Process reference models

The SCOR-model is a process reference model which is defined at different process levels. Besides the definition of the process, for each level indicators are proposed to allow to assess the performance of the process. In order to improve the process, best practices for the process elements are described which are based on the experience of the council members. 

Curran T, Keller G (1998) SAP R/3 Business Blueprint understanding the business process reference model, Prentice Hall, Upper Saddle River Datta S, Betts B, Dinning M, Erhun F, Gibbs T, Keskinocak P, Li H, Li M, Samuels M (2004) Adaptive Value Networks, In Chang YS, Makatsoris H, Richards H (eds) Evolution of Supply Chain Management. Kluwer Academic Publishers, Norwell, MA, pp 3-68... 

Still based on (Stewart 1997), to assist companies in increasing the effectiveness of their supply chain, and to support the move to process-based management, two consulting firms - PRTM and Advanced Manufacturing Research (AMR) - set out to consolidate within a process reference model their experience along with a group... 

The SCOR process reference model was established by the Supply Chain Council (SCC) in 1996 for standardization purposes. The model describes, characterizes, and evaluates acomplex management process. Such a model builds on the concepts of business process reengineering (BPR), benchmarking, and process measurement by integrating these techniques into a cross-functional framework. 

Figure 2. Integrated concepts for process reference model (Source Supply Chain Council, 2003)...

Process reference models integrate the well-known concepts of business process reengineering, benchmarking, and process measurement into a cross-functional framework. 

Supply Chain Operations Reference model (SCOR ) It is a process reference model that has been developed and endorsed by the Supply Chain Council as the cross-industry standard diagnostic tool for supply chain management. 

Identify the ways a reconstituted supply chain should support the strategy. Set a high level specification, or vision, for the new supply chain. Divide the supply chain into processes. The Supply-Chain Council offers a standard process reference model, described in further detail in Chapter 23. Examples... 

ISO 12207 Processes in software lifecycles offers a process reference model with the following categories ... 

Work continues on improving the efficiency of this process, such as for freeing the alkan olamine from heat-stable salts that can form (125). Formulations have been developed which inhibit degradation of mono- and diethanolamine in processing (126). Models (127), computer programs (128), and kinetics and enthalpies (129—136) have been developed to help determine equiUbria of the acid gas—alkanolamine—water system. Additional references relate to the use of tertiary alkan olamines, such as triethanolamine, for gas conditioning (137—139). 

The present book is devoted to both the experimentally tested micro reactors and micro reaction systems described in current scientific literature as well as the corresponding processes. It will become apparent that many micro reactors at first sight simply consist of a multitude of parallel channels. However, a closer look reveals that the details of fluid dynamics or heat and mass transfer often determine their performance. For this reason, besides the description of the equipment and processes referred to above, this book contains a separate chapter on modeling and simulation of transport phenomena in micro reactors. 

To address this situation, a data interpretation system was constructed to monitor and detect changes in the second stage that will significantly affect the product quality. It is here that critical properties are imparted to the process material. Intuitively, if the second stage can be monitored to anticipate shifts in normal process operation or to detect equipment failure, then corrective action can be taken to minimize these effects on the final product. One of the limitations of this approach is that disturbances that may affect the final product may not manifest themselves in the variables used to develop the reference model. The converse is also true—that disturbances in the monitored variables may not affect the final product. However, faced with few choices, the use of a reference model using the process data is a rational approach to monitor and to detect unusual process behavior, to improve process understanding, and to maintain continuous operation. 

To construct the reference model, the interpretation system required routine process data collected over a period of several months. Cross-validation was applied to detect and remove outliers. Only data corresponding to normal process operations (that is, when top-grade product is made) were used in the model development. As stated earlier, the system ultimately involved two analysis approaches, both reduced-order models that capture dominant directions of variability in the data. A PLS analysis using two loadings explained about 60% of the variance in the measurements. A subsequent PCA analysis on the residuals showed that five principal components explain 90% of the residual variability. 

As in example 1, the explained variance (the total variance minus the residual variance) is calculated by comparing the true process data with estimates computed from a reference model. This explained variance can be computed as a function of the batch number, time, or variable number. A large explained variance indicates that the variability in the data is captured by the reference model and that correlations exist among the variables. The explained variance as a function of time can be very useful in differentiating among phenomena that occur in different stages of the process operations. 

System Representation Errors. System representation errors refer to differences in the processes and the time and space scales represented in the model, versus those that determine the response of the natural system. In essence, these errors are the major ones of concern when one asks "How good is the model ". Whenever comparing model output with observed data in an attempt to evaluate model capabilities, the analyst must have an understanding of the major natural processes, and human impacts, that influence the observed data. Differences between model output and observed data can then be analyzed in light of the limitations of the model algorithm used to represent a particularly critical process, and to insure that all such critical processes are modeled to some appropriate level of detail. For example, a... 

FIGURE 5-12 A processive clamp model for the ATPase cycle of the ABC transporter Mdllp. ATP binding (step I) on NBD domains of both monomers induces formation of the dimer (step 2). After ATP hydrolysis by the first NBD (step 3), either the P, is released first (step 4), followed by hydrolysis of the second ATP step 5) and release of the second P, step 8), or the second ATP is hydrolyzed first step 6) and then both phosphates are set free steps 7 and 8). After both ATPs are hydrolyzed to ADP and both phosphates are released, the dimeric complex dissociates step 9) and ADP step 10) is released. The hydrolysis cycle can then start again with ATP binding. (With permission from Fig. 7 of reference [34].)... 

The Supply Chain Operations Reference (SCOR ) model is a reference model for supply chain planning and operations processes as well as performance management developed by the cross-industry organization Supply Chain Council (SCC) started in 1996 (Supply Chain Council 2006 reviewed by Siirie/Wagner 2004, pp. 41-49). The SCOR model structures... 

To develop a distributed modeling system, it is necessary to identify the primitive components essential to the fundamental operation of the system and its various appended and programmable functions, a process referred to as kernel identification. Studying such systems provides much of the basis for acquiring insight into what structure and function a kernel should possess (9). 

Process models are unfortunately often oversold and improperly used. Simulations, by definition, are not the actual process. To model the process, assumptions must be made about the process that may later prove to be incorrect. Further, there may be variables in the material or processing equipment that are not included in the model. This is especially true of complex processes. It is important not to confuse virtual reality with reality. The claim is often made that the model can optimize a cure cycle. The complex sets of differential equations in these models cannot be inverted to optimize the multiple properties they predict. It is the intelligent use of models by an experimenter or an optimizing routine that finds a best case among the ones tried. As a consequence, the literature is full of references to the development of process models, but examples of their industrial use in complex batch processes are not common. 

This is employed when the process is not well-known. The Model Reference Adaptive Controller contains a reference model to which the command signal or set point change is applied as well as to the process itself (Fig. 7.98)<4 ). The output of the reference model is postulated as the desired controlled process output and this is compared with the actual process output. The difference (or error) e , between the two outputs is used to adjust the controller parameters so as to minimise the relevant integral criterion. For example, if the ISE criterion is employed then the quantity... 

An early approach to music processing, referred to as additive synthesis [Moorer, 1977], used the sinusoidal model of a quasi-periodic music note... 

General References Seborg, Edgar, and Mellichamp, Process Dynamics and Control, Wiley, New York, 2004 Marlin, Process Control, McGraw-Hill, New York, 2000 Ogunnaike and Ray, Process Dynamics Modeling and Control, Oxford University Press, New York, 1994 Smith and Corripio, Principles and Practices of Automatic Process Control, Wiley, New York, 1997. 

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