Effects of grinding aids on model parameters of a cement ball mill and an air classifier

Accepted Manuscript Effects of grinding aids on model parameters of a cement ball mill and an air classifier

Nurettin Alper Toprak, Ahmet Hakan Benzer PII: DOI: Reference:

S0032-5910(18)31071-4 https://doi.org/10.1016/j.powtec.2018.12.039 PTEC 13984

To appear in:

Powder Technology

Received date: Revised date: Accepted date:

2 July 2018 12 November 2018 3 December 2018

Please cite this article as: Nurettin Alper Toprak, Ahmet Hakan Benzer , Effects of grinding aids on model parameters of a cement ball mill and an air classifier. Ptec (2018), https://doi.org/10.1016/j.powtec.2018.12.039

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ACCEPTED MANUSCRIPT Effects of Grinding Aids on Model Parameters of a Cement Ball Mill and an Air Classifier Nurettin Alper TOPRAKa*, Ahmet Hakan BENZERa Hacettepe University, Mining Engineering Department, 06800 Beytepe, Ankara, Turkey

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* Corresponding author. E-mail: [email protected]. Tel: +903122977600. Fax: +903122992155.

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Abstract This research focuses on investigating the effects of the three different grinding aids, consisting of a mixture of amine, glycol and polyol in different ratios, on the model

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parameters of a two-compartment cement ball mill and an air classifier. Within the content of this work, sampling campaigns were organized around a cement grinding circuit and each of

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the three grinding aids at varying dosage rates were tested. Then the ball mill and the air

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classifier were modelled by using perfect mixing approach and Whiten’s equation respectively. The relations between the types and dosage rates of grinding aids and model

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parameters were examined. The fact that such an examination has not been made previously by using industrial data, which makes this research unique. It was deduced that

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depending on the chemical composition of grinding aids, their effects on model parameters were varied and ultimately all of them improved the performance of the grinding and the

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classification operations.

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Keywords: Grinding aid; modelling; cement

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1. Introduction A considerable amount of energy is consumed for grinding during the manufacture of

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cement. Approximately 110 kWh/t of electric energy is consumed in the cement production. More than a half of that energy is used for the size reduction processes of the raw materials

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(%33) and clinker (%38). It is crucial to increase the efficiency of comminution process to

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reduce the amount of energy used and greenhouse gas emissions [1]. The cement industry has been taking advantages of the grinding aids for more than a half century and their

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utilization provides energy saving in grinding circuits [2,3].

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When grinding aids are mixed with the material to be ground, it is possible to achieve a finer product at the same production rate or to obtain a higher production rate at the same product fineness. [4]. Most of the grinding aids are consist of organic compounds and up to now,

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amines, glycols, and phenols have been used as raw materials which constitute the main

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ingredient of them. [5]. Many researchers extensively studied to clarify the mechanism of action of grinding aids. Rehbinder and Kalinkovaskaya [6] claimed that the adsorbed molecules on the particle surfaces could influence the bonding strength and the surface

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energy at the beginning point of cracking. Westwood and Stoloff [7,8] explained the mechanism of action of the grinding aids in a different way. They argued that the adsorption of grinding aids could constrict near-surface dislocations and thus the particle became more fragile. However, Locher and Seebach [9] came to a decision that the utilization of grinding aids could not influence the fracture characteristics of the materials, but it reduced the Van der Waal’s forces between the surfaces that facilitated the fracture process. The high polarity functioning groups of grinding aids (–COOR, –NH2, –OH, –SO3–) prevent the agglomeration by causing the propensity to adsorb on electrostatic surfaces formed by the breakage of the covalent bonds of Al-O, Si-O, and Ca-O [10]. Klimpel and Manfroy [11]

ACCEPTED MANUSCRIPT informed that the naturalization of the surface of the particles reduced the possibility of agglomeration. Moreover, the bulk material became fluidized and transported more easily through the mill. Weibel and Mishra [12] conducted variety of computer simulations in order to determine the physical and chemical behaviours of chemicals used as grinding aids. They reported that

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dispersion of organic molecules during grinding could occur via two basic mechanisms, such as gas phase transfer and surface contact transfer. Grinding aids reduced the polarity of the

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clinker surfaces; accordingly, surface energy was decreased. Their mechanism of action was

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explained by agglomeration energy. Grinding aids caused a decrease in agglomeration energy which correlated inversely with the grinding performance. Priziwara et al. [13]

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examined the influences of several grinding aids on particle and bulk properties of ground

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limestone. They concluded that as the polarity of the grinding aid decreases, the required grinding aid dosage should be higher. It was also reported that the use of grinding aids reduced the surface energy of the limestone. Due to the decrease in surface energy, the

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agglomerate size decreased and fluidity of the powder increased.

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Up to now, many laboratory-scale studies have been conducted with ball mill to examine the influences of the grinding aids on grinding performance. Sohoni et al. [14] investigated the influences of seven grinding aids on the batch grinding of limestone, Portland cement clinker

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and quartz. It was observed that the grinding aids had only a negligible impact on grinding of quartz although they had a considerably beneficial effect on the grinding of clinker and limestone. Also, they concluded that the effect of these grinding aids were to forestall agglomeration and media coatings of the powder. Sverak et al. [15] also conducted batch grinding tests on cement clinker with twelve different grinding aids and they reported that each of these had different effects on the grinding performance. The influences of grinding aids on the efficiency of the stirred media mill were also investigated by many researchers. Altun et al. and [16], Gokcen et al. [17], reported that the utilization of grinding aids enhanced the grinding performance and decreased the specific energy consumption.

ACCEPTED MANUSCRIPT Assaad et al.[18] performed laboratory scale grinding tests in order to compare the effects of amine and glycol based grinding aids on cement fineness in different specific energy consumptions. They reported that if the specific energy consumption was less than around 20 kWh/t and the Blaine values were lower than 3500 cm2/g, the fineness of cement changed according to Rittinger law. Beyond these values the agglomeration of cement

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particles increased and therefore the milling efficiency decreased. In addition, it has been concluded that the target product fineness and the applied grinding energy were important

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factors affecting the grinding aid performance. The amine-based grinding aid was found to be

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more efficient than the glycol based grinding aid at specific energy consumption values of more than 45 kWh/t.

