Alternative Solution Techniques Research Articles

Genetic Algorithm (GA) Approaches for the Transport Energy Demand Estimation: Model Development and Application

This study deals with estimating future transport energy demand using genetic algorithm (GA) approach. Genetic algorithm transport energy demand (GATENDM) model is developed based on socio-economic indicators (population, gross domestic product (GDP), import and export) and transportation indicators/parameters (car, bus, and truck sales). The GATENDM model developed is applied to Turkey, which is selected as an application country. This model in a quadratic form was found to provide the best fit solution to the observed data. It may be concluded that the model proposed can be used as an alternative solution and estimation technique to available estimation technique in predicting the future transportation energy utilization values of countries.

A variational approach to the stochastic aspects of cellular signal transduction

Cellular signaling networks have evolved to cope with intrinsic fluctuations, coming from the small numbers of constituents, and the environmental noise. Stochastic chemical kinetics equations govern the way biochemical networks process noisy signals. The essential difficulty associated with the master equation approach to solving the stochastic chemical kinetics problem is the enormous number of ordinary differential equations involved. In this work, we show how to achieve tremendous reduction in the dimensionality of specific reaction cascade dynamics by solving variationally an equivalent quantum field theoretic formulation of stochastic chemical kinetics. The present formulation avoids cumbersome commutator computations in the derivation of evolution equations, making the physical significance of the variational method more transparent. We propose novel time-dependent basis functions which work well over a wide range of rate parameters. We apply the new basis functions to describe stochastic signaling in several enzymatic cascades and compare the results so obtained with those from alternative solution techniques. The variational Ansatz gives probability distributions that agree well with the exact ones, even when fluctuations are large and discreteness and nonlinearity are important. A numerical implementation of our technique is many orders of magnitude more efficient computationally compared with the traditional Monte Carlo simulation algorithms or the Langevin simulations.

Estimating Petroleum Exergy Production and Consumption Using a Simulated Annealing Approach

This study deals with the development of the petroleum exergy production and consumption relations in order to better analyze exergy values and predict the future projections using the simulated annealing (SA) approach, which is a powerful technique used to solve many optimization problems. The exergy estimation is performed based on the indicators of gross domestic product (GDP) and the percentage of vehicle ownership figures in Turkey, which is given as an illustrative example. The so-called SA exergy production and consumption (SAPEX) model is developed, while the exergy values obtained using the SAPEX model are also compared with those using the genetic algorithm (GA) approach. It is determined that the SAPEX model developed predicts the exergy values better than the GA model. It may be concluded that the models proposed here can be used as an alternative solution and estimation technique to available estimation techniques in predicting the future energy and exergy utilization values of countries. This study is also expected to give a new direction to engineers, scientists, and policy makers in implementing energy planning studies and in dictating the energy strategies as a potential tool.

An Application of Genetic Algorithm Search Techniques to the Future Total Exergy Input/Output Estimation

Since 1975, there has been a great deal of interest, particularly during the past decade, in the promising genetic algorithm (GA) and its application to various disciplines from medicine to cogeneration. However, the studies performed on energy-related GA modeling are relatively low in numbers. The main objective of the present study is to develop the exergy input/output estimation equations in order to estimate the future projections based on the GA notion. In this regard, the GA Future Total EXergy Input/Output Estimation Models (GAFTEXIEM/GAFTEXOEM) are used to estimate total exergy input/output demand of Turkey, which is selected as an application country, based on the economic and social indicators of gross domestic product (GDP), population, import, export and house production figures. The future prediction of Turkey's total exergy input/output values are projected between 2003 and 2023. It may be concluded that the models proposed here can be used as an alternative solution and estimation techniques to available estimation techniques. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies.

Three different applications of genetic algorithm (GA) search techniques on oil demand estimation

This present study develops three scenarios to analyze oil consumption and make future projections based on the Genetic algorithm (GA) notion, and examines the effect of the design parameters on the oil utilization values. The models developed in the non-linear form are applied to the oil demand of Turkey. The GA Oil Demand Estimation Model (GAODEM) is developed to estimate the future oil demand values based on Gross National Product (GNP), population, import, export, oil production, oil import and car, truck and bus sales figures. Among these models, the GA-PGOiTI model, which uses population, GNP, oil import, truck sales and import as design parameters/indicators, was found to provide the best fit solution with the observed data. It may be concluded that the proposed models can be used as alternative solution and estimation techniques for the future oil utilization values of any country.

Thermal-Shock-Assisted Solid-State Process for the Production of BaMgAl10O17:Eu Phosphor

A new method, called the “thermal-shock method”, was introduced for the production of high-quality BaMgAl10O17:Eu (BAM) phosphor powder. A conventional solid-state process assisted by a thermal shock at 950 °C results in a pure BAM phase with no impurities such as BaAl2O4, MgAl2O4, or EuAlO3. When both AlF3 and H3BO3 are simultaneously used as the flux, this simple thermal-shock reaction yields the phosphor particles with highly improved morphology. Excellent emission intensity higher than that of commercial phosphor can be obtained after reduction at 1000 °C. Compared with other alternative liquid solution techniques, no use of any liquid medium that is harmful to the environment is one of advantages of the thermal-shock method for practical purposes. This synthetic process would be of great interest to the low-cost industrial production of high-performance BAM phosphor.

