David M. Himmelblau

Bio: David M. Himmelblau is an academic researcher from University of Texas System. The author has contributed to research in topics: Nonlinear programming & Metaheuristic. The author has an hindex of 10, co-authored 12 publications receiving 4624 citations.

Optimization of Chemical Processes

Abstract: I Problem Formulation 1 The Nature and Organization of Optimization Problems 2 Developing Models for Optimization 3 Formulation of the Objective Function II Optimization Theory and Methods 4 Basic Concepts of Optimization 5 Optimization for Unconstrained Functions: One- Dimensional Search 6 Unconstrained Multivariable Optimization 7 Linear Programming and Applications 8 Nonlinear Programming with Constraints 9 Mixed-Integer Programming 10 Global Optimization for Problems Containing Continuous and Discrete Variables IIIApplications of Optimization 11 Heat Transfer and Energy Conservation 12 Separation Processes 13 Fluid Flow Systems 14 Chemical Reactor Design and Operation 15 Optimization in Large-Scale Plant Design and Operations 16 Integrated Planning, Scheduling, and Control in the Process Industries Appendixes

Basic Principles and Calculations in Chemical Engineering

Abstract: (NOTE: Each chapter concludes with Supplementary References and Problems.) 1. Introduction to Chemical Engineering Calculations. Units and Dimensions. The Mole Unit. Conventions in Methods of Analysis and Measurement. Basis. Temperature. Pressure. The Chemical Equation and Stoichiometry. 2. Problem Solving. Techniques of Problem Solving. Computer-Based Tools. Sources of Data. 3. Material Balances. The Material Balance. Program of Analysis of Material Balance Problems. Solving Material Balance Problems That Do Not Involve Chemical Reactions. Solving Material Balance Problems That Involve Chemical Reactions. Solving Material Balance Problems Involving Multiple Subsystems. Recycle, Bypass, and Purge Calculations. 4. Gases, Vapors, Liquids, and Solids. Ideal Gas Law Calculations. Real Gas Relationships. Vapor Pressure and Liquids. Saturation. Vapor-Liquid Equilibria for Multicomponent Systems. Partial Saturation and Humidity. Material Balances Involving Condensation and Vaporization. 5. Energy Balances. Concepts and Units. Calculation of Enthalpy Changes. Applications of the General Energy Balance without Reactions Occurring. Energy Balances That Account for Chemical Reaction. Reversible Processes and the Mechanical Energy Balance. Heats of Solution and Mixing. Humidity Charts and Their Use. 6. Solving Simultaneous Material and Energy Balances. Analyzing the Degrees of Freedom in a Steady-State Process. Solving Material and Energy Balances Using Flowsheeting Codes. UNSTEADY- STATE MATERIAL AND ENERGY BALANCES. Unsteady-State Material and Energy Balances. Appendices. A. Answers tp Self-Assessment Tests. B. Atomic Weights and Numbers. Steam Tables. D. Physical Properties of Various Organic and Inorganic Substances. E. Heat Capacity Equations. F. Heats of Formation and Combustion. G. Vapor Pressures. H. Heats of Solution and Dilution. I. Enthalpy-Concentration Data. J. Thermodynamic Charts. K. Physical Properties of Petroleum Fractions. L. Solutions of Sets of Equations. L.1 Independent Linear Equations. L.2 Nonlinear Independent Equations. M. Fitting Functions to Data. N. Answers to Selected Problems. Index.

