Water minimization is conducted by exploiting all possibilities of water reuse and recycle to reduce the freshwater consumption, as well as the wastewater generation. Because the starting and finishing times of batch ...
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Water minimization is conducted by exploiting all possibilities of water reuse and recycle to reduce the freshwater consumption, as well as the wastewater generation. Because the starting and finishing times of batch water-using tasks are dependent on the production schedule as the inherent time dependence in batch processes, storage facilities are commonly equipped for the temporary storage of reusable water to partially bypass the time limitation. With a fixed production schedule, this paper presents a mathematical formulation for the synthesis of water-using networks in batch plants. Superstructures that incorporate all possible flow connections are built for modeling the batch water system, The proposed formulation is based on a continuous-time representation where different design objectives have been considered for an applicable network configuration. The design problems for the minimization of freshwater consumption, storage capacity, and the amount of connecting flows are formulated its nonlinear programs (NLPs), whereas the design problem for minimizing the number of connections will be a mixed-integer nonlinear program (MINLP). Representative examples from literature are provided to demonstrate the effectiveness of proposed formulation. Furthermore, the application of a fictitious contaminant is also developed. to address the forbidden match between assigned water-using tasks.
The objective of this study was to develop valid statistical collision models for multilane highway segments to examine the safety of curbs. For this, road geometric traffic and collision data for 2001 to 2003 were co...
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The objective of this study was to develop valid statistical collision models for multilane highway segments to examine the safety of curbs. For this, road geometric traffic and collision data for 2001 to 2003 were collected. The data included 2,274 collisions and 885 injury collisions that occurred on 191.85 mi of 199 directional segments in North Carolina. The authors applied a new modeling method of introducing variables into the model one by one in a multiplicative form. A nonlinear optimizing algorithm for estimating parameters using a negative binomial log likelihood function was adopted for the modeling. The functional form of the variable to be introduced was determined on the basis of the relationship between the recorded number of collisions and the number of collisions predicted by the current model without the variable. The integrate-differentiate method was applied to find candidate functional forms for each variable. Model selections were based on the -2 log likelihood and Bayesian information criterion statistics, and the cumulative residuals plot method to check the goodness of fit of the models was adopted. As a result of the modeling efforts, the annual average daily traffic, access point density, shoulder width, and shoulder type (including curb presence) variables were introduced to the final model for total collisions. The same variables except the shoulder type variable were introduced to the injury collision model. Overall, then, it appears that curbs mean fewer total collisions and no change in injury collisions as compared to no curbs on the sampled road segments.
In this paper a technical analysis of an ocean thermal energy conversion (OTEC) system is performed. Specifically, we present a general mathematical framework for the synthesis of OTEC power generating systems. The ov...
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In this paper a technical analysis of an ocean thermal energy conversion (OTEC) system is performed. Specifically, we present a general mathematical framework for the synthesis of OTEC power generating systems. The overall synthesis task is to minimize heat exchange area requirements, while generating some fraction of the maximum net power recoverable from hot and cold ocean water. The resulting problem formulation yields a nonlinear, nonconvex mathematical program;however, we show that globally optimal solutions for this program are easily obtained explicitly through a direct optimization approach with minimal computational effort over a wide range of thermodynamic conditions. The proposed analysis is demonstrated on a case study involving the generation of hydrogen by an OTEC system with a pure ammonia working fluid. (c) 2007 Elsevier Ltd. All rights reserved.
The short-term electric hydrothermal scheduling (STEHS) problem consists in optimizing the production of hydro and thermal electric generation units over a short time period (up to one week long). The problem describe...
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The short-term electric hydrothermal scheduling (STEHS) problem consists in optimizing the production of hydro and thermal electric generation units over a short time period (up to one week long). The problem described in this work can be modelled as a nonlinear network flow problem with linear and nonlinear side constraints. The minimization of this kind of problem can be performed by exploiting the efficiency of network flow techniques. It lies in minimizing approximately a series of augmented Lagrangian functions including only the side constraints, subject to balance constraints in the nodes and capacity bounds. One of the drawbacks of the multiplier methods with quadratic penalty function is that the augmented Lagrangian is not twice differentiable when it is applied to problems with inequality constraints. This article overcomes this difficulty by using the exponential multiplier method. In order to improve the performance some parameters are tuned. The efficiency of this method over STEHS test problems is illustrated by comparing its CPU-times with those of the quadratic multiplier method and with those of the general purpose codes MINOS, SNOPT, and KNITRO. Numerical results are promising.
