دانلود کتاب روش‌های عددی در مهندسی فرآیندهای شیمیایی: استفاده از ابزارهای پایتون برای مدل‌سازی، شبیه‌سازی و بهینه‌سازی

This university-level text has two main objectives: (i) to present concisely the most widely used numerical methods for solving the equations commonly encountered in Chemical Process Engineering (CPE). The emphasis is from the standpoint of someone who needs to solve such problems without delving into the details of numerical analysis itself (existence and uniqueness of solutions, convergence, etc.) and (ii) to show how Python and its scientific ecosystem (i.e., Matplotlib, NumPy, SciPy, pandas and Statsmodels) can be used to tackle the different categories of typical problems across the broad field of CPE. Python has become the most common language in introductory university programming courses globally and is now the de facto tool for research computing, from CERN’s beam-dynamics simulations to NASA’s prognostics packages. This explosive growth has been accompanied by a corresponding increase in university textbooks. Nevertheless, in our view there are still few books that link the most common numerical methods directly with the Python language, presenting the numerical technique and its implementation side by side, as this book does. The first part of the text reviews the available numerical methods and shows how they can be implemented with Python libraries built-in functions, providing a perspective that complements the software’s user manual and programming guides. Various techniques and algorithms are presented, highlighting which ones are best suited to each problem. Although “universal” algorithms rarely exist, we discuss the kinds of difficulties that may arise, how to diagnose them, and how to avoid them. Each chapter includes worked examples and a list of proposed problems; all have been tested in assessments for the courses “Applied Mathematics for Process Engineering” and “Computational Methods for Transport Phenomena,” taught by the authors at the School of Engineering of the Pontificia Universidad Católica de Chile for more than 20 years. The second part offers a deliberately selected set of solved problems, complete with code, results and figures. We have strived to include problems we consider most illustrative and engaging for students (parameter fitting with nonlinear equations and ordinary differential equations, confidence intervals, optimization of a bioreactor, etc.). As you will see, their level will challenge both undergraduate and graduate students. The problems are not limited to steady-state conditions; on the contrary, we have tried to include as many dynamic problems as possible. The general structure for solving each problem is straightforward: once the governing equations are derived or identified, the Python code is presented together with a brief explanation. Numerous graphics accompany the text, and in some cases the explanations go well beyond the immediate purpose of the problem, allowing students to explore related topics. We remind readers that programming—much like learning to integrate—is, in essence, an art. Other solutions may be completely valid; however, we have striven to offer the simplest and most direct ones.