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The temperature is also one of the important factors affecting performance of grinding aids. Assaad [19] compared the performance of the tertiary alkanolamines at ambient (23 ± 3 oC)

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and elevated (100 oC) temperature in laboratory conditions. The results showed that the more energy was required at elevated temperature than the ambient temperature to achieve

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the same product fineness. The loss in grinding aid performance due to elevated temperature was mostly attributed to decrease in the amount of organic compounds onto

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cement grains because of the thermal polymer degradation, partial evaporation and irreversible adsorption of them on grinding media [10].

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Although many researches have been conducted on the impacts of grinding aids on comminution, the works examining the effects of them on the classification are limited. Toprak et al. [20] performed extensive air classification tests with various chemicals used as grinding aid in the laboratory. It was concluded that while the grinding aids affected bypass and fishhook parameters considerably, there was almost no influence on cut size and sharpness parameters. Although laboratory tests are appropriate to compare different grinding aids with each other, these results cannot be adapted directly to the industry. In the laboratory tests, especially

ACCEPTED MANUSCRIPT performed by adding grinding aids, the specific energy consumption was much higher than in the industry and wider particle size distribution curves were obtained that shifted towards a higher diameter [21]. There were many factors that caused these differences and the most important of these was the scale-effect. Diameter and length of the mill, maximum ball size, ball filling ratio, design of the mill liners, and critical speed of the mill are the main factors

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affecting the grinding forces. Mill retention time is also another parameter that should be taken into consideration. The particles that reach the certain fineness inside the mill are

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taken out of the mill quickly with the help of air. Grinding aids also affect the retention time by

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increasing the cement fluidity [22]. These effects of grinding aids and ventilation on mill retention time cannot be examined by laboratory scale batch grinding tests. Furthermore, it is

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quite difficult to estimate the classifier performance and the circulating load of the circuit by only considering the laboratory test results, which is important to evaluate the milling

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efficiency and the fineness. For these reasons, it is essential to carry out industrial-scale

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tests to accurately assess the effects of grinding aids.

Toprak et al. [23] investigated the benefits of grinding aids in industrial applications by taking

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into consideration the improvements in cement quality, equipment performance, production rate and energy consumption. It was reported that the throughput of the circuit could be

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increased by 24% by using grinding aids. Assad [24] realized industrial test and developed a laboratory scale “locked-cycle” procedure that facilitated the testing and simulating of the closed circuit grinding operations. Even though the modelling and simulation studies of the cement grinding circuits have been carried out [25,26], the effects of the grinding aids on the model parameters have not been taken into consideration until today. The objective of this study is to improve the prediction capabilities of the simulations by revealing the relationships between model parameters and grinding aid dosage and type. In this way, the effects of different type of active ingredients used as grinding aid on the ball mill

ACCEPTED MANUSCRIPT and air classifier is better understood and it becomes easier to prepare the most effective chemical prescription for grinding aid. In this context, three different types of grinding aids were tested at various dosages in a cement plant and the impacts of grinding aids on model parameters of ball mill and air classifier were investigated. The results of this study are to be utilized in simulation of

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grinding circuits. In this way, by comparing the effects of doses of different types of grinding aids, the most appropriate grinding aid type and dosage can be predicted for the economy of

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the milling operation. This paper is thought to be beneficial for grinding aid suppliers, cement

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2. Sampling and Experimental Studies

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manufacturers and researchers dealing with modelling and simulation of grinding processes.

In order to examine the effects of grinding aids on grinding circuits, three types of grinding aids, consisting of a mixture of amine, glycol and polyol were tested in different doses.

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Grinding aids are specified in Table 1. Within the scope, 3 different sampling campaigns

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were conducted. The percentages of clinker, limestone, gypsum and ash used for cement production during the tests are given in Table 4. Fig. 1 illustrates the simplified flowsheet of

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the circuit and sampling points.

Table 1 Grinding aids used during tests Fig. 1. Simplified flow sheet and sampling points The circuit consists of a two compartment ball mill and a dynamic air classifier. There is a slotted diaphragm inside the ball mill that separates the two chambers from each other. The particles coarser than the size of the slots (10 mm) stay in the first chamber while the finer particles pass the second chamber for further size reduction. Liners of the first chamber are utilized to lift the grinding media to a certain height while the second chamber linings are classifying liners that allow segregation of the grinding media. The filter is used for ventilation

ACCEPTED MANUSCRIPT and helps to transport the material especially in the second compartment by collecting some of the fine particles. The coarser particles inside the mill that cannot be transported by filter are discharged by overflowing. The material collected in the filter is not in the fineness of the final product, so it is fed to air classifier. The operational parameters of the dynamic air classifier are adjusted to obtain the target product size. The fine stream of the classifier is

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Table 2 Technical data of the ball mill and the air classifier

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information of the mill and classifier are tabulated in Table 2.

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sent to silos as the end product while the coarse stream is returned to the mill. The technical

Tests conducted with each grinding aid took about one day to complete. The night before the

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tests started, the dispensing of grinding aid which had previously added to the system was stopped. The system was run about 10 hours without grinding aid until the morning. Before

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starting the sampling studies, operational parameters were monitored from the control room data. In order to verify the steady state condition at the targeted product fineness, cement

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samples were collected from the final product periodically. After establishing the steady-state conditions, reference samples were collected around the circuit. Then the dispensing of the

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lowest dose of the grinding aid was started. It took about 6-7 hours to establish the steady state conditions by determining the optimum throughput at the targeted cement fineness. It was easier to provide the optimum operating conditions when the dose of grinding aid was

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increased, because grinding aid was already in the system. Each test with a different dose of the same grinding aid lasted for about 4 hours.

During each sampling campaign, chemical assays of raw meal composition were undertaken periodically in order to provide a stable feeding with constant composition. Thus product quality of the cement plant was not deteriorated and the effects of grinding aid dosages on the throughput were more easily compared. The Blaine values and the chemical composition of cement samples collected from the final product during each sampling period are illustrated in Table 3. Thus, it can easily be seen from this table that there was no significant

ACCEPTED MANUSCRIPT change in the cement composition resulting from the raw materials. This means that grinding aid tests could be reliably compared with the reference conditions. Also, the Bond work index of clinker, limestone and gypsum determined as according to the Bond grindability test were 13.13, 10.82 and 9,88 kWh/t respectively. Table 3 Chemical composition and Blaine values of cement samples

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Also, the fluctuations on the flow rates (e.g., circulating load, fresh feed) and the power

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draws (e.g. classifier, mill and elevators) were all monitored from on the control room. Data

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recorded from control room when the steady-state conditions were established is tabulated in Table 4.

