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The field of process control has evolved gradually over the years,
with emphasis on key aspects including designing and tuning of
controllers. This textbook covers fundamental concepts of basic and
multivariable process control, and important monitoring and
diagnosis techniques. It discusses topics including state-space
models, Laplace transform to convert state-space models to transfer
function models, linearity and linearization, inversion formulae,
conversion of output to time domain, stability analysis through
partial fraction expansion, and stability analysis using Routh
table and Nyquits plots. The text also covers basics of relative
gain array, multivariable controller design and model predictive
control. The text comprehensively covers minimum variable
controller (MVC) and minimum variance benchmark with the help of
solved examples for better understanding. Fundamentals of diagnosis
of control loop problems are also explained and explanations are
bolstered through solved examples. Pedagogical features including
solved problems and unsolved exercises are interspersed throughout
the text for better understanding. The textbook is primarily
written for senior undergraduate and graduate students in the field
of chemical engineering and biochemical engineering for a course on
process control. The textbook will be accompanied by teaching
resource such a collection of slides for the course material and a
includsolution manual for the instructors.
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