Simulating and Comparing CO2/CH4 Separation Performance of Membrane-Zeolite Contactors by Cascade Neural Networks.

Separating carbon dioxide (CO2) from gaseous streams released into the atmosphere is becoming critical due to its greenhouse effect. Membrane technology is one of the promising technologies for CO2 capture. SAPO-34 filler was incorporated in polymeric media to synthesize mixed matrix membrane (MMM) and enhance the CO2 separation performance of this process. Despite relatively extensive experimental studies, there are limited studies that cover the modeling aspects of CO2 capture by MMMs. This research applies a special type of machine learning modeling scenario, namely, cascade neural networks (CNN), to simulate as well as compare the CO2/CH4 selectivity of a wide range of MMMs containing SAPO-34 zeolite. A combination of trial-and-error analysis and statistical accuracy monitoring has been applied to fine-tune the CNN topology. It was found that the CNN with a 4-11-1 topology has the highest accuracy for the modeling of the considered task. The designed CNN model is able to precisely predict the CO2/CH4 selectivity of seven different MMMs in a broad range of filler concentrations, pressures, and temperatures. The model predicts 118 actual measurements of CO2/CH4 selectivity with an outstanding accuracy (i.e., AARD = 2.92%, MSE = 1.55, R = 0.9964).

Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites.

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.

Applying a microfluidic device to improve the Ca2+ separation performance of the liquid-liquid extraction process.

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.

Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials.

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.