In this work, we address the problem of optimizing corn-based bioethanol plants through the use of heat integration and mathematical programming techniques. The goal is to reduce the operating costs of the plant. Capi...
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In this work, we address the problem of optimizing corn-based bioethanol plants through the use of heat integration and mathematical programming techniques. The goal is to reduce the operating costs of the plant. Capital cost, energy usage, and yields-all contribute to production cost. Yield and energy usage also influence the viability of corn-based ethanol as a sustainable fuel. We first propose a limited superstructure of alternative designs including the various process units and utility streams involved in ethanol production. Our objective is to determine the connections in the network and the flow in each stream in the network such that we minimize the energy requirement of the overall plant. This is accomplished through the formulation of a mixed-integer nonlinear programming problem involving short-cut models for mass and energy balances for all the units in the system, where the model is solved through two nonlinear programming subproblems. We then perform a heat integration study on the resulting flowsheet;the modified flowsheet includes multieffect distillation columns that further reduces energy consumption. The results indicate that it is possible to reduce the current steam consumption required in the transformation of corn into fuel grade ethanol by more than 40% compared to initial basic design. (C) 2008 American Institute of Chemical Engineers.
This article presents a new analytical method for estimating the location of a target using directional data. A three-dimensional (3-D) location estimation method is developed based on a nonlinear programming problem ...
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This article presents a new analytical method for estimating the location of a target using directional data. A three-dimensional (3-D) location estimation method is developed based on a nonlinear programming problem formulated for the line method, which is a well-known algorithm for 2-D location estimation. This method can be applied to situations in which one needs to find a target in space using observers on the ground or to find a target on the ground using observers at least one of which is not on the ground. In addition, based on the analysis of the maximum likelihood estimate of the target location, another 3-D location estimation method is developed for cases in which accuracies of directional data from different observers are different. The performance of the suggested methods is evaluated through simulation experiments;the results show that the methods give very accurate estimates in a reasonably short computation time.
Team Cornell's Skynet is an autonomous Chevrolet Tahoe built to compete in the 2007 DARPA Urban Challenge. Skynet consists of many unique subsystems, including actuation and power distribution designed in-house, a...
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Team Cornell's Skynet is an autonomous Chevrolet Tahoe built to compete in the 2007 DARPA Urban Challenge. Skynet consists of many unique subsystems, including actuation and power distribution designed in-house, a tightly coupled attitude and position estimator, a novel obstacle detection and tracking system, a system for augmenting position estimates with vision-based detection algorithms, a path planner based on physical vehicle constraints and a nonlinear optimization routine, and a state-based reasoning agent for obeying traffic laws. This paper describes these subsystems in detail before discussing the system's overall performance in the National Qualifying Event and the Urban Challenge. Logged data recorded at the National Qualifying Event and the Urban Challenge are presented and used to analyze the system's performance. (C) 2008 Wiley Periodicals, Inc.
The problem of time-optimal de-tumbling control (TODTC) of a rigid spacecraft moving between two attitudes is studied in this article. Unlike conventional approaches, which involve solving a set of differential equati...
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The problem of time-optimal de-tumbling control (TODTC) of a rigid spacecraft moving between two attitudes is studied in this article. Unlike conventional approaches, which involve solving a set of differential equations, a novel numerical method is introduced. In the proposed method, by fixing the count of control steps and treating the sampling period as a variable, the TODTC problem is formulated as a nonlinear programming ( NLP) problem by utilizing an iterative procedure. Generating initial feasible solutions systematically is also discussed, since these are usually needed in solving a NLP problem. In this manner, the optimization process of the NLP problem can be started from many different points when searching for the optimal solution. Simulation results are included, to show the feasibility of the proposed method.
We consider a class of convex programming problems whose objective function is given as a linear function plus a convex function whose arguments are linear functions of the decision variables and whose feasible region...
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We consider a class of convex programming problems whose objective function is given as a linear function plus a convex function whose arguments are linear functions of the decision variables and whose feasible region is a polytope. We show that there exists an optimal solution to this class of problems on a face of the constraint polytope of dimension not more than the number of arguments of the convex function. Based on this result, we develop a method to solve this problem that is inspired by the simplex method for linear programming. It is shown that this method terminates in a finite number of iterations in the special case that the convex function has only a single argument. We then use this insight to develop a second algorithm that solves the problem in a finite number of iterations for an arbitrary number of arguments in the convex function. A computational study illustrates the efficiency of the algorithm and suggests that the average-case performance of these algorithms is a polynomial of low order in the number of decision variables.
