Parameter model

Two generally accepted models for the vapor phase were discussed in Chapter 3 and one particular model for the liquid phase (UNIQUAC) was discussed in Chapter 4. Unfortunately, these, and all other presently available models, are only approximate when used to calculate equilibrium properties of dense fluid mixtures. Therefore, any such model must contain a number of adjustable parameters, which can only be obtained from experimental measurements. The predictions of the model may be sensitive to the values selected for model parameters, and the data available may contain significant measurement errors. Thus, it is of major importance that serious consideration be given to the proper treatment of experimental measurements for mixtures to obtain the most appropriate values for parameters in models such as UNIQUAC. 

The sum of the squared differences between calculated and measures pressures is minimized as a function of model parameters. This method, often called Barker s method (Barker, 1953), ignores information contained in vapor-phase mole fraction measurements such information is normally only used for consistency tests, as discussed by Van Ness et al. (1973). Nevertheless, when high-quality experimental data are available. Barker s method often gives excellent results (Abbott and Van Ness, 1975). 

If this criterion is based on the maximum-likelihood principle, it leads to those parameter values that make the experimental observations appear most likely when taken as a whole. The likelihood function is defined as the joint probability of the observed values of the variables for any set of true values of the variables, model parameters, and error variances. The best estimates of the model parameters and of the true values of the measured variables are those which maximize this likelihood function with a normal distribution assumed for the experimental errors. 

In a regression approach to material characterization, a statistical model which describes the relation between measurements and the material property is formulated and unknown model parameters are estimated from experimental data. This approach is attractive because it does not require a detailed physical model, and because it automatically extracts and optimally combines important features. Moreover, it can exploit the large amounts of data available. 

The results presented below were obtained using a 2 mm thick carbon fiber reinforced epoxy composite laminate with 16 layers. The laminate was quasi isotropic with fiber orientations 0°, 90° and 45°. The laminate had an average porosity content of approximately 1.7%. The object was divided in a training area and an evaluation area. The model parameters were determined by data solely from the training area. Both ultrasound tranducers used in the experiment had a center frequency of 21 MHz and a 6 dB bandwidth of 70%. 

In praetiee, one of the most important aspeets of interpreting experimental kinetie data in tenns of model parameters eoneems the temperature dependenee of rate eonstants. It ean often be deseribed phenomenologieally by the Arrhenius equation [39, 40 and 41]... 

The model suggested can be easily extended to the case of inhomogeneous NAs by means of introducing the dependence of the model parameters on the number of the NA unit in the chain and solving (4) and (7) for every NA unit. This seems important as natural NAs such as DNA and RNA are inhomogeneous. The extension of the model on the case of more than three conformations also can be done easily. 

Thermodynamic consistency requites 5 1 = q 2y but this requirement can cause difficulties when attempts ate made to correlate data for sorbates of very different molecular size. For such systems it is common practice to ignore this requirement, thereby introducing an additional model parameter. This facihtates data fitting but it must be recognized that the equations ate then being used purely as a convenient empirical form with no theoretical foundation. 

These models are usually categorized according to the number of supplementary partial differential transport equations which must be solved to supply the modeling parameters. The so-called zero-equation models do not use any differential equation to describe the turbulent quantities. The best known example is the Prandtl (19) mixing length hypothesis ... 

Cahbration is an important focus in analytical chemistry. It is the process that relates instmment responses to chemical concentrations. It consists of two basic steps estimation of the cahbration model parameters, and then prediction for new samples of unknown concentration. Cahbration refers to the step of the analytical process in Figure 2 where measurements are related to concentrations of chemical species or other chemical information. 

When viscometric measurements of ECH homopolymer fractions were obtained in benzene, the nonperturbed dimensions and the steric hindrance parameter were calculated (24). Erom experimental data collected on polymer solubiUty in 39 solvents and intrinsic viscosity measurements in 19 solvents, Hansen (30) model parameters, 5 and 5 could be deterrnined (24). The notation 5 symbolizes the dispersion forces or nonpolar interactions 5 a representation of the sum of 8 (polar interactions) and 8 (hydrogen bonding interactions). The homopolymer is soluble in solvents that have solubility parameters 6 > 7.9, 6 > 5.5, and 0.2 < <5.0 (31). SolubiUty was also determined using a method (32) in which 8 represents the solubiUty parameter... 

