Climate Change Reduction Using Carbon Capturing with Algae House Utilization and Artificial Neural Network Model Predictive Controller

Abstract

In response to the pressing need for effective carbon capture and emission reduction strategies, this study explores the integration of Artificial Neural Network-Model Predictive Control (ANN-MPC) technology with algae house monitoring systems. The overarching aim is to optimize carbon capture efficiency while simultaneously extracting oxygen for medical purposes. The study addresses key challenges including the need for advanced control strategies to maximize carbon capture efficiency and the utilization of algae as a natural carbon sink. The methodological approach involves the implementation of ANN-MPC control for a centrifugal fan system, coupled with real-time monitoring of carbon and oxygen responses within the algae house environment. Non-Dispersive Infrared (NDIR) sensors are employed to detect carbon concentration levels, while oxygen extraction processes are integrated for medical applications. Preliminary results indicate significant improvements in carbon capture efficiency, with the ANN-MPC controller achieving an impressive performance rating of 90%. Moreover, the algae house demonstrates an 80% increase in captured carbon concentration, showcasing the effectiveness of integrating advanced control strategies with natural carbon absorption processes. This study contributes to the advancement of carbon capture technologies by demonstrating the feasibility and efficacy of integrating ANN-MPC control with algae house monitoring systems. The findings highlight the potential for synergistic approaches to mitigate carbon emissions and extract valuable oxygen resources for medical applications, ultimately fostering a more sustainable and environmentally conscious future.