Corrigendum to "Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes".

[This corrects the article DOI: 10.1155/2021/7332776.].

Employing computational fluid dynamics technique for analyzing the PACK-1300XY with methanol and isopropanol mixture.

In this study, an innovative wire gauze structured packing, namely PACK-1300XY with a specific surface area of 1300 m2/m3 has been characterized by performing computational fluid dynamics (CFD) approach. Indeed, different features of this packing (height equivalent to a theoretical plate, wet/dry pressure drop, and mass transfer efficiency) were analyzed by analyzing the flow regime using the three-dimensional CFD approach with the Eulerian-Eulerian multiphase scenario. The results showed the mean relative deviation of 16% (for wet pressure drop), 14% (for dry pressure drop), and 17% (for mass transfer efficiency) between the CFD predictions and experimental measurements. These excellent levels of consistency between the numerical findings and experimental observations approve the usefulness of the CFD-based approach for reliable simulation of separation processes.

Comprehensive investigations of mixed convection of Fe-ethylene-glycol nanofluid inside an enclosure with different obstacles using lattice Boltzmann method.

In the present paper, nanofluid mixed convection is investigated in a square cavity with an adiabatic obstacle by using the Lattice Boltzmann method (LBM). This enclosure contains Fe-ethylene-glycol nanofluid and three constant temperature thermal sources at the left wall and bottom of the enclosure through a lateral wall. The fluid is incompressible, laminar, and Newtonian. The obtained results are presented in the constant Ra = 104 and a Pr = 0.71 for different Ri = 0.1, 1, and 10. The effects of the slope of the enclosure, volume fraction of nanoparticles [Formula: see text], the location of adiabatic obstacles, and nanoparticle diameter in the fluid are investigated on the value of heat transfer. A change in the attack angle of the enclosure leads to changes in the movement distance for fluid between hot and cold sources and passing fluid through case E, which affects the flow pattern strongly. In each attack angle, on colliding with an obstacle, the fluid heat transfers between two sources, which leads to uniform heat transfer in the enclosure. By increasing the velocity of the lid, the Richardson number decreases leading to improvement of the convective heat transfer coefficient and Nusselt number enhancement. The results so obtained reveal that by augmenting [Formula: see text] value the effect of Richardson number reduction can augment Nusselt number and the amount of absorbed heat from the hot surface. Consequently, in each state where a better flow mixture and lower depreciation of fluid velocity components, due to the penetration of lid movement and buoyancy force, occurs higher heat transfer rate is accomplished. Furthermore, it is shown that when Ri = 0.1, the effect of cavity angle is more important but when Ri = 10, the effect of the position of obstacle is more visible.

Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes.

Isentropic compressibility is one of the significant properties of biofuel. On the other hand, the complexity related to the experimental procedure makes the detection process of this parameter time-consuming and hard. Thus, we propose a new Machine Learning (ML) method based on Extreme Learning Machine (ELM) to model this important value. A real database containing 483 actual datasets is compared with the outputs predicted by the ELM model. The results of this comparison show that this ML method, with a mean relative error of 0.19 and R 2 values of 1, has a great performance in calculations related to the biodiesel field. In addition, sensitivity analysis exhibits that the most efficient parameter of input variables is the normal melting point to determine isentropic compressibility.

An insight into the estimation of drilling fluid density at HPHT condition using PSO-, ICA-, and GA-LSSVM strategies.

The present study evaluates the drilling fluid density of oil fields at enhanced temperatures and pressures. The main objective of this work is to introduce a set of modeling and experimental techniques for forecasting the drilling fluid density via various intelligent models. Three models were assessed, including PSO-LSSVM, ICA-LSSVM, and GA-LSSVM. The PSO-LSSVM technique outperformed the other models in light of the smallest deviation factor, reflecting the responses of the largest accuracy. The experimental and modeled regression diagrams of the coefficient of determination (R2) were plotted. In the GA-LSSVM approach, R2 was calculated to be 0.998, 0.996 and 0.996 for the training, testing and validation datasets, respectively. R2 was obtained to be 0.999, 0.999 and 0.998 for the training, testing and validation datasets, respectively, in the ICA-LSSVM approach. Finally, it was found to be 0.999, 0.999 and 0.999 for the training, testing and validation datasets in the PSO-LSSVM method, respectively. In addition, a sensitivity analysis was performed to explore the impacts of several variables. It was observed that the initial density had the largest impact on the drilling fluid density, yielding a 0.98 relevancy factor.