Process analysis and simulation : deterministic systems

Abstract: PrefaceThe methods used by chemical engineers for the conception, design, and operation ofnchemical processes have undergone significant changes in the past few years. Thenadvent of large-scale computing equipment has permitted vastly more detailed andnrealistic analyses to be carried out with a reasonable degree of effort and cost. Morenbasic (and complex) physical principles have been employed and new techniques havenbecome feasible, both of which involve advanced mathematical tools. The applicationnof many of these modern techniques has come to be called qprocess analysis,qnqsystems engineering,q or qsimulation.qWhile few aspects of process analysis are really new, the construction of complicatednmathematical models of real processes and the manipulation of the models by largescalencomputers do represent a new phase in engineering analysis. It is no longer thencustom to solve engineering problems solely with a slide rule or a piece of graph paper;ninstead, the engineer turns to the computer with increasing frequency. The use ofncomputers as process simulators is also now well established, thereby speeding processndevelopment. Statistical techniques are commonly used for data correlation. All thesenfeatures of the present-day scene demand from an engineer the best of his mathematicalnability as well as impose a severe burden on his judgment. They also permit thenengineer to make more complicated mistakes. Fortunately, the basic rules that governnprocess analysis and simulation remain invariant with the changing times -- it is thenapplication of these rules that becomes more sophisticated.The objectives of this book are twofold. First, in Part I, we have emphasized thenprinciples of model building in order to familiarize the engineer with the principles ofndevelopment and skills needed for the application of mathematical models. Variousntypes of models have been proposed in the literature, but it is qhard to see the forestnfor the trees.q One of the major features of this book is that we have organized thenvarious models into logical patterns based on both mathematical and physical principlesnso that they can be applied with confidence to real problems. The assumptions,nsimplifications, and degree of detail are all clearly stated and related to the complexitynof the particular problem.Second, we have tried to foster skills in the application of model building to anvariety of subsystems and systems. We believe that the most effective way to teachnengineers how to analyze processes is to indicate the analytical tools, provide clear-cutnexamples of applications, and point out the pitfalls that can arise in actual analysis. Anprospective engineer needs training and practice in finding what the problem is, inndefining the problem, in analyzing it and breaking down information, in assemblingnbasic principles into new patterns, and in testing the solutions. He must also know hownto collect and ask for data. To further this second objective, the principles described innPart I are closely followed by examples in Parts II and III, and, in addition, twonchapters solely dealing with applications have been assembled: Chapter 7 examinesnsubsystems analysis and Chapter 9 discusses systems analysis.We have tried to show how the solutions of problems depend upon the assumptionsnintroduced, and that problems have one or more answers depending upon thenassumptions made. Furthermore, we have demonstrated how various problems inndifferent fields may actually be solved by similar methods and how they may havensimilar solutions. Stress has been placed on breaking down problems into theirnfundamental concepts and manipulating the concepts to ask such questions as : Whatnhappens if I change this assumption ? What happens if I apply different limits ? Whatnhappens if I reverse the sequence? How does it fit in with other concepts in this field?nIt is particularly desirable for the reader to understand the reasons why he may fail tonapproach a problem in the right way. If emphasis is placed on the method of analysisnand on original thought, he can hope to avoid becoming a qhandbookq engineer.The book itself is divided into four parts :1. A brief general introduction and qualitative description of process analysis.2. Part I n -- Principles of model building based on physicochemical principles andnpopulation balances.3. Part II n-- Methodology and applications of subsystems analysis.4. Part III -- Methodology and applications of large-scale systems analysis.nIn addition, Appendix A provides sources from which model coefficients can benobtained while Appendix B describes some of the mathematical tools employed in thenmain part of the text. n n n n

Detection of abrupt changes: theory and application

Abstract: This book is downloadable from http://www.irisa.fr/sisthem/kniga/. Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established. We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.

How to Solve It: Modern Heuristics

Abstract: I What Are the Ages of My Three Sons?.- 1 Why Are Some Problems Difficult to Solve?.- II How Important Is a Model?.- 2 Basic Concepts.- III What Are the Prices in 7-11?.- 3 Traditional Methods - Part 1.- IV What Are the Numbers?.- 4 Traditional Methods - Part 2.- V What's the Color of the Bear?.- 5 Escaping Local Optima.- VI How Good Is Your Intuition?.- 6 An Evolutionary Approach.- VII One of These Things Is Not Like the Others.- 7 Designing Evolutionary Algorithms.- VIII What Is the Shortest Way?.- 8 The Traveling Salesman Problem.- IX Who Owns the Zebra?.- 9 Constraint-Handling Techniques.- X Can You Tune to the Problem?.- 10 Tuning the Algorithm to the Problem.- XI Can You Mate in Two Moves?.- 11 Time-Varying Environments and Noise.- XII Day of the Week of January 1st.- 12 Neural Networks.- XIII What Was the Length of the Rope?.- 13 Fuzzy Systems.- XIV Everything Depends on Something Else.- 14 Coevolutionary Systems.- XV Who's Taller?.- 15 Multicriteria Decision-Making.- XVI Do You Like Simple Solutions?.- 16 Hybrid Systems.- 17 Summary.- Appendix A: Probability and Statistics.- A.1 Basic concepts of probability.- A.2 Random variables.- A.2.1 Discrete random variables.- A.2.2 Continuous random variables.- A.3 Descriptive statistics of random variables.- A.4 Limit theorems and inequalities.- A.5 Adding random variables.- A.6 Generating random numbers on a computer.- A.7 Estimation.- A.8 Statistical hypothesis testing.- A.9 Linear regression.- A.10 Summary.- Appendix B: Problems and Projects.- B.1 Trying some practical problems.- B.2 Reporting computational experiments with heuristic methods.- References.