Improving energy efficiency in ammonia production plants using machine learning

Energy efficiency is becoming increasingly important nowadays due to the need for energy conservation and environmental sustainability. An existing ammonia plant was simulated using Aspen Hysys software, the simulated plant was used to produce large volumes of data to train and test our machine-learning model. In this work a benchmark methodology is proposed through machine-learning (ML) techniques to identify patterns and anomalies in energy consumption. Our ML model was developed in Python programming language using a multiple linear regression algorithm. Microsoft Power BI was used to build interactive visualizations to illustrate insights to users. The ML model was able to predict energy consumption by developing equations that relate the energy consumption and the operating variables for each significant energy user in the ammonia plant. In this study, actual versus optimum energy consumption was analyzed for four ammonia production plants. The ML model identified the ammonia plant operating costs and potential savings by adjusting operating conditions. An annual saving of up to 3.9 million dollars was reached in one of the ammonia production plants operating costs. A reduction in carbon dioxide emissions of up to 4.7 ton per hour was achieved due to energy efficiency adjustment.