Modeling of the Process

Once the flowsheet structure has been defined, a simulation of the process can be carried out. A simulation is a mathematical model of the process which attempts to predict how the process would behave if it was constructed (see Fig. 1.1b). Having created a model of the process, we assume the flow rates, compositions, temperatures, and pressures of the feeds. The simulation model then predicts the flow rates, compositions, temperatures, and pressures of the products. It also allows the individual items of equipment in the process to be sized and predicts how much raw material is being used, how much energy is being consumed, etc. The performance of the design can then be evaluated. 

When an oil or gas field has just been discovered, the quality of the information available about the well stream may be sparse, and the amount of detail put into the process design should reflect this. However, early models of the process along with broad cost estimates are needed to progress, and both design detail and cost ranges narrow as projects develop through the feasibility study and field development planning phases (see Section 12.0 for a description of project phases). 

The solution adopted by us is the use of computer simulations of mathematical models of the process and the mock-up situations. Eventually, simulation techniques will become so accurate, that the mock-up step can be discarded. For the time being it is reasonable to use such models to generate corrections for smaller differences between mock-up and process. 

An analytical model of the process has been developed to expedite process improvements and to aid in scaling the reactor to larger capacities. The theoretical results compare favorably with the experimental data, thereby lending vahdity to the appHcation of the model to predicting directions for process improvement. The model can predict temperature and compositional changes within the reactor as functions of time, power, coal feed, gas flows, and reaction kinetics. It therefore can be used to project optimum residence time, reactor si2e, power level, gas and soHd flow rates, and the nature, composition, and position of the reactor quench stream. 

Some of the inherent advantages of the feedback control strategy are as follows regardless of the source or nature of the disturbance, the manipulated variable(s) adjusts to correct for the deviation from the setpoint when the deviation is detected the proper values of the manipulated variables are continually sought to balance the system by a trial-and-error approach no mathematical model of the process is required and the most often used feedback control algorithm (some form of proportional—integral—derivative control) is both robust and versatile. 

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. 

The Smith dead-time compensator is designed to aUow the controUer to be tuned as tightly as it would be if there were no dead time, without the concern for cycling and stabUity. Therefore, the controUer can exert more reactive control. The dead-time compensator utilizes a two-part model of the process, ie, Gp, which models the portion of the process without dead time, and exp — sTp,pj ), which models the dead time. As seen from Figure 18b, the feedback signal is composed of the sum of the model (without dead time) and the error in the overaU model Gpj exp — sTppj )), ie, C —. Using... 

As a reactant molecule from the fluid phase surrounding the particle enters the pore stmcture, it can either react on the surface or continue diffusing toward the center of the particle. A quantitative model of the process is developed by writing a differential equation for the conservation of mass of the reactant diffusing into the particle. At steady state, the rate of diffusion of the reactant into a shell of infinitesimal thickness minus the rate of diffusion out of the shell is equal to the rate of consumption of the reactant in the shell by chemical reaction. Solving the equation leads to a result that shows how the rate of the catalytic reaction is influenced by the interplay of the transport, which is characterized by the effective diffusion coefficient of the reactant in the pores, and the reaction, which is characterized by the first-order reaction rate constant. 

Once the objective and the constraints have been set, a mathematical model of the process can be subjected to a search strategy to find the optimum. Simple calculus is adequate for some problems, or Lagrange multipliers can be used for constrained extrema. When a Rill plant simulation can be made, various alternatives can be put through the computer. Such an operation is called jlowsheeting. A chapter is devoted to this topic by Edgar and Himmelblau Optimization of Chemical Processes, McGraw-HiU, 1988) where they list a number of commercially available software packages for this purpose, one of the first of which was Flowtran. 

A key feature of MFC is that future process behavior is predicted using a dynamic model and available measurements. The controller outputs are calculated so as to minimize the difference between the predicted process response and the desired response. At each sampling instant, the control calculations are repeated and the predictions updated based on current measurements. In typical industrial applications, the set point and target values for the MFC calculations are updated using on-hne optimization based on a steady-state model of the process. Constraints on the controlled and manipulated variables can be routinely included in both the MFC and optimization calculations. The extensive MFC literature includes survey articles (Garcia, Frett, and Morari, Automatica, 25, 335, 1989 Richalet, Automatica, 29, 1251, 1993) and books (Frett and Garcia, Fundamental Process Control, Butterworths, Stoneham, Massachusetts, 1988 Soeterboek, Predictive Control—A Unified Approach, Frentice Hall, Englewood Cliffs, New Jersey, 1991). 

A dynamic model of the process is used to predict the future outputs over a prediction horizon consisting of the next p samphng periods. 

Categories 2 and 3 comprise the model of the process or equipment category I is sometimes called the economic model. 

Once fundamental data have been obtained, the goal is to develop a mathematical model of the process and to utilize it to explore such possibilities as produc t selectivity, start-up and shut-down behavior, vessel configuration, temperature, pressure, and conversion profiles, and so on. 

