Mathematical modeling of falling liquid film evaporation process

A mathematical model of evaporation process from a laminar falling liquid film on a vertical plate of constant temperature is presented. The model is developed with and without interfacial shear stress due to the vapor flow at the liquid film surface. The vapor pressure drop, vapor exit velocity and cooling rate are calculated for different liquid mass flow values. It is shown that lower liquid mass flow produces higher cooling rate. The results also show that the interfacial shear stress has a considerable negative effect on the cooling rate. It is proved that there exists an optimum distance between the plates, which gives the maximum volumetric cooling rate.     

Heat and Mass Transfer Analogy Studies of Binary Liquid Mixture in Comparison with Eteanol-n-Heptane Evaporating to Air

An expenmental and theoretical study of heat and mass transfer analogy and a comparison of that to a binary liquid mixture evaporation is presented. Common organic solvents, ethanol and n-heptane, were used to form an alcohol - hydrocarbon mixture. Studies were carried out in a horizontal rectangular channel having air flow velocities of 0.2 - 0.9 m/s. Heat transfer coefficients were measured with a copper plate resistor and mass transfer coefficients with a square pool. The heat and mass transfer analogy is presented for a system having two evaporating compounds with a fixed value of air flow and verified by measuring and comparing mass transfer coefficients for distilled water with air flow velocities of 0.2 - 0.9 mls. An illustrative example of the use of the theory for industrial ventilation is presented.     

Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison

This paper compares two Self-Organizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal context of the input signals. The learning and the convergence properties of the TKM and RSOM are studied and we show analytically that the RSOM is a significant improvement over the TKM, because the RSOM allows simple derivation of a consistent learning rule. The results of the analysis are demonstrated with experiments.     

A framework for generating tunable test functions for multimodal optimization

Multimodal function optimization, where the aim is to locate more than one solution, has attracted growing interest especially in the evolutionary computing research community. To evaluate experimentally the strengths and weaknesses of multimodal optimization algorithms, it is important to use test functions representing different characteristics and various levels of difficulty. The available selection of multimodal test problems is, however, rather limited and no general framework exists. This paper describes an attempt to construct a software framework which includes a variety of easily tunable test functions. The aim is to provide a general and easily expandable environment for testing different methods of multimodal optimization. Several function families with different characteristics are included. The framework implements new parameterizable function families for generating desired landscapes. Additionally the framework implements a selection of well known test functions from the literature, which can be rotated and stretched. The software module can easily be imported to any optimization algorithm implementation compatible with the C programming language. As an application example, 8 optimization approaches are compared by their ability to locate several global optima over a set of 16 functions with different properties generated by the proposed module. The effects of function regularity, dimensionality and number of local optima on the performance of different algorithms are studied.     

WATER FLOW AND BINDER MIGRATION DURING DRYING OF COATED PAPER

The aim of this study has been to develop a mathematical model for water flow and binder migration in coated paper. The model can be used for simulation of the stages starting after the coating applicator including infrared and air drying sections. On the basis of the model the moisture, temperature and binder distribution can be determined across the paper thickness. Approximating equations concerning the paper properties are used in the model. We have measured the diffusion coefficient between the water and binder substances. Representative numerical examples are presented for separate and combined infrared and air drying processes.     

Differential evolution based nearest prototype classifier with optimized distance measures for the features in the data sets

In this paper a further generalization of differential evolution based data classification method is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, for determining the optimal values for all free parameters of the classifier model during the training phase of the classifier. The earlier version of differential evolution classifier that applied individually optimized distance measure for each new data set to be classified is generalized here so, that instead of optimizing a single distance measure for the given data set, we take a further step by proposing an approach where distance measures are optimized individually for each feature of the data set to be classified. In particular, distance measures for each feature are selected optimally from a predefined pool of alternative distance measures. The optimal distance measures are determined by differential evolution algorithm, which is also determining the optimal values for all free parameters of the selected distance measures in parallel. After determining the optimal distance measures for each feature together with their optimal parameters, we combine all featurewisely determined distance measures to form a single total distance measure, that is to be applied for the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; A sample belongs to the class represented by the nearest prototype vector when measured with the above referred optimized total distance measure. During the training process the differential evolution algorithm determines optimally the class vectors, selects optimal distance metrics for each data feature, and determines the optimal values for the free parameters of each selected distance measure. Based on experimental results with nine well known classification benchmark data sets, the proposed approach yield a statistically significant improvement to the classification accuracy of differential evolution classifier.     

Alkaline fuel cell: A test plant,measurements and analysis of the results

An experimental study of an alkaline fuel cell test plant with a 0.5 kW fuel cell module is presented in this paper. The characteristics of the fuel cell module are measured in different steady state load conditions and with different electrolyte temperatures. The results are analysed by calculating the different efficiencies of the module.     

A new concept for an osmotic energy converter

A new concept for an osmosis power generation system is presented. While the power production of a conventional system is based on continuous, increasing volumetric flow of solution directed to a turbine, the new concept is based on the pressurizing of fluids by osmosis. Two different new concepts were studied. In the first case, the osmotic module consists of the osmotic membranes, fresh water and solution. In the second case, gas is included in the solution part of the module. Consequently, the new system without gas was found to result in more than 2.5 times higher power values than the conventional concept. Copyright © 2001 John Wiley & Sons, Ltd.     

Clustering properties of hierarchical self-organizing maps

A multilayer hierarchical self-organizing map (HSOM) is discussed as an unsupervised clustering method. The HSOM is shown to form arbitrarily complex clusters, in analogy with multilayer feedforward networks. In addition, the HSOM provides a natural measure for the distance of a point from a cluster that weighs all the points belonging to the cluster appropriately. In experiments with both artificial and real data it is demonstrated that the multilayer SOM forms clusters that match better to the desired classes than do direct SOM's, classical k-means, or Isodata algorithms.     

Exergy analysis for stationary flow systems with several heat exchange temperatures

A thermodynamic theory of exergy analysis for a stationary flow system having several heat inputs and outputs at different temperature levels is presented. As a new result a relevant reference temperature of the surroundings is derived for each case. Also a general formula which combines exergy analysis with a modified Carnot efficiency is derived. The results are illustrated by numerical examples for mechanical multi-circuit heat pump cycles, for a Brayton process and for an absorption heat pump.