Chemical Engineering Computation with MATLAB®, Second Edition continues to present basic to advanced levels of problem-solving techniques using MATLAB as the computation environment. The Second Edition provides even more examples and problems extracted from core chemical engineering subject areas and all code is updated to MATLAB version 2020. It also includes a new chapter on computational intelligence and: Offers exercises and extensive problem-solving instruction and solutions for various problems Features solutions developed using fundamental principles to construct mathematical models and an equation-oriented approach to generate numerical results Delivers a wealth of examples to demonstrate the implementation of various problem-solving approaches and methodologies for problem formulation, problem solving, analysis, and presentation, as well as visualization and documentation of results Includes an appendix offering an introduction to MATLAB for readers unfamiliar with the program, which will allow them to write their own MATLAB programs and follow the examples in the book Provides aid with advanced problems that are often encountered in graduate research and industrial operations, such as nonlinear regression, parameter estimation in differential systems, two-point boundary value problems and partial differential equations and optimization This essential textbook readies engineering students, researchers, and professionals to be proficient in the use of MATLAB to solve sophisticated real-world problems within the interdisciplinary field of chemical engineering. The text features a solutions manual, lecture slides, and MATLAB program files._

Partial differential equations (PDEs) describe technological phenomena and processes used for the analysis, design, and modeling of technical products. Solutions of spatial and transient PDEs are realized by using the PDE Toolbox included in the MATLAB® software. MATLAB® is introduced here as an essential foundation for PDE, and the Modeler of the PDE Toolbox, with appropriate explanatory solutions, is applied to engineering problems in mechanics, heat/mass transfer, tribology, materials science, physics, and biotechnology. The appendixes contain collections of commands and functions used to solve actual engineering problems. FEATURES Includes the PDE Modeler interface with example solutions of two- and three-dimensional PDEs Presents methodologies for all types of PDEs as representative of any engineering problem Describes the ordinate differential equation (ODE) solver for initial value and boundary value problems (IVP and BVP) through practical examples from mechanics and the thermodynamic properties of materials Covers the basics of MATLAB® to solve both ODEs and PDEs Reviews spatially the one-dimensional PDE solver with actual engineering examples PDE Toolbox Primer for Engineering Applications with MATLAB® Basics is aimed at scientists, students, professionals, practitioners, self-taught readers, and researchers who need concise and clear information to study and apply MATLAB® software and the PDE Toolbox in engineering.

MATLAB® in Quality Assurance Sciences fills a gap in the highly topical field of quality assurance (QA). It is a compact guide for students, engineers, and scientists in this field. It concentrates on MATLAB® fundamentals with examples of application to a wide range of current problems from general, nano and bio-technology, and statistical control, to medicine and industrial management. Examples cover both the school and advanced level; comprising calculations of total quality management, six sigma, time series, process improvement, metrology, quality control, human factors in quality assurance, measurement and testing techniques, quality project and function management, and customer satisfaction. This book covers key topics, including: the basics of software with examples; graphics and representations; numerical computation, scripts and functions for QA calculations; ODE and PDEPE solvers applied to QA problems; curve fitting and time series tool interfaces in calculations of quality; and statistics calculations applied to quality testing. - Includes MATLAB® fundamentals, matrices, arrays, general graphics and specialized plots in quality assurance problems, script files, ordinary and partial differential equations - Gives calculation of six sigma, total quality management, time series forecasting, reliability, process improvement, metrology, quality control and assurance, measurement and testing techniques - Provides tools for graphical presentation, basic and special statistics and testing, ordinary and partial differential solvers, and fitting tools

This textbook is designed for students and industry practitioners for a first course in optimization integrating MATLAB® software.

Techniques and Tools for Solving Acoustics Problems This is the first book of its kind that describes the use of ANSYS® finite element analysis (FEA) software, and MATLAB® engineering programming software to solve acoustic problems. It covers simple text book problems, such as determining the natural frequencies of a duct, to progressively more complex problems that can only be solved using FEA software, such as acoustic absorption and fluid-structure-interaction. It also presents benchmark cases that can be used as starting points for analysis. There are practical hints too for using ANSYS software. The material describes how to solve numerous problems theoretically, and how to obtain solutions from the theory using MATLAB engineering software, as well as analyzing the same problem using ANSYS Workbench and ANSYS Mechanical APDL. Developed for the Practicing Engineer Free downloads on http://www.mecheng.adelaide.edu.au/avc/software, including MATLAB source code, ANSYS APDL models, and ANSYS Workbench models Includes readers’ techniques and tips for new and experienced users of ANSYS software Identifies bugs and deficiencies to help practitioners avoid making mistakes Acoustic Analyses Using MATLAB® and ANSYS® can be used as a textbook for graduate students in acoustics, vibration, and related areas in engineering; undergraduates in mechanical and electrical engineering; and as an authoritative reference for industry professionals.

This book constitutes the refereed proceedings of the 5th International Symposium on Advances in Signal Processing and Intelligent Recognition Systems, SIRS 2019, held in Trivandrum, India, in December 2019. The 19 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 63 submissions. The papers cover wide research fields including information retrieval, human-computer interaction (HCI), information extraction, speech recognition.

Engineers are expected to design structures and machines that can operate in challenging and volatile environments, while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering, Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments. The first eight chapters cover probability and probability distributions, graphical displays of data and descriptive statistics, combinations of random variables and propagation of error, statistical inference, bivariate distributions and correlation, linear regression on a single predictor variable, and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies. Distinctive features include: All examples based on work in industry, consulting to industry, and research for industry Examples and case studies include all engineering disciplines Emphasis on probabilistic modeling including decision trees, Markov chains and processes, and structure functions Intuitive explanations are followed by succinct mathematical justifications Emphasis on random number generation that is used for stochastic simulations of engineering systems, demonstration of key concepts, and implementation of bootstrap methods for inference Use of MATLAB and the open source software R, both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applications Use of multiple regression for times series models and analysis of factorial and central composite designs Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooks Experiments designed to show fundamental concepts that have been tested with large classes working in small groups Website with additional materials that is regularly updated Andrew Metcalfe, David Green, Andrew Smith, and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering, mining, and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health, University of South Australia. Tony Greenfield, formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association, is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for Quality.

Since process models are nowadays ubiquitous in many applications, the challenges and alternatives related to their development, validation, and efficient use have become more apparent. In addition, the massive amounts of both offline and online data available today open the door for new applications and solutions. However, transforming data into useful models and information in the context of the process industry or of bio-systems requires specific approaches and considerations such as new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes in particular. The same can be said about the tools and software environments used to describe, code, and solve such models for their further exploitation. Going well beyond mere simulation tools, these advanced tools offer a software suite built around the models, facilitating tasks such as experiment design, parameter estimation, model initialization, validation, analysis, size reduction, discretization, optimization, distributed computation, co-simulation, etc. This Special Issue collects novel developments in these topics in order to address the challenges brought by the use of models in their different facets, and to reflect state of the art developments in methods, tools and industrial applications.