improved to be acceptable. 

Another interesting implementation of simulated annealing for continuous minimization (like a typical parameter estimation problem) utilizes a modification of the downhill simplex method. Press et al. (1992) provide a brief overview of simulated annealing techniques accompanied with listings of computer programs that cover all the above cases. 

The next two steps after the development of a mathematical process model and before its implementation to "real life" applications, are to handle the numerical solution of the model s ode s and to estimate some unknown parameters. The computer program which handles the numerical solution of the present model has been written in a very general way. After inputing concentrations, flowrate data and reaction operating conditions, the user has the options to select from a variety of different modes of reactor operation (batch, semi-batch, single continuous, continuous train, CSTR-tube) or reactor startup conditions (seeded, unseeded, full or half-full of water or emulsion recipe and empty). Then, IMSL subroutine DCEAR handles the numerical integration of the ode s. Parameter estimation of the only two unknown parameters e and Dw has been described and is further discussed in (32). 

Obtaining Eft), t, and of from experimental tracer data involves determining areas under curves defined continuously or by discrete data. The most sophisticated approach involves die use of E-Z Solve or equivalent software to estimate parameters by nonlinear regression. In this case, standard techniques are required to transform experimental concentration versus time data into Eft) or F(t) data the subsequent parameter estimation is based on nonlinear regression of these data using known expressions for Eft) and F t) (developed in Section 19.4). In the least sophisticated approach, discrete data, generated directly from experiment or obtained from a continuous response curve, are... 

This chapter contains examples of optimization techniques applied to the design and operation of two of the most common staged and continuous processes, namely, distillation and extraction. We also illustrate the use of parameter estimation for fitting a function to thermodynamic data. 

The future of on-line control of crystallization should see the use of parameter estimation for estimation and correction of model parameters along with higher level nonlinear control schemes. The major chtdlenge continues to be realistic measurement of the necessary variables such as the CSD or its moments. 

The observed transients of the crystal size distribution (CSD) of industrial crystallizers are either caused by process disturbances or by instabilities in the crystallization process itself (1 ). Due to the introduction of an on-line CSD measurement technique (2), the control of CSD s in crystallization processes comes into sight. Another requirement to reach this goal is a dynamic model for the CSD in Industrial crystallizers. The dynamic model for a continuous crystallization process consists of a nonlinear partial difference equation coupled to one or two ordinary differential equations (2..iU and is completed by a set of algebraic relations for the growth and nucleatlon kinetics. The kinetic relations are empirical and contain a number of parameters which have to be estimated from the experimental data. Simulation of the experimental data in combination with a nonlinear parameter estimation is a powerful 1 technique to determine the kinetic parameters from the experimental... 

Maximum likelihood (ML) is the approach most commonly used to fit a parametric distribution (Madgett 1998 Vose 2000). The idea is to choose the parameter values that maximize the probability of the data actually observed (for fitting discrete distributions) or the joint density of the data observed (for continuous distributions). Estimates or estimators based on the ML approach are termed maximum-likelihood estimates or estimators (MLEs). 

S. Vajda, P. Valkd) and K.R. Godfrey, Direct and indirect least squares methods in continuous-time parameter estimation, Automatica,... 

In this section, the model in Equation (9.18) is used to develop an analysis/synthesis system which will serve to test the accuracy of the sine-wave representation for audio signals. In the analysis stage, the amplitudes, frequencies, and phases of the model are estimated, while in the synthesis stage these parameter estimates are first matched and then interpolated to allow for continuous evolution of the parameters on successive frames. This sine-wave analysis/synthesis system forms the basis for the remainder of the chapter. 

Equations. (9.29)-(9,31) were solved numerically with parameter estimation routine s written for use with an IBM Continuous System Modeling Program (CSMP III)TRelative least-squares minimization was performed. 

Other recent developments in the field of adaptive control of interest to the processing industries include the use of pattern recognition in lieu of explicit models (Bristol (66)), parameter estimation with closed-loop operating data (67), model algorithmic control (68), and dynamic matrix control (69). It is clear that discrete-time adaptive control (vs. continuous time systems) offers many exciting possibilities for new theoretical and practical contributions to system identification and control. 

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