Mill Energy Consumption Research Articles

Investigation of the engineering and environmental properties of cement mortars incorporating ladle furnace steel slag

While the use of ladle steel slag in concrete offers several potential advantages, there are significant research gaps that need to be addressed to promote its safe and effective utilization in the construction industry, e.g. the variations in slag properties, such as chemical composition and physical characteristics, can influence concrete performance. Moreover, even there is some research on the mechanical properties of concrete containing ladle steel slag, more in-depth studies are needed to assess the long-term durability of such concrete and the research should focus more on evaluating the environmental impact of using ladle steel slag in concrete. The paper reports the fundamental findings of a research study focused on the treatment of ladle slag to be applied as a cement substitute in the manufacturing of cement products. The research aimed to determine the optimal proportion of admixture based on treated ladle slag and evaluate the environmental properties of the resulting cement composites. The treatment process involved mechanical activation, specifically grinding the ladle slag fraction of 0/8 mm using a vibrating mill (Coarse slag) and a ball mill (Fine slag). The treated ladle slag, as an admixture, was examined in conjunction with four types of cement (CEM I, CEM II, CEM III, and CEM V). The research findings revealed that the incorporation of coarse ladle slag enhanced the workability of fresh cement paste across all tested formulations. Moreover, the utilization of treated ladle slag as an admixture in cement paste (substituting cement) exhibited a retarding effect on the setting time. As the age of the test specimens progressed (90 to180 days), the strength properties of the samples with ladle slag admixture approached those of the cement composites prepared using natural aggregates. The climate change impact of cement mortars per strength unit spanned from 0.82 to 2.75 kg CO2eq/MPa for composites incorporating finely-ground slag, and from 0.9 to 3.1 kg CO2eq/MPa for composites utilizing coarse slag. The grinding of ladle slag, even up to 30 wt% cement replacement, did not significantly impact the overall global warming potential and climate change, considering the average mill energy consumption of 50 kWh per 1 t of material.

Prediction of specific energy consumption during face milling of steel

ABSTRACT A novel empirical equation is presented to evaluate the Specific Energy Consumption (SEC) in face milling of steel. The proposed approach introduces a corrective factor to estimate the softening of work materials due to thermal effects at large cutting velocity. An experimental campaign on AISI 304 was carried out to investigate the predictive capability of the proposed model. A complete design matrix varying cutting velocity, radial depth of cut and feed rate was performed while measuring the average power consumed as response variable. Experimental results show that SEC reaches a maximum with the increase in the cutting speed. It is demonstrated that the SEC reduction observed at high cutting speeds is due to thermal softening induced by the higher temperatures attained in the work material. The formula proposed aims at providing a simple and accurate expression to select the process parameters in an energy saving perspective, especially in high-speed milling.

Size reduction performance evaluation of HPGR/ball mill and HPGR/stirred mill for PGE bearing chromite ore

In the present study, size reduction experiments were performed on High-Pressure Grinding Rolls (HPGR), ball mill and stirred mill of PGE bearing chromite ore. The performance of HPGR was evaluated in two stages of size reduction to reduce energy consumption. In the first stage of HPGR, the effect of operating variables such as the gap between the rolls, roll speed, and specific pressing force on product size (P80) and energy consumption (ECS) was investigated. The process to get the smallest product size was optimized within the experimental range of investigation. The crushed product of HPGR was subjected to grinding in the second stage in a ball mill and stirred mill. The effect of mill speed, grinding time, and ball size on the performance of the ball mill was investigated and the product was further investigated in the second stage. A comparative analysis of the ball mill and stirred mill performance and energy consumption at different grinding time intervals was also performed. It was found that the ball mill consumed 54.67 kWh/t energy to reduce the F80 feed size of 722.2 µm to P80 product size of 275.4 µm while stirred mill consumed 32.45 kWh/t of energy to produce the product size of 235.6 µm. It also showed that stirred mill produced finer product than the ball mill at around 40% lesser consumption of energy. It can be concluded that the HPGR-Stirred mill combination was a more energy-efficient grinding circuit than the HPGR-Ball mill combination for PGE bearing chromite ore.

