Optimizing building stone-cutting in quarries: a study on estimation of maximum electric current using ABC and SC algorithms

Abstract

In today's context, due to the extensive construction projects, there is a surging demand for building stones. Within quarry-based processing facilities, a pivotal aspect influencing the production of these building stones pertains to evaluating the performance of band saw machines, particularly concerning the cutting of these stones. In this context, Maximum Electric Current (MEC) emerges as a critical variable. To identify this crucial factor, it necessitates a comprehensive grasp of the inherent properties of the stone since it profoundly influences costs, equipment depreciation, and production rates. Estimating the MEC poses numerous challenges and complications due to the uncertainty inherent in geological and geotechnical parameters at each point. Conventional and traditional methods, such as numerical, experimental, analytical, and regression methods, have limitations as they often overlook the uncertainty in rock parameters, leading to the construction of simplistic and non-linear models with simplified assumptions in analytical methods that may lack high accuracy. Consequently, this article employs intelligent methods to overcome these challenges and achieve an optimal solution with high accuracy. Using intelligent methods, it becomes possible to create complex and non-linear models efficiently, minimizing both time and cost. Consequently, this study addresses these challenges by employing two optimization algorithms: Artificial Bee Colony (ABC) and Sine Cosine (SC) Algorithms to estimate MEC specifically in quarry operations. In pursuit of this objective, 120 test samples drawn from 12 distinct types of carbonate rocks obtained from a marble factory in the Mahalat region of Iran were utilized. The considered input parameters encompassed Young's modulus, Mohs hardness, uniaxial compressive strength (UCS), production rate and F-Schimazek abrasion factors. The dataset was partitioned, allocating 80% (70 data points) for model development and reserving 20% (18 data points) for model validation. The analysis of modeling outcomes involved three statistical criteria: squared correlation coefficient, mean square error, and root mean square error. The results revealed that the developed model demonstrates a high level of accuracy and minimal error, closely approximating real values. Hence, it can serve as a valuable tool for engineers engaged in the field of rock engineering. In a final step, to assess sensitivity and evaluate the model's output, the @RISK software was employed. The analyses unveiled that among the input parameters within the quarry context, UCS exerts the most substantial influence on the model's output. Even slight variations in UCS can lead to significant alterations in MEC within quarry operations.