Wolfe and Mond-Weir type second-order symmetric duals are formulated and appropriate duality theorems are established under eta-bonvexity/eta-pseudobonvexity assumptions. This formulation removes several omissions in ...
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Wolfe and Mond-Weir type second-order symmetric duals are formulated and appropriate duality theorems are established under eta-bonvexity/eta-pseudobonvexity assumptions. This formulation removes several omissions in an earlier second-order primal dual pair introduced by Devi [Symmetric duality for nonlinear programming problems involving eta-bonvex functions, European J. Oper. Res. 104 (1998) 615-621]. (c) 2007 Elsevier B.V. All rights reserved.
uv-decomposition method for solving a mathematical program with equilibrium constraints （MPEC） problem with linear complementarity constraints is presented. The problem is first converted into a nonlinear programmin...
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uv-decomposition method for solving a mathematical program with equilibrium constraints （MPEC） problem with linear complementarity constraints is presented. The problem is first converted into a nonlinear programming one. The structure of subdifferential a corresponding penalty function and results of its uv-decomposition are given. A conceptual algorithm for solving this problem with a superUnear convergence rate is then constructed in terms of the obtained results.
This paper investigates a discrete-time neural network model for solving nonlinear convex programming problems with hybrid constraints. The neural network finds the solution of both primal and dual problems and conver...
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This paper investigates a discrete-time neural network model for solving nonlinear convex programming problems with hybrid constraints. The neural network finds the solution of both primal and dual problems and converges to the corresponding exact solution globally. We prove here that the proposed neural network is globally exponentially stable. Furthermore, we extend the proposed neural network for solving a class of monotone variational inequality problems with hybrid constraints. Compared with other existing neural networks for solving such problems, the proposed neural network has a low complexity for implementation without a penalty parameter and converge an exact solution to convex problem with hybrid constraints. Some numerical simulations for justifying the theoretical analysis are also given. The numerical simulations are shown that in the new model note only the cost of the hardware implementation is not relatively expensive, but also accuracy of the solution is greatly good. (c) 2007 Elsevier Inc. All rights reserved.
Many object recognition and localization techniques utilize multiple levels of local representations. These local feature representations are common, and one way to improve the efficiency of algorithms that use them i...
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Many object recognition and localization techniques utilize multiple levels of local representations. These local feature representations are common, and one way to improve the efficiency of algorithms that use them is to reduce the size of the local representations. There has been previous work on selecting Subsets of image features, but the focus here is on a systematic study of the feature selection problem. We have developed a combinatorial characterization of the feature subset selection problem that leads to a general optimization framework. This framework optimizes multiple objectives and allows the encoding of global constraints. The features selected by this algorithm are able to achieve improved performance on the problem of object localization. We present a dataset of synthetic images, along with ground-truth information, which allows us to precisely measure and compare the performance of feature subset algorithms. Our experiments show that Subsets of image features produced by our method, stable bounded canonical sets (SBCS), outperform subsets produced by K-Means clustering and threshold-based methods for the task of object localization under occlusion. (C) 2008 Elsevier Inc. All rights reserved.
This paper develops a non-linear programming optimization model with an integrated soil water balance, to determine the optimal reservoir release policies, the irrigation allocation to multiple crops and the optimal c...
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This paper develops a non-linear programming optimization model with an integrated soil water balance, to determine the optimal reservoir release policies, the irrigation allocation to multiple crops and the optimal cropping pattern in irrigated agriculture. Decision variables are the cultivated area and the water allocated to each crop. The objective function of the model maximizes the total farm income, which is based on crop-water production functions, production cost and crop prices. The proposed model is solved using the simulated annealing (SA) global optimization stochastic search algorithm in combination with the stochastic gradient descent algorithm. The rainfall, evapotranspiration and inflow are considered to be stochastic and the model is run for expected values of the above parameters corresponding to different probability of exceedence. By combining various probability levels of rainfall, evapotranspiration and inflow, four weather conditions are distinguished. The model takes into account an irrigation time interval in each growth stage and gives the optimal distribution of area, the water to each crop and the total farm income. The outputs of this model were compared with the results obtained from the model in which the only decision variables are cultivated areas. The model was applied on data from a planned reservoir on the Havrias River in Northern Greece, is sufficiently general and has great potential to be applicable as a decision support tool for cropping patterns of an irrigated area and irrigation scheduling.
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