## Contents of Modeling and simulation of chemical process systems by Ghasem, Nayef pdf

- Introduction
- Lumped Parameter Systems
- Theory and Applications of Distributed Systems
- Computational Fluid Dynamics
- Mass Transport of Distributed Systems
- Heat Transfer Distributed Parameter Systems
- Case Studies
- Computing Solutions of Ordinary Differential Equations
- Higher-Order Differential Equations

## Modeling and simulation of chemical process systems by Ghasem, Nayef PDF

Modeling and simulation of chemical process systems refer to translating the actual process behavior into mathematical expressions (process modeling) and solving that model numerically with the help of a computer (simulation). Modeling and simulation support analysis, experimentation, and training and can facilitate understanding the behavior of the system. Modeling and simulation are valuable tools; it is safer and cheaper to perform tests on the model using computer simulations rather than carrying out repetitive experimentations and observations on the real system.

Modeling and Simulation of Chemical Process Systems covers modeling and simulation of both lumped parameter systems and distributed parameter systems. Lumped parameter systems include processes where the parameters of the system, such as temperature and concentration, are uniform throughout the process unit. The process model equations of the lumped parameters system originate from the transient material and energy balance equations. Lumped parameter systems, in general, use a single ordinary differential equation or a set of ordinary differential equations; in certain cases these equations can be simplified and solved manually. The students are also encouraged to solve the system model equations by using the MATLAB®/Simulink® software package. Students will be able to compare analytical and simulated results, which will give them self-confidence in their work.

This text also covers modeling and simulation of distributed parameter systems. The state variables of these systems, such as temperature, concentration, and momentum, generally have spatial variation. For these systems, students will learn not to start from shell momentum balance but from equations of change of heat, mass, and momentum. The equations to be simplified are based on the question information, and in general the resultant equations are partial differential equations. The generated simplified partial differential equations are solved by the COMSOL Multiphysics 5.3a software package, an effective tool for solving partial differential equations using the fine element method.

This textbook uses the transport phenomena approach to develop mathematical models of chemical process systems. Mathematical models contain equations that include known and unknown variables to be determined. Known variables are usually called parameters, and unknown variables are called decision variables. In this approach, chemical engineers and scientists start from the general three-dimensional conservation equations of mass, energy, and momentum. With the physical description of the system and with the assumptions based on the objectives of modeling, one sets several terms in general balance equations equal to zero to obtain the model equations of the system under consideration. Appropriate initial and boundary conditions are also to be stated. Once the process model is developed, it is essential to obtain the solution of the model equations to study the effect of system parameters and operating conditions on the performance of chemical process systems.

Students will practice using COMSOL and MATLAB software to obtain the solution of model equations. This approach and practice are also essential because it is frequently impossible to solve model equations analytically. Students should learn to use both numerical methods and available software tools. Numerical computational methods are also explained in the last two chapters of the book.

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