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Fig. 2. illustrates an observed graph during one of the sampling period. While GA-1 was being tested, the energy consumed by the mill was lower because the mill was not on the

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original media filling. On the other hand, it was aimed to produce finer product than the other campaigns. Therefore, this campaign was resulted in lower throughput.

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Fig. 2. Observed control room trends during one of the sampling period

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It was aimed to achieve the similar product fineness with the reference condition while testing the different dosage rates of each grinding aid (Fig. 3). During the tests, samples were taken from the final product every 30 minutes and the cement fineness was controlled by

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determining sieve residue values. In order to obtain the desired product fineness, the rotor speed of the air classifier was adjusted by keeping air flow rate constant. When the cement fineness was coarser than the desired value, the rotor speed was increased and a finer product was obtained. In this case, more materials were returned to the mill and the circulating load increased in the system. In the presence of grinding aids, the speed of the rotor was increased slightly in order to achieve the same cement fineness as the reference conditions. Fig. 3. Particle size distributions of the final products

ACCEPTED MANUSCRIPT Combination of two different measurement techniques were used to analyze the samples collected from around the circuit. Firstly, particle size of materials from top size to 150 µm was measured by dry sieving. Then, laser diffractometry method was applied for finer particles than 150 µm. Fig. 4 illustrates the measured particle size distributions around the circuit as an example. Similar plots were also drawn for the other grinding aids as well. The

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particle size distributions were then used for modelling studies.

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3. The Influences on Overall Circuit Performance

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Fig. 4. Measured particle size distributions around the circuit (GA-1, 400 g/t)

Mass balance studies were carried out around the circuit by using the control room data and

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the particle size distributions. In this way, flow rates in each stream were computed. In

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addition, the measured size distributions were justified by eliminating any experimental errors that might have been resulted from both the sampling works and process conditions.

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During the mass balance studies, JK-SimMet mass balance module was utilized. The algorithm behind the mass balancing was based on Quasi-Newton approach. Instead of

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obtaining an estimate of the Hessian matrix at a single point, this approach gradually builds up an approximate Hessian matrix by using gradient information from some or all of the

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previous iterates visited by the algorithm. Fig. 5 illustrates the calculated cumulative passing values against the measured values. A good fit of the measured and calculated values indicated that the data could be utilized for further considerations. Mass balance results are tabulated in Table 5. Fig. 5 Calculated and measured cumulative passing values Table 5 Calculated flow rates around the grinding circuit The sampling campaigns conducted without using any grinding aid represented the reference conditions. All results indicated that grinding aids improved the performance of the

ACCEPTED MANUSCRIPT circuit (Fig. 6). The optimum production rate of the circuit was determined considering the targeted fineness of the final product. The production rate increased depending on the grinding aid dosage. Grinding aids used during the tests showed similar effects at various dosages because they had different chemical constituents.

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The maximum increase in the throughput was reached by using 500 g/t of GA-2. It was approximately 24% more than the reference condition. Considering increase in throughput,

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GA-1 was the least effective among the three grinding aids. The highest rate of increase

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achieved by GA-1 was 10 %. At the highest dosage of the GA-3 (400 g/t), the throughput was 16 % more than that of the reference condition. The increase in throughput was

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approximately 23 % when GA-2 was used at the same dosage as GA-3. In connection with

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reduced from 34 kWh/t to 28 kWh/t.

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the increase in throughput due to grinding aids, specific energy consumption of the circuit

Previous studies have revealed that applied grinding energy and temperature were important

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factors affecting the performance of grinding aids [18,19,21]. During the tests, the cement temperature changed between 100 and 105 oC and the specific energy consumption values changed in the range between 28 and 34 kWh/t. It should be borne in mind that the

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performance of these grinding aids could also change if these tests were performed at different temperatures and the targeted cement fineness.

Fig. 6 Variations in finished product rate and specific energy consumption with grinding aid dosage Until the amount of grinding aid reached a certain degree of saturation, with increasing dosage, the production rate of the circuit has also increased. For example, when 100 g/t GA2 was added to the system for the first time, the production rate increased by 13.5%.

ACCEPTED MANUSCRIPT However, when the dosage of the GA-2 was increased from 400 g/t to 500 g/t, there was no significant increase in the production rate. It has been understood that increasing the grinding aid dosage had no significant effect on the production rate after the amount of grinding aid in the system reached a certain degree of saturation. When the economic aspect of production is considered, it is very important to prevent the use of excess grinding aid by

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determining the optimum dosage. Mishra [27] found that 0.56% and 0.19% of the energy consumed for milling dry tricalcium

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silicate (C3S) was spent for surface and agglomeration energy, respectively. In this case, the

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specific energy consumption of grinding circuit must have been reduced only 0.75%, even if the grinding aids had completely prevented the consumption of the surface and

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agglomeration energy of the cement particles. However, industrial test results showed that

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the utilization of grinding aids reduced the specific energy consumption of the grinding circuit between 10% and 18 %. This was an indication that grinding aids did not only reduce the agglomeration and surface energies on the cement particles, but also provided a rheological

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environment that allowed the equipment in the grinding circuit to operate more efficiently.

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With the addition of grinding aids to the system, the circulating load at first increased due to the mill emptying out, then settled down to a lower value than reference condition. Variations in circulating load of the circuit depending on the grinding aid dosage at steady state

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conditions can be seen in Fig. 7. It was possible to reduce the circulating load rate by almost one third of the reference condition by using grinding aids. The main reason for the reduction of circulating load was to increase the classifier and mill efficiency due to grinding aids. Fig.7 Variations in circulating load of the circuit with grinding aid dosage 3.1. The Influences on Ball Mill Performance It is known that grinding aid improves the flowability of the powder thus the fine particles inside the mill can be transported more easily. In addition, the fine particles are ground more efficiently due to the decrease in coatings, and therefore grinding efficiency enhances [28].

ACCEPTED MANUSCRIPT Finer mill product was obtained when grinding aids were used. Also, reduction ratio of the mill was increased gradually due to increase in grinding aid dosage (Fig. 8).

Fig. 8 The variation of reduction ratio of mill with specific energy consumption and grinding

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aid dosage Another factor that allowed the milling process to be more efficient was the decrease in the

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circulating load. Depending on the decrease in circulating load, rate of material flow through

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the ball mill was reduced and retention time was increased. Besides, reduction ratio of the mill was increased as a result of increase of the energy applied to the unit material in the mill.

