Process optimization of chemical looping combustion of solid waste/biomass using machine learning algorithm

Chemical Looping Combustion (CLC) is a carbon capture technology that uses an oxygen carrier to transfer the oxidizing agent to the fuel for combustion. This study used different machine learning algorithms, Artificial neural network and Response surface methodology to estimate the surface region process performance and optimize the process condition for the CLC of different solid fuels waste paper, plastic waste, and sugarcane bagasse blends. Based on the combustion efficiency, CO₂ yield and CO₂ capture efficiency responses, A high performance correlation (R² > 0.8) was obtained for all the combustion parameters analyzed. The perturbation plot derived from the RSM analysis indicated that the most significant input parameters include the steam to fixed carbon, blend ratio and the fuel reaction temperature. The CLC process was optimized using RSM. For blends of SCB/WP, the best operating conditions were found to be 800 °C, a solid flow rate of 197.7 kg/h, an oxygen carrier to fuel ratio of 1.1, a steam to fixed carbon ratio of 2.16, and a blend ratio of 1. Similarly, for blends of SCB/PW, the optimal operating conditions were 800 °C, a solid flow rate of 199.4 kg/h, an oxygen carrier to fuel ratio of 1.3, steam to fixed carbon ratio of 2, and a blend ratio of 0.3. The optimum combustion performance was found to be 0.98, 0.78, and 0.96 for SCB/WP and 0.99, 0.62, and 0.96 for SCB/PW, respectively.