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Dynamic Optimization (dynamic + optimization)

Distribution by Scientific Domains

Engineering	53%
Chemistry	30%

Terms modified by Dynamic Optimization

dynamic optimization problem

Selected Abstracts

Dynamic Optimization in Chemical Processes Using Region Reduction Strategy and Control Vector Parameterization with an Ant Colony Optimization Algorithm

CHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 4 2008
A. Asgari
Abstract Two different approaches of the dynamic optimization for chemical process control engineering applications are presented. The first approach is based on discretizing both the control region and the time interval. This method, known as the Region Reduction Strategy (RRS), employs the previous solution in its next iteration to obtain more accurate results. Moreover, the procedure will continue unless the control region becomes smaller than a prescribed value. The second approach is called Control Vector Parameterization (CVP) and appears to have a large number of advantages. In this approach, control action is generated in feedback form, i.e., a set of trial functions of the state variables are expanded by multiplying by some unknown coefficients. By utilizing an optimization method, these coefficients are calculated. The Ant Colony Optimization (ACO) algorithm is employed as an optimization method in both approaches. [source]

Dynamic optimization of N -joint robotic limb deployments

JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 3 2010
Wolfgang Fink
We describe an approach using a stochastic optimization framework (SOF) for operating complex mobile systems with several degrees of freedom (DOFs), such as robotic limbs with N joints, in environments that can contain obstacles. As part of the SOF, we have employed an efficient simulated annealing algorithm normally used in computationally highly expensive optimization and search problems such as the traveling salesman problem and protein design. This algorithm is particularly suited to run onboard industrial robots, robots in telemedicine, remote spacecraft, planetary landers, and rovers, i.e., robotic platforms with limited computational capabilities. The robotic limb deployment optimization approach presented here offers an alternative to time-intensive robotic arm deployment path planning algorithms in general and in particular for robotic limb systems in which closed-form solutions do not exist. Application examples for a (N = 4)-DOF arm on a planetary exploration rover are presented. © 2009 Wiley Periodicals, Inc. [source]

Dynamic optimization of the methylmethacrylate cell-cast process for plastic sheet production

AICHE JOURNAL, Issue 6 2009
Martín Rivera-Toledo
Abstract Traditionally, the methylmethacrylate (MMA) polymerization reaction process for plastic sheet production has been carried out using warming baths. However, it has been observed that the manufactured polymer tends to feature poor homogeneity characteristics measured in terms of properties like molecular weight distribution. Nonhomogeneous polymer properties should be avoided because they give rise to a product with undesired wide quality characteristics. To improve homogeneity properties force-circulated warm air reactors have been proposed, such reactors are normally operated under isothermal air temperature conditions. However, we demonstrate that dynamic optimal warming temperature profiles lead to a polymer sheet with better homogeneity characteristics, especially when compared against simple isothermal operating policies. In this work, the dynamic optimization of a heating and polymerization reaction process for plastic sheet production in a force-circulated warm air reactor is addressed. The optimization formulation is based on the dynamic representation of the two-directional heating and reaction process taking place within the system, and includes kinetic equations for the bulk free radical polymerization reactions of MMA. The mathematical model is cast as a time dependent partial differential equation (PDE) system, the optimal heating profile calculation turns out to be a dynamic optimization problem embedded in a distributed parameter system. A simultaneous optimization approach is selected to solve the dynamic optimization problem. Trough full discretization of all decision variables, a nonlinear programming (NLP) model is obtained and solved by using the IPOPT optimization solver. The results are presented about the dynamic optimization for two plastic sheets of different thickness and compared them against simple operating policies. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source]

