Chemical Process Modelling and Computer Simulation by Amiya K. Jana

By Amiya K. Jana

This accomplished and carefully revised textual content, now in its moment variation, maintains to provide the basic options of the way mathematical versions of chemical methods are developed and exhibit their purposes to the simulation of 2 of the vitally important chemical engineering platforms: the chemical reactors and distillation structures.

The ebook offers an built-in therapy of approach description, mathematical modelling and dynamic simulation of real looking difficulties, utilizing the powerful technique version method and its simulation with effective numerical strategies. Theoretical historical past fabrics on task coefficient versions, equation of nation versions, response kinetics, and numerical resolution innovations wanted for the improvement of mathematical versions also are addressed within the e-book.

the themes of dialogue regarding tanks, warmth exchangers, chemical reactors (both non-stop and batch), biochemical reactors (continuous and fed-batch), distillation columns (continuous and batch), equilibrium flash vaporizer, and refinery debutanizer column comprise a number of worked-out examples and case stories to educate scholars how chemical strategies could be measured and monitored utilizing desktop programming.

the hot version comprises extra chapters Reactive Distillation Column and Vaporizing Exchangers with a view to additional increase the textual content.

This booklet is designed for senior point undergraduate and first-year postgraduate point classes in Chemical technique Modelling and Simulation . The e-book may also be invaluable for college students of petrochemical engineering, biotechnology, and biochemical engineering. it could actually function a advisor for examine scientists and training engineers in addition.

His components of analysis comprise nonlinear keep an eye on, modelling and simulation, and effort engineering.

desk Of Contents:

1. advent to Modelling and Simulation 2. Numerical equipment


three. Batch Reactor four. non-stop Stirred Tank Reactor five. Bioreactor


6. Compartmental Distillation version 7. excellent Binary Distillation Column eight. job Coefficient types nine. Binary Batch Distillation Column 10. Binary non-stop Distillation Column eleven. Multicomponent Batch Distillation Column 12. Equilibrium Flash Vaporization thirteen. Equation of country versions 14. Refinery Debutanizer Column 15. Reactive Distillation Column

half IV-VAPORIZING tactics

sixteen. Vaporizing Exchangers Index

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Additional info for Chemical Process Modelling and Computer Simulation

Example text

Ca = Concentration of species A ! Cb = Concentration of species B ! Fc = Coolant flow rate ! Kc = PI controller gain ! T = Reactor temperature ! TAUI = Integral time constant ! Td = Desired (set-point) temperature ! Ts = Steam temperature ! U = Parametric variable (manipulated variable) ! Uc = Overall heat transfer coefficient of cooling coil PROGRAM BATCH_REACTOR IMPLICIT NONE ! dat”) ! ———————————————Starting of main loop——————————————————! ———————————————————————————————————————————————————————!

Realistic mathematical process models in chemical engineering field usually consist of ordinary differential equations (ODEs) coupled with nonlinear algebraic equations. The ODEs are generally derived making mass and energy balance, and the supporting algebraic equations usually correlate the parameters with the process variables. Let us take an example of a distillation column. An ODE is formed if a mass balance is made around a tray, whereas an algebraic equation correlates the vapour–liquid equilibrium coefficient with the phase composition, temperature and pressure.

Although this approach is similar to the Bisection method, instead of using the average of the interval endpoints to select the next root estimate, it is required to construct a secant line and find its x-intercept as the next root estimate. 1). Let xk–1 and xk are two approximations to the root. Now construct a straight line through the points (xk–1, fk–1) and (xk, fk), where fk–1 = f(xk–1) and fk = f(xk). Note that this line is a secant (or chord) of the function f. 2) Now take the point of intersection of the straight line with the x-axis as the next approximation to the root.

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