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Optimization Formulation (optimization + formulation)
Selected AbstractsA Path-Based Algorithm for the Cross-Nested Logit Stochastic User Equilibrium Traffic AssignmentCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 1 2009Shlomo Bekhor A SUE assignment based on the Cross-Nested Logit (CNL) route choice model is presented. The CNL model can better represent route choice behavior compared to the Multinomial Logit (MNL) model, while keeping a closed form equation. The article uses a specific optimization formulation developed for the CNL model, and develops a path-based algorithm for the solution of the CNL-SUE problem based on adaptation of the disaggregate simplicial decomposition (DSD) method. The article illustrates the algorithmic implementation on a real size network and discusses the trade-offs between MNL-SUE and CNL-SUE assignment. [source] Optimization-based dynamic human walking prediction: One step formulationINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 6 2009Yujiang Xiang Abstract A new methodology is introduced in this work to simulate normal walking using a spatial digital human model. The proposed methodology is based on an optimization formulation that minimizes the dynamic effort of people during walking while considering associated physical and kinematical constraints. Normal walking is formulated as a symmetric and cyclic motion. Recursive Lagrangian dynamics with analytical gradients for all the constraints and objective function are incorporated in the optimization process. Dynamic balance of the model is enforced by direct use of the equations of motion. In addition, the ground reaction forces are calculated using a new algorithm that enforces overall equilibrium of the human skeletal model. External loads on the human body, such as backpacks, are also included in the formulation. Simulation results with the present methodology show good correlation with the experimental data obtained from human subjects and the existing literature. Copyright © 2009 John Wiley & Sons, Ltd. [source] Application of support vector regression for developing soft sensors for nonlinear processes,THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 5 2010Saneej B. Chitralekha Abstract The field of soft sensor development has gained significant importance in the recent past with the development of efficient and easily employable computational tools for this purpose. The basic idea is to convert the information contained in the input,output data collected from the process into a mathematical model. Such a mathematical model can be used as a cost efficient substitute for hardware sensors. The Support Vector Regression (SVR) tool is one such computational tool that has recently received much attention in the system identification literature, especially because of its successes in building nonlinear blackbox models. The main feature of the algorithm is the use of a nonlinear kernel transformation to map the input variables into a feature space so that their relationship with the output variable becomes linear in the transformed space. This method has excellent generalisation capabilities to high-dimensional nonlinear problems due to the use of functions such as the radial basis functions which have good approximation capabilities as kernels. Another attractive feature of the method is its convex optimization formulation which eradicates the problem of local minima while identifying the nonlinear models. In this work, we demonstrate the application of SVR as an efficient and easy-to-use tool for developing soft sensors for nonlinear processes. In an industrial case study, we illustrate the development of a steady-state Melt Index soft sensor for an industrial scale ethylene vinyl acetate (EVA) polymer extrusion process using SVR. The SVR-based soft sensor, valid over a wide range of melt indices, outperformed the existing nonlinear least-square-based soft sensor in terms of lower prediction errors. In the remaining two other case studies, we demonstrate the application of SVR for developing soft sensors in the form of dynamic models for two nonlinear processes: a simulated pH neutralisation process and a laboratory scale twin screw polymer extrusion process. A heuristic procedure is proposed for developing a dynamic nonlinear-ARX model-based soft sensor using SVR, in which the optimal delay and orders are automatically arrived at using the input,output data. Le domaine du développement des capteurs logiciels a récemment gagné en importance avec la création d'outils de calcul efficaces et facilement utilisables à cette fin. L'idée de base est de convertir l'information obtenue dans les données d'entrée et de sortie recueillies à partir du processus dans un modèle mathématique. Un tel modèle mathématique peut servir de solution de rechange économique pour les capteurs matériels. L'outil de régression par machine à vecteur de support (RMVS) constitue un outil de calcul qui a récemment été l'objet de beaucoup d'attention dans la littérature des systèmes d'identification, surtout en raison de ses succès dans la création de modèles de boîte noire non linéaires. Dans ce travail, nous démontrons l'application de la RMVS comme outil efficace et facile à utiliser pour la création de capteurs logiciels pour les procédés non linéaires. Dans une étude de cas industrielle, nous illustrons le développement d'un capteur logiciel à indice de fluidité à état permanent pour un processus d'extrusion du polymère d'acétate de vinyle-éthylène à l'échelle industrielle en utilisant la RMVS. Le capteur logiciel fondé sur la RMVS, valide sur une vaste gamme d'indices de fluidité, a surclassé le capteur logiciel fondé sur les moindres carrés non linéaires existant en matière d'erreurs de prédiction plus faibles. Dans les deux autres études de cas, nous démontrons l'application de la RMVS pour la création de capteurs logiciels sous la forme de modèles dynamiques pour deux procédés non linéaires: un processus de neutralisation du pH simulé et un processus d'extrusion de polymère à deux vis à l'échelle laboratoire. Une procédure heuristique est proposée pour la création d'un capteur logiciel fondé sur un modèle ARX non linéaire dynamique en utilisant la RMVS, dans lequel on atteint automatiquement le délai optimal et les ordres en utilisant les données d'entrée et de sortie. [source] Integration of Solar Energy into Absorption Refrigerators and Industrial ProcessesCHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 9 2010E. A. Tora Abstract Absorption refrigeration is gaining increasing attention in industrial facilities to use process heat for partially or completely driving a cooling cycle. This paper introduces a systematic approach to the design of absorption refrigeration systems for industrial processes. Three sources of energy are considered to drive absorption refrigerators: excess process heat, solar energy, and fossil fuels. To handle the dynamic nature of solar energy, hot water tanks are used for energy storage and dispatch. Thermal pinch analysis is performed to determine the amount of available excess heat and the required refrigeration duty. Next, a multiperiod optimization formulation is developed for the entire system. The procedure determines the optimal mix of energy forms (solar versus fossil) and the dynamic operation of the system. Three case studies are solved to demonstrate the effectiveness and applicability of the devised procedure. [source] | |||||||||||||