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Nonlinear Nature (nonlinear + nature)

Distribution by Scientific Domains

Chemistry	50%

Selected Abstracts

A study on a new AVR parameter tuning concept using on-line measured data with the real-time simulator

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 3 2006
Joong-Moon Kim
Abstract Automatic voltage regulator (AVR) parameter tuning for voltage control of power system generators has generally been performed with the analytic methods and the simulation methods, which mostly depend on off-line linear mathematical models of power system. However, due to the nonlinear nature of power system, the mathematical models of the excitation system may not be correct. So the excitation control system performances with the parameter set that is tuned by using the mathematical model, may not be appropriate for some operating conditions. This paper presents an AVR parameter tuning method using on-line measured data of the excitation control system with parameter optimization technique. As this method utilizes on-line operating data, it can overcome the limitation of model uncertainty problems of conventional method. To validate the proposed tuning concept, a scaled model excitation system is connected to the real-time power system simulator, and the proposed tuning concept is tested. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Non-linear approaches for reducing large power systems

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 3 2001
X. Lei
Issues on the establishment of equivalent networks are becoming essential for the deregulated power market. This paper presents a comprehensive tool for network reduction of large power systems. Through integrating different methodologies into a simulation program, the dynamic equivalent can be established by adopting one common database. With a readily integrated modified Gauss-Newton algorithm, network reduction can be executed under the dynamic conditions either in the time domain or in the frequency domain in coping with nonlinear nature of the system involved. Furthermore, a novel algorithm based on dynamic coherency approach implemented readily into the simulation program is also presented. This novel approach determines coherent generators on non-linear basis in the time domain using the cross correlation technique, taking dynamic characteristics of the system involved into consideration. Two case studies are presented in this paper. Each of the non-linear approaches presented is applied for one of the case studies as application example. The results achieved validate the functionality of the approaches presented. [source]

Artificial neural networks model for the prediction of steady state phenol biodegradation in a pulsed plate bioreactor

JOURNAL OF CHEMICAL TECHNOLOGY & BIOTECHNOLOGY, Issue 9 2008
K Vidya Shetty
Abstract BACKGROUND: A recent innovation in fixed film bioreactors is the pulsed plate bioreactor (PPBR) with immobilized cells. The successful development of a theoretical model for this reactor relies on the knowledge of several parameters, which may vary with the process conditions. It may also be a time-consuming and costly task because of their nonlinear nature. Artificial neural networks (ANN) offer the potential of a generic approach to the modeling of nonlinear systems. RESULTS: A feedforward ANN based model for the prediction of steady state percentage degradation of phenol in a PPBR by immobilized cells of Nocardia hydrocarbonoxydans (NCIM 2386) during continuous biodegradation has been developed to correlate the steady state percentage degradation with the flow rate, influent phenol concentration and vibrational velocity (amplitude × frequency). The model used two hidden layers and 53 parameters (weights and biases). The network model was then compared with a Multiple Regression Analysis (MRA) model, derived from the same training data. Further these two models were used to predict the percentage degradation of phenol for blind test data. CONCLUSIONS: The performance of the ANN model was superior to that of the MRA model and was found to be an efficient data-driven tool to predict the performance of a PPBR for phenol biodegradation. Copyright © 2008 Society of Chemical Industry [source]

Efficient use of nonequilibrium measurement to estimate free energy differences for molecular systems

JOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 14 2004
F. Marty Ytreberg
Abstract A promising method for calculating free energy differences ,F is to generate nonequilibrium data via "fast-growth" simulations or by experiments,and then use Jarzynski's equality. However, a difficulty with using Jarzynski's equality is that ,F estimates converge very slowly and unreliably due to the nonlinear nature of the calculation,thus requiring large, costly data sets. The purpose of the work presented here is to determine the best estimate for ,F given a (finite) set of work values previously generated by simulation or experiment. Exploiting statistical properties of Jarzynski's equality, we present two fully automated analyses of nonequilibrium data from a toy model, and various simulated molecular systems. Both schemes remove at least several kBT of bias from ,F estimates, compared to direct application of Jarzynski's equality, for modest sized data sets (100 work values), in all tested systems. Results from one of the new methods suggest that good estimates of ,F can be obtained using 5,40-fold less data than was previously possible. Extending previous work, the new results exploit the systematic behavior of bias due to finite sample size. A key innovation is better use of the more statistically reliable information available from the raw data. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 1749,1759, 2004 [source]

Modeling of turbulent precipitation: A transported population balance-PDF method

AICHE JOURNAL, Issue 4 2010
Giovanni di Veroli
Abstract Turbulent precipitation is a complex problem, whose mathematical description of precipitation requires a coupling of fluid dynamics with the population balance equation (PBE). In the case of turbulent flow, this coupling results in unclosed equations due to the nonlinear nature of precipitation kinetics. In this article, we present a methodology for modeling turbulent precipitation using the concept of the transported probability density function (PDF) in conjunction with a discretized PBE, simulated via a Lagrangian stochastic method. The transported PBE-PDF approach resolves the closure problem of turbulent precipitation for arbitrarily complex precipitation kinetics, while retrieving the full particle size distribution (PSD). The method is applied to the precipitation of BaSO4 in a turbulent pipe flow and comparisons are made with the experimental results of Baldyga and Orciuch (Chem Eng Sci. 2001;56:2435-2444) showing excellent agreement, while insight is drawn into the mechanisms that determine the evolution of the product PSD. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source]

Maximum Likelihood Estimation of VARMA Models Using a State-Space EM Algorithm

JOURNAL OF TIME SERIES ANALYSIS, Issue 5 2007
Konstantinos Metaxoglou
Abstract., We introduce a state-space representation for vector autoregressive moving-average models that enables maximum likelihood estimation using the EM algorithm. We obtain closed-form expressions for both the E- and M-steps; the former requires the Kalman filter and a fixed-interval smoother, and the latter requires least squares-type regression. We show via simulations that our algorithm converges reliably to the maximum, whereas gradient-based methods often fail because of the highly nonlinear nature of the likelihood function. Moreover, our algorithm converges in a smaller number of function evaluations than commonly used direct-search routines. Overall, our approach achieves its largest performance gains when applied to models of high dimension. We illustrate our technique by estimating a high-dimensional vector moving-average model for an efficiency test of California's wholesale electricity market. [source]