Enhancing Energy Sustainability and Operational Efficiency of Ammonia Production using Machine Learning

Ammonia production is vital to the global chemical industry. Its CO2 removal unit remains an energy-intensive bottleneck, improving sustainability and operational costs. Traditional control strategies struggle with nonlinear dynamics and multivariable interactions, leading to inefficiencies i n CO2 removal processes. This study introduces a machine learning (ML) enabled framework tailored for CO2 removal in ammonia systems using Hot Potassium Carbonate (HPC) solution. Advanced ML models such as Linear Regression, Artificial Neural Networks (ANN),and XGBoost are employed to predict CO2 removal efficiency and energy consumption. E valuated on real operational data from an Egyptian ammonia plant, ANN and XGBoost achieved R² scores of 0.99 for energy savings and over 0.82 for energy consumption forecasts. The framework reduced steam usage from 12.4 ton/h to 10.1 ton/h, achieving an 18.5% energy saving, and improved CO2 monitoring efficacy from 88.2% to 94.7%, a 6.5% increase. These results demonstrate the potential of ML to enhance energy sustainability and CO2 removal efficiency, addressing the unique challenges of HPC solution based systems and advancing sustainable industrial practices.