Digital Twin of Chemical Absorption Column Coupled with Machine Learning for Real-Time Emission Prediction

Abstract

The growing demand for efficient emission monitoring and process optimization in chemical industries has accelerated the adoption of digitalization and intelligent predictive technologies. This study presents the development of a digital twin framework for a chemical absorption column integrated with machine learning techniques for real-time emission prediction and operational analysis. The proposed system combines process simulation, real-time sensor data acquisition, and data-driven predictive modeling to create a dynamic virtual representation of the absorption column under varying industrial operating conditions. The digital twin continuously monitors process parameters such as gas flow rate, solvent concentration, temperature, pressure, and mass transfer characteristics to evaluate system behavior and emission trends. Machine learning algorithms were incorporated to improve prediction accuracy for gaseous pollutant emissions and identify nonlinear relationships between operational variables and process performance. The framework was evaluated using historical and real-time process datasets to analyze predictive reliability, anomaly detection capability, and adaptive process optimization. Results demonstrated that the integrated digital twin model significantly enhanced emission forecasting accuracy and enabled rapid identification of operational deviations compared to conventional monitoring methods. Furthermore, the system supported predictive maintenance planning, energy-efficient operation, and improved process control through continuous feedback and intelligent decision-making mechanisms. The integration of machine learning with digital twin technology also facilitated real-time optimization of absorption efficiency and pollutant reduction performance.