Falah Alobaid

In this research, experimental investigation and the computational fluid dynamic (CFD) simulation of a cryogenic condenser for oxygen liquefaction was carried out. The liquid nitrogen was used as a cooling fluid. In the simulation section, a three-dimensional model with a structured mesh with high mesh quality for aspect ratio and skewness was considered. The multi-phase flow inside the condenser was studied numerically, using the volume of fluid (VOF) method. This work also examined the assessment of the vapor generation rate during the condensation of oxygen, based on the boiling heat transfer mechanism and the unique physical characteristics. The experiment was conducted to examine the simulation results. The effect of liquid nitrogen height on the oxygen mass flows was investigated using computational fluid dynamics (CFD). The average deviation of the CFD predictions from the available experimental oxygen mass flows was 17%.

The higher heating value (HHV) is the main property showing the energy amount of biomass samples. Several linear correlations based on either the proximate or the ultimate analysis have already been proposed for predicting biomass HHV. Since the HHV relationship with the proximate and ultimate analyses is not linear, nonlinear models might be a better alternative. Accordingly, this study employed the Elman recurrent neural network (ENN) to anticipate the HHV of different biomass samples from both the ultimate and proximate compositional analyses as the model inputs. The number of hidden neurons and the training algorithm were determined in such a way that the ENN model showed the highest prediction and generalization accuracy. The single hidden layer ENN with only four nodes, trained by the Levenberg–Marquardt algorithm, was identified as the most accurate model. The proposed ENN exhibited reliable prediction and generalization performance for estimating 532 experimental HHVs with a low mean absolute error of 0.67 and a mean square error of 0.96. In addition, the proposed ENN model provides a ground to clearly understand the dependency of the HHV on the fixed carbon, volatile matter, ash, carbon, hydrogen, nitrogen, oxygen, and sulfur content of biomass feedstocks.