With the development and widespread use of large-scale nonlinear programming (NLP) tools for process optimization, there has been an associated application of NLP formulations with complementarity constraints in order...
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With the development and widespread use of large-scale nonlinear programming (NLP) tools for process optimization, there has been an associated application of NLP formulations with complementarity constraints in order to represent discrete decisions. Also known as mathematical programs with equilibrium constraints (MPECs), these formulations can be used to model certain classes of discrete events and can be more efficient than a mixed integer formulation. However, MPEC formulations and solution strategies are not yet fully developed in process engineering. In this study, we discuss MPEC properties, including concepts of stationarity and linear independence that are essential for well-defined NLP formulations. nonlinear programming based solution strategies for MPECs are then reviewed and examples of complementarity, drawn from chemical engineering applications are presented to illustrate the effectiveness of these formulations. (C) 2008 Elsevier Ltd. All rights reserved.
Most pharmaceutical companies that rely heavily on their sales force for success do not fully understand the effect of details made in previous quarters have on the current quarter, which is also known as the carryove...
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Most pharmaceutical companies that rely heavily on their sales force for success do not fully understand the effect of details made in previous quarters have on the current quarter, which is also known as the carryover effect. This paper proposes an expert system that utilizes neural networks with nonlinear programming to accurately derive the carryover effect at the customer level. Results suggest that using this adaptive and easy-to-implement expert system helped a firm increase its sales by 3.4% while reducing its sales force expenditure by 8.9%, compared to the control group. The implications of this approach are considered. (C) 2007 Elsevier Ltd. All rights reserved.
Optimization methods that employ the classical Powell-Hestenes-Rockafellar augmented Lagrangian are useful tools for solving nonlinear programming problems. Their reputation decreased in the last 10 years due to the c...
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Optimization methods that employ the classical Powell-Hestenes-Rockafellar augmented Lagrangian are useful tools for solving nonlinear programming problems. Their reputation decreased in the last 10 years due to the comparative success of interior-point Newtonian algorithms, which are asymptotically faster. In this research, a combination of both approaches is evaluated. The idea is to produce a competitive method, being more robust and efficient than its 'pure' counterparts for critical problems. Moreover, an additional hybrid algorithm is defined, in which the interior-point method is replaced by the Newtonian resolution of a Karush-Kuhn-Tucker (KKT) system identified by the augmented Lagrangian algorithm. The software used in this work is freely available through the Tango Project web page:http://***/similar to egbirgin/tango/.
The filled function method is an approach to find global minima of multidimensional multimodal functions. This paper proposes a class of new filled functions that are continuously differentiable and do not include exp...
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The filled function method is an approach to find global minima of multidimensional multimodal functions. This paper proposes a class of new filled functions that are continuously differentiable and do not include exponential terms. The performance of the new function in numerical experiments for a large set of testing functions up to 40 dimensions is quite satisfactory.
In this paper, we propose a distributed algorithm to solve the yet explored distributed optimal power flow problem with discrete control variables of large distributed power systems. The proposed algorithm consists of...
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In this paper, we propose a distributed algorithm to solve the yet explored distributed optimal power flow problem with discrete control variables of large distributed power systems. The proposed algorithm consists of two distinguished features: 1) a distributed algorithm for solving continuous distributed optimal power flow to serve as a core technique in the framework of ordinal optimization (OO) strategy, and 2) implementing the OO strategy in a distributed power system to select a good enough discrete control variable solution. We have tested the proposed algorithm for several cases on the IEEE 118-bus and Tai Power 244-bus systems using a 4-PC network. The test results demonstrate the validity, robustness, and excellent computational efficiency of the proposed distributed algorithm in getting a good enough feasible solution.
In this article we solve a nonlinear cutting stock problem which represents a cutting stock problem that considers the minimization of, both, the number of objects used and setup. We use a linearization of the nonline...
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In this article we solve a nonlinear cutting stock problem which represents a cutting stock problem that considers the minimization of, both, the number of objects used and setup. We use a linearization of the nonlinear objective function to make possible the generation of good columns with the Gilmore and Gomory procedure. Each time a new column is added to the problem, we solve the original nonlinear problem by an Augmented Lagrangian method. This process is repeated until no more profitable columns is generated by Gilmore and Gomory technique. Finally, we apply a simple heuristic to obtain an integral solution for the original nonlinear integer problem.
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