This study correlated biomass heat capacity (Cp) with the chemistry (sulfur and ash content), crystallinity index, and temperature of various samples. A five-parameter linear correlation predicted 576 biomass Cp samples from four different origins with the absolute average relative deviation (AARD%) of ~1.1%. The proportional reduction in error (REE) approved that ash and sulfur contents only enlarge the correlation and have little effect on the accuracy. Furthermore, the REE showed that the temperature effect on biomass heat capacity was stronger than on the crystallinity index. Consequently, a new three-parameter correlation utilizing crystallinity index and temperature was developed. This model was more straightforward than the five-parameter correlation and provided better predictions (AARD = 0.98%). The proposed three-parameter correlation predicted the heat capacity of four different biomass classes with residual errors between −0.02 to 0.02 J/g∙K. The literature related biomass Cp to temperature using quadratic and linear correlations, and ignored the effect of the chemistry of the samples. These quadratic and linear correlations predicted the biomass Cp of the available database with an AARD of 39.19% and 1.29%, respectively. Our proposed model was the first work incorporating sample chemistry in biomass Cp estimation.

This study proposes a simple correlation for approximating hydrogen solubility in biomaterials as a function of pressure and temperature. The pre-exponential term of the proposed model linearly relates to the pressure, whereas the exponential term is merely a function of temperature. The differential evolution (DE) optimization algorithm helps adjust three unknown coefficients of the correlation. The proposed model estimates 134 literature data points for the hydrogen solubility in biomaterials with an excellent absolute average relative deviation (AARD) of 3.02% and a coefficient of determination (R) of 0.99815. Comparing analysis justifies that the developed correlation has higher accuracy than the multilayer perceptron artificial neural network (MLP-ANN) with the same number of adjustable parameters. Comparing analysis justifies that the Arrhenius-type correlation not only needs lower computational effort, it also has higher accuracy than the PR (Peng-Robinson), PC-SAFT (perturbed-chain statistical associating fluid theory), and SRK (Soave-Redlich-Kwong) equations of state. Modeling results show that hydrogen solubility in the studied biomaterials increases with increasing temperature and pressure. Furthermore, furan and furfuryl alcohol show the maximum and minimum hydrogen absorption capacities, respectively. Such a correlation helps in understanding the biochemical–hydrogen phase equilibria which are necessary to design, optimize, and control biofuel production plants.

This study applies a hybridized wavelet transform-artificial neural network (WT-ANN) model to simulate the acetone detecting ability of the Indium oxide/Iron oxide (In2O3/Fe2O3) nanocomposite sensors. The WT-ANN has been constructed to extract the sensor resistance ratio (SRR) in the air with respect to the acetone from the nanocomposite chemistry, operating temperature, and acetone concentration. The performed sensitivity analyses demonstrate that a single hidden layer WT-ANN with nine nodes is the highest accurate model for automating the acetone-detecting ability of the In2O3/Fe2O3 sensors. Furthermore, the genetic algorithm has fine-tuned the shape-related parameters of the B-spline wavelet transfer function. This model accurately predicts the SRR of the 119 nanocomposite sensors with a mean absolute error of 0.7, absolute average relative deviation of 10.12%, root mean squared error of 1.14, and correlation coefficient of 0.95813. The In2O3-based nanocomposite with a 15 mol percent of Fe2O3 is the best sensor for detecting acetone at wide temperatures and concentration ranges. This type of reliable estimator is a step toward fully automating the gas-detecting ability of In2O3/Fe2O3 nanocomposite sensors.

This study investigates the application of extraction solvent in a new microfluidic apparatus to separate calcium ions (Ca2+). Indeed, a serpentine microfluidic device has been utilized to separate calcium ions. The flow regime map shows that it is possible to completely separate organic and aqueous phases using the serpentine microfluidic device. The suggested microfluidic device reaches the extraction efficiency of 24.59% at 4.2 s of the residence time. This research also employs the Box–Behnken design (BBD) strategy in the response surface methodology (RSM) for performing the modeling and optimization of the suggested extraction process using the recorded experimental data. Flow rate and pH of the aquatic phase as well as Dicyclohexano-18-crown-6 (DC18C6) concentration are those independent features engaged in the model derivation task. The optimum values of pH 6.34, the DC18C6 concentration of 0.015 M, and the flow rate = 20 µl/min have been achieved for the aquatic phase. The results indicated that the extraction efficiency of Ca2+ is 63.6%, and microfluidic extraction is 24.59% in this optimum condition. It is also observed that the microfluidic extraction percentage and experimental efficiency achieved by the suggested serpentine microchannel are higher than the previous separation ranges reported in the literature.

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%.