Process modelling and simulation

All process systems respond to various disturbances in different ways. Certain types of response are characteristic of specific types of process. Two of the most common personalities are those for first- and second-order systems. The single tank that was mathematically modelled in the previous section is an example of a first-order type of system. 

If a step input is applied to a capacity-dominated process such as a single tank, the output begins to change instantaneously but does not reach its steady-state value for a period of time. This is true of any process that is capacitive in nature. It takes approximately 5t for the output of the capacity process to reach its final, steady-state value. The time constant t is defined as the amount of time it takes the output of the system to reach 63.2 per cent of its steady-state value, t is a basic characteristic of capacity-dominated physical systems. The time constant x can be defined in electrical terms as the product of the resistance times the capacitance  

3 FUNDAMENTALS OF SINGLE INPUT-SINGLE OUTPUT SYSTEMS 

Higher-order responses are the result of multi-capacitance processes that contain vessels in series, fluid or mechanical components of a process that are subjected to accelerations causing inertial effects to become important, or tbe addition of controllers to a system. In a chemical plant, higher-order systems that result from a combination of capacities and controllers are very common. Typical examples are reactors in series, heat exchangers and distillation columns. In the case of distillation columns, when controllers are attached to the column, very high-order, nonlinear differential equations result when the system is mathematically modelled. Mechanical conqtonent time constant and natural frequencies are very small relative to the process time constants and frequencies, and, as such, the resultant effects are typically minor. 

In order to derive the system equation we first apply Newton s second law, which 

Chapter 4 presents models of different complexity for BREAD processes. For other types of models and underlying assumptions, the readers are directed to Cuille and Reklaitis (1986), Albet et al. (1991), Basualdo and Ruiz (1995), Wajge and Reklaitis (1999), Leversund et al. (1993). An example of BREAD process simulation from Greaves (2003) using the model of Mujtaba and Macchietto (1997) is presented in Chapter 4. 

Ethylene Oxide Water Ethylene Glycol Inverted 

Acetic Acid Methanol Methyl Acetate Water Conventional 

Acetic Acid Ethanol Ethyl Acetate Water Conventional 

Acetic Acid Propanol Propyl Acetate Water N.S. 

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Polymer processing modeling and simulation / Tim A. Osswald, Juan P. Hernandez-Oritz.- lsted. p. cm. 

In gratitude to Professor R.B. Bird, the teacher and the pioneer who laid the groundwork for polymer processing — modeling and simulation... 

J.Savkovic-Stevanovic,1995, Process Modelling and Simulation, Faculty of Technology and Metallurgy, Belgrade. 

The research works performed for several years within the PSE research department (LGC) on process modelling and simulation have led to the development of PrODHyS. This environment provides a library of classes dedicated to the dynamic hybrid simulation of processes. Based on object concepts, PrODHyS offers extensible and reusable software components allowing a rigorous and systematic modelling of processes. The primal contribution of these works consisted in determining and designing the foundation buildings classes. 

Zimmerman, W. B. J., Process Modelling and Simulation with Finite Element Methods. World... 

In die perspective of process synthesis, process control should be viewed am as a separate element in process desiga and optimization bas rather as a component of a coordinated approach. Therefore, the design and sequencing of separation processes me si consider the relationship that process control will have to the final process structure. This can be done only through process modeling and simulation,... 

Simprosys 3.0 provides a comprehensive and integrated but simple and easy tool not only for the calculation of humid gas properties of 14 solvent-gas systems (including combustion gases from any specified fossil fuel) but also for the process modeling and simulation of these 14 solvent-gas drying systems. 

Process modeling and simulation ate nevertheless extremely important tools in the design and evaluation of process control strategies for separation processes. There is a strong need, however, for better process mo ls for a variety of separations as well as process data with which to confirm tiiese models. Confidence in complex process models, especially those that can be used to study process dynamics, can come only from experimental verification of these models. This will require more sophisticated process sensors than those commonly available for temperature, pressure, pH, and differential pressure. Direct, reliable measurement of stream composition, viscosity, turbidity, conductivity, and so on is important not only for process model verification but also for actual process control applications. Other probes, which could be used to provide a better estimate of the state of the system, are needed to contribute to the understanding of the process in the same time frame as that of changes occurring in the process. 

There are still many obstacles to overcome before the use of process modeling, simulation, and control reaches the potential that many think it holds. One of the most interesting possibilities is the development of systems capable of performing on-line optimization functions. Althou on-line plant optimization is still in the future, optimization of subprocesses is already a possibility. This is true for both continuous and batch processes, and there are now many examples of how process modeling and simulation have enabled process control strategy and design to move beyond strictly performance considerations. 

Heat and Mass Transport Mechanistic Process Modeling and Simulation Considerations for Process Integration and Economic Viability... 

Osswald, T. A. Hernandez-Ortiz, J. P. 2006. Polymer processing modeling and simulation, Munich Cincinnati, Hanser Publishers. 

Hsiao, K.-T, Devillard, M. and Advani, S. G. (2004), Simulation based flow distribution network optimization for vacuum assisted resin transfer molding process . Modeling and Simulation in Materials Science and Engineering, 12(3), S175-S190. DOI 10.1088/0965-0393/12/3/S08. 

Osswald, T.A., Hemandez-Ortiz, J.P., 2006. Polymer Processing Modeling and Simulation. Hanser PubUsbers, Municb Cincinnati. 

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