User Models

When the design engineer needs to specify a unit operation that is not represented by a library model and cannot be approximated by a simple model such as a component splitter or a combination of library models, then it is necessary to construct a user model. All of the commercial simulators allow the user to build add-in models of varying sophistication. 

Models that require no internal iteration are easily coded as spreadsheets. Most of the simulators offer some degree of spreadsheet capability, ranging from simple calculation blocks to full Microsoft Excel functionality. 

For more sophisticated spreadsheet models, Aspen Plus allows the user to link a spreadsheet to a simulation via a user model known as a USER block. The designer can create a new spreadsheet or customize an existing spreadsheet to interact with an Aspen Plus simulation. The USER block is much easier to manipulate when handling large amounts of input and output data, such as streams with many components or unit operations that involve multiple streams. The procedure for setting up a USER MS Excel model is more complex than using a calculator block but avoids having to 

Models that require internal convergence are best written as subroutines rather than spreadsheets, as more efficient solution algorithms can be used. Most user subroutines are written in FORTRAN or Visual Basic, though some of the simulators allow other programming languages to be used. 

It is generally a good practice to compile and test a user model in a simplified flowsheet or as a standalone program before adding it to a complex flowsheet with recycles. It is also a good practice to check the model carefully over a wide range of input values, or else constrain the inputs to ranges where the model is valid. 

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The selection of the descriptors can happen in a forward or backward manner. A model created using the forward method starts with one descriptor followed by the addition of descriptors to the model until the model meets the specifications of the user. Models created in the backward method start with all the possible descriptors descriptors are taken away as they are deemed unnecessary. It is safe to assume that most QSAR models are created using the forward method due to the sheer number of descriptors and the desire for only a few in the model. Models can be pruned using the backward method specifically, once the model is created, the user wants to reduce the number of descriptors yet keep the same level of validity for the model. 

Heat Exchangers Columns Reactor Premiretihahgrtrs j MampUalois ] Solids ] User Models... 

User models User, User , UserS User Unit Op... 

Detailed models of commercial reactors are usually written as user models. These are described in Section 4.6. 

The commercial simulation programs were originally developed mainly for petrochemical applications, and none of them has a complete set of solids-handling operations. Although models for filters, crystallizers, decanters, and cyclones are present in most of the simulators, the designer may have to add user models for operations such as... 

Figure D. 1 shows the opening screen of FEMLAB. Look at the options across the top. The New tab is what is shown. If you click the Model Library tab, you get a menu of solved examples. These are ideal to investigate the program. The User Models tab provides a place you can put your examples, if you wish. Settings lets you choose a white or black background.

The use of expert systems for both on-line and off-line risk analysis and decision support becomes much more complicated in an international environment. Human input and output interpretation becomes a much more challenging exercise. The user model upon which the system depends most certainly will have to account for both linguistic and cultural factors if the system is to operate reliably and effectively. Multicultural human factors must certainly be addressed if we are to succeed in international trade and multinational enterprise. 

The quality of fit of these USER models is excellent and the error is comparable to the uncertainties in experimental methods of determining solubility, and has been claimed to have "reached the level of exhaustive fit" ( . 

Figure 2.11 presents the concept of an open integrated system. The simulation environment could come from the Vendor A, to which in-house unit operations can be coupled. One of the user-model unit calls the library of the vendor B, which is linked to a thermo-server supplied by the vendor C, who in turn could consider an equation-of-state model from the supplier D. The vendor E might supply a special solver, while the database with information about physical properties could come from the vendor F, etc. It is clear that the PMC s should be provided with compatible plug-and-play interfaces. This can be done nowadays at best with an object-oriented technology. 

Developed user models that are relevant, accurate, adaptable to changes in user behavior, and generalizable... 

AUen, R. B. (1997), Mental Models and User Models, in Handbook of Human-Computer Interaction, 2nd Ed., M. Helander, T. K. Landauer, and R V. Prabhu, Eds., North-HoUand, Amsterdtun, pp. 49-63. 

Floudas, C.A. (2000) Deterministic Global Optimization - Theory, Methods and Applications, Kluwer Academic Publishers, DordrechtFontalvo, J. (2014) Using user models in MATLAB within the aspen plus interface with an excel link. Ingenieria EInvestigacion, 34 (2), 39-43. 

Such systems require a user model that explains the user behavior from various aspects of cognitive processes such as awareness, memory, user knowledge and experience, context recognition, planning, intention formation, and even consciousness. User models can be used to predict users cognitive processes which in turn can be used to better support them (Forsythe et al, 2006, Haikonen, 2003). 

Taking into account the recent and spectacular advances in informatic instruments which permit their full use by non-specialists in informatics by way of a more transparent and more convivial access, and basing ourselves on the premise that these instruments were essentially based on concerns which were intuitive rather than experimentally tested on the user model, we believe that the effective improvement of their mediatic conception currently requires, above all, studies centered on the natural and effective ways of using them. 

With licenses organized bythe concurrent user model, only active user accounts are counted, which are concurrently working with the CDS. By this model, the same licenses could first be used by a site in, for example, Japan and later on in the United States due to the time shift. 

Using licenses according to the named user model, every single active account in the system is counted, no matter if this user has currently registered in the systems or not. Costs for licenses are saved by keeping unused licenses centrally. For example, each single site does not need to keep three spare-licenses, but centrally five licenses are kept that can be offered to any site, if requested. 

As a consequence, FFSs researchers, should try to incorporate into their work the results from natural language understanding [1], man-machine communication [17] and psychological theories of belief [20]. Within this framework, user modelling will be equivalent to belief revision. 

Self, J., Knowledge, Belief and User Modelling, in Artificial Intelligence III methodology, systems, applications, O Shea, T. Sgurev, V. (eds.), North-Holland, 1988. 

Dr. Marlene Jones is a senior researcher in Artificial Intelligence in the Alberta Research Council s Department of Advanced Technologies and an adjunct professor in the Department of Computational Science at the University of Saskatchewan. Prior to joining the Alberta Research Council in January 1987, Dr. Jones was a tenured Associate Professor at the University of Waterloo. Her qualifications include a PhD (Computer Science) from the University of Toronto and an MEd (location of Exceptional Children) from the University of Saskatchewan. Her research experience expands mote than a decade, with the last 8 years focussed upon research in the area of AI and Education. Dr Jones recent research projects include the development of expert systems for educational diagnosis, expert environments for curriculum and course development, and user modelling. 

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