# Energy Optimisation of Industrial Limestone Grinding Using ANN
### Motivation for Adaptive AI-Enhanced Optimisation  
The introduction of new control and optimisation strategies for mineral grinding systems using intelligent adaptive methods based on deep learning is a response to dynamic and unpredictable changes in production strategies. These changes are a consequence of unstable market conditions in the mineral resources industry, such as significant increases or decreases in product demand. Classical approaches to grinding process control and optimisation, based on heuristic models of process relationships, are effective only within a narrow range of production parameters, often specified by mill manufacturers. Under rapidly changing production conditions, these methods prove insufficient, as they lack adaptability to new and often extreme process situations [5; 11]. In such circumstances, the most effective solutions are based on artificial intelligence techniques. Optimisation of operating parameters under dynamic conditions in mineral processing thus becomes a complex task of dynamic constrained multi-objective optimisation (DCMOP). This variability includes, among others, fluctuations in raw-material quality, shifting production goals, and technological failures or disturbances. To solve such problems, evolutionary multi-objective optimisationmethods--such as MP-DCMOEA--and Long Short-Term Memory (LSTM) neural networks are effectively used [19]. These methods enable effective adaptation of control systems to changing operational conditions, ensuring process stability and production efficiency. Recent work has highlighted the limitations of purely black-box models such as ANN, especially in terms of physical consistency and generalization. Paper [54] reviewed hybrid modelling approaches that combine data-driven learning with physics-based constraints. Physics-informed neural networks (PINNs) offer a promising solution by embedding physical laws directly into training, improving robustness and interpretability [55]. In industrial applications, such as gas turbines, PINNs have outperformed conventional ANNs in modelling dynamic thermodynamic behaviour [56], supporting their use in energy-intensive process optimisation. Artificial intelligence is increasingly enhancing industrial automation and process optimisation. The work in [57] highlights key challenges and benefits of integrating AI with SCADA systems, including improved fault detection and autonomous decision-making. Reinforcement learning is emerging as an effective tool for adaptive control, with [58] reviewing its use in real-time optimisation and anomaly detection. In mining, AI-driven predictive maintenance is gaining ground, for example ref. [44] reports the widespread adoption of digital twins and intelligent asset management. These developments support the shift toward energy-efficient, self-adaptive industrial systems. Also, recent studies further reinforce the growing applicability and competitiveness of black-box and hybrid data-driven models in energy-intensive industrial processes. The authors of [59] reviewed simulation tools for energy management and highlighted how black-box models, particularly those based on neural networks, are increasingly adopted in consulting practice due to their flexibility and lower dependence on structural system data. [PERSON] et al. [60] performed a comparative study between grey-box and ANN-based models in indoor temperature prediction, showing that while grey-box models offer better interpretability, ANN approaches provide superior accuracy under varying dynamic conditions. A domain-specific application of AI-based black-box modelling has been proposed by [61], who developed a NARX neural network model to identify dynamic behavior in photovoltaic inverter systems without access to internal system parameters, demonstrating the effectiveness of neural networks in high-fidelity dynamic prediction tasks.