Jerzy Weres

Inverse analysis of the transient bound water diffusion in wood

Abstract The methods so far applied to determine the bound water diffusion coefficient in wood do not provide credible results on this coefficient as well as on the boundary condition. An alternative approach based on the concept of solving the inverse transfer problems was recently applied. Two European species were investigated in the present study. A series of sorption experiments was performed and followed by the numerical identification of the coefficients. Several case studies were carried out for the constant and bound water content dependent diffusion coefficients. The obtained results were validated by comparison to a set of experimental data.

Neural identification of selected apple pests

The study was based on neural network modeling methods, including image analysis.Artificial neural networks as a powerful tool to identify pests.The color of pests as the dominant input variable of a neural model.The best classification ability was achieved by MLP topology. The subject of this study was to investigate the possibility of using artificial neural networks as a tool for classification, designed to identify apple orchard pests. The paper presents a classification neural model using optimized learning sets acquired on the basis of the information encoded in the form of digital images of selected pests. This study predominantly deals with the problem of the identification of 6 selected apple pests which are most commonly found in Polish orchards. Neural modeling techniques, including digital image analysis, were used to classify the pests.The qualitative analysis of neural models produced, indicates that multi-layered perceptron (MLP) neural network topology achieve the best classification ability. Representative features, allowing for effective pest identification are 23 visual parameters in the form of 7 selected coefficients of shape and 16 color characteristic of pests. The dominant input variables of a neural model, determining the correct identification of the features, contain information about the color of pests.Our results support the hypothesis that artificial neural networks are an effective tool that supports the process of identification of pests in apple orchards. The resulting neural classifier has been created to assist in the decision-making processes that take place during the production of apples, in the context of protection against pests.

Effects of thermal conductivity data on accuracy of modeling heat transfer in wood

Summary Data sets of wood thermal properties differing in their complexity are presented and discussed. A number of numerical experiments of the heat transfer in wood are performed, and the predicted temperatures are compared to the experimental data obtained for European beech and Scots pine wood. The analysis of similarity of the heat transfer model together with the different empirical data of wood thermal properties to the results of the experiments showed that the lowest accuracy of temperature prediction was obtained for the constant data. Application of advanced models of the thermal conductivity required a large amount of input data and sometimes gave relatively low accuracy in temperature prediction. Application of the thermal conductivity models developed by the authors with the use of the Inverse Heat Transfer Problem approach produced the best temperature prediction accuracy.

IDENTIFICATION OF MATHEMATICAL MODEL COEFFICIENTS IN THE ANALYSIS OF THE HEAT AND MASS TRANSPORT IN WOOD

ABSTRACT A method based on the concept of solving inverse heat and mass transport problems was proposed to identify wood physical properties, with the use of empirical data, a mathematical model corresponding to the direct problem, and an optimization procedure. The computer software was developed to solve transient, three-dimensional, quasi-linear direct and inverse problems of heat and mass transport in wood as an anisotropic body together with initial and boundary conditions of any kind. The software was adapted to data parallel processing environment of high performance computers. Identification of thermal conductivity was performed for beech wood in three principal anatomic directions, and accuracy of temperature distribution predictions was significantly increased (the global relative error of prediction was reduced to 1.0 - 1.9%).

Implementation of a relaxation equilibrium term in the convective boundary condition for a better representation of the transient bound water diffusion in wood

In this work, a relaxation term was added to the convective boundary condition to increase the accuracy of the transient bound water diffusion modeling in wood. The implemented term accounts for a relaxation time constant in the equilibrium moisture content. The inverse finite element analysis approach was used to determine the values of all coefficients of the modified diffusion model. This procedure was performed for beech wood (Fagus sylvatica L.) in the radial and longitudinal directions. The experimental data obtained by Perré et al. (2007) for transient diffusion configurations were used here. The accurate control of moist air parameters and the improved procedure for mass measurements of a sample during sorption experiments were used. The influence of the modification of the boundary condition on accuracy of diffusion modeling was analyzed.

