# Integrating Process Re-Engineering Models in Cement Production to Improve Energy Efficiency
## 4 Discussion  
According to the data collected and analysed, the annual energy loss for Plant A accumulates to 1889.07 MJ. The four major factors that contribute to this energy loss are overload flue gas temperature (OFgT), clinker discharge temperature (CDT), recovery point flue gas temperature (FgTR), and flue gas temperature (FgTK). Pareto analysis indicates that these four variables account for 96% of all energy losses. A significant reduction in energy loss could be achieved by implementing improvement strategies that are related to these four variables. The improvement strategies must include insulation and heat recovery. In recent studies [41; 42; 43] and other studies [44; 45; 46; 47], it has been shown that optimising heat recovery systems and improving insulation are effective strategies for improving  
Figure 14: Response function for the kiln system for Plant B.  
Figure 13: Response function for the PID-controlled system of the kiln for Plant A.  
energy efficiency in industrial processes. Therefore, by focusing on these areas, Plant A can significantly reduce its energy consumption and align with best practices in cement manufacturing energy management. Regression analysis for Plant A indicates that there is a significant relationship (R\({}^{2}\) = 0.749) between clinker production and energy consumption. The analysis indicates that energy consumption per ton reduces with increasing production volume. This is because fixed energy costs are distributed across higher production volumes, indicating substantial efficiencies in production. Plant B, on the other hand, has an annual energy loss of 1856.5 MJ, with a different set of causes. The tertiary air duct damper temperature (TaDT), cooler air exhaust temperature (CaET), flue gas temperature at the recovery point (FgTR), and overload flue gas temperature (OFgT) all contribute significantly to energy losses, accounting for 95% of the total. Plant B has different production and energy consumption trends compared to Plant A. The regression model shows a stronger association (R\({}^{2}\) = 0.83) between energy usage and clinker production. This implies that Plant B benefits from a lower drop in energy consumption per unit produced, indicating different operational constraints or efficiency compared to Plant A. The variations in operational efficiency could be attributed to differences in equipment, process technology, or operational methods. Using Pareto analysis, Plants A and B can identify solutions tailored to their specific energy challenges and prioritise them accordingly. The dynamic simulations of the cement kiln system using Simulink indicate that the use of a PID-controlled system is effective at managing kiln temperature and maintaining process stability. The burner and kiln transfer functions define how well the system responds to input changes, resulting in a quick temperature rise to the desired setpoint with little overshoot. This rapid stabilisation within a short timeframe indicates that the PID controller is precise and effective in minimising fluctuations and maintaining steady-state conditions, as supported by studies [48; 49; 50]. The PID controller helps optimise energy usage by minimising temperature variations, which has been highlighted in the literature as an important component in improving system sustainability [50]. The rapid stabilisation is consistent with research findings [51; 52], which indicate that well-tuned PID systems can greatly enhance energy efficiency, adding to the sustainability goals emphasised in current research. Furthermore, the absence of significant fluctuations and overshoot in the response of the system demonstrates precision in achieving desired operating conditions. This level of control indicates that the model is both energy-efficient and capable of delivering consistent controls required for product quality.