{"product_id":"chemical-and-biomedical-engineering-calculations-using-python-isbn-9781119267065","title":"Chemical and Biomedical Engineering Calculations Using Python","description":"\u003cp\u003ePresents standard numerical approaches for solving common mathematical problems in engineering using Python.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eCovers the most common numerical calculations used by engineering students\u003c\/li\u003e \u003cli\u003eCovers Numerical Differentiation and Integration, Initial Value Problems, Boundary Value Problems, and Partial Differential Equations\u003c\/li\u003e \u003cli\u003eFocuses on open ended, real world problems that require students to write a short report\/memo as part of the solution process\u003c\/li\u003e \u003cli\u003eIncludes an electronic download of the Python codes presented in the book\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Problem Solving in Engineering 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Equation Identification and Categorization 4\u003c\/p\u003e \u003cp\u003e1.1.1 Algebraic versus Differential Equations 4\u003c\/p\u003e \u003cp\u003e1.1.2 Linear versus Nonlinear Equations 5\u003c\/p\u003e \u003cp\u003e1.1.3 Ordinary versus Partial Differential Equations 6\u003c\/p\u003e \u003cp\u003e1.1.4 Interpolation versus Regression 8\u003c\/p\u003e \u003cp\u003eProblems 10\u003c\/p\u003e \u003cp\u003eAdditional Resources 11\u003c\/p\u003e \u003cp\u003eReferences 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Programming with Python 12\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Why Python? 12\u003c\/p\u003e \u003cp\u003e2.1.1 Compiled versus Interpreted Computer Languages 13\u003c\/p\u003e \u003cp\u003e2.1.2 A Note on Python Versions 14\u003c\/p\u003e \u003cp\u003e2.2 Getting Python 15\u003c\/p\u003e \u003cp\u003e2.2.1 Installation of Python 17\u003c\/p\u003e \u003cp\u003e2.2.2 Alternative to Installation: SageMathCloud 18\u003c\/p\u003e \u003cp\u003e2.3 Python Variables and Operators 19\u003c\/p\u003e \u003cp\u003e2.3.1 Updating Variables 21\u003c\/p\u003e \u003cp\u003e2.3.2 Containers 23\u003c\/p\u003e \u003cp\u003e2.4 External Libraries 25\u003c\/p\u003e \u003cp\u003e2.4.1 Finding Documentation 27\u003c\/p\u003e \u003cp\u003eProblems 28\u003c\/p\u003e \u003cp\u003eAdditional Resources 29\u003c\/p\u003e \u003cp\u003eReferences 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Programming Basics 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Comparators and Conditionals 31\u003c\/p\u003e \u003cp\u003e3.2 Iterators and Loops 34\u003c\/p\u003e \u003cp\u003e3.2.1 Indentation Style 39\u003c\/p\u003e \u003cp\u003e3.3 Functions 39\u003c\/p\u003e \u003cp\u003e3.3.1 Pizza Example 43\u003c\/p\u003e \u003cp\u003e3.3.2 Print Function 44\u003c\/p\u003e \u003cp\u003e3.4 Debugging or Fixing Errors 45\u003c\/p\u003e \u003cp\u003e3.5 Top 10+ Python Error Messages 45\u003c\/p\u003e \u003cp\u003eProblems 47\u003c\/p\u003e \u003cp\u003eAdditional Resources 49\u003c\/p\u003e \u003cp\u003eReferences 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 External Libraries for Engineering 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Numpy Library 51\u003c\/p\u003e \u003cp\u003e4.1.1 Array and Vector Creation 51\u003c\/p\u003e \u003cp\u003e4.1.2 Array Operations 55\u003c\/p\u003e \u003cp\u003e4.1.3 Getting Helping with Numpy 55\u003c\/p\u003e \u003cp\u003e4.1.4 Numpy Mathematical Functions 