Exploring the effect of a polyacrylic acid-based grinding aid on magnetite-quartz flotation separation

It is well documented that the use of grinding aids (GAs) can reduce milling energy consumption. However, the impact of GAs on downstream processes must be addressed in view of complex processes such as froth flotation separation. This study investigates the effects of polyacrylic-based grinding aids (Zalta™ GR20-587: AAG) on the grinding performance and quartz flotation from magnetite. Various AAG dosages and conditions were examined. The grinding results showed lower energy consumption and a finer, more uniform product size with roughened surfaces for AAG compared to grinding without the grinding aid. Flotation tests of single pure minerals showed that AAG enhanced quartz collection with minimal effect on magnetite. Mixed mineral flotation showed that by using AAG, Fe recovery of 92.1 % and 64.5 % Fe grade could be achieved with a lower collector dosage of 100 g/t compared to 200 g/t in the absence of AAG. Zeta potentials and stability measurements showed that AAG shifts the potential, thus improving the stability and dispersion of the suspension. Adsorption tests illustrated that AAG adsorbed on both quartz and magnetite, the former having a higher capacity. FTIR indicated the physisorption interaction between AAG and the minerals. Therefore, the presence of AAG not only improved grinding efficiency but could potentially decrease the amount of collector required to achieve comparable metallurgical performance.

Investigation of the effects of rock dynamic hardness on the energy consumption in the crushing unit, case study: Midouk copper mine

ABSTRACT The mounted semi autogenous (SAG) mill in the processing unit of the Midouk copper mine which consumes 35% of operational costs has a critical size within the range of 2.5–5 cm. To optimize the energy consumption in the milling unit of the mine, the data have been collected from 36 blast blocks. These blocks are selected in the main ore body area to monitor their effects on energy consumption in the milling unit. Owing to the dependence of mill energy consumption on the feed strength properties, a criterion for mill performance (MPC) has been defined and compared with the obtained dynamic hardness of seven specific blasting blocks. The results show a linear relationship with the accuracy of R 2 = 0.91 between the dynamic hardness and the MPC. For mines that use SAG or AG mills, the energy consumption for crushing the unit weight of feed depends on their dynamic stiffness.

The effect of a dacitic (rhyolitic) tuff breccia (Azomite®) in corn, soybean, and DDGS based diets that vary in inorganic phosphate source on pellet mill energy use, 0 to 21-day broiler performance, and apparent ileal amino acid digestibility

Past research has shown that Azomite ( AZM ) can increase pellet mill production rate in diets that include inorganic phosphate sources ( IPS ) of dicalcium phosphate ( DCP ) or tricalcium phosphate ( TCP ). The authors hypothesized that if production rate were held constant then pellet mill energy consumption would decrease for diets that contained AZM. The objective was to determine the effect of AZM (0.25%) in diets with DCP and TCP on pellet mill energy consumption, subsequent live bird performance, and apparent ileal amino acid digestibility ( AIAAD ) when fed to broiler chicks for 21 d. Feed was manufactured in a 2 (IPS) × 2 (AZM inclusion) factorial across 4 days in a Latin Square Design. Post manufacture, 320 one-day-old Ross 708 males were allocated to 8 replicate pens, 10 chicks per pen, in a randomized complete block design. Dietary treatments were arranged in a 2 (DCP or TCP) × 2 (AZM or no AZM) factorial. On d 21 ileal contents were collected for amino acid and titanium dioxide analysis. Pellet mill motor load decreased by 5% in diets containing TCP compared to DCP ( P < 0.001), and 1% when AZM was included ( P < 0.001). Pellet durability was increased in diets containing DCP when AZM was included, while TCP diets were unaffected ( P < 0.001). Hot pellet temperature increased for TCP diets with AZM, whereas DCP diets did not change ( P < 0.05). AIAAD of cysteine increased for DCP diets with AZM, but not TCP diets ( P < 0.05). Similar trends were observed for lysine, isoleucine, and valine ( P < 0.10). An increase in FCR by 0.02 was observed in DCP diets with AZM (1.283 vs. 1.309, P < 0.05), while TCP diets were unaffected (1.294 vs. 1.287, P > 0.05). Decreased pellet mill motor load that resulted from AZM inclusion suggests that feed was subjected to less frictional heat and/or pressure within the pellet die. This reduction in frictional heat and pressure likely preserved amino acid conformation, but the improvement was not translated to growth efficiency.