Dynamic optimization and Skiba sets in economic examples

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 5-6 2001
Wolf-Jürgen Beyn
Abstract We discuss two optimization problems from economics. The first is a model of optimal investment and the second is a model of resource management. In both cases the time horizon is infinite and the optimal control variables are continuous. Typically, in these optimal control problems multiple steady states and periodic orbits occur. This leads to multiple solutions of the state,costate system each of which relates to a locally optimal strategy but has its own limiting behaviour (stationary or periodic). Initial states that allow different optimal solutions with the same value of the objective function are called Skiba points. The set of Skiba points is of interest, because it provides thresholds for a global change of optimal strategies. We provide a systematic numerical method for calculating locally optimal solutions and Skiba points via boundary value problems. In parametric or higher dimensional systems Skiba curves (or manifolds) appear and we show how to follow them by a continuation process. We apply our method to the models above where Skiba sets consist of points or curves and where optimal solutions have different stationary or periodic asymptotic behaviour. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Dynamic operation plan of a combined fuel cell cogeneration, solar module, and geo-thermal heat pump system using Genetic Algorithm

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 13 2007
Shin'ya Obara
Abstract A chromosome model that simulates the operation patterns of an energy system was introduced into a simple Genetic Algorithm, and a method of dynamic optimization was developed. This paper analyses the operation planning of an energy system that uses in combination a solar power module, proton-exchange membrane fuel cell cogeneration (PEMFC-CGS) with methanol steam reforming, a geo-thermal heat pump, heat storage and battery, commercial power, and a kerosene boiler. The hours of operation of each energy device and the rate of the energy output were calculated by having introduced the analysis program developed by this study. Three objective functions: (a) minimization of operation cost; (b) minimization of the error of demand-and-supply balance; and (c) minimization of the amount of greenhouse gas discharge were given to the optimization analysis of the system. Furthermore, the characteristics of the system operation planning under each objective function are described. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Fast implementations and rigorous models: Can both be accommodated in NMPC?

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 8 2008
Victor M. Zavala
Abstract In less than two decades, nonlinear model predictive control has evolved from a conceptual framework to an attractive, general approach for the control of constrained nonlinear processes. These advances were realized both through better understanding of stability and robustness properties as well as improved algorithms for dynamic optimization. This study focuses on recent advances in optimization formulations and algorithms, particularly for the simultaneous collocation-based approach. Here, we contrast this approach with competing approaches for online application and discuss further advances to deal with applications of increasing size and complexity. To address these challenges, we adapt the real-time iteration concept, developed in the context of multiple shooting (Real-Time PDE-Constrained Optimization. SIAM: Philadelphia, PA, 2007; 25,52, 3,24), to a collocation-based approach with a full-space nonlinear programming solver. We show that straightforward sensitivity calculations from the Karush,Kuhn,Tucker system also lead to a real-time iteration strategy, with both direct and shifted variants. This approach is demonstrated on a large-scale polymer process, where online calculation effort is reduced by over two orders of magnitude. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Design and Analysis of Bioenergy Networks

JOURNAL OF INDUSTRIAL ECOLOGY, Issue 2 2009
A Complex Adaptive Systems Approach
Summary This article presents a new methodology for designing industrial networks and analyzing them dynamically from the standpoint of sustainable development. The approach uses a combination of optimization and simulation tools. Assuming "top-down" overarching control of the network, we use global dynamic optimization to determine which evolutionary pathways are preferred in terms of economic, social, and environmental performance. Considering the autonomy of network entities and their actions, we apply agent-based simulation to analyze how the network actually evolves. These two perspectives are integrated into a powerful multiscale modeling framework for evaluating the consequences of new policy instruments or different business strategies aimed at stimulating sustainable development as well as identifying optimal leverage points for improved performance of the network in question. The approach is demonstrated for a regional network of interdependent organizations deploying a set of bioenergy technologies within a developing-economy context. [source]

Dynamic optimization of the methylmethacrylate cell-cast process for plastic sheet production