56\u003c\/p\u003e \u003cp\u003e4.1.5 Random Vectors with Numpy 57\u003c\/p\u003e \u003cp\u003e4.1.6 Sorting and Searching 57\u003c\/p\u003e \u003cp\u003e4.1.7 Polynomials 58\u003c\/p\u003e \u003cp\u003e4.1.8 Loading and Saving Arrays 59\u003c\/p\u003e \u003cp\u003e4.2 Matplotlib Library 60\u003c\/p\u003e \u003cp\u003e4.3 Application: Gillespie Algorithm 63\u003c\/p\u003e \u003cp\u003eProblems 66\u003c\/p\u003e \u003cp\u003eAdditional Resources 68\u003c\/p\u003e \u003cp\u003eReferences 68\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Symbolic Mathematics 70\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 70\u003c\/p\u003e \u003cp\u003e5.2 Symbolic Mathematics Packages 71\u003c\/p\u003e \u003cp\u003e5.3 An Introduction to SymPy 72\u003c\/p\u003e \u003cp\u003e5.3.1 Multiple Equations 75\u003c\/p\u003e \u003cp\u003e5.4 Factoring and Expanding Functions 76\u003c\/p\u003e \u003cp\u003e5.4.1 Equilibrium Kinetics Example 77\u003c\/p\u003e \u003cp\u003e5.4.2 Partial Fraction Decomposition 78\u003c\/p\u003e \u003cp\u003e5.5 Derivatives and Integrals 78\u003c\/p\u003e \u003cp\u003e5.5.1 Reaction Example 79\u003c\/p\u003e \u003cp\u003e5.5.2 Symbolic Integration 80\u003c\/p\u003e \u003cp\u003e5.5.3 Reactor Sizing Example 80\u003c\/p\u003e \u003cp\u003e5.6 Cryptography 81\u003c\/p\u003e \u003cp\u003eProblems 83\u003c\/p\u003e \u003cp\u003eReferences 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Linear Systems 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Example Problem 88\u003c\/p\u003e \u003cp\u003e6.2 A Direct Solution Method 91\u003c\/p\u003e \u003cp\u003e6.2.1 Distillation Example 95\u003c\/p\u003e \u003cp\u003e6.2.2 Blood Flow Network Example 95\u003c\/p\u003e \u003cp\u003e6.2.3 Computational Cost 98\u003c\/p\u003e \u003cp\u003e6.3 Iterative Solution Methods 100\u003c\/p\u003e \u003cp\u003e6.3.1 Vector Norms 100\u003c\/p\u003e \u003cp\u003e6.3.2 Jacobi Iteration 100\u003c\/p\u003e \u003cp\u003e6.3.3 Gauss–Seidel Iteration 103\u003c\/p\u003e \u003cp\u003e6.3.4 Relaxation Methods 105\u003c\/p\u003e \u003cp\u003e6.3.5 Convergence of Iterative Methods 105\u003c\/p\u003e \u003cp\u003eProblems 107\u003c\/p\u003e \u003cp\u003eReferences 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Regression 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Motivation 113\u003c\/p\u003e \u003cp\u003e7.2 Fitting Vapor Pressure Data 114\u003c\/p\u003e \u003cp\u003e7.3 Linear Regression 115\u003c\/p\u003e \u003cp\u003e7.3.1 Alternative Derivation of the Normal Equations 118\u003c\/p\u003e \u003cp\u003e7.4 Nonlinear Regression 119\u003c\/p\u003e \u003cp\u003e7.4.1 Lunar Disintegration 122\u003c\/p\u003e \u003cp\u003e7.5 Multivariable Regression 126\u003c\/p\u003e \u003cp\u003e7.5.1 Machine Learning 127\u003c\/p\u003e \u003cp\u003eProblems 129\u003c\/p\u003e \u003cp\u003eReferences 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Nonlinear Equations 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 135\u003c\/p\u003e \u003cp\u003e8.2 Bisection Method 137\u003c\/p\u003e \u003cp\u003e8.3 Newton’s Method 140\u003c\/p\u003e \u003cp\u003e8.4 Broyden’s Method 143\u003c\/p\u003e \u003cp\u003e8.5 Multiple Nonlinear Equations 146\u003c\/p\u003e \u003cp\u003e8.5.1 The Point Inside a Square 149\u003c\/p\u003e \u003cp\u003eProblems 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Statistics 156\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 156\u003c\/p\u003e \u003cp\u003e9.2 Reading Data