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Therefore, it was possible to increase the amount of energy applied to particles without

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increasing the overall energy consumption of the circuit (Fig. 9). Fig. 9 Effects of grinding aid dosage on specific energy consumption of the circuit

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The impacts of the grinding aids on the ball mill are mathematically expressed using the obtained data from industrial tests. The particles at a certain size in the mill are formed by

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both particles at that size present in feed and the particles that are broken from coarser size to that size. While some of the particles formed at a certain size are carried outside the mill

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as a product, the other part is broken into finer dimensions. When the system is dynamically balanced, this loop can be expressed as in Eq. 1. Bj + İj = Tj = Aj + Uj

Bj : The amount of particles in j size coming from the mill feed İj : The amount of particles broken in j size from coarser sizes Tj : The amount of total particles in j size that is transiently in the mill Aj : The amount of particles broken into finer sizes than j

(1)

ACCEPTED MANUSCRIPT Uj : The amount of particles in size j that leave the mill as product The amount of any size fraction in the mill feed (B j) and in the product (Uj) were known from the results of mass balance previously performed. Breakage size distribution of clinker was determined by a new method developed by Ekşi [29]. Size depending breakage distribution matrix can be seen in Table 6. By using breakage size distribution, the amount of each size

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fraction instantaneously present inside the mill feed (T j) was calculated iteratively. Some of the particles in each size fraction inside the mill were broken into finer sizes while the

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unbroken particles were transported to outside the mill. Therefore, the rate of breakage (Aj/Tj)

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and discharge (Uj/Tj) of particles in each size fraction were calculated. Variation in breakage

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rate of particles depending on the size was mathematically expressed in Eq.2. Breakage rate = kxm

(2)

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k : a variable depending on operational conditions of mill

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x : particle size

m: a variable depending on mill design (mill diameter and length, media charge, liner type

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ect. )

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Table 6. Size dependent breakage distribution matrix Fig. 10 Breakage and discharge probabilities calculated by perfect mixing model Fig.10 illustrates the calculated breakage and discharge probabilities for the each grinding aid type and dosage. It was thought that grinding aids made the powder inside the mill more fluid and prevented fine cement particles from adhering to the mill liner and media. In addition, the use of grinding aids decreased the circulating load. Thus, the mill feed rate was reduced and the retention time of the cement particles were increased, so the force acting on the unit material inside the mill was increased. Fig. 10 indicates that regardless of grinding

ACCEPTED MANUSCRIPT aid type, the breakage probability increased and the discharge probability was reduced. For GA-1 and GA-3, the highest breakage probabilities were reached at a dose rate of 400 g/t, while the highest breakage probabilities for GA-2 were obtained at a dosage rate of 250 g/t. When the discharge probabilities were examined, it was observed that the use of GA-1 and GA-3 at a higher dosage rate than 250 g/t did not lead a significant difference. However, the

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use of GA-2 at doses higher than 250 g/t reduced the effectiveness of GA-2. Addition of GA2 at doses of 100 g/t and 400 g/t had almost the same effect on the discharge probability. In

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fact, the effect of adding 500 g/t of GA-2 was even less than that of 100 g/t of GA-2.

Fig.11 represents the graphs of particle size distributions of ball mill feed and discharge

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which were drawn as a result of both mass balance and modelling studies. The ball mill feed consisted of fresh feeds (clinker, gypsum and other additives) and the air classifier reject.

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Since it was not physically possible to take samples from the ball mill feed, the particle size distribution of the mill feed was calculated by mass balance. Although the properties of the

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fresh feeds used in the tests were the same as the reference conditions, particle size distributions of mill feed for each test were different. Since the classifier coarse flow was finer

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than the fresh feeds, the particle size distribution of the mill feed also became finer as the amount of circulating load increased. Fig.11 illustrates the good agreement between mass

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balance and model results, indicating success of modelling.

Fig.11 Size distribution of mill feed and discharge after model fit Tests were carried out in three different periods and within that particular time some minor changes in mill conditions took place unavoidably (such as media filing, ball size distribution, lining wear ect.). For this reason, each test period was evaluated within itself and a different m parameter was determined for each period. Firstly, breakage rates were calculated for each industrial test condition and then k and m parameters were calculated using nonlinear regression technique (Table 7).

ACCEPTED MANUSCRIPT The variations in k parameter depending on mill throughput and grinding aid type and dosage are illustrated in Fig.12. While k value was inversely proportional to mill throughput, it was directly proportional to grinding aid dosage. The effect of GA-2 on the k parameter was different from the other grinding aids. When GA-2 was used up to 250 g/t the value of k increased, whereas at doses higher than 250 g/t it decreased.

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Fig.12. The variation of “k” parameter with ball mill feed rate and grinding aid dosage

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The m values were equal to each other for the tests performed in the same period. m

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parameters were calculated for GA-1, GA-2 and GA-3 as 0.72, 0.65 and 0.63 respectively. During the time between three test periods, there were some minor changes in media charge

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and liner conditions because of the wear and maintenance works. Therefore, the value of m varied for each period.

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Table 7 Effects of grinding aids on mill model parameters

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3.2. The Influence on Air Classifier Performance One of the most important factors to increase the throughput was the better classification

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process due to use of grinding aids. In order to evaluate the performance of the classification operation, actual efficiency curves were drawn for each case. Actual efficiency curves indicates the part of the feeding material which is forced to flow the coarse or fine streams at

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a certain size fraction [30,31]. An example of a plot of the efficiency curve is illustrated in Fig.13. Efficiency parameters such as cut size (d50), bypass and fish hook are shown on graph. The fine material that flow coarse stream and re-circulated to the mill due to the inefficient classification process is defined as bypass [31-33]. The poor dispersion of the classifier feed and the agglomeration of fine particles cause an increase in the amount of bypass fraction [34]. Also, bypass is directly affected by the dust load of the classifier [32,35]. Equal forces act on the particles at the size which is defined as the cut size (d50) and their probabilities of flowing to coarse or fine stream are the same.