Optimal control and design of a cold store using dynamic optimization

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 1 2009
Leo Lukasse
Abstract The design of controlled processes is a combined optimal control and design problem (OCDP). Literature on solving large OCDPs is rare. This paper presents an algorithm for solving large OCDPs. For this algorithm system dynamics, objective function and their first-order derivatives must be continuous in the state, control and design parameters. The algorithm is successfully applied to the combined control and design problem of a cold store with three possible refrigeration technologies: mechanical refrigeration, ventilation and evaporative cooling. As a result, insight into cost effectiveness of the refrigeration technologies is generated. It is concluded that for this cold store in the Netherlands evaporative cooling is too expensive. Ventilation is economically viable if the cold store is to be used in January only. In case the cold store is to be operated all year then it is most economical to rely on mechanical refrigeration only and use the overcapacity during most part of the year to shift refrigeration to low-tariff hours. Copyright © 2008 John Wiley & Sons, Ltd. [source]

An economic application of the Dubovitskii,Milyutin version of the Maximum Principle

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 6 2007
Siu Fai Leung
Abstract The Pontryagin Maximum Principle is well known in economics. There is a different and more general version of the Maximum Principle, first established by Dubovitskii and Milyutin (Dokl. Acad. Nauk SSSR 1963; 149:759,762; Zh. Vychisl. Mat. Mat. Fiz. 1965; 5:393,453), which is little known in economics and has never been applied to solve an economic optimal control problem. This paper introduces the Dubovitskii,Milyutin version of the Maximum Principle to economics and offers an economic application to illustrate the limitation of the conventional Maximum Principle and the usefulness of the Dubovitskii,Milyutin version. The Dubovitskii,Milyutin Maximum Principle should be an integral part of the economist's toolbox and be included in economics textbooks on dynamic optimization. Copyright © 2007 John Wiley & Sons, Ltd. [source]

An analysis of simplified muscle activation profile parameterization

PROCEEDINGS IN APPLIED MATHEMATICS & MECHANICS, Issue 1 2006
Daniel Strobach
This paper analyzes a simplified method for rough identification of muscle activation profiles of general motor tasks by means of dynamic optimization. Muscle activation profiles are parameterized with six parameters per muscle, using linear combinations of two smooth C, functions closely related to the GAUSSian distribution function used in stochastics and fuzzy control. The method is applied to a simplified subsystem of the human leg consisting of pelvis, thigh shank and foot, interconnected by planar joints at hip, knee and ankle. The system comprises one antagonistic muscle pair at the knee for knee flexion and extension (vastus intermedius and biceps femoris caput brevis). To simulate the swing phase of gait, rheonomic constraints are imposed on pelvis (translation and rotation), hip (rotation) and ankle (rotation). The optimization results show that, (1) the method is suitable to map typical muscle activation time histories that are recorded via EMG, (2) the method can reduce the number of design parameters and CPU-time consumption significantly in comparison to other parameterizations and (3) this reduction in CPU-time consumption additionally coinncides with an improved approximation quality to the target motion. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

Optimization of Fed-Batch Saccharomyces cerevisiae Fermentation Using Dynamic Flux Balance Models

BIOTECHNOLOGY PROGRESS, Issue 5 2006
Jared L. Hjersted
We developed a dynamic flux balance model for fed-batch Saccharomyces cerevisiae fermentation that couples a detailed steady-state description of primary carbon metabolism with dynamic mass balances on key extracellular species. Model-based dynamic optimization is performed to determine fed-batch operating policies that maximize ethanol productivity and/or ethanol yield on glucose. The initial volume and glucose concentrations, the feed flow rate and dissolved oxygen concentration profiles, and the final batch time are treated as decision variables in the dynamic optimization problem. Optimal solutions are generated to analyze the tradeoff between maximal productivity and yield objectives. We find that for both cases the prediction of a microaerobic region is significant. The optimization results are sensitive to network model parameters for the growth associated maintenance and P/O ratio. The results of our computational study motivate continued development of dynamic flux balance models and further exploration of their application to productivity optimization in biochemical reactors. [source]

Dynamic Optimization in Chemical Processes Using Region Reduction Strategy and Control Vector Parameterization with an Ant Colony Optimization Algorithm