Research on the influence of convector factors on a panel radiator’s heat output and total weight with a machine learning algorithm

Çalışır, Tamer; Çolak, Andaç Batur; Aydın, Devrim; Dalkılıç, Ahmet Selim; Baskaya, Senol

doi:10.1140/epjp/s13360-022-03622-6

Cited by 4 publications

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Hybrid approach for gas viscosity in Yemeni oil fields

Al-Gathe,

Baarimah,

Al-Khudafi

et al. 2023

Earth Sci Inform

View full text Add to dashboard Cite

The estimation of a gas viscosity experimentally is often difficult. So, accurate determination of gas viscosity has been the main challenge in a gas reservoir development. There are many correlations to estimate this property. Often time, the results of these correlations do not agree with experimental data, thereby causing a considerable amount of error. The difficulty of these correlations can be propagated simply by tuning against some experimental data using artificial intelligent model. Currently, the achievements of artificial neural networks (ANN) techniques alone to predict gas viscosity open the door to use the hybrid system. In this model, the Particle Swarm Optimization (PSO) algorithm is employed to search for optimal connection weighs and thresholds for the neural networks (NN), then the back-propagation learning rule and training algorithm is used to adjust the final weights. A total of about 868 data points obtained from the laboratory measurements of gas viscosity were used. The data include measured gas viscosity, specific gas gravity, temperature, pressure, molecular weight, pseudo-critical temperature and pressure and non-hydrocarbon components (H2S, CO2, and N2). The performance of the PSONN model is compared with performance of ANN and other empirical model to show the most general and accurate model for predicting gas viscosity. From the results of this study, we found that the PSONN model is more reliable and accurate with the absolute present relative (APRE) error and mean square error (RMS) of 2.76 and 5.49 respectively.

show abstract