Modeling Effective Moisture Diffusivity of Orange Slice (Thompson Cv.)

The present study was conducted to compute effective moisture diffusivity and activation energy of orange slices during convection drying. The thin-layer drying experiments were carried out at five air temperatures of 40, 50, 60, 70, and 80^ºC, three air velocities of 0.5, 1.0, and 2.0 m/s and three orange slice thicknesses of 2, 4, and 6 mm. Results indicated that drying took place in the falling rate period. Moisture transfer from orange slices was described by applying the Fick's diffusion model. The effective diffusivity values were increased from 6.27 × 10^?10 to 3.50 × 10^?9 m²/s for the temperature range used in this study. An Arrhenius relation with an activation energy value of 16.47 to 40.90 kJ/mol and the diffusivity constant value of 7.74 × 10^?7 to 3.93 × 10^?3 m²/s were obtained. It was found that with increasing the temperature and air velocity the effective diffusivity increases while slice thickness showed no considerable changes on the effective diffusivity.     

Maximum likelihood estimation of synchronous machine parametersfrom standstill time response data

This paper presents a systematic approach for identification of a three-phase salient-pole synchronous machine rated at 5 kVA from standstill time-domain data. Machine time constant models and the equivalent circuit models are identified and their parameters are estimated. The initialization of the estimated parameters is achieved by the Laplace transformation of the recorded standstill time-response data and the derivation of the well-known operational inductances. The estimation is performed using the Maximum Likelihood algorithm. Based on the best estimated equivalent circuit models, simulation studies using the measured on-line dynamic responses are performed to validate the identified machine models     

Development of a neural network based saturation model forsynchronous generator analysis

This paper presents a new approach to model synchronous generator saturation based on a feedforward artificial neural network (ANN) model. The machine loading conditions, excitation levels and rotor positions are all included in the modeling process. The nonlinear saturation characteristics of a three-phase salient-pole synchronous machine rated at 5 kVA and 240 V is studied using the ANN model. An appropriate selection of input/output pattern for the ANN model training based on an error back-propagation scheme is developed using the on-line small-disturbance responses and the well-known maximum-likelihood estimation algorithm. The developed ANN model is implemented in the generator dynamic transient stability study requiring only small computational alteration in saturation model representation     

Identification of variable frequency induction motor models fromoperating data

The parameters of the induction motor model vary as operating conditions change. Accurate knowledge of these parameters and their dependency on operating conditions is critical for optimal field oriented control. This paper presents a systematic approach to modeling an induction motor considering operating conditions. All parameters are assumed to vary as a function of the operating conditions. The parameters are estimated from transient data using a constrained optimization algorithm. The parameters are mapped to the operating conditions using polynomial functions and artificial neural networks. The model is validated for both steady state and transient conditions     

An integrated virtual learning system for the development of motordrive systems

This paper describes the basic concept of an integrated virtual learning system as an instructional tool for the development of digital signal processor (DSP)-based control schemes for motor drive applications. The system is comprised of a graphical user interface (GUI) front-end and a hardware-in-the-loop custom DSP for rapid prototyping and efficient testing of digital control algorithms. The circuit design and control algorithm development of a pulse width modulation (PWM) voltage source inverter (VSI) for 3-phase brushless DC (BLDC) motor control applications is used to describe the functions of the system. It is shown that the virtual learning system provides a cost-effective learning tool for students or engineers in training and can serve as a supplement to a conventional laboratory based system     

Study of fast decoupled load flow algorithms with substantially reduced memory requirements

The convergence pattern of fast decoupled load flow (FDLF) algorithms with substantially reduced memory requirements is presented in this paper. The convergence performance of FDLF algorithms as compared with the Newton-Raphson algorithm under various loading conditions is demonstrated. Furthermore, one particular formulation of an FDLF algorithm which allows substantial reduction in memory requirements with approximately the same convergence rate as the current FDLF method is described. The proposed method is simple and can easily be added to the existing FDLF programs.     

The chitin catabolic cascade in the marine bacterium Vibrio furnissii. Molecular cloning, isolation, and characterization of a periplasmic chitodextrinase

Chitin catabolism in Vibrio furnissii comprises several signal transducing systems and many proteins. Two of these enzymes are periplasmic and convert chitin oligosaccharides to GlcNAc and (GlcNAc)2. One of these unique enzymes, a chitodextrinase, designated EndoI, is described here. The protein, isolated from a recombinant Escherichia coli clone, exhibited (via SDS-polyacrylamide gel electrophoresis) two enzymatically active, close running bands ( approximately mass of 120 kDa) with identical N-terminal sequences. The chitodextrinase rapidly cleaved chitin oligosaccharides, (GlcNAc)4 to (GlcNAc)2, and (GlcNAc)5,6 to (GlcNAc)2 and (GlcNAc)3. EndoI was substrate inhibited in the millimolar range and was inactive with chitin, glucosamine oligosaccharides, glycoproteins, and glycopeptides containing (GlcNAc)2. The sequence of the cloned gene indicates that it encodes a 112,690-kDa protein (1046 amino acids). Both proteins lacked the predicted N-terminal 31 amino acids, corresponding to a consensus prokaryotic signal peptide. Thus, E. coli recognizes and processes this V. furnissii signal sequence. Although inactive with chitin, the predicted amino acid sequence of EndoI displayed similarities to many chitinases, with 8 amino acids completely conserved in 10 or more of the homologous proteins. There was, however, no "consensus" chitin-binding domain in EndoI.     

Discrete time sliding mode,continuous time sliding mode and vector control of induction motors

Three control strategies are implemented and compared in this paper: vector control, continuous time sliding mode control and discrete time sliding mode control of an induction motor. The induction motor model used for control takes into account magnetizing inductance saturation and rotor resistance variation. While the magnetizing inductance is mapped to the operating conditions, the rotor resistance is estimated on-line. The motor is controlled with optimal efficiency flux. Flux weakening is employed above rated speed. Experimental results are drawn to show the advantages and disadvantages of the control methods.     

Synchronous machine parameter estimation using the Hartley series

This paper presents a novel alternative to estimate armature circuit parameters of large utility generators using real time operating data. The proposed approach uses the Hartley series for fitting operating data (voltage and currents measurements). The essence of the method is the use of linear state estimation to identify the coefficients of the Hartley series. The approach is tested for noise corruption likely to be found in measurements. The method is found to be suitable for the processing of digital fault recorder data to identify synchronous machine parameters