Energy consumption modelling in milling of variable curved geometry

The accurate estimation of energy consumption is beneficial to manufacturing enterprises economically as well as to overcome global energy crisis. The present work concentrates on developing an energy consumption model in milling of variable curved geometries where magnitudes and directions of workpiece curvature vary along tool contact path of a component. The current work deals with estimation and analysis of energy consumption in peripheral milling of variable curved surfaces where cutting forces differ along tool contact path in the presence of workpiece curvature. The proposed hybrid model developed in MATLAB involves process mechanics, cutting forces and energy consumption and has modules for idle, auxiliary and cutting power. The proposed model is validated by the experimental work. The model is generic and versatile in nature and is useful for milling of straight, circular and curved surfaces. In addition to it, the influence of workpiece curvature on power consumption has been investigated to realize the variation of power consumption along the tool contact path. The developed model offers a basic platform to understand and characterize the energy consumption for general peripheral milling considering workpiece geometry. The comparison of predicted and measured results indicates that the model is capable to estimate the power consumption accurately. The proposed model will be used by the practitioners to find the optimum cutting conditions to reduce power consumption during the machining of curved geometries – a pragmatic condition but not much researched condition in machining.

Analysis and Optimization of Grinding Performance of Vertical Roller Mill Based on Experimental Method

This work concentrates on the energy consumption and grinding energy efficiency of a laboratory vertical roller mill (VRM) under various operating parameters. For design of experiments (DOE), the response surface method (RSM) was employed with the VRM experiments to systematically investigate the influence of operating parameters on the energy consumption and grinding energy efficiency. The prediction models of energy consumption (Ecs) and grinding energy efficiency (η) were established respectively with the operating parameters (loading pressure, rotation speed and moisture content). Analysis of variance (ANOVA) was performed to obtain useful knowledge in designing operating parameters. Moreover, the multi-objective optimization design (MOD) method was conducted to seek out the optimal parameters of the VRM, and a set of optimal parameters was gained based on the desirability approach by Design-Expert. It is proved that the optimized prediction results match the experimental results well, which indicates this research offers a reliable guidance for reducing energy consumption and improving grinding energy efficiency.

Large-scale production of hexagonal boron nitride nanosheets by dry vibration milling at room temperature

Stable hexagonal boron nitride nanosheets (h-BNNSs) were directly exfoliated from bulk h-BN through a simple method of dry vibration milling at room temperature. SEM and TEM images demonstrate the as-prepared h-BNNSs can reach 1 ∼ 3 nm with a few layers when increase the vibration milling time. XRD measurements show a h-BNNSs lattice distortion after 30 h vibration milling due to increased strain and pressure on the nanosheets. Owing to the low energy consumption of dry vibration milling without the use of solvents, the nanosheets well maintain the crystal phase with a low level of defects. We believe that vibration mills are potentially attractive for high-volume production of two-dimensional materials.

A Metaheuristic Optimization Algorithm for energy efficiency in Digital Twins

This work aims to study the role of Digital Twins (DTs) technology combined with the Metaheuristic Optimization Algorithm in manufacturing energy efficiency optimization. Firstly,a machine tool model is established based on DTs technology to study the energy consumption of the milling process of Computer Numerical Machine Tools. Besides, the Particle Swarm Optimization (PSO) algorithm is introduced to optimize the milling parameters of the machining process. Meanwhile, the tool machining path is optimized by combining the Optimized Genetic algorithm and Simulated Annealing algorithm and find the optimal solution of the machining path. On the premise of ensuring the machining quality, this scheme improves the machining efficiency, reduces the energy consumption of the processing process, and improve the energy efficiency. The results demonstrate that the optimized milling parameters can ensure the lowest milling power while considering the maximum material removal rate. Take the plane model as an example. The Improved Genetic Algorithm-Simulated Annealing algorithm can significantly reduce the number of empty walking knife by adopting projection machining and helical machining. The total length of the milling path is reduced by 69.45 ​mm at most, a relative reduction of 10.01%. The average measured energy consumption of milling is reduced by 5.62W∗h compared with the empirical value; the measured average energy consumption of the optimized idle tool is reduced by 0.17W∗h; the total measured energy consumption of milling processing is reduced by 5.73W∗h compared with the empirical value. It can be seen that the optimization algorithm can improve the processing efficiency and reduce the energy consumption.

