This book covers several bases at once. It is useful as a textbook for a second course in experimental optimization techniques for industrial production processes. In addition, it is a superb reference volume for use by professors and graduate students in Industrial Engineering and Statistics departments. It will also be of huge interest to applied statisticians, process engineers, and quality engineers working in the electronics and biotech manufacturing industries. In all, it provides an in-depth presentation of the statistical issues that arise in optimization problems, including confidence regions on the optimal settings of a process, stopping rules in experimental optimization, and more.
A unique text covering basic and advanced concepts of optimization theory and methods for process systems engineers. With examples illustrating key concepts and algorithms, and exercises involving theoretical derivations, numerical problems and modeling systems, it is ideal for single-semester, graduate courses in process systems engineering.
Experimental Design and Process Optimization delves deep into the design of experiments (DOE). The book includes Central Composite Rotational Design (CCRD), fractional factorial, and Plackett and Burman designs as a means to solve challenges in research and development as well as a tool for the improvement of the processes already implemented. Appr
This book is an update of a successful first edition that has been extremely well received by the experts in the chemical process industries. The authors explain both the theory and the practice of optimization, with the focus on the techniques and software that offer the most potential for success and give reliable results. Applications case studies in optimization are presented with new examples taken from the areas of microelectronics processing and molecular modeling. Ample references are cited for those who wish to explore the theoretical concepts in more detail.
This work presents the concepts of process design, problem identification, problem-solving and process optimization. It provides the basic tools needed to increase the consistency and profitability of manufacturing options, stressing the paradigms of improvement and emphasizing the hands-on use of tools furnished. The book introduces basic experimental design principles and avoids complicated statistical formulae.
Exploring methods and techniques to optimize processing energy efficiency in process plants, Energy and Process Optimization for the Process Industries provides a holistic approach that considers optimizing process conditions, changing process flowschemes, modifying equipment internals, and upgrading process technology that has already been used in a process plant with success. Field tested by numerous operating plants, the book describes technical solutions to reduce energy consumption leading to significant returns on capital and includes an 8-point Guidelines for Success. The book provides managers, chemical and mechanical engineers, and plant operators with methods and tools for continuous energy and process improvements.
All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.
Erfahren Sie in diesem Buch über Prozessoptimierung für produzierende Unternehmen alles über die neue Methode LEO-3DIn seinem Buch „Prozessoptimierung für produzierende Unternehmen“ beschreibt Klaus R. Stoesser einen neuen, kombinierten Ansatz, mit dem Firmen aus dem produzierenden Gewerbe ihre Geschäftsprozesse optimieren können. Der Autor stellt hierfür Philosophien wie Lean Management, Six Sigma und SCRUM vor. Darauf aufbauend führt er seine eigens entwickelte Philosophie LEO-3D ein, welche Unternehmen vielseitige Möglichkeiten bietet.Der Inhalt im ÜberblickIm Kern geht es in Stoessers Buch über Prozessoptimierung für produzierende Unternehmen um folgende Themen:• Entwicklung der Prozessoptimierung• LEO-3D• Ausgewählte Methoden, Tools und VorgehensweisenEs vermittelt diese Ansätze, um Prozesse in Unternehmen ganzheitlich im zu verbessern und damit das Ergebnis nachhaltig zu steigern.
Stochastic Process Optimization using Aspen® Plus Bookshop Category: Chemical Engineering Optimization can be simply defined as "choosing the best alternative among a set of feasible options". In all the engineering areas, optimization has a wide range of applications, due to the high number of decisions involved in an engineering environment. Chemical engineering, and particularly process engineering, is not an exception; thus stochastic methods are a good option to solve optimization problems for the complex process engineering models. In this book, the combined use of the modular simulator Aspen® Plus and stochastic optimization methods, codified in MATLAB, is presented. Some basic concepts of optimization are first presented, then, strategies to use the simulator linked with the optimization algorithm are shown. Finally, examples of application for process engineering are discussed. The reader will learn how to link the process simulator Aspen® Plus and stochastic optimization algorithms to solve process design problems. They will gain ability to perform multi-objective optimization in several case studies. Key Features: • The book links simulation and optimization through numerical analyses and stochastic optimization techniques • Includes use of examples to illustrate the application of the concepts and specific guidance on the use of software (Aspen® Plus, Excel, MATLB) to set up and solve models representing complex problems. • Illustrates several examples of applications for the linking of simulation and optimization software with other packages for optimization purposes. • Provides specific information on how to implement stochastic optimization with process simulators. • Enable readers to identify practical and economic solutions to problems of industrial relevance, enhancing the safety, operation, environmental, and economic performance of chemical processes.
