Multi-objective optimization of high-quality lithium extraction from lepidolite roasting based on neural network coupled modeling

• • PSO-BP neural network models achieved MAPE of 0.278 for lithium conversion rate and 0.284 for natural gas consumption intensity, outperforming standard BP and enabling accurate prediction under wide process fluctuations. • • NSGA-II multi-objective optimization yielded Pareto-optimal solutions that stabilize lithium conversion rate between 82.45% and 87.96%, an average increase of 3.61 percentage points over baseline, while reducing natural gas consumption intensity to as low as 53.7 m3 per ton of clinker. • • For a 3.2×105 t/a lepidolite processing plant, optimized parameters enable an additional 127.1 tons of lithium metal recovery, a reduction of 1,964,912 m3 in natural gas consumption, and a CO2 emission cut of 3,763.84 tons annually, demonstrating significant resource and environmental benefits. • • Grey relational analysis identified kiln head temperature (correlation 0.98) and natural gas flow rate (correlation 0.97) as the most influential factors on lithium conversion rate and energy intensity, respectively, guiding input variable selection for the neural network models.