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

Abstract

In this research, artificial neural networks (ANN) and response surface methodology (RSM) were applied for modeling and optimization of carbon dioxide (CO2) absorption using KOH-Pz-CO2 system. In the RSM approach, the central composite design (CCD) describes the performance condition in accordance with the model using the least-squares technique. The experimental data was placed in second-order equations applying multivariate regressions and appraised applying analysis of variance (ANOVA). The p-value for all dependent variables was obtained to be less than 0.0001, indicating that all models were significant. Furthermore, the experimental values obtained for the mass transfer flux satisfactorily matched the model values. The R2 and Adj-R2 models are 0.9822 and 0.9795, respectively, which, it means that 98.22% of the variations for the NCO2 is explained by the independent variables. Since the RSM does not create any details about the quality of the solution acquired, the ANN method was applied as the global substitute model in optimization problems. The ANNs are versatile utensils that can be utilized to model and anticipate different non-linear and involved processes. This article addresses the validation and improvement of an ANN model and describes the most frequently applied experimental plans, about their restrictions and generic usages. Under different process conditions, the developed ANN weight matrix could successfully forecast the behavior of the CO2 absorption process. In addition, this study provides methods to specify the accuracy and importance of model fitting for both methodologies explained herein. The MSE values for the best integrated MLP and RBF models for the mass transfer flux were 0.00019 and 0.00048 in 100 epochs, respectively.

Velocity Augmentation Model for an Empty Concentrator-Diffuser-Augmented Wind Turbine and Optimisation of Geometrical Parameters Using Surface Response Methodology

“…Figure 14B depicts the normal probability chart of the studentised residuals utilised to assess the adequacy and suitability of the velocity augmentation model. As shown in the graph, the points form a straight line, affirming that the errors were distributed normally with a mean of zero and a fixed value, thereby confirming the model's adequacy [37,62,65,86,89]. Similar to the findings of [7], the results show no evidence to dispute any independence violations or hypotheses of permanent variance.…”

“…In Figure 14C, a random distribution of points is displayed, varying up and down the x-axis within the range of −3.59982 to +3.59982, enclosed by the red line, without exhibiting any noticeable trends. This observation serves as an assessment of the adequacy and reliability of the velocity augmentation model [65]. This scenario suggests that the velocity augmentation model is reasonably exempt from any violations of the independence or constant variance assumptions [86].…”

“…To assess the validity and significance of the wind speed augmentation model developed using response surface methodology (RSM), this study employed analysis of variance (ANOVA) and a lack-of-fit test [65]. The model's fitness was crucial for optimising the geometrical parameters of the CDaugWT and the velocity augmentation ratio.…”

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

“…Intermediate and hidden layers connect the input and output layers, where various forms of activation functions can be applied 92 . Additional information about MLP and RBF algorithms can be found in the literature 93 , 94 .…”

Performance of Water-Lean Solvent for Postcombustion Carbon Dioxide Capture in a Process-Intensified Absorber: Experimental, Modeling, and Optimization Using RSM and ML

“…15 Recently, Pashaei et al have applied artificial neural network (ANN) and response surface methodology (RSM) methods for modeling of postcombustion carbon capture process using alkanolamine solvent to recognize the process variables and optimal conditions. 16 Moreover, Hosseinpour et al evaluated and classified the various ML approaches utilized in the postcombustion process (PCC). They focused on simulation, optimization, and solvent management in PCC by the ML technique.…”