Statistics for Chemical and Process Engineers: A Modern Approach
Autor Yuri A.W. Shardten Limba Engleză Hardback – 27 oct 2015
This book shows the reader how to develop and test models, design experiments and analyse data in ways easily applicable through readily available software tools like MS Excel® and MATLAB®. Generalized methods that can be applied irrespective of the tool at hand are a key feature of the text.
The reader is given a detailed framework for statistical procedures covering:
· data visualization;
· probability;
· linear and nonlinear regression;
· experimental design (including factorial and fractional factorial designs); and
· dynamic process identification.
Main concepts are illustrated with chemical- and process-engineering-relevant examples that can also serve as the bases for checking any subsequent real implementations. Questions are provided (with solutions available for instructors) to confirm the correct use of numerical techniques, and templates for use in MS Excel and MATLAB can also be downloaded from extras.springer.com.
With its integrative approach to system identification, regression and statistical theory,Statistics for Chemical and Process Engineersprovides an excellent means of revision and self-study for chemical and process engineers working in experimental analysis and design in petrochemicals, ceramics, oil and gas, automotive and similar industries and invaluable instruction to advanced undergraduate and graduate students looking to begin a career in the process industries.
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Specificații
ISBN-13: 9783319215082
ISBN-10: 3319215086
Pagini: 414
Ilustrații: 85 schwarz-weiße und 48 farbige Abbildungen, Bibliographie
Dimensiuni: 155 x 235 x 30 mm
Greutate: 0.94 kg
Ediția:1st ed. 2015
Editura: Springer
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3319215086
Pagini: 414
Ilustrații: 85 schwarz-weiße und 48 farbige Abbildungen, Bibliographie
Dimensiuni: 155 x 235 x 30 mm
Greutate: 0.94 kg
Ediția:1st ed. 2015
Editura: Springer
Colecția Springer
Locul publicării:Cham, Switzerland
Public țintă
Professional/practitionerCuprins
1.
Introduction
to
Statistics
and
Data
Visualisation.-
2.
Theoretical
Foundation
for
Statistical
Analysis.-
3.
Regression.-
4.
Design
of
Experiments.-
5.
Modelling
Stochastic
Processes
with
Time
Series
Analysis.-
6.
Modelling
Dynamic
Processes
Using
System
Identification
Methods.-
7.-
Using
MATLAB®
for
Statistical
Analysis.-
8
:
Using
Excel®
to
do
Statistical
Analysis.
Notă biografică
Prof.
Dr.Yuri
A.
W.
Shardtis
currently
the
chair
of
the
Department
of
Automation
Engineering
(DE:
Fachgebiet
Automatisierungstechnik)
in
the
Faculty
of
Computer
Science
and
Automation
(DE:
Fakultät
Informatik
und
Automatisierung)
at
the
Technical
University
of
Ilmenau
(DE:
Technische
Universität
Ilmenau),
working
in
the
fields
of
big
data,
including
process
identification
and
monitoring
with
an
emphasis
on
the
development
and
industrial
implementation
of
soft
sensors;
holistic
control,
including
the
development
of
advanced
control
strategies
for
complex
industrial
process;
and
the
smart
world,
including
such
implementations
as
smart
factories,
smart
home,
Industry
4.0,
and
smart
grids.
Previously,
he
worked
at
the
University
of
Waterloo
in
the
Department
of
Chemical
Engineering
and
at
the
University
of
Duisburg-Essen
in
the
Institute
of
Control
and
Complex
Systems
(DE:
Fachgebiet
Automatisierungstechnik
und
komplexe
Systeme,
AKS)
as
an
Alexander
von
Humboldt
Fellow.
He
has
written
30
papers
appearing
in
such
journals
as
Automatica,
Journal
of
Process
Control,
IEEE
Transactions
on
Industrial
Electronics,
and
Industrial
and
Engineering
Chemistry
Research
on
topics
ranging
from
system
identification,
soft
sensor
development,
to
process
control.
