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Process controls is a mixture between the statistics and engineering discipline that deals with the mechanism, architectures, and algorithms for controlling a process. Some examples of controlled processes are: •Controlling the temperature of a water stream by controlling the amount of steam added to the shell of a heat exchanger. •Operating a jacketed reactor isothermally by controlling the mixture of cold water and steam that flows through the jacket of a jacketed reactor. •Maintaining a set ratio of reactants to be added to a reactor by controlling their flow rates. •Controlling the height of fluid in a tank to ensure that it does not overflow.
- Subjects:
- Chemistry
- Keywords:
- Chemical process control Chemical processes Textbooks
- Resource Type:
- e-book
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e-book
This is a "first course" in the sense that it presumes no previous course in probability. The mathematical prerequisites are ordinary calculus and the elements of matrix algebra. A few standard series and integrals are used, and double integrals are evaluated as iterated integrals. The reader who can evaluate simple integrals can learn quickly from the examples how to deal with the iterated integrals used in the theory of expectation and conditional expectation. Appendix B provides a convenient compendium of mathematical facts used frequently in this work. And the symbolic toolbox, implementing MAPLE, may be used to evaluate integrals, if desired. In addition to an introduction to the essential features of basic probability in terms of a precise mathematical model, the work describes and employs user defined MATLAB procedures and functions (which we refer to as m-programs, or simply programs) to solve many important problems in basic probability. This should make the work useful as a stand-alone exposition as well as a supplement to any of several current textbooks. Most of the programs developed here were written in earlier versions of MATLAB, but have been revised slightly to make them quite compatible with MATLAB 7. In a few cases, alternate implementations are available in the Statistics Toolbox, but are implemented here directly from the basic MATLAB program, so that students need only that program (and the symbolic mathematics toolbox, if they desire its aid in evaluating integrals). Since machine methods require precise formulation of problems in appropriate mathematical form, it is necessary to provide some supplementary analytical material, principally the so-called minterm analysis. This material is not only important for computational purposes, but is also useful in displaying some of the structure of the relationships among events.
- Subjects:
- Mathematics and Statistics
- Keywords:
- MATLAB Textbooks Probabilities
- Resource Type:
- e-book
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e-book
This book is intended for the Risk Management and Insurance course where Risk Management is emphasized. When we think of large risks, we often think in terms of natural hazards such as hurricanes, earthquakes or tornados. Perhaps man-made disasters come to mind such as the terrorist attacks in the U.S. on September 11, 2001. Typically we have overlooked financial crises, such as the credit crisis of 2008. However, these types of man-made disasters have the potential to devastate the global marketplace. Losses in multiple trillions of dollars and in much human suffering and insecurity are already being totaled, and the global financial markets are collapsing as never before seen. We can attribute the 2008 collapse to financially risky behavior of a magnitude never before experienced. The 2008 U.S. credit markets were a financial house of cards. A basic lack of risk management (and regulators' inattention or inability to control these overt failures) lay at the heart of the global credit crisis. This crisis started with lack of improperly underwritten mortgages and excessive debt. Companies depend on loans and lines of credit to conduct their routine business. If such credit lines dry up, production slows down and brings the global economy to the brink of deep recession—or even depression. The snowballing effect of this failure to manage the risk associated with providing mortgage loans to unqualified home buyers have been profound, indeed. When the mortgages failed because of greater risk- taking on the Street, the entire house of cards collapsed. Probably no other risk-related event has had, and will continue to have, as profound an impact world wide as this risk management failure. How was risk in this situation so badly managed? What could firms and individuals have done to protect themselves? How can government measure such risks (beforehand) to regulate and control them? These and other questions come to mind when we contemplate the consequences of this risk management fiasco. Standard risk management practice would have identified sub-prime mortgages and their bundling into mortgage-backed-securities as high risk. People would have avoided these investments or would have put enough money into reserve to be able to withstand defaults. This did not happen. Accordingly, this book may represent one of the most critical topics of study that the student of the 21st century could ever undertake. Risk management will be a major focal point of business and societal decision—making in the 21st century. A separate focused field of study, it draws on core knowledge bases from law, engineering, finance, economics, medicine, psychology, accounting, mathematics, statistics and other fields to create a holistic decision-making framework that is sustainable and value- enhancing. This is the subject of this book.
- Subjects:
- Management
- Keywords:
- Risk management Risk (Insurance) Textbooks
- Resource Type:
- e-book
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Courseware
This course aims to give students the tools and training to recognize convex optimization problems that arise in scientific and engineering applications, presenting the basic theory, and concentrating on modeling aspects and results that are useful in applications. Topics include convex sets, convex functions, optimization problems, least-squares, linear and quadratic programs, semidefinite programming, optimality conditions, and duality theory. Applications to signal processing, control, machine learning, finance, digital and analog circuit design, computational geometry, statistics, and mechanical engineering are presented. Students complete hands-on exercises using high-level numerical software.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Mathematical optimization Convex functions
- Resource Type:
- Courseware