Prediction Of Critical Points Research Articles

Development of a generalised PV model in MATLAB/Simulink using datasheet values

This study proposes an improved single-diode modelling approach for photovoltaic (PV) modules suitable for a broad range of the PV technologies available today, including modules based on tandem cell structures. After establishing the model (which has an overall of seven parameters), this study devises a methodology to estimate its parameters using Standard Test Conditions (STC) data, Nominal Operating Cell Temperature (NOCT) data, and temperature coefficient values as provided in most manufacturers’ datasheets. Simulation results and their comparison with a previous work show a very accurate prediction of critical points in the current-voltage characteristics curve. The precise prediction happens for both STC and NOCT conditions and the error in predicting maximum power point (MPP) lies within 1% limit, and the error in its corresponding voltage and current is almost always within 2% limit. Further, for both MPP and open-circuit voltage, the statistical variance around manufacturer measurements due to temperature changes is demonstrated to be low for five various module technologies.

Computation of Critical Points of Mixtures Using Particle Swarm Optimization with Low-Discrepancy Sequences

The prediction of critical points of thermodynamic systems is an important tool for modeling many high-pressure processes of theoretical and practical interest. In this article, the calculation of critical points of multicomponent mixtures is treated as a global minimization problem of a modified merit function associated with the criticality conditions obtained from the Gibbs tangent plane criterion, designed to discriminate the scale of the problem. The methodology used to solve the optimization problem is based on two versions of the particle swarm optimization (PSO), equipped with low-discrepancy sequences to prevent the sensitivity of the swarm with respect to the location of the initial population. To avoid a rapid decrease in the weight inertia, and to prevent stagnations near undesirable local minimizers, we present a modification of the PSO method, which uses different search cycles with the same inertia weight. This new version developed here is a fast and robust algorithm for solving the critical-point problem, via global optimization.

The neural network and multivariate linear regression approach for observing phase transitions of polymers with the differential thermal analysis method

The aim of this study was to correlate the results of experimental data using DTA method and predictions of artificial neural network (ANN) and multivariate linear regression (MLR). Thermal decomposition of polymers was analyzed by simultaneous DTA method, and kinetic parameters (critical points, the change of enthalpy and entropy) of polymers were investigated. A computer model based on multilayer feed forwarding back propagation and multilayer linear regression model were used for the prediction of critical points, phase transitions of low-density polyethylene (LDPE) and mid-density polyethylene. As a result of our study, we concluded that ANN model is more suitable than MLR about prediction of experimental data.

Novel approach for the calculation of critical points in binary mixtures using global optimization

Critical point calculations in binary mixtures is a very important subject in phase equilibria study. A robust method for this task is certainly a powerful tool for calculation of critical loci. These critical diagrams are, usually, employed in the characterization of the behavior in two-component systems. In this paper, a new methodology for critical point calculation in binary systems, via global optimization, is presented. This approach is a particular case of the recently proposed formulation in chemical engineering literature by Henderson et al. [N. Henderson, L. Freitas, G.M. Platt, Prediction of critical points: a new methodology using global optimization, AIChE J. 50 (2004) 1300]. This novel methodology provides a robust algorithm, which does not involve derivatives of the objective function and is insensitive to the initial guesses. We obtain results for six binary systems and perform comparisons among experimental data (for systems n-butane/carbon dioxide, ethane/propane and ethane/n-butane). We also present new calculations for systems methane/hydrogen sulfide, carbon dioxide/n-octane and carbon dioxide/n-hexadecane, and showing a detailed analysis of the critical phenomena for the system methane/hydrogen sulfide. We detected that this last system can exhibits up to three critical points, for some compositions. The results illustrate the capability of the simulated annealing algorithm to find several critical points for binary systems.

Prediction of critical points: A new methodology using global optimization

AbstractIn the present work we develop, implement, and analyze the results of a new methodology destined to calculate critical points of phase transitions. Our criticality conditions are developed from a slight modification of the Gibbs tangent plane criterion. We formulate the critical points calculation as an optimization problem. Such formulation offers many advantages, among them we can mention the capability of determining more than one critical point, the use of robust numerical methods, and the possibility of visualization of the critical phenomenon from a two‐variable (T and P) objective function. To solve the optimization problems we use the simulated annealing algorithm, a stochastic method whose convergence does not depend on initial guesses and gives preference to global optima. Several samples of mixtures were used to test the present methodology. The results of our simulations were compared, in a satisfactory way, with experimental data and with some results calculated in other approaches. © 2004 American Institute of Chemical Engineers AIChE J, 50: 1300–1314, 2004