Smart Carbon Capture System

The adaptive and effective carbon mitigation strategies are needed because atmospheric CO₂ levels have been increasing very quickly since the start of the Industrial Revolution. The paper introduces a Smart Carbon Capture System which combines post combustion carbon capture with Internet of Things (IoT) technologies to support the automation of the control system, real time monitoring and prediction of emission. This system uses gas sensors, flow sensors, pressure sensors, voltage sensors and current sensors connected to the Raspberry Pi Zero 2W, to monitor CO₂ levels, energy consumption and storage conditions continuously. A vacuum air pump is controlled adaptively using the Pulse Width Modulation (PWM) signal that is generated according to the detected CO₂ concentration. This is a linear regression model that is light (because of NumPy's polyfit function) and predicts short time emission behaviour, allowing for adaptive decision making in resource-constrained environments. Real-time visualization and data logging in the cloud are supported by the Blynk platform and Google Sheets. The results of experiments show that it is possible to achieve a capture efficiency higher than 90%, with an adaptive energy consumption ranging from 3 W in conditions of low emission to 11 W in the maximum emission conditions of CO₂. The coefficient of determination R2 is 0.93, which is an indication of the reliability of the emission short-term prediction. The system is a prototype that demonstrates the potential of an adaptive, IoT-enabled carbon capture system and provides a scalable basis for its industrial adoption.