ACCEPTED MANUSCRIPT Fig.13 Actual efficiency curve of an air classifier Altun [36] investigated the prediction capabilities of different efficiency curve approaches and among them the Whiten’s approach was found to have improved correlation with the industrial data. From this perspective, Whiten’s approach that is presented in Eq.3 was used

(1+𝛽∗𝛽 ̽∗ 𝑑 ) ∗( exp( 𝛼) −1) 𝑑 50𝑐

exp(𝛼∗𝛽 ∗𝑑 𝑑 )+exp( 𝛼) −2

)

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𝐸𝑜𝑎

= 𝐶∗(

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for the classification model [30].

(3)

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50𝑐

Eoa : The actual efficiency to overflow

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Where;

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C : Fraction subjected to real classification ; (100 - bypass)

 : Parameter that controls the initial rise of the curve in fine sizes (fish-hook)

: Size

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d

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  : Parameter that preserves the definition of d50c ; d=d50c when E=(1/2)C

d50c : Corrected cut size

 : Sharpness of separation Hereafter, the effects of the grinding aids on parameters in Eq. 3 were discussed. Sharpness, bypass, fish hook and cut size parameters were taken into account in this regard. Within the scope of study, actual efficiency curves of each grinding aid dosage were modelled. Fig. 14 illustrates the fitted data with actual efficiency curves. As can be followed

ACCEPTED MANUSCRIPT from the graphics the modelling results fitted the actual values very well. The results of the modelling studies performed for air classifier are summarized in Table 8. Fig.14 Actual efficiency curves of the air classifier Table 8 Efficiency curve model parameters of air classifier

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3.2.1.  parameter

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Lynch and Rao [37] normalized the efficiency curve by dividing the each particle size to the cut size (d/d50c ) to examine the variations in the slope of the curve that is a demonstration of

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the sharpness of separation. Since this curve is called reduced efficiency curve, the

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parameter represents the sharpness of the classification. The sharper the classification, the higher the  value is. The reduced efficiency curves plotted for each type of grinding aid and

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dosage are illustrated in Fig.15.

It was understood that the slope of reduced efficiency curve was not affected from the

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grinding aid type and dosage. Therefore, it was assumed that  parameters were the same

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(2.26) for both grinding aid and reference conditions. Fig.15 Reduced efficiency curves

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3.2.2.  parameter

parameter is directly influenced by the agglomeration of powders, which leads to the formation of the fish hook on the efficiency curve [30]. All three grinding aids used in the tests were effective in dispersing agglomerations and reducing fishhook. Fig.16 Effects of grinding aids on parameter As can be seen in Fig.16, the addition of the grinding aids to the system resulted in a significant decrease in the value of  parameter. GA-2 was the most effective on the

parameter whereas GA-1 was the least effective on it. It was also found that the use of the

ACCEPTED MANUSCRIPT all three types of grinding aids at dosage rates higher than 250 g/t did not provide an extra benefit in reducing the value of the  parameter. Moreover  value, just like bypass, was increased slightly when GA-2 was added more than 400 g/t. This was a result of adding excessive amount of grinding aid.

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3.2.3. C parameter

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The literature reports that the parameter bypass (100- C) and the dust load of the classifier

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are directly proportional to each other [35]. With the increase in the dust loading, the classifiers get close to their operational ranges. The efficiency of the classification reduces

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because there are too many particles in the separation region that air flow cannot cope with. As a result, bypass increases or C parameter reduces. Fig.17 İllustrates the bypass values

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for each grinding aid.

Fig.17 Variation of bypass with grinding aid type and dosage

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Fig.18 Effects of the type and dosage of grinding aid on C parameter

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Fig.18 indicates that grinding aids have influence on classifier performance since the bypass decreases depending on the grinding aid dosage. Grinding aids are adsorbed onto the

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particle surfaces to forestall the formation of adhesion forces between them and cause a decrease in agglomeration tendency of the particles. For this reason, particles are welldispersed and forces (centripetal and drag) in the classification zone act equally on each particle [18]. In addition to this, declines in the classifier feed rate also increases the efficiency of classification. Thus, particles in the product size leave the system and circulating load is decreased (Fig.7). GA-1, GA-2 and GA-3 were effective in reducing the bypass in ascending order. The highest C values were obtained with GA-3 whereas the values of C parameter were lowest when GA1 was used. When the GA-2 was used at dosages higher than 250 g/t, it was observed that

ACCEPTED MANUSCRIPT bypass increased as the grinding aid dosage was increased. If other type grinding aids had been used in overdose, probably similar situations would have been encountered. The use of grinding aids in extreme doses can have unfavourable effects. Deckers and Stettner [38] pointed out that overdoses of grinding aids could cause agglomeration by forming capillary forces between the particles. Consequently, agglomerated powders in the classifier feed tend

PT

to increase the amount of bypass fraction acting as a bunch.

RI

3.2.4. d50c parameter

SC

The cut size is the size that the particles have equal probability of being subjected to either coarse or fine stream. When the shape of the actual Tromp curve is considered, %50

NU

passing size is called as the cut size however it is also in interaction with the bypass

MA

parameter. Consequently, the actual curve is normalized by extracting the bypass from each size fraction. This curve is named as corrected curve and the corrected cut size is

corrected cut size parameter.

ED

determined from this plot [30,39]. In this study the evaluations were carried out with the

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Fig.19 Decrease in d50c by increase of rotor speed The corrected cut size parameter (d50c ) was considered to be constant for the tests

AC C

conducted with the same product fineness. Within the study, it was found that the cut size parameter was inversely proportional to the air classifier rotor speed. As it is depicted in Fig. 19 d50c parameter was not depending on the grinding aid type and no matter what the dosage rate was. As can be understood, increasing the air classifier rotor speed lead to lower value of d50c parameter meaning that finer product is obtained.

4. Conclusions Within the scope of this study, the influences of grinding aids on model parameters of an air classifier and a ball mill were investigated. For this purpose, three different type of grinding aids were examined at varying dosage rates in a closed cement grinding circuit.

ACCEPTED MANUSCRIPT With the use of grinding aids, production rate of the cement grinding circuit was increased at the same product fineness and the specific energy consumption was decreased. Grinding aids reduced the formation of agglomeration so that the efficiency of ball mill and air classifier was improved. Grinding aids prevented the fine particles from report to coarse flow of the air classifier by reducing the bypass and fish hook. Thus, circulating load of the circuit

PT

decreased. Modelling studies were performed in order to evaluate this effects of grinding aids on classifier and mill model parameters.

