Analysing real-time exergy of a cement production plant using a machine learning approach

The cement industry ranks among the most energy-intensive sectors worldwide, accounting for extensive consumption of non-renewable fuels and the release of substantial untapped waste heat. 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 indicate 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 findings demonstrate that combining exergy analysis with machine learning particularly python and Artificial neural network (ANN) and Artificial intelligence (AI) enables practical, real-time assessments of process inefficiencies, offering valuable insights for industrial decision-making. The proposed framework provides a pathway for optimizing operating conditions, minimizing exergy losses, and advancing sustainable in one of the world's most energy-demanding industries.