Characterization of the Composition, Structure, and Mechanical Properties of Endocarp Biomass

HighlightsEndocarps have higher lignin content, cellular and bulk density, and hardness than typical biomass feedstocks.The impacts of lignin content, bulk density, and mechanical properties on energy consumption are discussed.Endocarps can be a potential feedstock for a biorefinery coproducing biofuel and bioproducts.Abstract. Lignin is an abundant biopolymer and a promising source of feedstock for high-value chemicals and materials. This study aims to characterize the lignin-rich endocarp biomass and identify features of this unique feedstock that are relevant to feedstock preprocessing and logistics. The chemical composition and cellular structure of walnut and peach endocarps were characterized using HPLC and scanning electron microscopy (SEM) imaging. Mechanical properties of the endocarps were investigated using nanoindentation. Mechanical tests revealed hardness values of up to 0.48 and 0.40 GPa for walnut and peach endocarps, respectively. With screen sizes of 1 and 2 mm, the specific energy consumption was 9.21 and 1.86 MJ kg-1 for walnut and 12.6 and 2.72 MJ kg-1 for peach, respectively, as determined using a knife mill. Milling energy consumption was correlated to screen size, lignin content, bulk density, and mechanical properties. This study provides critical information on feedstock supply logistics necessary to implement a novel feedstock in biorefineries and evaluate the economic feasibility for coproduction of biofuels and lignin-derived products. Keywords: Biomass feedstock, Lignin, Mechanical properties, Nanoindentation, Size reduction.

Adaptive Fuzzy Logic Control for Grinding Process Based on Grinding Sound Trend

Advanced control is significant to improve the process performance for grinding and classification process. Due to frequent variation of ore property, many advanced control methods cannot work effectively in a long period. To overcome this problem, an adaptive fuzzy logic control method based on the trend of grinding sound is proposed for grinding process. In this method, the trend of grinding sound is extracted by qualitative trend analysis (QTA), grinding sound signal and mill power are used as controller inputs to control ore feed and water feed, and improved fuzzy C-means (FCM) is adopted to adapt parameters of fuzzy controller according to actual process conditions. Simulation results show that this proposed approach achieves less fluctuation of ore feed than manual control, ensures that ore throughput meets the target of this concentrator and reduces energy consumption of ball mill.

Modelling of spindle energy consumption in CNC milling

In manufacturing industries, machine tools are frequently used and required a lot of energy to work. Spindle acceleration is a common process when machine tools are in use. It generates a high-energy intensive power peak. The total energy consumption of machine tools in the machining process is strongly affected by these high-power peaks of short duration. Many researchers have overlooked the energy consumption of spindle acceleration resulting into inaccuracies in the prediction of overall energy consumption of machine tools. Therefore, the present study aims to develop a model to predict the spindle acceleration energy consumption of computer numerical control (CNC) milling machines. The proposed model is based on the principle of spindle motor control and includes the computation of moment of inertia of the spindle drive system. To validate the effectiveness of the proposed model, machining experiments are carried out on a CNC milling machine. Without performing time-consuming experiments, the proposed models can be utilized to estimate the power, time, and energy consumption of spindle acceleration. The proposed model helps to determine total energy consumption during machining process correctly.

A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment.

Prediction models for energy consumption and surface quality in stainless steel milling

Stainless steel is a kind of difficult-to-machine material, and the work hardening in milling easily leads to high energy consumption and poor surface quality. Thus, the influence of machined surface hardness on energy consumption and surface quality cannot be ignored. To solve this problem, the prediction models for machine tool specific energy consumption and surface roughness are developed with tool wear and machined surface hardness considered firstly. Then, the validity of the models is verified through AISI 304 stainless steel milling experiments. The results show that the prediction accuracy of the machine tool specific energy consumption model can reach 98.7%, and the roughness model can reach 96.8%. Later, according to the developed prediction models, the influence of milling parameters, surface hardness, and tool wear on the machine specific energy consumption and surface roughness is studied. Results show that in stainless steel milling, the most significant parameter for surface roughness is the machined surface hardness, while that for energy consumption is the feed per tooth. The machine specific energy consumption increases linearly with the increase of the tool wear and the machined surface hardness gradually. The proposed models are helpful to optimize the process parameters for energy-saving and high-quality machining of stainless steel.

