# Energy Optimisation of Industrial Limestone Grinding Using ANN
### Industry 4.0 and Digital Transformation of Open-Pit Mining  
Modern open-pit mining, like other industrial sectors, is introducing the broad automation of facilities along with production optimisation. The highly competitive market forces enterprises to reduce production costs and increase product quality. To meet these increasingly demanding requirements, from the perspective of automation, a well-functioning control, monitoring, and optimisation system is essential. For this reason, intelligent control systems based on neural networks and machine learning are being implemented. The implementation of digital and industrial transformation--described in the literature as Industry 4.0--in the mining sector creates new development opportunities and introduces modern knowledge-based management supported by accurate data [40]. Beyond the first wave of automation, today's Mining 4.0 initiatives combine cyber-physical systems with high-fidelity digital twins that stream real-time sensor data into physics-informed process models. Multi-site case studies report downtime cuts of 15-20%, energy savings of 10-15%, and significantly faster decision cycles when haulage, ventilation, and plant-wide control are driven by continuously updated virtual replicas [41; 42]. A critical enabler of these gains is the rapid rollout of private 5G/edge-computing architectures, which guarantee sub-10 ms latency for thousands of IIoT devices spread across large pits and tunnels. This connectivity supports autonomous trucks, drone fleets and mobile-robot-as-a-sensor networks, while keeping compute resources close to the ore face for low-power AI inference and adaptive control loops [43]. Parallel advances in AI-driven predictive-maintenance frameworks--often ensembles of CNN-LSTM models fed by vibration, acoustic and thermal imagery--have lifted the mean time between failure by roughly 25% across crushers, conveyors and SAG mills, as summarised in a 2025 systematic review [44]. From a sustainability perspective, integrating energy, water and emissions metrics directly into these data pipelines allows operations to track ESG key performance indicators (KPIs) in near-real time. Mining companies that embed such dashboards into daily planning routines report not only lower Scope 1 and 2 emissions but also improved licence-to-operate scores and have easier access to green financing [45]. Complementary research highlights that aligning digital-twin objectives with multi-objective optimisation--balancing throughput, energy intensity and safety risk--produces more robust strategies than traditional single-target set-point policies [46; 47; 48; 49]. Collectively, these developments show that Industry 4.0 in mining is evolving from isolated automation projects to enterprise-wide, AI-enhanced ecosystems that continuously optimise resource efficiency, worker safety and environmental performance. Establishing coherent KPI frameworks, investing in resilient 5G/edge infrastructures and coupling physics-based twins with transparent AI models emerge as critical success factors for the sector's long-term competitiveness and sustainable growth. Recent studies emphasise that the real breakthrough comes from integrating cyber-physical systems with high-fidelity digital twins, which continuously synchronise sensor data with physics-based models to optimise haulage, ventilation and asset-health decisions--reducing unplanned downtime by up to 20% and cutting energy use by 10-15% [50; 51]. At the same time, AI-driven predictive-maintenance frameworks built on deep-learning ensembles are now being deployed at large open-pit operations. A 2023 systematic review reports mean-time-between-failure improvements of 25% across crushers, conveyors and grinding mills [52]. Such data-intensive applications depend on low-latency connectivity: private 5G/edge-computing architectures tested in underground block-cave mines demonstrate sub-10 ms control-loop delays while supporting thousands of IIoT devices, enabling fully autonomous truck and drone fleets [53].