Measurement Selection The identification of which measurements to make is an often overlooked aspect of plant-performance analysis. The end use of the data interpretation must be understood (i.e., the purpose for which the data, the parameters, or the resultant model will be used). For example, building a mathematical model of the process to explore other regions of operation is an end use. Another is to use the data to troubleshoot an operating problem. The level of data accuracy, the amount of data, and the sophistication of the interpretation depends upon the accuracy with which the result of the analysis needs to oe known. Daily measurements to a great extent and special plant measurements to a lesser extent are rarelv planned with the end use in mind. The result is typically too little data of too low accuracy or an inordinate amount with the resultant misuse in resources. 

In the material balance example, the matrix B contains the material balance coefficients for the component flows based on the implicit model of the process. These adjustments can be done by hand or by... 

MATHEMATICAL MODELING OF THE PROCESS TAKEN PLACE IN THE SOLID SUPPORT - SOLUTION , TYPE INDICATOR PIPES... 

In a continuous reaction process, the true residence time of the reaction partners in the reactor plays a major role. It is governed by the residence time distribution characteristic of the reactor, which gives information on backmixing (macromixing) of the throughput. The principal objectives of studies into the macrokinetics of a process are to estimate the coefficients of a mathematical model of the process and to validate the model for adequacy. For this purpose, a pilot plant should provide the following ... 

In general, the optimum conditions cannot be precisely attained in real reactors. Therefore, the selection of the reactor type is made to approximate the optimum conditions as closely as possible. For this purpose, mathematical models of the process in several different types of reactors are derived. The optimum condition for selected parameters (e.g., temperature profile) is then compared with those obtained from the mathematical expressions for different reactors. Consequently, selection is based on the reactor type that most closely approaches the optimum. 

An event tree is a model of the process response to an accident initiator. The initiators are... 

The frequency of an initiating event is usually based on industrial experience. If the process is new or rare, it may be estimated by a system model of the process steps (e.g., a fault tree) and using data from similar experience to give the probability of failure of the steps. Either of these estimates should consider the possibility of mitigating actions to prevent the hazard from having detrimental effects. 

An intuitive model of the process can be given. Consider the proton, with / = i then there are two states, characterized by m = +5 and m = —5. In the absence of an applied field, these states are equally populated. The states may be pictured as corresponding to opposite orientations of a tiny bar magnet, which is a crude way of visualizing the magnetic moment vector. Clearly in the absence of an applied field, the orientation of the moment should not affect the energy of the nucleus. 

Chapter 8 consists of the following in Sect. 8.2 the physical model of the process is described. The governing equations and conditions of the interface surface are considered in Sects. 8.3 and 8.4. In Sect. 8.5 we present the equations transformations. In Sect. 8.6 we display equations for the average parameters. The quasi-one-dimensional model is described in Sect. 8.7. Parameter distribution in characteristic zones of the heated capillary is considered in Sect. 8.8. The results of a parametrical study on flow in a heated capillary are presented in Sect. 8.9. 

As mentioned in Section 11.3, fluidized-bed reactors are difficult to scale. One approach is to build a cold-flow model of the process. This is a unit in which the solids are fluidized to simulate the proposed plant, but at ambient temperature and with plain air as the fluidizing gas. The objective is to determine the gas and solid flow patterns. Experiments using both adsorbed and nonadsorbed tracers can be used in this determination. The nonadsorbed tracer determines the gas-phase residence time using the methods of Chapter 15. The adsorbed tracer also measures time spent on the solid surface, from which the contact time distribution can be estimated. See Section 15.4.2. 

A process for the depolymerisation of Nylon 6 carpet fibre in the presenee of steam under medium pressure (800 to 1500 KpA, 100 to 200 psig) is described. The feasibility of the seheme was demonstrated using a small laboratory apparatus and the best run produced a 95% yield of crude eaprolaetam. The data obtained were used to construct a computer model of the process for both batch and continuous flow stirred reactors. 6 refs. 

Dr. G. A. LeBlanc of North Carolina State University is evaluating effects of potentially endocrine-disrupting chemicals, including endosulfan, on steroid hormone biotransformation/elimination processes in daphnids, fish, and mice, and is constructing models of the processes. The work is being funded by the U.S. Department of Agriculture. 

Based on the shapes of the responses to step changes in controller output, and reasoning from the physical configuration of the extruder barrel, a reduced order dynamic model of the process was postulated. One can think of the Topaz program as order 80 (the number of nodes in the finite element subdivision), and the reduced model of order 4 (the number of dynamic variables). The figure below illustrates the model. 

The key recognitive skill required to carry out the above tasks is the formation of a mental model of the process operations that fits the current facts about the process and enables the operators to correctly assess process behavior and predict the effects of possible control actions. Correct mental models of process operations have allowed operators to overcome the weakness of lost sensors and conflicting trends, even under the pressure of an emergency (Dvorak, 1987), whereas most of the operational mishandlings are due to an erroneous perception as to what is going on in the process (O Shima, 1983). 

A schematic one-dlmenslonal model of the process Is Illustrated In Fig, 8. Here It Is envisioned that the process 4B +L 3L+2B2 Is... 

Adamska-Rutkowska, D., 1992, An Identification of the Model of the Process for Oxidation of Benzene to Maleic Anhydride , Ph.D. Thesis, Warsaw University of Technology, Warsaw. 

Equation 2.1, the mathematical model of the process, is very general and it covers many cases, namely,... 

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