RI

It was observed that the parameter α which expresses the sharpness of separation in the

SC

model based on Whiten’s [30] equation was independent of both the grinding aids and operational conditions. The parameter d50c , which represents the cut size, was also

NU

independent of the effects of the grinding aids but increased inversely with the classifier rotor speed. On the other hand, bypass parameter (C) parameter increased with the increase of

MA

the grinding aid dosage, while the fish hook parameter ( β) decreased.

ED

Grinding aids used in industrial tests have facilitated the transport of fine particles inside the mill and prevented them from adhering to mill liners and ball surfaces. Depending on the

EP T

decrease in circulating load, material flow rate inside the mill was also decreased and retention time was increased. Therefore energy applied to unit material inside the mill was increased. The possibility of breaking each particle size inside the mill was calculated by

AC C

using perfect mixing model. Evaluations showed that possibility of particle breakage was increased owning to grinding aids. When the effects of different types of grinding aids were compared with each other, the most effective grinding aid type on bypass parameter (C) was GA-3. The value of bypass dropped below 10%. It was GA-2, which reduced fish hook (parameter β) to the lowest levels. Also, the most significant increase in breakage probability was achieved with GA-2. As a result, the highest improvement on production rate was reached by using GA-2 at a dosage of 500 g/t and the specific energy consumption of the circuit decreased from 34 to 28 kWh/t.

ACCEPTED MANUSCRIPT As a result of the modelling studies, it was understood that although the relationship between grinding aids and model parameters showed similar trends, the effect of each grinding aid was unique. It come to a conclusion that, it is possible to manufacture certain type of grinding aids for certain type of cement, which is beneficial for both the cement producers and grinding aid manufacturers side.

PT

Acknowledgements

RI

Authors appreciate contributions of Dr. Okay ALTUN (Hacettepe University) for his

SC

assistance in writing, Tracim Cement Plant for the assistance in industrial tests, CHRYSO for the supply of the grinding aids prepared in various chemical compounds and Hacettepe

MA

NU

University Mining Engineering Department for providing laboratory facilities.

References

ED

[1] N.A. Madlool, R. Saidur, M.S. Hossain, N.A. Rahim, A critical review on energy use

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savings in the cement industries, Renew. Sust. Energ. Rev. 15 (4) (2011) 2042–2060. [2] N.A. Toprak, O. Altun, N. Aydogan, H. Benzer, The influences and selection of grinding chemicals in cement grinding circuits, Constr. Build. Mater. 68 (15) (2014) 199–205.

AC C

[3] F.C. Lai, M.R. Karim, M. Jamil, M.F.M. Zain, Production yield, fineness and strength of cement as influenced by strength enhancing additives, Aust. J. Basic Appl. Sci. 7 (4) (2013) 253–259.

[4] L.G. Austin, R.R. Klimpel, P.T. Luckie, Process engineering of size reduction: ball milling. Society of mining engineers of the American Institute of Mining, Metallurgical and Petroleum Engineers; 1984. p. 561. [5] C. Engelsen, Quality improvers in cement making-State of the art, Building and infrastructure coin project report 2008.

ACCEPTED MANUSCRIPT [6] P. Rehbinder and N. Kalinkovskay, 1932 Journal of Technology and Physics, Vol.2, USSR, pp. 726-755. [7] A. Westwood, 1974, Tewksbury Lecture: Control and Application of EnvironmentSensitive Fracture Processes, Journal of Material and Science, Vol. 9. Pp. 1871-1895 A. Westwood and N. Stoloff, 1966, Environmental Sensitive Mechanical Behaviour,

PT

[8]

Gordon and Breach Publihing, New York, pp. 1-65.

RI

[9] F. Locher, and H.M. von Seebach, 1972, Influence of Adsorption Industrial Grinding,

SC

Industrial and Engineering Chemistry, Process Design and Development. Vol. 11. pp 190 -

NU

197.

[10] A.A. Jeknavorian, E.F. Bary, F. Serafin, Determination of grinding aids in Portland

MA

cement by pyrolysis gas chromatography-mass spectrometry, Cem. Concr. Res. 28 (9) (1998) 1335–1345.

ED

[11] R.R. Klimpel, W. Manfroy, Chemical grinding aids for increasing throughput in the wet grinding of ores. Ind Eng Chem Proc Des Dev 1978;17(4):518–23.

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[12] M. Weibel, R.K. Mishra, Comprehensive understanding of grinding aids, ZKG International, Volume 67, Issue 6, (2014) 28–39.

AC C

[13] P. Prziwara, S. Breitung-Faes, A. Kwade, Impact of grinding aids on dry grinding performance, bulk properties and surface energy, Advanced Powder Technology 29 (2018) 416-425

[14] S. Sohoni, R. Sridhar, G. Mandal, The effect of grinding aids on the fine grinding of limestone, quartz and Portland cement clinker, Powder Technol. 67 (September 1991) 277– 286. [15] T.S. Sverak, D.G.J. Baker, O. Kozdas, Efficiency of grinding stabilizers in cement clinker processing, Miner. Eng. 43–44 (2013) 52–57.

ACCEPTED MANUSCRIPT [16] O. Altun, H. Benzer, A. Toprak, U. Enderle, Utilization of grinding aids in dry horizontal stirred milling, Powder Technol. 286 (2015) 610–615. [17] H.S. Gökcen, S. Cayırlı, Y. Ucbas, K. Kayaci, The effect of grinding aids on dry micro fine grinding of feldspar, Int. J. Miner. Process. 136 (2015) 42–44. [18] J.J. Assaad, S.E. Asseily, J. Harb, Effect of specific energy consumption on fineness of

PT

Portland cement incorporating amine or glycol-based grinding aids, Materials and Structures

RI

(2009) 42: 1077-1087.

