## 4 Hybrid Thematic Analysis: Key Findings from NLP and Expert Review
### Energy Efficiency in Industrial Processes  
Energy efficiency in industrial processes is essential for ensuring sustainability in modern industry, particularly in mining operations. The integration of artificial intelligence (AI) tools, including machine learning and deep learning, has facilitated resource optimization across various applications [53, 63]. For instance, fault monitoring in bearings within hydroelectric plants has led to reduced energy consumption through advanced detection algorithms [51]. These advancements have demonstrated strong potential for adaptation in mining applications and can be further enhanced by leveraging Industry 4.0 methodologies.  
Recent studies have underscored that integrating data-driven predictive models with real-time sensor feedback can further optimize energy usage in heavy machinery [24, 25, 64]. Specifically, advanced signal processing techniques, combined with multi-sensor fusion, allow for more precise adjustments in operational parameters, minimizing energy-intensiveconditions while maintaining throughput [65, 66]. Furthermore, researchers have explored the application of explainable AI approaches to validate energy-consumption models, enhancing both transparency and scalability for industrial adoption [32, 67].  
The literature highlights how advanced mechatronic designs, in conjunction with predictive analytics, can reduce total energy usage in mineral processing lines by up to 15% [52, 53]. Additionally, novel automation architectures in mill circuits employing unsupervised clustering have successfully identified suboptimal torque ranges [8, 9], enabling targeted corrective actions and improved sustainability.  
Data collection and transformation are also key factors in the development of predictive models that uncover behavioral patterns in critical components, ultimately optimizing energy management [6, 18]. This approach has been successfully implemented in mining, where automated monitoring of bit wear has significantly improved energy efficiency [4], leading to better resource utilization and enhanced operational performance.  
Moreover, pitting detection in gearboxes has not only improved system reliability but has also led to a notable reduction in energy consumption during operations [54]. Similarly, deep learning applied to vibration monitoring has been shown to enhance energy performance in industrial processes, extending equipment lifespan while lowering energy expenditure [63].  
Furthermore, advanced strategies that integrate real-time sensor data with multi-objective optimization algorithms [10, 11] have enabled more granular control over conveyor speeds and ventilation demands, effectively minimizing excess power use [12]. These strategies serve as a critical link between conventional mechanical retrofits and modern data-driven analytics [13], fostering robust solutions that align with integrated sustainability goals.  
In particular, the introduction of edge-based computing architectures, combined with federated learning, has been shown to drastically reduce the bandwidth needed for real-time data transfer while maintaining reliable optimization loops [40, 68]. Such configurations further aid in distributing computational loads across geographically dispersed sites, thereby improving resource allocation in power-intensive environments [15, 17].