A Group Contribution Method for Predicting the Freezing Point of Ionic Liquids

A simple group contribution method for the prediction of the freezing point for several ionic liquids is presented. Liquids have a characteristic temperature, known as their freezing point, at which they turn into solids. The melting point of a solid should theoretically be the same as the freezing point for the liquid. Greater differences between these quantities can be observed in ionic liquids. Some ionic liquids display substantial supercooling while being cooled at relatively high temperature. Experimental data from the freezing point (not melting point) for 40 ionic liquids were used to obtain the contributions for the cation-anion groups in a correlation set. The optimum parameters of the method were obtained using a genetic algorithm-based on multivariate linear regression. Then, the freezing points for another 23 ionic liquids were predicted, and the results were compared with experimental data available in the literature. The results show an average deviation of 5 %

Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network

In this study, an artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the short-term $x(t+6)$. The performance prediction was evaluated and compared with another studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called {\it stochastic} hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute uncertainties of predictions for noisy Mackey--Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level ($\sigma_{N}$) from 0.01 to 0.1.Comment: 11 pages, 8 figure

Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm

An artificial neural network was used for forecasting of long-term wind speed data (24 and 48 hours ahead) in La Serena City (Chile). In order to obtain a more effective correlation and prediction, a particle swarm algorithm was implemented to update the weights of the network. 43800 data points of wind speed were used (years 2003- 2007), and the past values of wind speed, relative humidity, and air temperature were used as input parameters, considering that these meteorogical parameters are more readily available around the globe. Several neural network architectures were studied, and the optimum architecture was determined by adding neurons in systematic form and evaluating the root mean square error (RMSE) during the learning process. The results show that the meteorological variables used as input parameters, have influential effects on the good training and predicting capabilities of the chosen network, and that the hybrid neural network can forecast the hourly wind speed with acceptable accuracy, such as: RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 and R2 =0.97 for 24-hours-ahead wind speed prediction, and RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 for 48-hours-ahead wind speed prediction.Una red neuronal artificial fue utilizada para la predicción de datos de la velocidad de viento a largo plazo (24 y 48 horas en adelanto) en la Ciudad de La Serena (Chile). Para obtener una efectiva correlación y predición, se implementó una optimización de enjambre de particulas para actualizar los pesos de la red. Se emplearon 43800 datos de velocidad de viento (años 2003-2007), y los valores pasados de velocidad del viento, humedad relativa y temperatura del aire fueron utilizados como parámetros de entrada, considerando que estos parámetros meteorológicos se encuentran fácilmente disponibles en todo el mundo. Se estudiaron varias arquitecturas de redes neuronales y la arquitectura optima se determine añadiendo neuronas de forma sistemática y evaluando la raíz del error cuadrático medio (RMSE) durante el proceso de aprendizaje. Los resultados muestran que las variables meteorológicas utilizadas como parámetros de entrada, tienen un efecto positivo sobre el correcto entrenamiento y capacidades predictivas de la red, y que la red neural híbrida puede pronosticar la velocidad del viento horaria con una precisión aceptable, como un RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 y R2 =0.97 para la predicción de la velocidad del viento de 24 horas en adelanto, y un RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 para la predicción de la velocidad del viento de 48 horas en adelanto

General method for prediction of thermal conductivity for well-characterized hydrocarbon mixtures and fuels up to extreme conditions using entropy scaling

A general and efficient technique is developed to predict the thermal conductivity of well-characterized hydrocarbon mixtures, rocket propellant (RP) fuels, and jet fuels up to high temperatures and high pressures (HTHP). The technique is based upon entropy scaling using the group contribution method coupled with the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state. The mixture number averaged molecular weight and hydrogen to carbon ratio are used to define a single pseudo-component to represent the compounds in a well-characterized hydrocarbon mixture or fuel. With these two input parameters, thermal conductivity predictions are less accurate when the mixture contains significant amounts of iso-alkanes, but the predictions improve when a single thermal conductivity data point at a reference condition is used to fit one model parameter. For eleven binary mixtures and three ternary mixtures at conditions from 288 to 360 K and up to 4,500 bar, thermal conductivities are predicted with mean absolute percent deviations (MAPDs) of 16.0 and 3.0% using the two-parameter and three-parameter models, respectively. Thermal conductivities are predicted for three RP fuels and three jet fuels at conditions from 293 to 598 K and up to 700 bar with MAPDs of 14.3 and 2.0% using the two-parameter and three-parameter models, respectively

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods

Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm

An artificial neural network was used for forecasting of long-term wind speed data (24 and 48 hours ahead) in La Serena City (Chile). In order to obtain a more effective correlation and prediction, a particle swarm algorithm was implemented to update the weights of the network. 43800 data points of wind speed were used (years 2003- 2007), and the past values of wind speed, relative humidity, and air temperature were used as input parameters, considering that these meteorogical parameters are more readily available around the globe. Several neural network architectures were studied, and the optimum architecture was determined by adding neurons in systematic form and evaluating the root mean square error (RMSE) during the learning process. The results show that the meteorological variables used as input parameters, have influential effects on the good training and predicting capabilities of the chosen network, and that the hybrid neural network can forecast the hourly wind speed with acceptable accuracy, such as: RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 and R2 =0.97 for 24-hours-ahead wind speed prediction, and RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 for 48-hours-ahead wind speed prediction

GA-optimized neural network for forecasting the geomagnetic storm index

Se desarrolló un método que combina una red neuronal artificial y un algoritmo genético (ANN+GA) con el fin de pronosticar el índice de tiempo de perturbación de tormenta (Dst). A partir de esta técnica, la ANN fue optimizada por GA para actualizar los pesos de la ANN y para pronosticar el índice Dst a corto plazo de 1 a 6 horas de antelación usando los valores de la serie temporal del índice Dst y del índice de electrojet auroral (AE). La base de datos utilizada contiene 233,760 datos de índices geomagnéticos por hora desde 00 UT del 01 de enero de 1990 hasta las 23 UT del 31 de agosto de 2016. Se analizaron diferentes topologías de ANN y se seleccionó la arquitectura óptima. Se encontró que el método propuesto ANN+GA puede ser adecuadamente entrenado para pronosticar Dst (t+1 a t+6) con una precisión aceptable (con errores cuadrático medio RMSE≤10nT y coeficientes de correlación R≥0.9), y que los índices geomagnéticos utilizados tienen efectos influyentes en la buena capacidad de entrenamiento y predicción de la red elegida. Los resultados muestran una buena aproximación entre las variaciones medidas y modeladas de Dst tanto en la fase principal como en la fase de recuperación de una tormenta geomagnética. doi: https://doi.org/10.22201/igeof.00167169p.2018.57.4.210