Bonus Algorithm For Large Scale Stochastic Nonlinear Programming Problems
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BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems
This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world.
Optimization in Chemical Engineering
Author: Fernando Israel Gómez-Castro
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2025-04-21
Optimization is an area in constant evolution. The search for robust optimization techniques to deal with the highly non-convex models that represent the systems related to Chemical Engineering has led to important advances in the area. The need for developing economically feasible processes which are simultaneously environmentally friendly, safe, and controllable requires for adequate optimization strategies. Moreover, finding a global optimum is still a challenge for a diversity of cases. Thus, this book presents a compilation of classic and emerging optimization techniques, focusing on their application to systems related to the Chemical Engineering. The book shows the applications of classic mathematical programming, metaheuristic optimization methods and machine learning-based strategies. The analysis of the described techniques allows the reader identifying the advantages and disadvantages of each approach. Moreover, the book will discuss the perspectives for future developments on the area.
Life Cycle Analysis of Nanoparticles
Author: Ashok Vaseashta
language: en
Publisher: DEStech Publications, Inc
Release Date: 2015-03-30
Investigative tools for analyzing environmental nanoparticles with health impactsBasic theories and models of life cycle analysis applied to nanomaterialsConnects LCA, detection technologies and sustainability This book addresses the ways life cycle assessment (LCA) concepts can be applied to analyze the fate of nanoparticles in a variety of environmental and manufacturing settings. After introducing LCA theory and modeling concepts, the work discusses risks associated with carbon nanotubes, graphene, silver, fullerenes, iron oxides and other particles generated by manufacturing or medical diagnostics. Chapters in the text discuss biomolecules and the application of in vivo biosensors. Also covered are fate analysis, risk assessment, toxicology and nanopathology with a focus on human health and disease.