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MixedInteger Nonlinear Optimization in Process Synthesis
, 1998
"... The use of networks allows the representation of a variety of important engineering problems. The treatment of a particular class of network applications, the process synthesis problem, is exposed in this paper. Process Synthesis seeks to develop systematically process flowsheets that convert raw ma ..."
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The use of networks allows the representation of a variety of important engineering problems. The treatment of a particular class of network applications, the process synthesis problem, is exposed in this paper. Process Synthesis seeks to develop systematically process flowsheets that convert raw materials into desired products. In recent years, the optimization approach to process synthesis has shown promise in tackling this challenge. It requires the development of a network of interconnected units, the process superstructure, that represents the alternative process flowsheets. The mathematical modeling of the superstructure has a mixed set of binary and continuous variables and results in a mixedinteger optimization model. Due to the nonlinearity of chemical models, these problems are generally classified as MixedInteger Nonlinear Programming (MINLP) problems. A number of local optimization algorithms, developed for the solution of this class of problems, are presented in this pap...
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 1 EnergyEfficient MultiChannel Cooperative Sensing Scheduling with Heterogeneous Channel Conditions for Cognitive Radio Networks
"... Abstract—Spectrum sensing is an important aspect of Cognitive Radio Networks. Secondary users should sense the channels periodically in order to ensure primary user protection. Sensing with cooperation among several secondary users is more robust and less error prone. However, cooperation also incr ..."
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Abstract—Spectrum sensing is an important aspect of Cognitive Radio Networks. Secondary users should sense the channels periodically in order to ensure primary user protection. Sensing with cooperation among several secondary users is more robust and less error prone. However, cooperation also increases the energy spent for sensing. Considering the periodic nature of sensing, even a small amount of savings in each sensing period leads to considerable improvement in the long run. In this paper, we consider the problem of energyefficient spectrum sensing scheduling with satisfactory primary user protection. Our model exploits the diversity of secondary users in their received signaltonoiseratio value of the primary signal to determine the sensing duration for each user/channel pair for higher energy efficiency. We model the mentioned problem as an optimization problem with two different objectives. The first one minimizes the energy consumption whereas the second one minimizes the spectrum sensing duration in order to maximize the remaining time for data transmission. We solve both problems using outer linearization method. In addition, we present two suboptimal but efficient heuristic methods. We provide an extensive performance analysis of our proposed methods under various number of secondary users, average channel signaltonoiseratio, and channel sampling frequency. Our analysis reveal that all proposals with energy minimization perspective provide significant energy savings compared to a pure transmission time maximization technique. Index Terms—Cooperative sensing scheduling, energyefficient sensing, sensing task assignment, heterogeneous sensing. I.
Nonlinear and MixedInteger Optimization in Chemical Process Network Systems
, 1998
"... The use of networks allows the representation of a variety of important engineering problems. The treatment of a particular class of network applications, the Process Synthesis problem, is exposed in this paper. Process Synthesis seeks to develop systematically process flowsheets that convert raw ..."
Abstract
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The use of networks allows the representation of a variety of important engineering problems. The treatment of a particular class of network applications, the Process Synthesis problem, is exposed in this paper. Process Synthesis seeks to develop systematically process flowsheets that convert raw materials into desired products. In recent years, the optimization approach to process synthesis has shown promise in tackling this challenge. It requires the development of a network of interconnected units, the process superstructure, that represents the alternative process flowsheets. The mathematical modeling of the superstructure has a mixed set of binary and continuous variables and results in a mixedinteger optimization model. Due to the nonlinearity of chemical models, these problems are generally classified as MixedInteger Nonlinear Programming (MINLP) problems. A number of local optimization algorithms for MINLP problems are outlined in this paper: Generalized Benders Decompositi...