Fitting Dynamic Models to E erimental Data In developing empirical transfer functions, it is necessary to identify model parameters from experimental data. There are a number of approaches to process identification that have been pubhshed. The simplest approach involves introducing a step test into the process and recording the response of the process, as illustrated in Fig. 8-21. The i s in the figure represent the recorded data. For purposes of illustration, the process under study will be assumed to be first order with deadtime and have the transfer func tion ... 

The response produced by Eq. (8-26), c t), can be found by inverting the transfer function, and it is also shown in Fig. 8-21 for a set of model parameters, K, T, and 0, fitted to the data. These parameters are calculated using optimization to minimize the squarea difference between the model predictions and the data, i.e., a least squares approach. Let each measured data point be represented by Cj (measured response), tj (time of measured response),j = 1 to n. Then the least squares problem can be formulated as ... 

The Smith predictor is a model-based control strategy that involves a more complicated block diagram than that for a conventional feedback controller, although a PID controller is still central to the control strategy (see Fig. 8-37). The key concept is based on better coordination of the timing of manipulated variable action. The loop configuration takes into account the facd that the current controlled variable measurement is not a result of the current manipulated variable action, but the value taken 0 time units earlier. Time-delay compensation can yield excellent performance however, if the process model parameters change (especially the time delay), the Smith predictor performance will deteriorate and is not recommended unless other precautions are taken. 

A real-time optimization (RTO) system determines set point changes and implements them via the computer control system without intervention from unit operators. The RTO system completes all data transfer, optimization c culations, and set point implementation before unit conditions change and invahdate the computed optimum. In addition, the RTO system should perform all tasks without upsetting plant operations. Several steps are necessaiy for implementation of RTO, including determination of the plant steady state, data gathering and vahdation, updating of model parameters (if necessaiy) to match current operations, calculation of the new (optimized) set points, and the implementation of these set points. 

Whiting, W.B., TM. Tong, and M.E. Reed, 1993. Effect of Uncertainties in Thermodynamic Data and Model Parameters on Calculated Process Performance, Industiial and Engineeiing Chemistiy Reseaieh, 32, 1993, 1367-1371. (Relational model development)... 

The three vertices are the operating plant, the plant data, and the plant model. The plant produces a product. The data and their uncertainties provide the histoiy of plant operation. The model along with values of the model parameters can be used for troubleshooting, fault detection, design, and/or plant control. 

History The histoiy of a plant forms the basis for fault detection. Fault detection is a monitoring activity to identify deteriorating operations, such as deteriorating instrument readings, catalyst usage, and energy performance. The plant data form a database of historical performance that can be used to identify problems as they form. Monitoring of the measurements and estimated model parameters are typic fault-detection activities. 

The second classification is the physical model. Examples are the rigorous modiiles found in chemical-process simulators. In sequential modular simulators, distillation and kinetic reactors are two important examples. Compared to relational models, physical models purport to represent the ac tual material, energy, equilibrium, and rate processes present in the unit. They rarely, however, include any equipment constraints as part of the model. Despite their complexity, adjustable parameters oearing some relation to theoiy (e.g., tray efficiency) are required such that the output is properly related to the input and specifications. These modds provide more accurate predictions of output based on input and specifications. However, the interactions between the model parameters and database parameters compromise the relationships between input and output. The nonlinearities of equipment performance are not included and, consequently, significant extrapolations result in large errors. Despite their greater complexity, they should be considered to be approximate as well. 

Required Sensitivity This is difficult to establish a priori. It is important to recognize that no matter the sophistication, the model will not be an absolute representation of the unit. The confidence in the model is compromised by the parameter estimates that, in theoiy, represent a limitation in the equipment performance but actually embody a host of limitations. Three principal limitations affecting the accuracy of model parameters are ... 

Interaction between measurement error and model parameters... 

Interaction between model and model parameters Three examples are discussed. 

The first two examples show that the interaction of the model parameters and database parameters can lead to inaccurate estimates of the model parameters. Any use of the model outside the operating conditions (temperature, pressures, compositions, etc.) upon which the estimates are based will lead to errors in the extrapolation. These model parameters are effec tively no more than adjustable parameters such as those obtained in linear regression analysis. More comphcated models mav have more subtle interactions. Despite the parameter ties to theoiy, tliey embody not only the uncertainties in the plant data but also the uncertainties in the database. 

The third example shows how the uncertainties in plant measurements compromise the model parameter estimates. Minimal temperature differences, veiy low conversions, and hmited separations are all instances where errors in the measurements will have a greater impact on the parameter estimate. 

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