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

Ammonia production is vital to the global chemical industry. Its CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> 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 CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> removal processes. This study introduces a machine learning (ML) enabled framework tailored for CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> 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 CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> removal efficiency and energy consumption. E valuated on real operational data from an Egyptian ammonia plant, ANN and XGBoost achieved R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 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 CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> monitoring efficacy from 88.2% to 94.7%, a 6.5% increase. These results demonstrate the potential of ML to enhance energy sustainability and CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> removal efficiency, addressing the unique challenges of HPC solution based systems and advancing sustainable industrial practices.