SC

[19] Joseph.J. Assaad, Effect of energy and temperature on performance of alkanolamine

NU

processing additions, Minerals Engineering, 102 (2017) 30 - 41

[20] N.A.Toprak, O.Altun, A.H. Benzer, The effects of grinding aids on modeling of air

MA

classification of cement, Construction and Building Materials, 160 (2018) 564-573 [21] J.J.Assaad, Industrial versus Laboratory Clinker Processing Using Grinding Aids (Scale

ED

Effect), Advance in Material Science and Engineering, 2015, 1-12 [22] L.Sottili, D.Padovani, Effect of Grinding Aids in the Cement Industry, ZKG International,

AC C

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2001, 54(3) : 146-151

[23] N.A. Toprak, O. Altun, N. Aydogan, H. Benzer, The influences and selection of grinding chemicals in cement grinding circuits, Construction Building Materials, 68 (2014) 199–205 [24] Joseph Jean Assaad, Quantifying the effect of clinker grinding aids under laboratory conditions, Minerals Engineering, 81 (2015) 40-51 [25] H. Dundar, H. Benzer, N.A. Aydogan, O. Altun, N.A. Toprak, O. Ozcan, D. Eksi, A. Sargın, Simularion assisted capacity improvement of cement grinding circuit: Case study cement plant, Minerals Engineering, 24 (2011) 205-210

ACCEPTED MANUSCRIPT [26] Okay Altun, Simulation aided flow sheet optimization of a cement grinding circuit by considering the quality measurements, Powder Technology 301 (2016) 1242-1251 [27] R. K. Mishra, Simulation of interfaces in construction materials: tricalcium silicate, gypsum and organic modifiers. 2012, Doctor of Philosophy, University of Akron, Polymer Engineering.

PT

[28] L.G. Austin, R.R. Klimpel, P.T. Luckie, 1984. Process engineering of size reduction: Ball

RI

milling, Society of Mining Engineers of the American Institute of Mining, Metallurgical and

SC

Petroleum Engineers, p.561

[29] D. Eksi, A. H. Benzer, A. Sargin, O. Genc, A new method for determination of fine

T.J. Napier-Munn, S. Morrell, R.D. Morrison, T. Kojovic, Mineral comminution circuits-

MA

[30]

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particle breakage, Minerals Engineering 24 (2011) 216–220

Their operation and optimization. JKMRC monograph series in mining and mineral

[31]

ED

processing, Brisbane, Australia, 1996

M.R. Dunn, A method for analyzing the performance of a mechanical air separator.

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World Cement, 1985; (16) 8: 326-332.

E. Onuma, and M. Ito, Separators in grinding circuits. ZKG, 1994; 47 (9): 535-542.

[33]

M. Ito, K. Sutoh, T. Matsuda, 1996. Classification Efficiency of Cage-Type Air

AC C

[32]

Classifier. ZKG, 1996; 49 (3): 134-141. [34]

M. Kershaw, and J. Yardi, Analysis of O-Sepa separators at Blue Circle. World

Cement, 1989; 11: 400-404. [35] O. Altun, and H. Benzer, Selection and mathematical modeling of high efficiency aid classifiers. Powder Technology, 2014; 264: 1-8 [36] O. Altun, Comparison of different efficiency curve approaches in modelling of airclassifiers, Hacettepe University, MSc. Thesis, Ankara, Turkey, 2007.

ACCEPTED MANUSCRIPT [37] A.J. Lynch and T.C. Rao, in Proceedings Eleventh International Mineral Processing Congress, Cagliari, Italy, 1975, pp. 2 45-269. [38] M. Deckers, W. Stettiner, Die Wirkung von Mahlhilfsmitteln unter besonderer Berücksichtigung der Mühlenbedingungen (Effect of grinding aids with special consideration of the mill conditions). Aufbereitungs-Technik, 10 (1979), pp. 545-550.

PT

[39] K. Nageswararao, K. Normalisation of the efficiency curves of hydrocyclone classifiers.

AC C

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NU

SC

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Minerals Engineering, vol 1, 12 (1999), pp. 107-108.

ACCEPTED MANUSCRIPT Table 1 Grinding aids used during tests Active ingredients Amine-glycol mixture (equal amounts) Amine-glycol-polyol mixture (minority of polyol) Amine major component

GA-1 GA-2 GA-3

Overall concentration

Specific Gravity 3 (kg/dm )

pH

50% active matter

1.080 ± 0.015

7.35 ± 2.00

50% active matter

1.085 ± 0.015

7.30 ± 2.00

50% active matter

1.055 ± 0.015

8.10 ± 2.00

PT

Grinding Aid

Dynamic Classifiers Diameter (m) Max. rotor speed (rpm) Installed Power (kW) Classifier air flow (m3/h)

SC

ED

2 281 355 240000

2nd Comp. 4.2 9.0 50-20 33.0

NU

1st Comp. 4.2 4.5 90-60 32.5 15.3 3800

MA

Ball Mill Internal diameter (m) Internal length (m) Ball size ( mm) Media filling (%) Mill speed (rpm) Installed power (kW)

RI

Table 2 Technical data of the ball mill and the air classifier

GA

Dosage (g/t)

Reference

-

Reference

GA-2

Reference GA-3

SO3 (%)

SiO2 (%)

Al2O3 (%)

Fe 2O3 (%)

CaO (%)

MgO (%)

Na2O (%)

K2O (%)

Blaine (cm 2/g)

3.12

19.98

4.78

3.83

64.65

1.10

0.35

0.81

3710

3.04

19.82

4.72

3.93

64.83

1.10

0.35

0,82

3710

400

3.16

19.74

4.74

3.95

64.60

1.11

0.34

0.78

3610

500

3.20

19.46

4.78

3.82

64.25

1.10

0.34

0.79

3670

-

2.92

16.23

3.85

3.32

66.10

0.90

0.37

0.78

3600

100

3.23

15.82

3.70

3.27

65.89

0.87

0.37

0.80

3830

250

3.09

15.92

3.66

3.22

65.91

0.86

0.37

0.84

3830

400

2.91

15.89

3.73

3.29

65.98

0.86

0.36

0.80

3800

500

3.14

15.80

3.70

3.32

65.96

0.87

0.35

0.77

3880

-

3.69

19.11

4.43

3.33

64.10

1.22

0.60

0.91

3992

250

3.65

18.61

4.28

3.13

64.47

1.17

0.61

0.90

3750

400

3.73

19.00

4.46

3.31

64.20

1.23

0.60

0.90

3780

AC C

250

GA-1

EP T

Table 3 Chemical composition and Blaine values of cement samples

ACCEPTED MANUSCRIPT

Table 4 Data recorded from the control room under steady-state conditions Dosage Fresh feed Clinker Gypsum Ash Limestone (g/t) (t/h) (%) (%) (%) (%) -

