To accurately predict the critical flow velocity of Sijiaying's large-capacity super fine tailings slurry in long self-flowing transportation, a new ELM prediction model is developed. The ELM model takes pipe diameter, grain diameter, slurry density and volume concentration as input factors, and critical flow velocity as output factor. By comparing it with traditional BP neural networks and support vector machines (SVMs), the superiority of ELM in improving precision and efficiency is demonstrated. It is revealed that ELM model's relative error is blow 5%, which is lower than BP model's 9.56%. With the hidden node number being 110 and 200, the training times of ELM are 0.02 s and 0.05 s, respectively, which both are far below the corresponding SVM's 0.04 s and 0.095 s. The random choice and good adaptability of hidden node number makes the new ELM model superior in improving precision and efficiency.

large-capacity  /  critical flow velocity  /  extreme learning machine  /  prediction accuracy  /  computational efficiency