Computer image analysis in the quality in procedure for selected carrot varieties

In our daily lives we often assess our surroundings to classify the situations we encounter. We do so based on the observations we make of our surroundings and information we obtain from other sources, using our knowledge and abilities. While this process is natural to us, if we want to give a similar task to a computer system then we have to take various steps in order to enable our computers to partially emulate the human capacity for observation, learning and making final decisions based on knowledge. As information complexity increases, there is an increasing demand for systems which can recognize and classify the objects presented to them. Recently there has been an increase in interest in application of computer image analysis in various research areas. One of these applications is food quality assessment, which aims to replace traditional instrumental methods. A computer visual system was developed to assess carrot quality, based on a single variety. Characteristic qualities of the variety were chosen to describe a suitable root. In the course of the study, digital photographs of carrot roots were taken, which were used as input data for the assessment performed by a dedicated computer program created as a part of the study.

A stand for the image acquisition of composted material based on the sewage sludge

Composting is one of the best methods for sewage sludge management. The early identification of the young compost stage in composted material is important. The method for determining the degree of maturity of composted material containing sewage sludge will use the selected topologies of artificial neural networks. The learning processes of these networks will be carried out with the use of the information contained in digital images of composted material. It is important that acquisition of these images was carried out under constant lighting and exposure conditions on a suitable acquisition stand. The objectives of presented study were: to develop a stand for image acquisition of composted material, to determine the spectral distribution for used light sources and illuminance distribution for visible light, to determine the parameters for image acquisition of composted material. A suitable stand, consisted of three photographic chambers illuminated with visible light, UV-A light and mixed light, was developed. The spectral distribution of the used light sources and the illuminance distribution for visible light were analyzed and considered satisfactory. Image acquisition parameters, such as focal length, ISO sensitivity, aperture and exposure time, were specified.

Inverse finite element analysis of technological processes of heat and mass transport in agricultural and forest products

Abstract Due to complexity of agricultural and forest products, the mathematical model coefficients are often dubious, as experimental determination of their values leads to erroneous results. To solve this problem an inverse finite element analysis software was developed to identify coefficient values of the heat and mass transport model and to predict and visualize the processes. The model reflected 3D structure of investigated systems comprising the heat conduction and moisture diffusion in heterogeneous, anisotropic, and irregularly shaped products represented by wood and cereal grain kernels. Test cases used to validate the software covered identification of the thermal conductivity, convective heat transfer coefficient, diffusion coefficient, equilibrium moisture content, and convective mass transfer coefficient in pine and beech wood, and also in corn. Implementation of the proposed optimization algorithm and improvement of the software functionality resulted in more effective and accurate identification of the coefficient values, demonstrated by increased accuracy and reliability of predicting the heat conduction and water diffusion processes.

Thermal properties of wood-based panels: thermal conductivity identification with inverse modeling

Accuracy and effectiveness of predicting the heat transfer in wood-based panels is increasingly important for describing their behavior, especially for varying environmental conditions. To model the heat transfer in wood-based panels it is essential to input credible data on their thermal properties. Therefore, proper estimation of the specific heat and thermal conductivity is fundamental. A finite element inverse analysis procedure was developed. The procedure was designed in such a way that anisotropy of the thermal conductivity was accounted for. For all analyzed wood-based panels, in-plane thermal conductivity was significantly higher compared to the transverse one, and it was recommended to consider the anisotropy, and to use both in-plane and transverse thermal conductivity for modeling heat transfer. The effect of temperature on thermal conductivity was not clearly manifested. The thermal conductivity values were decreasing or increasing with temperature. In some cases this influence was practically insignificant (i.e. OSB), while for low density fiberboard the effect of temperature on thermal conductivity was the highest. The identification procedure was validated and its credibility was assessed. It was shown that data on thermal properties available in the literature should not be recommended to model the heat transfer.

Computer-aided identification of the water diffusion coefficient for maize kernels dried in a thin layer

Abstract Uncertainties in mathematical modelling of water transport in cereal grain kernels during drying and storage are mainly due to implementing unreliable values of the water diffusion coefficient and simplifying the geometry of kernels. In the present study an attempt was made to reduce the uncertainties by developing a method for computer-aided identification of the water diffusion coefficient and more accurate 3D geometry modelling for individual kernels using original inverse finite element algorithms. The approach was exemplified by identifying the water diffusion coefficient for maize kernels subjected to drying. On the basis of the developed method, values of the water diffusion coefficient were estimated, 3D geometry of a maize kernel was represented by isoparametric finite elements, and the moisture content inside maize kernels dried in a thin layer was predicted. Validation of the results against experimental data showed significantly lower error values than in the case of results obtained for the water diffusion coefficient values available in the literature.