Process models information flow

It is important to note that none of the flows (organization, function, output, and information flow, respectively) illustrated above is capable of completely modeling the entire business process. We must therefore combine all these perspectives. To this end, one of the views should be selected as a foundation and then be integrated into the others. The function view is closest to the definition of a business process and is therefore typically used as a starting point. However, in the context of object-oriented enterprise modeling information, flows can serve as a starting point as well. 

The feedforward control strategy (Fig. lb) addresses the disadvantages of the feedback control strategy. The feedforward control strategy measures the disturbance before it affects the output of the process. A model of the process determines the adjustment ia the manipulated variables(s) to compensate for the disturbance. The information flow is therefore forward from the disturbances, before the process is affected, to the manipulated variable iaputs. 

Human Information-Processing A view of the human operator as an information-processing system. Information-processing models are convention-aUy expressed in terms of diagrams which indicate the flow of information throu stages such as perception, decision-making, and action. 

To address these challenges, chemical engineers will need state-of-the-art analytical instruments, particularly those that can provide information about microstmctures for sizes down to atomic dimensions, surface properties in the presence of bulk fluids, and dynamic processes with time constants of less than a nanosecond. It will also be essential that chemical engineers become familiar with modem theoretical concepts of surface physics and chemistry, colloid physical chemistry, and rheology, particrrlarly as it apphes to free surface flow and flow near solid bormdaries. The application of theoretical concepts to rmderstanding the factors controlling surface properties and the evaluation of complex process models will require access to supercomputers. 

Contextual design is a flexible software design approach that collects multiple customer-centered techniques into an integrated design process [7]. The approach is centered around contextual inquiry sessions in which detailed information is gathered about the way individual people work and use systems and the associated information flow. The data from each contextual inquiry session are used to create sequence models that map the exact workflow in a session along with any information breakdowns, flow models that detail the flow of information between parties and systems (much akin to but less formal... 

In production and logistics we find some typical objects Products, processes, BOMs, work flows, resources, shift models, lockups, departments/business units, locations, demands (anonymous, orders). Most of these objects are discrete in nature, for example orders and batches are typical discrete objects. All these objects can be used to group and attach information. Objects which are not discrete may be approximated by discrete quants. 

The solution developed (see Figure 5.5) considers simultaneously, and in an optimal way, the most important aspects affecting the copper production. In order to cover the process itself and the necessary information and decision flow, the solution builds on a valid and robust process model that captures the main chemical reactions and is able to link the variable material amounts with predicted processing times. The main input data comprises ... 

For aerobic gravity sewers, procedure 4 is the ultimate calibration of the sewer process model. This is based on procedures 1 to 3 using information from upstream and downstream wastewater samples and by including local sewer systems and flow characteristics, temperature and DO concentration values of the wastewater in the sewer. Example 7.2 outlines the results of calibration and validation performed on a 5 km intercepting sewer line. 

Decomposition leads to a rearrangement of the process equations from their flow chart sequence to a natural sequence based on the information flow among the equations. The ultimate goal is to set up an iterative scheme in which each equation is solved for a single variable (by some appropriate root identification method), and where values of unknown variables that must be assumed are checked cyclically. The greatest reduction in the number of iterates that must be assumed, and therefore the greatest reduction in computer storage and time requirements, takes place for those systems of process equations in which the number of variables per equation is small compared to the total number of variables in the system. Clearly, when each of the system equations contains every process variable, no effective decomposition can take place. Fortunately, most models used in the process industries are of such a character that extensive decomposition can be effected. 

We first describe in Section II how the information flow takes place in process models, give a compact method of representation of the system of equations, and point out the correspondence between a system of equations and a linear diagraph. In Section III, methods for finding an output set... 

The older modular simulation mode, on the other hand, is more common in commerical applications. Here process equations are organized within their particular unit operation. Solution methods that apply to a particular unit operation solve the unit model and pass the resulting stream information to the next unit. Thus, the unit operation represents a procedure or module in the overall flowsheet calculation. These calculations continue from unit to unit, with recycle streams in the process updated and converged with new unit information. Consequently, the flow of information in the simulation systems is often analogous to the flow of material in the actual process. Unlike equation-oriented simulators, modular simulators solve smaller sets of equations, and the solution procedure can be tailored for the particular unit operation. However, because the equations are embedded within procedures, it becomes difficult to provide problem specifications where the information flow does not parallel that of the flowsheet. The earliest modular simulators (the sequential modular type) accommodated these specifications, as well as complex recycle loops, through inefficient iterative procedures. The more recent simultaneous modular simulators now have efficient convergence capabilities for handling multiple recycles and nonconventional problem specifications in a coordinated manner. 

In order for a process to be controllable by machine, it must represented by a mathematical model. Ideally, each element of a dynamic process, for example, a reflux drum or an individual tray of a fractionator, is represented by differential equations based on material and energy balances, transfer rates, stage efficiencies, phase equilibrium relations, etc., as well as the parameters of sensing devices, control valves, and control instruments. The process as a whole then is equivalent to a system of ordinary and partial differential equations involving certain independent and dependent variables. When the values of the independent variables are specified or measured, corresponding values of the others are found by computation, and the information is transmitted to the control instruments. For example, if the temperature, composition, and flow rate of the feed to a fractionator are perturbed, the computer will determine the other flows and the heat balance required to maintain constant overhead purity. Economic factors also can be incorporated in process models then the computer can be made to optimize the operation continually. 

Figure 2-1 presents a model of the clairvoyance process. The rectangular blocks (also used in subsequent figures) indicate processes or mechanisms of some sort by which information is transformed or used. The arrows indicate the direction of information flow within the model. We will look at this first model in considerable detail because many points relevant to it will also apply to other models. 

Like our other models of psi processes, the model of telepathy has been chosen to fit in with commonsense assumptions about the nature of the universe, particularly about the identity of brain and nervous system functions and mental processes. And like the other models, this may not reflect reality so much as it reflects our attachment to our conceptions. Thus, the information-flow route that seems most likely (from external target stimulus via the sense organs to the brain to the transmitting process to the percipient, possibly with representation in the conscious or unconscious mind) may not be the case. The information-flow... 

The interconnections between the unit modules may represent information flow as well as material and energy flow. In the mathematical representation of the plant, the interconnection equations are the material and energy balance flows between model subsystems. Equations for models such as mixing, reaction, heat exchange, and so on, must also be listed so that they can be entered into the computer code used to solve the equation. Table 5.1 lists the common type of equations that might be used for a single subsystem. In general, similar process units repeatedly occur in a plant and can be represented by the same set of equations, which differ only in the names of variables, the number of terms in the summations, and the values of any coefficients in the equations. 

I have made an attempt to provide sufficient information to understand and to define the specific role of computational flow modeling in reactor engineering applications. Discussions on the main features of reactor engineering, computational flow modeling and their interrelationship will help to select appropriate models, and to apply these computational models to link reactor hardware to reactor performance. Mathematical modeling of flow processes (including turbulent flows, multiphase flows and reactive flows) and corresponding numerical methods to solve these model... 

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