Cement Energy & Environment

25 early 2000s introduced Advanced Process Control (APC) systems, laboratory automation, and centralizeddigitalcontrolrooms.Today,theindustry stands at the threshold of AI-driven predictive maintenance, machine learning algorithms, digital twins, and Industry 4.0 ecosystems that promise near-real-time optimization. However, despite this impressive technological arsenal, many plants continue to operate at suboptimal levels. While best-in-class cement kilns achieve thermal energy consumption near 680 kcal/kg clinker, numerous plants still consume 720–740 kcal/kg clinker. Similarly, the Thermal Substitution Rate (TSR) in some advanced regions exceeds 40 percent, but several plants, even in technology-richmarkets,operatebelow20percent. This persistent gap underscores a critical realization: technology alone cannot deliver transformation without equally strong human and organizational systems. 2. THE TECHNOLOGY UTILIZATION GAP Every major technology introduced into cement manufacturing—be it APC, robotic laboratories, or predictive maintenance— has demonstrated tangible potential. Yet, outcomes often fall short of expectations. Advanced Process Control (APC) Global studies reveal that APC can lower fuel consumption by 3–5 percent and increase throughput by 2–3 percent. However, these savings are not always fully realized because many plants lackconsistentoperatortraining,resultinginfrequent manual overrides that nullify system benefits. Laboratory Automation Robotic laboratories can reduce clinker quality variability by 30–40 percent, ensuring tighter control of free lime, alkalis, and silicates. Nevertheless, inseveral cases, laboratoriesareused primarily for reporting rather than as analytical hubs supporting real-time decision-making. Outcome High Low Advanced Process Control (APC) Insu cient operator training and reliance on manual overrides Technology Outcome Utilization Gap Laboratory Automation Primarily used for reporting instead of real-time decision-making Predictive Maintenance Failure to act on early alerts and continued reactive maintenance Cement Plants 100% Technology 50% 0 Utilization Gap Outcome 85% Technology 50% 0 Utilization Gap Outcome Cement Kilns Figure 1 A: Technology vs Utilization Gap Figure 1 B: Technology vs Utilization Gap PREDICTIVE MAINTENANCE AI-enabled maintenance systems can reduce unplanned downtime by up to 20 percent, but their effectiveness depends entirely on the organization’s willingness to act on early alerts rather than continue with reactive maintenance habits. These examples collectively suggest a utilization gap—where the capability of machines outpaces the readiness of people and processes.