Parametric Sensitivity In Chemical Systems

The behavior of a chemical system is affected by many physicochemical parameters. The sensitivity of the system's behavior to changes in parameters is known as parametric sensitivity. When a system operates in a parametrically sensitive region, its performance becomes unreliable and changes sharply with small variations in parameters. Thus, it is of great value to those who design and operate chemical systems to be able to analyze and predict their sensitivity behavior. This book is the first to provide a thorough treatment of the concept of parametric sensitivity and the mathematical tool it generated, sensitivity analysis. The emphasis is on applications to real situations. The book begins with definitions of various sensitivity indices and describes the numerical techniques most commonly used for their evaluation. Extensively illustrated chapters discuss sensitivity analysis in a variety of chemical reactors--batch, tubular, continuous-flow, fixed-bed--and in combustion systems, mechanistic studies, air pollution, and metabolic processes. Seniors and graduate students in various fields of science and engineering, researchers, and practicing engineers will welcome this valuable resource.

The behavior of a chemical system is affected by many physicochemical parameters. The sensitivity of the system's behavior to changes in parameters is known as parametric sensitivity. When a system operates in a parametrically sensitive region, its performance becomes unreliable and changes sharply with small variations in parameters. Thus, it is of great value to those who design and operate chemical systems to be able to analyze and predict their sensitivity behavior. This book is the first to provide a thorough treatment of the concept of parametric sensitivity and the mathematical tool it generated, sensitivity analysis. The emphasis is on applications to real situations. The book begins with definitions of various sensitivity indices and describes the numerical techniques most commonly used for their evaluation. Extensively illustrated chapters discuss sensitivity analysis in a variety of chemical reactors--batch, tubular, continuous-flow, fixed-bed--and in combustion systems, mechanistic studies, air pollution, and metabolic processes. Seniors and graduate students in various fields of science and engineering, researchers, and practicing engineers will welcome this valuable resource.

An introduction to the mathematical concepts and techniques needed for the construction and analysis of models in molecular systems biology. Systems techniques are integral to current research in molecular cell biology, and system-level investigations are often accompanied by mathematical models. These models serve as working hypotheses: they help us to understand and predict the behavior of complex systems. This book offers an introduction to mathematical concepts and techniques needed for the construction and interpretation of models in molecular systems biology. It is accessible to upper-level undergraduate or graduate students in life science or engineering who have some familiarity with calculus, and will be a useful reference for researchers at all levels. The first four chapters cover the basics of mathematical modeling in molecular systems biology. The last four chapters address specific biological domains, treating modeling of metabolic networks, of signal transduction pathways, of gene regulatory networks, and of electrophysiology and neuronal action potentials. Chapters 3–8 end with optional sections that address more specialized modeling topics. Exercises, solvable with pen-and-paper calculations, appear throughout the text to encourage interaction with the mathematical techniques. More involved end-of-chapter problem sets require computational software. Appendixes provide a review of basic concepts of molecular biology, additional mathematical background material, and tutorials for two computational software packages (XPPAUT and MATLAB) that can be used for model simulation and analysis.

Real life phenomena in engineering, natural, or medical sciences are often described by a mathematical model with the goal to analyze numerically the behaviour of the system. Advantages of mathematical models are their cheap availability, the possibility of studying extreme situations that cannot be handled by experiments, or of simulating real systems during the design phase before constructing a first prototype. Moreover, they serve to verify decisions, to avoid expensive and time consuming experimental tests, to analyze, understand, and explain the behaviour of systems, or to optimize design and production. As soon as a mathematical model contains differential dependencies from an additional parameter, typically the time, we call it a dynamical model. There are two key questions always arising in a practical environment: 1 Is the mathematical model correct? 2 How can I quantify model parameters that cannot be measured directly? In principle, both questions are easily answered as soon as some experimental data are available. The idea is to compare measured data with predicted model function values and to minimize the differences over the whole parameter space. We have to reject a model if we are unable to find a reasonably accurate fit. To summarize, parameter estimation or data fitting, respectively, is extremely important in all practical situations, where a mathematical model and corresponding experimental data are available to describe the behaviour of a dynamical system.

Based on the author's many years of experience in practicing safety assessment in industry and teaching students or professionals in this area, the topic of this book is seldom found on university curricula and many professionals do not have the knowledge required to interpret thermal data in terms of risks. For this reason, Francis Stoessel adopts a unique systematic how-to-do approach: Each chapter begins with a case history illustrating the topic and presenting the lessons learned from the incident. In so doing, he analyzes a goldmine of numerous examples stemming from industrial practice, additionally providing a series of problems or case studies at the end of each chapter. Divided into three distinct sections, part one looks at the general aspects of thermal process safety, while Part 2 deals with mastering exothermal reactions. The final section discusses the avoidance of secondary reactions, including heat accumulation and thermal confinement.

Dynamic Risk Analysis in the Chemical and Petroleum Industry focuses on bridging the gap between research and industry by responding to the following questions: What are the most relevant developments of risk analysis? How can these studies help industry in the prevention of major accidents? Paltrinieri and Khan provide support for professionals who plan to improve risk analysis by introducing innovative techniques and exploiting the potential of data share and process technologies. This concrete reference within an ever-growing variety of innovations will be most helpful to process safety managers, HSE managers, safety engineers and safety engineering students. This book is divided into four parts. The Introduction provides an overview of the state-of-the-art risk analysis methods and the most up-to-date popular definitions of accident scenarios. The second section on Dynamic Risk Analysis shows the dynamic evolution of risk analysis and covers Hazard Identification, Frequency Analysis, Consequence Analysis and Establishing the Risk Picture. The third section on Interaction with Parallel Disciplines illustrates the interaction between risk analysis and other disciplines from parallel fields, such as the nuclear, the economic and the financial sectors. The final section on Dynamic Risk Management addresses risk management, which may dynamically learn from itself and improve in a spiral process leading to a resilient system. Helps dynamic analysis and management of risk in chemical and process industry Provides industry examples and techniques to assist you with risk- based decision making Addresses also the human, economic and reputational aspects composing the overall risk picture