Boosting CO2 Uptake from Waste Concrete Powder Using Artificial Intelligence and the Marine Predators Algorithm

Abstract

Waste concrete powder (WCP) is emerging as a potential method of adoption for CO2 sequestration due to its ability to chemically react with carbon dioxide and trap it within its structure. This study explores the application of artificial intelligence (AI) and the Marine Predators Algorithm (MPA) to maximize the absorption of CO2 from waste concrete powder generated by recycling plants for building and demolition debris. Initially, a model is developed to assess CO2 uptake according to carbonation time (CT) and water-to-solid ratio (WSR), utilizing the adaptive neuro-fuzzy inference system (ANFIS) modeling approach. Subsequently, the MPA is employed to estimate the optimal values for CT and WSR, thereby maximizing CO2 uptake. A significant improvement in modeling accuracy is evident when the ANOVA method is replaced with ANFIS, leading to a substantial increase of approximately 19% in the coefficient of determination (R-squared) from 0.84, obtained through ANOVA, to an impressive 0.9999 obtained through the implementation of ANFIS; furthermore, the utilization of ANFIS yields a substantial reduction in the root mean square error (RMSE) from 1.96, as indicated by ANOVA, to an impressively low value of 0.0102 with ANFIS. The integration of ANFIS and MPA demonstrates impressive results, with a nearly 30% increase in the percentage value of CO2 uptake. The highest CO2 uptake of 3.86% was achieved when the carbonation time was 54.3 h, and the water-to-solid ratio was 0.27. This study highlights the potential of AI and the MPA as effective tools for optimizing CO2 absorption from waste concrete powder, contributing to sustainable waste management practices in the construction industry.