Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting

• Published in: Engineering with computers Vol. 37; no. 1; pp. 265 - 274
• Main Authors: Zhou, Jian, Li, Chuanqi, Arslan, Chelang A., Hasanipanah, Mahdi, Bakhshandeh Amnieh, Hassan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.01.2021; Springer Nature B.V
• Subjects: Adaptive systems; Artificial neural networks; Blasting; CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Classical Mechanics; Computer Science; Computer-Aided Engineering (CAD; Control; Fragmentation; Fuzzy logic; Fuzzy systems; Genetic algorithms; Heuristic methods; Math. Applications in Chemistry; Mathematical and Computational Engineering; Mining industry; Model accuracy; Original Article; Particle size; Particle size distribution; Performance evaluation; Prediction models; Root-mean-square errors; Support vector machines; Systems Theory; ANFIS; Blasting; Genetic algorithm; Firefly algorithm; Rock fragmentation
• DOI: 10.1007/s00366-019-00822-0
• ISSN: 0177-0667

Accurately predicting the particle size distribution of a muck-pile after blasting is always an important subject for mining industry. Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic intelligent system. The main contribution of this paper is to optimize the premise and consequent parameters of ANFIS by firefly algorithm (FFA) and genetic algorithm (GA). To the best of our knowledge, no research has been published that assesses FFA and GA with ANFIS for fragmentation prediction and no research has tested the efficiency of these models to predict the fragmentation in different time scales as of yet. To show the effectiveness of the proposed ANFIS-FFA and ANFIS-GA models, their modelling accuracy has been compared with ANFIS, support vector regression (SVR) and artificial neural network (ANN). Intelligence predictions of fragmentation by ANFIS-FFA, ANFIS-GA, ANFIS, SVR and ANN are compared with observed values of fragmentation available in 88 blasting event of two quarry mines, Iran. According to the results, both ANFIS-FFA and ANFIS-GA prediction models performed satisfactorily; however, the lowest root mean square error (RMSE) and the highest correlation of determination ( R 2 ) values were obtained from ANFIS-GA model. The values of R 2 and RMSE obtained from ANFIS-GA, ANFIS-FFA, ANFIS, SVR and ANN models were equal to (0.989, 0.974), (0.981, 1.249), (0.956, 1.591), (0.924, 2.016) and (0.948, 2.554), respectively. Consequently, the proposed ANFIS-GA model has the potential to be used for predicting aims on other fields.