Estimating Energy and Exergy Production and Consumption Values Using Three Different Genetic Algorithm Approaches. Part 1: Model Development

The present study, consisting of two parts, proposes new models for estimating energy and exergy production and consumption values using the genetic algorithm approach. Part 1 of this study deals with the model development, while the application and testing with various scenarios will be treated in Part 2. In this regard, the genetic algorithm energy (GAEN) and genetic algorithm exergy (GAEX) estimating models have been proposed. During the energy and exergy estimation, independent variables are the GDP, population, and the ratio of export to import. The three forms of the GAEN and GAEX are developed, of which one is linear, second is exponential and the third is a mix of the exponential and linear form of the equations. Among them, the best fit models in terms of average relative errors and for the testing period are selected for future estimation and proposed both for GAEN and GAEX. It may be concluded that the models proposed here can be used as an alternative solution and estimation techniques to available estimation techniques.

Residential-commercial energy input estimation based on genetic algorithm (GA) approaches: an application of Turkey

The main objective of the present study is to develop the energy input estimation equations for the residential-commercial sector (RCS) in order to estimate the future projections based on genetic algorithm (GA) notion and to examine the effect of the design parameters on the energy input of the sector. For this purpose, the Turkish RCS is given as an example. The GA Energy Input Estimation Model (GAEIEM) is used to estimate Turkey’s future residential-commercial energy input demand based on GDP, population, import, export, house production, cement production and basic house appliances consumption figures. It may be concluded that the three various forms of models proposed here can be used as an alternative solution and estimation techniques to available estimation techniques. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies.

Estimating the Turkish residential–commercial energy output based on genetic algorithm (GA) approaches

The present study develops three forms of equations to better analyze energy use and make future projections based on genetic algorithm (GA) notion, and examines the effect of the design parameters on the energy utilization values. The models developed in the quadratic form are applied to Turkey, which is selected as an application country. Turkey's future residential energy output demand is estimated based on GDP, population, import, export, house production, cement production and basic house appliances consumption figures. Among these models, the so-called GA-RWTVR model, which uses residential housing production, house appliances sales of washing machine, television, vacuum cleaner and refrigerator as design parameters/indicators, was found to provide the best fit solution to the observed data. It may be concluded that the models proposed can be used as an alternative solution and estimation techniques to available estimation techniques in predicting the future energy utilization values of countries.

Computational modeling of growth

The present contribution is dedicated to the computational modeling of growth phenomena typically encountered in modern biomechanical applications. We set the basis by critically reviewing the relevant literature and classifying the existing models. Next, we introduce a geometrically exact continuum model of growth which is not a priori restricted to applications in hard tissue biomechanics. The initial boundary value problem of biomechanics is primarily governed by the density and the deformation problem which render a nonlinear coupled system of equations in terms of the balance of mass and momentum. To ensure unconditional stability of the required time integration procedure, we apply the classical implicit Euler backward method. For the spatial discretization, we suggest two alternative strategies, a node-based and an integration point–based approach. While for the former, the discrete balance of mass and momentum are solved simultaneously on the global level, the latter is typically related to a staggered solution with the density treated as internal variable. The resulting algorithms of the alternative solution techniques are compared in terms of stability, uniqueness, efficiency and robustness. To illustrate their basic features, we elaborate two academic model problems and a typical benchmark example from the field of biomechanics.

Routing and wavelength assignment in optical networks from edge disjoint path algorithms

Routing and wavelength assignment (RWA) problems in wavelength-routed optical networks are typically solved using a combination of integer programming and graph coloring. Such techniques are complex and make extensive use of heuristics. We explore an alternative solution technique in the well-known maximum edge disjoint paths (EDP) problem which can be naturally adapted to the RWA problem. Maximum EDP is NP-hard, but now it is known that simple greedy algorithms for it are as good as any of the more complex heuristic solutions. In this paper we investigate the performance of a simple greedy maximum edge disjoint paths algorithm applied to the RWA problem and compare it with a previously known solution method.

Robust algorithm for load estimation in distribution networks

A new mathematical model is proposed which allows distribution network loads to be estimated using the available information from metering and other data sources. The application of this model offers a compromise between conventional load allocation methods and network state estimation. A novel approach to the modelling of current magnitude measurements is introduced. The problem is formulated as a weighted least absolute values estimation problem which is solved numerically using weighted least squares with an iterative reweighting scheme. The nonlinear effect of power losses is accounted for by applying a Newton–Raphson approach. Alternative sparse matrix solution techniques are compared and a robust and computationally efficient approach is described which is suitable for online applications.

