Model-Based Approaches

Borodina Y, Rudik A, Filimonov D, Kharchevnikova N, Dmitriev A, Blinova V, et al. A new statistical approach to predicting aromatic hydroxylation sites. Comparison with model-based approaches. J Chem Inf Comput Sci 2004 44 1998-2009. 

With the view that a KBS interpreter is a method for mapping from input data in the form of intermediate symbolic state descriptions to labels of interest, four families of approaches are described here, each offering inference mechanisms and related knowledge representations that can be used to solve interpretation problems namely, model-based approaches, digraphs, fault trees, and tables. These methods have been heavily used... 

Catino, C. A., and Ungar, L. H., Model-based approach to automated hazard identification of chemical plants, AIChE J. 41(1), 97-109 (1995). 

The parsing of the transporter sequences into the TM domains shown in Fig. 1A represents the consensus result of three different methods. Average hydrophobicity was calculated with ProperTM using different window sizes and the Kyte and Doolittle scale (7). TMHMM, a hidden Markov model-based approach (8), and PHDHTM, a profile-based neural network method (9), were then utilized to refine the predictions. 

A model-based approach for hydrogen-infrastructure analysis - the MOREHyS model... 

The case study from chapter 7 is concerned with the design and improvement of chemically-active ship bottom paints known as antifouling paints. A hybrid experiment-model based approach is employed here. Experiments and use of expert knowledge are employed to identify product alternatives, whose evaluation in terms of performance as a marine biofouling protector is verified through a model-based approach. 

In analytical redundancy schemes, the resulting difference generated from the consistency checking of different variables is called a residual signal. The residual should be by convention zero-valued when the system is normal and should diverge from zero when a fault occurs. This zero and non-zero property of the residual is used to determine whether or not a fault has occurred. Analytical redundancy makes use of a quantitative model of the monitored process and is therefore often referred to as the model-based approach to fault diagnosis. 

The models used can be either fixed or adaptive and parametric or non-parametric models. These methods have different performances depending on the kind of fault to be treated i.e., additive or multiplicative faults). Analytical model-based approaches require knowledge to be expressed in terms of input-output models or first principles quantitative models based on mass and energy balance equations. These methodologies give a consistent base to perform fault detection and isolation. The cost of these advantages relies on the modeling and computational efforts and on the restriction that one places on the class of acceptable models. 

Several approaches have been proposed to deal with population stratification by using unlinked markers. In general, these methods fall into two categories model-based and non-model-based approaches. We briefly describe and explain three major methods and discuss their advantages as well as disadvantages. The basic understanding of these model-based and non-model-based methods is necessary and helpful when users apply them to analyze genetic data. 

Compared with non-model-based methods, model-based approaches work better when no adjustment is needed or negative confounding exists. However, success of the algorithm depends on accurate subgroup classification. If random markers are used, then a large number of markers are required for the subgroup classification. 

Figure 15.1 Data-based versus model-based approach.

To exhaustively test all possibilities is very expensive, because for every 10,000-mile test the engine must be disassembled to measure the IVD. Every single data point costs about 8,000 to 10,000 and a considerable amount of time. Therefore, we were asked to develop a model-based approach to this problem in 1995. 

If the assumptions made above are not valid, and/or information about the rate constants of the investigated reactions is required, model-based approaches have to be used. Most of the model-based measurements of the calorimetric signal are based on the assumption that the reaction occurs in one single step of nth order with only one rate-limiting component concentration in the simplest case, this would be pseudo-first-order kinetics with all components except one in excess. The reaction must be carried out in batch mode (Vr = constant) in order to simplify the determination, and the general reaction model can, therefore, be written as Equation 8.14 with component A being rate limiting ... 

Haddad, S., M. Beliveau, R. Tardif, and K. Krishnan. A PBPK modeling-based approach to account for interactions in the health risk assessment of chemical mixtures. Toxicol. Sci. 63 125-131, 2001. 

Kenig EY, Gorak A. A film model based approach for simulation of multicomponent reactive separation. Chem Eng Process 1995 34 97-103. 

