Mohammad Esmaeili

Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting

Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. Backbreak can be affected by various parameters such as the rock mass properties, blasting geometry and explosive properties. In this study, the application of the artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS) for prediction of backbreak, was described and compared with the traditional statistical model of multiple regression. The performance of these models was assessed through the root mean square error, correlation coefficient (R2) and mean absolute percentage error. As a result, it was found that the constructed ANFIS exhibited a higher performance than the ANN and multiple regression for backbreak prediction.

Application of PCA, SVR, and ANFIS for modeling of rock fragmentation

Fragmentation has direct effects not only on the drilling and blasting costs but also on the economy of subsequent operations. In the present study, two soft computing-based models, so called “support vector machines (SVM)” and “adaptive neuro-fuzzy inference system (ANFIS)” were used and compared with Kuz-Ram method. In this regard, six effective parameters including specific charge, stemming length, total delays per number of rows ratio, hole diameter, spacing to burden ratio, and blastability index were considered as input parameters containing a database of 80 variables from the blasting operation of the Chadormalu iron mine of Iran. Principal component analysis (PCA) was performed to clarify the effective parameters on the fragmentation. As statistical indices, root mean square error (RMSE), correlation coefficient (R2), bias, variance account for (VAF), and mean absolute percentage error (MAPE) were used to evaluate the efficiency of the addressed models between measured and predicted values of rock fragmentation. The results confirmed the ANFIS and SVM as accurate predictive tools for rock fragmentation in open-pit mines. Correlation coefficient, bias, VAF, and MAPE generated by the ANFIS model (respectively 0.89, 0.257, 88.19, and 10.37) were higher than referred values for the SVM model (0.83, 1.87, 75.24, and 16.25, respectively) as well as Kuz-Ram inference.

Utilising of linear and non-linear prediction tools for evaluation of penetration rate of Tunnel Boring Machine in hard rock condition

Predicting Tunnel Boring Machine (TBM) penetration rate is a crucial issue for the successful fulfilment of a mechanical tunnel project. Penetration rate depends on many factors such as intact rock properties, rock mass conditions and machine specifications. In this paper, linear and non-linear multiple regression as well as Artificial Neural Network (ANN) techniques were applied to predict the penetration rate of TBM. In developing of the proposed models, five parameters, which include Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), peak slope index (punch penetration), spacing of discontinuities (of weakness planes) and orientation of discontinuities with respect to the tunnel axis (β angle), were incorporated. For this study, 46 datasets were collected. Performance of these models was assessed through the R2, RMSE and MAPE. As a result, these indices revealed that the prediction performance of the ANN model is higher than that of the non-linear and linear multiple regression models.

Optimal energy aware clustering in circular wireless sensor networks

Abstract The lifetime of a wireless sensor node refers to the duration after which the nodes energy has ended. Since battery replacement in most applications of wireless sensor networks is not possible, designing an energy-efficient communication protocol in these networks is very important. Therefore, many studies have been conducted to find a solution to increase the lifetime of these networks. Clustering is a useful technique for partitioning the network to areas, called clusters and entrusting energy-waste issues (e.g. data gathering, aggregating and routing to the sink) to some specific nodes, the cluster heads. In this paper, a new method for Optimal Clustering in Circular Networks (OCCN) is proposed which aims to mitigate energy consumption and increase the lifetime of wireless sensor networks. In this method, which is proposed for a circular area surrounding a sink, one hop communication between the cluster heads and the sink is replaced by an optimal multi-hop communications. Moreover, the optimal number of clusters is computed and the energy consumption is optimized by partitioning the network into nearly the same size clusters in a distributed manner. Simulation results indicate that the proposed method achieved more than 35% improvements in terms of energy consumption in comparison to other well-known clustering techniques.

An integrated production and procurement design for a multi-period multi-product manufacturing system with machine assignment and warehouse constraint

Abstract Economic production and on-time ordering are among the most important topics related to production and inventory control issues. An economical production needs a comprehensive and precise planning to be implemented in all production stages. To have a controlled and comprehensive planning system, economic order quantity (EOQ) or economic production quantity (EPQ) models are usually used in various production-inventory environments to minimize costs, avoid delays in orders, and achieve high performance. To meet the demands, sometimes a multiperiod production-inventory planning that involves several products requires outsourcing. In this paper, a production-procurement plan that integrates EOQ with EPQ is proposed for a multiperiod multi-product production-inventory system with a limited warehouse capacity. The design aims to determine the optimal values of decision variables including the production quantity, the order quantity, inventory, shortages, and machine assignments to produce the products in a planning horizon. The problem is formulated as a non-linear mixed-integer programming model. As the problem becomes NP-Hard, a genetic algorithm (GA) is utilized to find a near-optimal solution. In addition, a random search (RS) algorithm is applied to validate the results obtained and to justify the efficiency of the GA. Since there is no benchmark available in the literature, some randomly generated numerical examples are solved and a sensitivity analysis is performed in order to demonstrate the effectiveness of the proposed methodology and the solution algorithm.