Numerical Analysis of Strategic Contingent Claims Models

We study the numerical properties of a class of models recently introduced to calculate the values of corporate bonds and other corporate liabilities. Starting from a discrete-time extensive form game representing the consequences of financial distress, these ’’strategic contingent claims models" are associated with a particular free-boundary problem. Here we consider the properties of alternative solution techniques applied to this problem. We discuss four solution techniques of the finite difference type: explicit solutions, explicit solutions of the log transformed model, implicit solutions on a regular grid, and dynamically remeshed implicit solutions. To our knowledge this last method has not previously been employed in financial applications. We find that the use of dynamic remeshing can speed calculation solutions enormously. This opens the way to applying strategic contingent claims models in practical applications.

Book review: Intelligent Scheduling by M. Zweben and M. S. Fox (Morgan Kaufmann, 1994)

As a domain for applied AI, scheduling combines a conceptually rich domain that lends itself well to symbolic manipulation; a set of problems that are provably NP-hard and thus good candidates for heuristics; a tradition of alternative solution techniques to permit comparison of results; and the potential for strong practical impact on industrial competitiveness. The editors of this volume, who are themselves leaders in this domain, have gathered twenty-five papers by prominent researchers and developers world- wide to provide a snapshot of the state-of-the-art in intelligent scheduling. The collection is more unified than many similar volumes, with a short (9 page) but meaty introduction and an integrated 24-page index. An integrated bibliography would be a helpful addition.

Global optimization of multipleiers and buses in interconnect synthesis

In this paper, we consider the problem of optimizing interconnection complexity in behavioral level synthesis of digital systems. We assume that, as a result of other steps in synthesis, logical connection requirements have already been determined, with a corresponding level of multiplexing implied. We further reduce the total amount of multiplexing by combining logical connections into shared (physical) connections wherever possible. Our measure of interconnection complexity is the number of equivalent 2×1 multiplexers. Using this criterion, we can guarantee an optimal solution to this problem. The problem is modelled with a graph, which is then pruned extensively before being used as an input to any one of the two alternative solution techniques. The primary (and optimum) technique employs integer linear programming. We also provide a faster heuristic solution that yields near-optimal results. Since the actual implementation of shared connections may vary widely, we also show how the linear program can be modified to optimize and count the total number of n×1 multiplexers ( n⩾2) or the total number of tri-state buffers in a bus-based interconnect implementation.

Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods

The purpose of this paper is to report on a comparison of several alternative numerical solution techniques for nonlinear rational expectations models. The comparison was made by asking individual researchers to apply their different solution techniques to a simple representative agent, optimal, stochastic growth model. Decision rules as well as simulated time series are compared. The differences among the methods turned out to be quite substantial for certain aspects of the growth model. Therefore, researchers might want to be careful not to rely blindly on the results of any chosen numerical solution method in applied work.

Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods

The purpose of this paper is to report on a comparison of several alternative numerical solution techniques for nonlinear rational expectations models. The comparison was made by asking individual researchers to apply their different solution techniques to a simple representative agent, optimal, stochastic growth model. Decision rules as well as simulated time series are compared. The differences among the methods turned out to be quite substantial for certain aspects of the growth model. Therefore, researchers might want to be careful not to rely blindly on the results of any chosen numerical solution method in applied work.

Supply Identification for Water Distribution Systems

Identifying the precise source of delivered flow in water distribution systems is increasingly important. The necessity for this arises when individual supplies exceed allowable levels of certain chemical or biological constituents. An explicit methodology to make these calculations for complex water distribution systems is presented. The methodology requires the solution of a set of linear equations of order equal to the number of junctions. The equation coefficients are based on the flow distribution for the system demands under consideration. Because the explicit solution procedure requires the simultaneous solution of large equation sets for large networks, an alternative solution technique was developed. This technique solved the equations in a simple, cyclic manner requiring little computer storage and proved to be an efficient scheme for carrying out the calculations.

Solution to boundary value problems using the method of maximum entropy

The method of maximum entropy is used to solve a class of linear boundary value problems. The method is based on using various moments of the differential equation as constraints when maximizing the entropy. Various examples are presented and compared to exact solutions for varying numbers of moments. It is found that the maximum entropy approximation is, in many cases, better than a Fourier series solution for a given number of expansion terms and moments. The method is very general and will find applications in many areas of physics. A comparison of the amount of work necessary for the maximum entropy solution versus finite difference techniques is presented and it is found that the maximum entropy technique shows promise as an alternative solution technique.

Advances in building energy simulation in the U.K. - The science and engineering research council's programme

This paper describes the recent developments in building energy simulation in the U.K., which have resulted from a government-funded research program in this area. It first considers the models themselves, the evolutionary process of their formation, the moves towards more flexible software structures and inclusion of plant modelling, alternative solution techniques, and the importance of documentation. Next, some related studies in areas of building physics where great uncertainties remain are outlined. The new perspectives on model validation are then considered, with possible alternative measures of model credibility to complement the normal, but often inconclusive, technical studies. Suggestions are made regarding ways in which users might deal with the uncertainty which is now seen as inevitable in modelling. Finally, some possible work for the future on the current generation of models is given, and for the longer term, the proposed Energy Kernel System is set in the context of current knowledge and concerns.