He
has
presented
his
research
at
numerous
conferences
and
taught
various
courses
in
the
intersection
between
statistics,
chemical
engineering,
process
control,
EXCEL®,
and
MATLAB®.
Prof.
Dr.
Shardt
completed
his
doctoral
degree
under
the
supervision
of
Prof.
Dr.
Biao
Huang
at
the
University
of
Alberta.
His
thesis
examined
the
methods
for
extracting
valuable
data
for
system
identification
from
data
historians
for
application
to
soft
sensor
design.
In
addition
to
his
academic
work,
he
has
spent
considerable
time
in
industry
working
on
implementing
various
process
control
solutions.
He
also
has
interests
in
linguistics,
as
well
as
software
internationalisation
and
localisation.
Textul de pe ultima copertă
This
book
shows
the
reader
how
to
develop
and
test
models,
design
experiments
and
analyse
data
in
ways
easily
applicable
through
readily
available
software
tools
like
MS
Excel®
and
MATLAB®.
Generalized
methods
that
can
be
applied
irrespective
of
the
tool
at
hand
are
a
key
feature
of
the
text.
The reader is given a detailed framework for statistical procedures covering:
· data visualization;
· probability;
· linear and nonlinear regression;
· experimental design (including factorial and fractional factorial designs); and
· dynamic process identification.
Main concepts are illustrated with chemical- and process-engineering-relevant examples that can also serve as the bases for checking any subsequent real implementations. Questions are provided (with solutions available for instructors) to confirm the correct use of numerical techniques, and templates for use in MS Excel and MATLAB can also be downloaded from extras.springer.com.
With its integrative approach to system identification, regression and statistical theory,Statistics for Chemical and Process Engineersprovides an excellent means of revision and self-study for chemical and process engineers working in experimental analysis and design in petrochemicals, ceramics, oil and gas, automotive and similar industries and invaluable instruction to advanced undergraduate and graduate students looking to begin a career in the process industries.
The reader is given a detailed framework for statistical procedures covering:
· data visualization;
· probability;
· linear and nonlinear regression;
· experimental design (including factorial and fractional factorial designs); and
· dynamic process identification.
Main concepts are illustrated with chemical- and process-engineering-relevant examples that can also serve as the bases for checking any subsequent real implementations. Questions are provided (with solutions available for instructors) to confirm the correct use of numerical techniques, and templates for use in MS Excel and MATLAB can also be downloaded from extras.springer.com.
With its integrative approach to system identification, regression and statistical theory,Statistics for Chemical and Process Engineersprovides an excellent means of revision and self-study for chemical and process engineers working in experimental analysis and design in petrochemicals, ceramics, oil and gas, automotive and similar industries and invaluable instruction to advanced undergraduate and graduate students looking to begin a career in the process industries.
Caracteristici
Covers
all
concepts
required
by
the
American
Fundamentals
of
Engineering
Examination
Helps the reader perform correct data analysis by providing detailed guidance frameworks in addition to the conceptual presentation
Emphasizes examples relevant to chemical and process engineers especially those new to statistical analysis
Microsoft Excel Templates facilitate the use of the methods presented without requiring the practitioner to have access to specialized software
Generalized exposition of results means they can be put to use in the widest range of applications possible
Integrative approach to system identification, linear regression and statistical theory helps the reader to understand the similarities and differences in the methods used
Includes supplementary material: sn.pub/extras
Helps the reader perform correct data analysis by providing detailed guidance frameworks in addition to the conceptual presentation
Emphasizes examples relevant to chemical and process engineers especially those new to statistical analysis
Microsoft Excel Templates facilitate the use of the methods presented without requiring the practitioner to have access to specialized software
Generalized exposition of results means they can be put to use in the widest range of applications possible
Integrative approach to system identification, linear regression and statistical theory helps the reader to understand the similarities and differences in the methods used
Includes supplementary material: sn.pub/extras