from a File 156\u003c\/p\u003e \u003cp\u003e9.2.1 Numpy Library 157\u003c\/p\u003e \u003cp\u003e9.2.2 CVS Library 159\u003c\/p\u003e \u003cp\u003e9.2.3 Pandas 159\u003c\/p\u003e \u003cp\u003e9.2.4 Parsing an Array 162\u003c\/p\u003e \u003cp\u003e9.3 Statistical Analysis 162\u003c\/p\u003e \u003cp\u003e9.4 Advanced Linear Regression 164\u003c\/p\u003e \u003cp\u003e9.5 U.S. Electrical Rates Example 168\u003c\/p\u003e \u003cp\u003eProblems 172\u003c\/p\u003e \u003cp\u003eReferences 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Numerical Differentiation and Integration 176\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 176\u003c\/p\u003e \u003cp\u003e10.2 Numerical Differentiation 176\u003c\/p\u003e \u003cp\u003e10.2.1 First Derivative Approximation 177\u003c\/p\u003e \u003cp\u003e10.2.2 Second Derivative Approximation 180\u003c\/p\u003e \u003cp\u003e10.2.3 Scipy Derivative Approximation 181\u003c\/p\u003e \u003cp\u003e10.3 Numerical Integration 183\u003c\/p\u003e \u003cp\u003e10.3.1 Trapezoid Rule 185\u003c\/p\u003e \u003cp\u003e10.3.2 Numerical Integration Using Scipy 186\u003c\/p\u003e \u003cp\u003e10.3.3 Error Function 187\u003c\/p\u003e \u003cp\u003eProblems 190\u003c\/p\u003e \u003cp\u003eReference 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Initial Value Problems 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 193\u003c\/p\u003e \u003cp\u003e11.2 Biochemical Reactors 193\u003c\/p\u003e \u003cp\u003e11.3 Forward Euler 195\u003c\/p\u003e \u003cp\u003e11.4 Modified Euler Method 198\u003c\/p\u003e \u003cp\u003e11.5 Systems of Equations 199\u003c\/p\u003e \u003cp\u003e11.5.1 The Lorenz System and Chaotic Solutions 200\u003c\/p\u003e \u003cp\u003e11.5.2 Second-Order Initial Value Problems 203\u003c\/p\u003e \u003cp\u003e11.6 Stiff Differential Equations 203\u003c\/p\u003e \u003cp\u003eProblems 206\u003c\/p\u003e \u003cp\u003eReferences 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Boundary Value Problems 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 211\u003c\/p\u003e \u003cp\u003e12.2 Shooting Method 212\u003c\/p\u003e \u003cp\u003e12.3 Finite Difference Method 216\u003c\/p\u003e \u003cp\u003e12.3.1 Reactions in Spherical Catalysts 220\u003c\/p\u003e \u003cp\u003eProblems 224\u003c\/p\u003e \u003cp\u003eReference 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Partial Differential Equations 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Finite Difference Method for Steady-State PDEs 227\u003c\/p\u003e \u003cp\u003e13.1.1 Setup 228\u003c\/p\u003e \u003cp\u003e13.1.2 Matrix Assembly 230\u003c\/p\u003e \u003cp\u003e13.1.3 Solving and Plotting 232\u003c\/p\u003e \u003cp\u003e13.2 Convection 233\u003c\/p\u003e \u003cp\u003e13.3 Finite Difference Method for Transient PDEs 236\u003c\/p\u003e \u003cp\u003eProblems 241\u003c\/p\u003e \u003cp\u003eReference 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Finite Element Method 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 A Warning 245\u003c\/p\u003e \u003cp\u003e14.2 Why FEM? 246\u003c\/p\u003e \u003cp\u003e14.3 Laplace’s Equation 246\u003c\/p\u003e \u003cp\u003e14.3.1 The Mesh 246\u003c\/p\u003e \u003cp\u003e14.3.2 Discretization 247\u003c\/p\u003e \u003cp\u003e14.3.3 Wait! Why Are We Doing This? 248\u003c\/p\u003e \u003cp\u003e14.3.4 FEniCS Implementation 248\u003c\/p\u003e \u003cp\u003e14.4 Pattern Formation 249\u003c\/p\u003e \u003cp\u003eAdditional Resources 253\u003c\/p\u003e \u003cp\u003eReferences 254\u003c\/p\u003e \u003cp\u003eIndex 255\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJeffrey J. Heys\u003c\/b\u003e is currently the department head in Chemical and Biological Engineering at Montana State University. He has taught numerous courses in Chemical and Biological Engineering for 15 years. He also taught courses in Applied Mathematics at the University of Colorado at Boulder, including Numerical Analysis, for three years. Jeff has been creating mathematical models of biological systems for approximately 20 years, published more than 40 peer reviewed papers, and has programmed extensively in FORTRAN, C, C++, MATLAB\u003csup\u003e®\u003c\/sup\u003e, and Python\u003csup\u003e®\u003c\/sup\u003e.\t   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eChemical and Biomedical Engineering Calculations Using Python\u003csup\u003e®\u003c\/sup\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003ePresents standard numerical approaches for solving common mathematical problems in engineering using Python\u003csup\u003e®\u003c\/sup\u003ePython\u003csup\u003e®\u003c\/sup\u003e is a clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, and Java. The Python programming language is ideal due to its rapid growth and strong recent interest among practitioners in areas as diverse as numerical modeling, data science, and bioinformatics. \u003ci\u003eChemical and Biomedical Engineering Calculations Using Python\u003csup\u003e®\u003c\/sup\u003e\u003c\/i\u003e presents standard numerical approaches for solving common mathematical problems in engineering.\t \u003c\/p\u003e\u003cp\u003eThe book covers the most common engineering calculations used by students and utilizes the freely available Python software and its supporting libraries. \u003ci\u003eChemical and Biomedical Engineering Calculations Using Python\u003csup\u003e®\u003c\/sup\u003e\u003c\/i\u003e features topics on: \t \u003c\/p\u003e\u003cul\u003e \u003cli\u003eProgramming in Python\u003c\/li\u003e \u003cli\u003eCommon External Libraries for Engineering\u003c\/li\u003e \u003cli\u003ePlotting\u003c\/li\u003e \u003cli\u003eSymbolic Mathematics\u003c\/li\u003e \u003cli\u003eLinear Systems\u003c\/li\u003e \u003cli\u003eRegression\u003c\/li\u003e \u003cli\u003eNonlinear Equations\u003c\/li\u003e \u003cli\u003eStatistics\u003c\/li\u003e \u003cli\u003eNumerical Differentiation and Integration\u003c\/li\u003e \u003cli\u003eInitial Value Problems\u003c\/li\u003e \u003cli\u003eBoundary Value Problems\u003c\/li\u003e \u003cli\u003ePartial Differential Equations\u003c\/li\u003e \u003cli\u003eFinite Element Method\u003c\/li\u003e \t\u003c\/ul\u003e\t   \u003cp\u003e\u003ci\u003eChemical and Biomedical Engineering Calculations Using Python\u003csup\u003e®\u003c\/sup\u003e\u003c\/i\u003e is written to be accessible to engineering students in a numerical methods or computational methods course as well as for practicing engineers who want to learn to solve common problems using Python. Also included is an electronic download of the Python codes presented in the book.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988904820965,"sku":"NP9781119267065","price":65.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119267065.jpg?v=1761781994","url":"https:\/\/k12savings.com\/es\/products\/chemical-and-biomedical-engineering-calculations-using-python-isbn-9781119267065","provider":"K12savings","version":"1.0","type":"link"}