Specific Energy Consumption of Material handling by Excavator
Autor: Vjekoslav Herceg, PhD mag. ing. min.
In modern mining, it is increasingly clear that competitiveness is no longer based solely on productivity, but also on energy efficiency and the reduction of CO₂ emissions. Excavators, as key machines in crushed stone quarries, consume significant amounts of diesel, and every liter directly represents both a cost and an emission. For this reason, understanding how different materials influence the energy consumption of excavation has become highly important. Field measurements carried out in an active quarry provided insight into how much energy is actually consumed when excavating five typical material types—overburden, blasted rock material, boulders, crushed stone, and crushed stone with clay.
The measurements for this research were conducted under real operating conditions, on a hydraulic excavator equipped with a precise measurement system containing hydraulic pressure transmitters and draw‑wire displacement sensors mounted on the hydraulic cylinders. Combined with knowledge of the very complex kinematics of the excavator (Figure 1), this enables direct monitoring of forces and bucket trajectory during excavation.

Figure 1 Kinematics of the excavator (Herceg et.al. 2023)
The system records data every 0.1 seconds, making it possible to calculate the digging energy in great detail, not only as an average value but also as instantaneous changes throughout the digging cycle. The digging mechanism and the trajectory of interaction between the material and the bucket are shown in Figure 2. Since only the portion of the cycle in which the bucket actually digs the material is taken into account, the resulting data reflect the pure bucket–material interaction, without the influence of lifting, swinging, or dumping. Additionally, each material was measured through approximately twenty repetitions to determine repeatability, and the excavator was operated by the same person throughout the entire study, thereby eliminating operator-related variability. After excavation, the mass of material in the bucket was determined from the boom cylinder pressure, enabling the calculation of specific energy (kJ/t), which is the key indicator of true efficiency and fuel consumption. This level of detail provides data reliable enough for use in planning, optimization, and CO₂ emission assessment in everyday quarry operations.

Figure 2 Mechanism of digging (Herceg et.al.2023)
The results show a very clear and practically important difference between materials. Blasted rock material was found to be the most energy‑demanding: a wide range of fragment sizes, well size distribution and irregular behavior during bucket penetration create high resistance, which means higher energy consumption and higher CO₂ emissions per ton of loaded material. Immediately behind it is overburden, whose main issue is the high plasticity of clay—clay causes adhesion and increases the force required for digging.
Contrary to expectations, boulders require less energy than blasted rock material. Despite their large dimensions, they are uniform, and during loading the bucket typically contacts them at only a few points, effectively rolling them into the bucket. The lowest energy consumption was recorded for crushed stone and crushed stone with 15% clay. For clean crushed material, the reason is straightforward: the fine grain-size distribution and small size range allow smooth bucket penetration. Interestingly, even a relatively small amount of clay (only 15%) increases energy consumption by approximately one-fifth, which over long-term excavator operation can represent a significant difference in fuel usage. A symbolic representation of the results is shown in Figure 3.

Figure 3 Symbolic representation of energy consumption for each material
The practical application of these results is very direct. Knowing that digging blasted rock material consumes the most energy, it becomes possible to better plan the allocation of machinery—for example, assigning equipment with newer engines and lower specific fuel consumption to the most demanding tasks, while less demanding materials can be handled by more economical or older machines. This yields two benefits: overall fuel consumption is reduced and the service life of machines is extended.
This research also reveals another important point: optimizing emissions in mining does not always require expensive machinery upgrades or switching to alternative fuels. Sometimes it is sufficient to understand how certain materials behave during excavation and then intelligently plan work tasks. Likewise, operations can be scheduled during periods with lower soil moisture, which reduces adhesion forces and digging resistance, especially for clay-rich materials.
In a broader sense, this type of analysis contributes to the development of modern, energy‑efficient mining. By monitoring digging energy and understanding how different materials create different energy demands, it becomes possible to produce precise energy‑demand maps and calculate expected fuel costs and CO₂ emissions for each stage of the mining process. Ultimately, such studies confirm that efficiency and ecology in mining are not mutually exclusive but mutually reinforcing: everything that reduces energy consumption also reduces emissions while increasing productivity and long‑term sustainability.
References
Herceg, V.; Klanfar, M.; Herceg, K.; Domitrović, D. (2023): Specific energy consumption of material handling by excavator in the quarrying of crushed stone. Rudarsko-geološko-naftni zbornik, 83–92. https://doi.org/10.17794/rgn.2023.1.8.
Dadhich, S.; Bodin, U.; Andersson, U. (2016): Key challenges in automation of earth-moving machines. Automation in Construction, 68. https://doi.org/10.1016/j.autcon.2016.05.009.
Du, Y.; Dorneich, M. C.; Steward, S. (2016): Virtual operator modeling method for excavator trenching. Automation in Construction, 70, 14–25
Jassim, H.S.H.; Lu, W.; Olofsson, T. (2018): Quantification of Energy Consumption and Carbon Dioxide Emissions During Excavator Operations. In: Advanced Computing Strategies for Engineering, LNCS 10863, 431–453.
Kim, B. Y., Ha, J., Kang, H., Kim, P. Y., Park, J., Park, F. C. (2013): Dynamically optimal trajectories for earthmoving excavators. Automation in Construction, 35, 568–578.
Klanfar, M., Herceg, V., Kuhinek, D., & Sekulić, K. (2019). Construction and testing of the measurement system for excavator productivity. Rudarsko-geološko-naftni zbornik, 34(2).
Koivo, A. J. (1994): Kinematics of excavators (backhoes) for transferring surface material. Journal of Aerospace Engineering, 7, 17–32.
Kujundžić, T.; Klanfar, M.; Korman, T.; Briševac, Z. (2021): Influence of Crushed Rock Properties on Productivity of a Hydraulic Excavator. Applied Sciences, 11, 2345.
Lee, S.; Hong, D.; Park, H.; Bae, J. (2008): Optimal path generation for excavator with neural networks based soil models. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 632–637.



















































































