High-Temperature CO₂ Separation from Flue Gas Using Ceramic Membranes: Experimental Insights and Artificial Neural Network Modeling

The effective capturing of carbon dioxide (CO₂) from flue gases represents an important environmental and economic challenge, where such emissions are considered a major contributor to global climate issue. Traditional capturing techniques such as amine scrubbing are energy-intensive with high cost, due to the necessity of cooling high temperature flue gases before the separation process. In this study, we investigated the utilization of ceramic membranes fabricated from Saudi red clay which considered an available, cost-effective local material as a sustainable solution for high-temperature CO₂ capture. The research evaluates separation efficiency under high temperatures and different pressure values, which enable direct CO₂ capturing from hot flue streams without precooling processes. Experimental results demonstrate the membranes efficacy in separating CO₂ from flue gas, in which the presence of iron oxide (Fe₂O₃) constituents in the clay enhancing capture efficiency through weak chemisorption. In addition, the membranes showed robust structural integrity and consistent performance under high temperature conditions, compared to polymeric membranes that degrade thermally and offer advantages over metal-organic framework-enhanced ceramics, which incur higher costs and lower thermal tolerance. An ANN model is constructed to estimate the membrane performance (CO2 concentration (%) in permeate) using results obtained from the present experimental results and utilizing pressure and temperature as ANN input parameters. The process of training incorporates the analysis of the loss function on training and validation data for controlling the weights and biases using backpropagation while feed forward propagate the selected input parameters. A total of 8 hidden layers consisting of 12 neurons each has been used in constructing the ANN, and training process is optimized using the ADAM algorithm to minimize the loss function. The Final layer uses the linear activation function while all the hidden layers use the rectified Linear Units Activation function (ReLU). The ANN model demonstrates excellent predictive performance, yielding values close to 1 for R2 and r, along with extremely low values for MSE, MAPE, MSLE, and log-cosh loss (0.00033, 0.146%, 4.1×10⁻6, 0.00016 respectively), demonstrating the ANN model's high predictive accuracy.