In the case study, the adaptive model-based approach is designed on the basis of a reduced model of the phenol-formaldehyde reaction introduced in the Chap. 2. Noticeably, the results show that the model-based control scheme achieves very good performance even when a strongly simplified mathematical model of the reactive system is adopted for the design. 

Early approaches to fault diagnosis were often based on the so-called physical redundancy [11], i.e., the duplication of sensors, actuators, computers, and softwares to measure and/or control a variable. Typically, a voting scheme is applied to the redundant system to detect and isolate a fault. The physical redundant methods are very reliable, but they need extra equipment and extra maintenance costs. Thus, in the last years, researchers focused their attention on techniques not requiring extra equipment. These techniques can be classified into two general categories, model-free data-driven approaches and model-based approaches. 

Model-based approaches to fault diagnosis can be divided into qualitative methods [51] and quantitative methods [35, 36],... 

A similar, but more significant, effect is expected for multiphase reactors. A general conclusion may be that the more the reactors are complex, the more advantageous is the use of model-based approaches, when compared to more empirical ones. This is true from a practical point of view as well, since the increasing availability of fast and low-price computing devices allows improving the complexity of the models it is deemed that the limits to this approach depend essentially on the quality of the experimental data available for identification purposes. 

In the fifth chapter, a general overview of temperature control for batch reactors is presented the focus is on model-based control approaches, with a special emphasis on adaptive control techniques. Finally, the sixth chapter provides the reader with an overview of the fundamental problems of fault diagnosis for dynamical systems, with a special emphasis on model-based techniques (i.e., based on the so-called analytical redundancy approach) for nonlinear systems then, a model-based approach to fault diagnosis for chemical batch reactors is derived in detail, where both sensors and actuators failures are taken into account. 

This chapter describes in a step-by-step manner a generic strategy that we have been using for the development of various industrial processes. The chapter begins with a discussion of the overall workflow for process development, followed by a description of the way in which a process is synthesized, which relies heavily on the use of phase diagrams. The deviation from equilibrium behavior is accounted for using a model-based approach. This is illustrated with an example on the asymmetric transformation of an enantiomer. 

Candy, J., Signal Processing. The Model-based Approach, McGraw-Hill Series in Electrical Engineering, McGraw-Hill, Singapore, 2nd ed., 1987. 

Figure 16 Comparison of the trace-element composition of bulk continental crust from seismological and model-based approaches. All data normalized to the new composition given here (Table 10, R G ). Gray shading depicts 30% variation from Rudnick and Gao composition (this work), (a) Transition metals, (b) high-field strength elements, (c) alkali and alkaline earth metals, and (d) REEs, (e) actinides and heavy metals, and (f) siderophile and...

To assess the magnitude of fluxes of reactive chlorine from sea salt aerosol, Erickson et al. (1999) used a general circulation-model-based approach to calculate the release of HCl by acid displacement and formation of CINO2 by surface reaction of N2O5 from sea salt aerosol in different size classes under different ambient conditions. They found net HCl fluxes from... 

Perhaps the major bottleneck in modifier- and direct-input-adaptation schemes lies in the estimation of this gradient information. The finite-difference scheme used in the original ISOPE paper [19] is known to be inefficient for large-scale, slow and noisy processes. Hence, alternative techniques have been developed, which can be classified as either model-based approaches or perturbation-based approaches. 

Model-based approaches allow fast derivative computation by relying on a process model, yet only approximate derivatives are obtained. In self-optimizing control [12,21], the idea is to use a plant model to select linear combinations of outputs, the tracking of which results in optimal performance, also in the presence of uncertainty in other words, these linear combinations of outputs approximate the process derivatives. Also, a way of calculating the gradient based on the theory of neighbouring extremals has been presented in [13] however, an important limitation of this approach is that it provides only a first-order approximation and that the accuracy of the derivatives depends strongly on the reliability of the plant model. 

State feedback control is commonly used in control systems, due to its simple structure and powerful functions. Data-driven methods such as neural networks are useful only for situations with fully measured state variables. For this system in which state variables are not measurable and measurement function is nonlinear, we are dependant on system model for state estimation. On the other hand, as shown in figure 2, in open-loop situations, system has limit cycle behavior and measurements do not give any information of system dynamics. Therefore, we use model-based approach. 

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