Modeling and optimization of CO2 mass transfer flux into Pz-KOH-CO2 system using RSM and ANN

Optimizing neural network algorithms for submerged membrane bioreactor: A comparative study of OVAT and RSM hyperparameter optimization techniques

Hyperparameter tuning is an important process to maximize the performance of any neural network model. This present study proposed the factorial design of experiment for screening and response surface methodology to optimize the hyperparameter of two artificial neural network algorithms. Feed-forward neural network (FFNN) and radial basis function neural network (RBFNN) are applied to predict the permeate flux of palm oil mill effluent. Permeate pump and transmembrane pressure of the submerge membrane bioreactor system are the input variables. Six hyperparameters of the FFNN model including four numerical factors (neuron numbers, learning rate, momentum, and epoch numbers) and two categorical factors (training and activation function) are used in hyperparameter optimization. RBFNN includes two numerical factors such as a number of neurons and spreads. The conventional method (one-variable-at-a-time) is compared in terms of optimization processing time and the accuracy of the model. The result indicates that the optimal hyperparameters obtained by the proposed approach produce good accuracy with a smaller generalization error. The simulation results show an improvement of more than 65% of training performance, with less repetition and processing time. This proposed methodology can be utilized for any type of neural network application to find the optimum levels of different parameters.

Adsorption optimization and modeling of Hg2+ ions from aqueous solutions using response surface methodology by SNPs-CS bionanocomposite produced from rice husk agro-industrial waste as a novel environmentally-friendly bionanoadsorbent

In the present research, extraction of silica (SiO2) from rice husk (RH) was optimized and silica nanoparticles (SNPs) was produced using it and functionalized by chitosan (CS) functional groups to obtain CS functionalized SNPs (SNPs-CS) bionanocomposite for the first time. The physical and chemical characteristics of the produced materials were examined using structural analyses. The results of structural analyses confirmed the fine structure of the produced materials. The SNPs-CS bionanocomposite was applied to effectively remove Hg2+ ions from aqueous solutions as an environmentally-friendly bionanoadsorbent and optimization and modeling of the adsorption conditions was explored using designed experiments by Design-Expert software with central composite design (CCD) and response surface methodology (RSM). Optimum adsorption conditions were obtained as solution pH of 6, SNPs-CS dosage of 0.1 g L-1 and Hg2+ ions concentration of 100 mg L-1 by removal efficiency of 85% and desirability function of 0.876. The results of adsorption kinetic showed a better fit of the pseudo-second-order model with experimental data, indicating the chemisorption of the adsorption process. The better fit of the Langmuir model with experimental data was confirmed by the results of adsorption isotherms, demonstrating monolayer adsorption on the homogeneous surface. The adsorption thermodynamic results illustrated the exothermicity and spontaneity of the adsorption reaction. The results of SNPs-CS recovery depicted its excellent recovery ability of removal efficiency with more than 90% after five consecutive adsorption and desorption cycles, which proved high potential of the produced bionanocomposite for industrial applications.

Investigating the effect of textural properties on CO2 adsorption in porous carbons via deep neural networks using various training algorithms

The adsorption of carbon dioxide (CO2) on porous carbon materials offers a promising avenue for cost-effective CO2 emissions mitigation. This study investigates the impact of textural properties, particularly micropores, on CO2 adsorption capacity. Multilayer perceptron (MLP) neural networks were employed and trained with various algorithms to simulate CO2 adsorption. Study findings reveal that the Levenberg-Marquardt (LM) algorithm excels with a remarkable mean squared error (MSE) of 2.6293E-5, indicating its superior accuracy. Efficiency analysis demonstrates that the scaled conjugate gradient (SCG) algorithm boasts the shortest runtime, while the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm requires the longest. The LM algorithm also converges with the fewest epochs, highlighting its efficiency. Furthermore, optimization identifies an optimal radial basis function (RBF) network configuration with nine neurons in the hidden layer and an MSE of 9.840E-5. Evaluation with new data points shows that the MLP network using the LM and bayesian regularization (BR) algorithms achieves the highest accuracy. This research underscores the potential of MLP deep neural networks with the LM and BR training algorithms for process simulation and provides insights into the pressure-dependent behavior of CO2 adsorption. These findings contribute to our understanding of CO2 adsorption processes and offer valuable insights for predicting gas adsorption behavior, especially in scenarios where micropores dominate at lower pressures and mesopores at higher pressures.

Enhanced CO2 capture potential of UiO-66-NH2 synthesized by sonochemical method: experimental findings and performance evaluation

The excessive release of greenhouse gases, especially carbon dioxide (CO2) pollution, has resulted in significant environmental problems all over the world. CO2 capture technologies offer a very effective means of combating global warming, climate change, and promoting sustainable economic growth. In this work, UiO-66-NH2 was synthesized by the novel sonochemical method in only one hour. This material was characterized through PXRD, FT-IR, FE-SEM, EDX, BET, and TGA methods. The CO2 capture potential of the presented material was investigated through the analysis of gas isotherms under varying pressure conditions, encompassing both low and high-pressure regions. Remarkably, this adsorbent manifested a notable augmentation in CO2 adsorption capacity (3.2 mmol/g), achieving an approximate enhancement of 0.9 mmol/g, when compared to conventional solvothermal techniques (2.3 mmol/g) at 25 °C and 1 bar. To accurately represent the experimental findings, three isotherm, and kinetic models were used to fit the experimental data in which the Langmuir model and the Elovich model exhibited the best fit with R2 values of 0.999 and 0.981, respectively. Isosteric heat evaluation showed values higher than 80 kJ/mol which indicates chemisorption between the adsorbent surface and the adsorbate. Furthermore, the selectivity of the adsorbent was examined using the Ideal Adsorbed Solution Theory (IAST), which showed a high value of 202 towards CO2 adsorption under simulated flue gas conditions. To evaluate the durability and performance of the material over consecutive adsorption-desorption processes, cyclic tests were conducted. Interestingly, these tests demonstrated only 0.6 mmol/g capacity decrease for sonochemical UiO-66-NH2 throughout 8 consecutive cycles.