# Energy Optimisation of Industrial Limestone Grinding Using ANN
### Deep-Learning--Driven Optimisation Across Energy-Intensive Industries  
Artificial neural network and deep learning (DL) techniques have moved far beyond the proof-of-concept stage and are now embedded in real-time digital twins that supervise entire value chains. A recent case study on a gold-flotation plant showed that an encoder-decoder LSTM surrogate can forecast the concentrate grade 30 min ahead with an \(R^{2}\) of 0.94, enabling feed-forward set-point adjustments and cutting specific energy by 12% [23]. Similar multivariate, multistep DL surrogates are being ported to grinding circuits and thickener networks, providing mill-wide optimisation layers that are orders of magnitude faster than first-principle simulators.  
A complementary thread focuses on predictive maintenance. In a packaging-plant conveyor, a CNN-BiLSTM digital twin flagged 88% of chain-slack and loop-rupture eventsat least one shift in advance, allowing maintenance planners to halve unplanned stoppages and save 6% of annual electricity consumption [24]. The study demonstrates how edge-deployed DL models can operate under strict latency budgets (<10 ms) while streaming thousands of sensor tags over private 5G.  
Within mineral comminution itself, [PERSON] et al. combined channel-attention CNNs with stacked LSTMs (CACN-LSTM) to predict SAG-mill power draw; plant trials at a 26 MW mill achieved a mean absolute error below 2.5% and supported energy-aware MPC retuning [25]. Earlier work by [PERSON] and co-authors used a pure LSTM encoder to classify forthcoming \"hard\" versus \"soft\" ore events, reaching 80% accuracy and giving operators up to 30 min of warnings to reconfigure feed blending [26].  
Deep learning is also reshaping soft-sensing. Transformer-based vision models trained on froth-image sequences now estimate tailings grade in iron reverse-flotation circuits with 7% lower RMSE than ResNet baselines, unlocking reagent-dosing savings of roughly $0.07 t\({}^{-1}\) of the ore [27]. A complementary deep-ensemble sensor for gold-antimony flotation boosted condition-recognition accuracy to 97%, offering actionable feedback for chemical-dosage control [28].  
Beyond unit-operation scale, deep reinforcement learning (DRL) is emerging for large-scale, constraint-rich scheduling. [PERSON] et al. framed the daily allocation of 40 haul trucks under safety corridors as a Markov decision process and trained a proximal-policy-optimisation (PPO) agent that cut cycle times by 11% and reduced idle fuel burn by 9% compared with mixed-integer programming [29]. Comparable DRL schedulers are being piloted in steel rolling, hot forging, and cement kilns, pointing toward unified multi-objective controllers that weigh energy, throughput, and emissions in real time.  
Collectively, these ANN and DL applications illustrate a clear pattern: coupling high-capacity learners with first-principle or rule-based layers, often within a synchronised digital-twin framework, delivers measurable gains in energy efficiency, asset utilisation, and sustainability across multiple heavy-industry contexts. Ongoing challenges include rigorous uncertainty quantification, life-of-mine dataset drift, and the transparent reconciliation of black-box predictions with operator intuition, but the trajectory toward fully autonomous, energy-optimised plants is now unmistakable.