Forecasting and Analysing Real-Time Exergy of a Cement Plant Using a Machine Learning Approach

The cement industry is ranked among the most energy-intensive sectors worldwide, which accounts for extensive consumption of non-renewable fuels and the release of substantial untapped heat from the waste of the cement industry. Traditional energy balance methods are inadequate for identifying system inefficiencies, whereas exergy analysis provides a more rigorous evaluation by assessing energy quality losses. Conventional exergy models relay on enthalpy- based formulations derived from thermodynamics principles, correlating exergy with enthalpy and temperature through either Carnot factor. However, enthalpy cannot be measured directly with standard plant instruments, limiting the practicality of such methods for real-time monitoring. This study proposes a novel methodology for real-time exergy analysis of cement rotary kiln based exclusively on plant-measured variables, including temperature, pressure, and mass flowrates, integrated with machine learning tools. Real-time operational plant data of rotary kiln is analyzed using Python-based artificial intelligence algorithms to develop model. Results indicated that 454.38 MJ of exergy entered the kiln, primarily through raw materials and fuel combustion, while 3.9 MJ exited, leading to a calculated loss of 450.48 MJ. The integration of exergy analysis with machine learning, particularly Python-based ANN and AI models, enables real-time evaluation of process inefficiencies and system performance. The ANN predictions for both chemical and physical exergy show strong agreement with measured data, validating model accuracy. These models effectively capture non-linear thermodynamics relationships, enhancing predictive reliability. The proposed framework offers a pathway to optimize operations, reduce exergy losses, and promote sustainable industrial efficiency.