Effect of methionine chelated Zn and Mn and corn particle size on Large White male turkey live performance and carcass yields

Most turkey research has been conducted with a regular corn particle size set through phase-feeding programs. This study's first objective was to determine the effect of increasing corn particle size through the feed phases on performance, processing yield, and feed milling energy usage in Large White commercial male turkey production. Zinc (Zn) and manganese (Mn) are essential microminerals for animals' healthy growth. The source in which these elements are supplied to the bird will determine their bioavailability, effect on bird growth, and subsequent environmental impact. This study's second objective was to measure both inorganic and chelated Zn and Mn sources on turkey performance, turkey carcass processing yields, and subsequent litter residues. Twelve hundred Nicolas Select male poults were randomly assigned to 48 concrete; litter-covered floor pens. The experimental design was a completely randomized block design with a 2 × 2 factorial arrangement of 2 sources of minerals (organic blend vs. inorganic) formulated to match breeder recommendations and 2 types of corn mean particle size (coarse corn [1,000–3,500 µm] vs. fine corn [276 µm]). The ASABE S319.4 standard was used to measure corn mean particle size. Bird performance, carcass processing yield, litter content of Zn and Mn, and pellet mill energy consumption were analyzed in SAS 9.4 in a mixed model. There was a reduction of pellet mill energy usage of 36% when coarse corn was added post-pelleting. Birds fed increasing coarse corn mean particle size were 250 g lighter on average in body weight (BW) than birds fed a constant control mean particle size. No difference was found in feed intake (FI) or feed conversion ratio (FCR). Birds fed methionine chelated Zn and Mn blended with inorganic mineral sources were 250 g heavier on average than birds fed only an inorganic source of minerals. In addition, feeding an organic blend of Zn and Mn resulted in greater breast meat yield. Litter from birds fed the control corn mean particle size, and inorganic minerals had a higher concentration of Zn in the litter but were not different when the chelated Zn/Mn were fed. In conclusion, increasing the corn mean particle size and adding it post pellet could save money during feed milling; however, birds might have a slightly lower BW. A combination of inorganic and chelated Zn and Mn may improve performance and increase total breast meat yields.

Multi-objective integrated optimization of tool geometry angles and cutting parameters for machining time and energy consumption in NC milling

Multi-objective integrated optimization of tool geometry angles and cutting parameters for machining time and energy consumption in NC milling

Application of singularity vibration for minimum energy consumption in high-speed milling

To provide a model for real-time prediction of cutting forces, vibrations and optimal energy consumption (EC) model, a new hybrid algorithm based on Back-Propagation Neural Network and Multi-Objective Particle Swarm Optimization (BPNN-MOPSO) was developed to determine the optimal cutting parameters with minimal total energy consumption. High-speed milling experiments were conducted to confirm the proposed online monitoring and optimal model’s accuracy and availability. The proposed optimal method, based on the proposed improvement model, can reduce EC by 10.49% compared to the empirical option method. The feasibility and effectiveness of the proposed model have been verified through experimental processes.

On-Line Optimization of Energy Consumption in Electromagnetic Mill Installation

Milling is one of the most energy consuming stages of the value production chain in many industries. To minimize the specific energy required, new and more efficient devices and circuits are designed and dedicated optimizing control strategies are applied. This research presents the results of innovative electromagnetic mill energy consumption reduction with dedicated supervisory on-line optimizing control algorithm. The paper describes an algorithm that uses the active power measurement and searches for the minimum on the active constraints of the optimization problem. The constraints follow from the product quality, mill supply voltage and magnetic induction requirements. Algorithm performance was tested in simulations, but the main validation was performed on a semi-industrial dry grinding and classification circuit equipped with an electromagnetic mill. The results of the experiments presented in this paper show that the application of the on-line optimization algorithm allows for even a 40% reduction in the electromagnetic mill energy consumption when compared to the nominal operating point.

Machine-Specific Estimation of Milling Energy Consumption in Detailed Design

AbstractManufacturing is a significant contributor to global greenhouse gas emissions and there is an urgent need to reduce the energy consumption of production processes. An important step towards this goal is proactively estimating process energy consumption at the detailed design stage. This is a challenging task as variabilities in factors such as process specifications, machine tool architecture, and workpiece geometry can significantly reduce the accuracy of the estimated energy consumption. This paper discusses a methodology for machine-specific energy estimation in milling processes at the detailed design stage based on the unit process life cycle inventory (UPLCI) model. We develop an adjusted UPLCI model that includes adjustment factors for uncertainties in machine tool specifications and the specific cutting energy of a workpiece material. These adjustment factors are calculated through experimental measurement of energy consumption for a reference test part on a specific machine tool. To validate the adjusted UPLCI model, we conducted a case study that measured the energy consumption for machining three parts made of Aluminum 6082 on two separate three-axis vertical milling machines, a Chevalier QP2040-L and a Leadwell MCV-OP. Results show that the UPLCI model consistently overestimated the total energy consumption for machining the three validation parts across both machine tools. We also found that the adjusted UPLCI model significantly reduced the estimation errors for the same tests for both machine tools.