A Mass Transport Model for Drying Wood under Isothermal Conditions

Mass transport in wood during drying can have different mechanisms at different periods of drying. Depending on the current moisture content (MC) and the structure of the wood, the driving forces for the mass transport are essentially different. Above the fiber saturation point (FSP), the lumens are partially saturated and the transport of liquid (free) water occurs as a consequence of capillary action. On the other hand, below the FSP, bound water within the cell walls is conveyed by diffusion, and water vapor in the lumens moves under influence of pressures gradient. Based on these considerations, a unified model is presented that takes into account the transport of the different moisture phases. Simulation of the drying of a Norway spruce sample at 50°C from about 135 to 7% MC is carried out using the finite element method (FEM). Comparison between the simulated average MC and the experimental observations obtained from X-ray computed tomography (CT) shows reasonable agreement. Possible simplifications in the model are briefly discussed as well as some aspects of the numerical implementation. Finally, the influence of absolute permeability on the average MC is studied.     

Generation of adaptive gait patterns for quadruped robot with CPG network including motor dynamic model

This paper describes the generation of adaptive gait patterns using new Central Pattern Generators (CPGs) including motor dynamic models for a quadruped robot under various environments. The CPGs act as the flexible oscillators of the joints and adjust joint angles to required values. The CPGs are interconnected with each other and sets of their coupling parameters are adjusted by a genetic algorithm so that the quadruped robot can realize stable and adequate gait patterns. Generation of gait patterns results in the formation of the CPG networks suitable for the formation of not only a straight walking pattern but also of rotating gait patterns. Experimental results demonstrate that the proposed CPG networks are effective for the automatic adjustment of the adaptive gait patterns for the tested quadruped robot under various environments. Furthermore, the target tracking control based on image processing is achieved by combining the general gait patterns. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 155(1): 35–43, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20225     

Simulation of water and nitrogen dynamics in soils during wastewater applications by using a finite-element model

A mathematical model was developed to simulate water movement, mass transport, and nitrogen transformations in soils during wastewater applications. The model is one-dimensional and based on the Galerkin finite-element method. The submodel of mass transport of nitrogen incorporates the convection-dispersion processes of ammonium and nitrate nitrogen, nitrification, denitrification, ammonium exchange and uptake of ammonium and nitrate ions. The accuracy and validity of the proposed model was examined by comparison with an explicit-implicit finite-difference model results. The model was used for simulation of water and nitrogen dynamics during wastewater application in homogeneous and multi-layered soils under different N concentration, rate, duration and scheduling of application.     

Isolation and preliminary characterization of Pichia pinus mutants insensitive to glucose repression

A new method for the isolation of glucose repression-insensitive mutants in the methylotrophic yeast Pichia pinus was developed. The method is based on screening of small suspension samples derived from 2-deoxyglucose-resistant colonies for alcohol oxidase activity. Alcohol oxidase activity was evaluated by determination of formaldehyde excreted by cells. Mutants with glucose non-repressible alcohol oxidase and catalase synthesis were obtained. All mutants grew poorly on D -xylose compared to the wild type, whereas growth on L -arabinose was similar to the wild type. Changes in the glucose transport system were suggested to be responsible for altered growth characteristics and defective glucose repression.     

TRVs and fault clearing stresses in extra-high-voltage radial networks

To cope with the high rate of increase of power demand in the main industrialized districts in Japan, 550-kV transmission systems covering the districts have been reinforced, with most of the main power plants connected directly to these systems. Through 550-/300-kV substations, the majority of power to the districts is supplied by 300-kV systems. To limit the excess short-circuit capacity in the 300-kV systems, they tend to be reconstructed as so-called radial networks. In radial networks with high short-circuit capacity and relatively small number of transmission lines connected to the substation busbars, the rate of rise of TRV can be far higher than standard value. This paper analyzes the transient recovery voltages (TRV) in such extra-high-voltage radial networks in Japan, together with the relevant stresses to circuit-breakers during fault clearings. Future system conditions have also been introduced. As the typical rate of rise of the TRV values, more than twice that of today's standard ones are probable.     

Reactor modelling for electrochemical processes

This paper presents a relatively straightforward approach to the modelling of electrochemical reactors operated in batch or continuous modes. The models are based on ideal flow assumptions of either well-mixed or plug flow and incorporate reaction rate models based on electrochemical kinetics and mass transport at one electrode. General characteristics of the reactor models are described, particularly with regard to the need for good mass transport in metal recovery applications. An example is given on the use of the model in the recovery of a heavy metal (Cd²⁺) from an acidified solution containing Cd(II) and Fe(III) ions. The reaction rate model is based on experimental data.     

Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor

Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was used to classify the experimental cutting force data of milling operations previously. Preprocessing (PreP) of the approximation coefficients of the WT is proposed just before the classification by using the Adaptive Resonance Theory (ART2) type NNs. Genetic Algorithm (GA) was used to estimate the weights of each coefficient of the PreP. The WT-PreP-NN (ART2) combination worked at lower vigilances by creating only a few meaningful categories without any errors. The WT-NN (ART2) combination could obtain the same error rate only if very high vigilances are used and many categories are allowed.     

Thermodynamic state of reinforced interpenetrating polymer networks

The free energies of mixing of two networks in the interpenetrating polymer network based on crosslinked polyurethane and poly(ester acrylate) have been determined by the vapour sorption method. It was established that the constituent networks in the IPN are not miscible. The introduction of fillers of different chemical nature increases the compatibility. The thermodynamic affinity of the fillers to the individual networks and IPN was estimated. It was established that when the free energy of interaction of one or both components of the IPN with the filler is negative, reinforcement leads to the formation of a compatible and equilibrium system. For fillers having no affinity to the polymers, compatibilization is observed, which is connected with slowing down of phase separation in the system in the presence of filler.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008