70.30

92

6

2

-

325.70

1057

3256

250

70.95

92

6

2

-

255.56

1297

3287

400

71.88

92

6

2

-

222.67

1326

3274

500

77.61

92

6

2

-

225.84

1297

3338

-

102.42

84

5

-

11

321.75

922

3542

100

116.22

84

5

-

11

178.99

921

3605

250

119.23

84

5

-

11

160.80

400

126.86

84

5

-

11

500

127.02

84

5

-

11

-

103.02

92

6

-

2

250

118.36

92

6

-

2

400

120.10

92

6

-

2

Reference

GA-2

Reference

931

3625

198.37

939

3597

236.91

939

3549

342.31

1061

3526

343.94

1062

3543

344.84

1072

3552

ED

MA

NU

GA-3

Mill m otor pow er (KW)

PT

GA-1

Classifier rotor speed (rpm )

RI

Reference

Classifier coarse (tph)

SC

GA

Table 5 Calculated flow rates around the grinding circuit

GA-1

Reference

GA-2

Reference GA-3

EP T

Reference

Dosage Fresh Mill Filter Classifier Classifier (g/t) Feed (t/h) Overflow (t/h) Product (t/h) feed (t/h) coarse (t/h)

Finished Mill feed product (t/h) (t/h)

-

70.30

349.19

25.49

374.68

304.38

70.30

374.68

250

70.95

288.82

10.01

298.83

227.88

70.95

298.83

400

71.88

266.25

18.78

285.03

213.15

71.88

285.03

500

77.61

242.72

49.33

292.05

214.44

77.61

292.05

AC C

GA

-

102.42

408.98

8.59

417.57

315.15

102.42

417.57

100

116.22

310.25

9.55

319.80

203.58

116.22

319.80

250

119.23

270.85

11.55

282.40

163.17

119.23

282.40

400

126.86

300.40

17.58

317.99

191.13

126.86

317.99

500

127.02

335.89

21.54

357.43

230.41

127.02

357.43

-

103.02

323.99

13.54

337.53

234.51

103.02

337.53

250

118.36

275.99

21.47

297.46

179.10

118.36

297.47

400

120.10

266.58

23.21

289.79

169.69

120.10

289.99

ACCEPTED MANUSCRIPT Table 6 Size dependent breakage distribution matrix 2360

1700

1180

850

600

425

300

121

150

102

72

50

36

25

18

12

8.6

4.4

2.6

1.8

2360

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1700

0.301

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1180

0.141

0.316

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

850

0.089

0.127

0.316

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

600

0.084

0.096

0.140

0.334

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

425

0.068

0.088

0.098

0.146

0.361

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

300

0.060

0.083

0.095

0.104

0.156

0.391

0

0

0

0

0

0

0

0

0

0

0

0

0

0

121

0.061

0.058

0.090

0.098

0.110

0.168

0.421

0

0

0

0

0

0

0

150

0.047

0.059

0.063

0.091

0.097

0.118

0.180

0.446

0

0

0

0

0

0

102

0.040

0.049

0.060

0.070

0.093

0.104

0.139

0.210

0.495

0

0

0

0

0

72

0.032

0.033

0.039

0.047

0.058

0.073

0.086

0.131

0.195

0.503

0

0

0

50

0.024

0.028

0.029

0.035

0.039

0.053

0.063

0.081

0.143

0.214

0.517

0

0

36

0.015

0.018

0.020

0.022

0.025

0.028

0.040

0.045

0.065

0.136

0.203

0.507

0

25

0.012

0.014

0.015

0.017

0.019

0.022

0.024

0.035

0.039

0.072

0.159

0.232

0.528

18

0.008

0.009

0.010

0.011

0.011

0.013

0.015

0.016

0.025

0.026

0.065

0.148

0.212

12

0.007

0.008

0.008

0.009

0.010

0.010

0.012

0.013

0.014

0.022

0.024

0.076

8.6

0.004

0.004

0.005

0.005

0.006

0.006

0.006

0.007

0.008

0.008

0.014

4.4

0.005

0.005

0.006

0.006

0.007

0.007

0.007

0.008

0.008

0.010

0.010

2.6

0.002

0.002

0.003

0.003

0.003

0.003

0.003

0.003

0.003

0.003

1.8

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.002

0.002

GA-2

Reference GA-3

m

374.68

0.98 0.72

298.83

1.29 0.72

400

285.03

1.36 0.72

500

292.05

1.32 0.72

-

417.57

0.86 0.65

100

319.80

1.19 0.65

250

282.40

1.38 0.65

400

317.99

1.20 0.65

500

357.43

1.04 0.65

-

337.53

1.11 0.63

250

297.46

1.30 0.63

400

289.79

1.34 0.63

AC C

Reference

k

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

RI

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.520

0

0

0

0

0

0

0.182

0.260

0.569

0

0

0

0

0

0.014

0.051

0.147

0.210

0.546

0

0

0

0

0.015

0.018

0.061

0.207

0.376

0.749

0

0

0

0.004

0.004

0.005

0.007

0.008

0.070

0.234

0.766

0

0

0.002

0.002

0.002

0.002

0.003

0.003

0.011

0.173

0.622

0

NU

SC

0

MA

ED

250

GA-1

EP T

Reference

Dosage (g/t) Mill Feed (tph)

0

0

Table 7 Effects of grinding aids on mill model parameters GA

PT

X (µm)

ACCEPTED MANUSCRIPT Table 8 Efficiency curve model parameters of air classifier GA

Dosage (g/t)

Reference

-

3.42 1.36 0.28 0.024 2.26

250

1.29 1.54 0.54 0.024 2.26

400

1.25 1.49 0.57 0.024 2.26

500

1.23 1.49 0.59 0.024 2.26

250

0.80 1.37 0.69 0.038 2.26

400

0.82 1.47 0.74 0.038 2.26

500

0.94 1.56 0.70 0.038 2.26

-

2.21 1.66 0.41 0.032 2.26

250

0.94 1.39 0.78 0.032 2.26

400

0.88 1.37 0.82 0.032 2.26

PT

1.25 1.47 0.58 0.038 2.26

RI

2.32 1.71 0.38 0.038 2.26

SC

100

NU

GA-3



MA

Reference

d50c

ED

GA-2

C

EP T

Reference



AC C

GA-1



ACCEPTED MANUSCRIPT Highlights •

Industrial scale tests were carried out with different types of grinding aids.

• Effects of grinding aids on grinding and classification operations were evaluated. • Effects of the type and dosage of grinding aids on model parameters were examined.

PT

• Grinding aids had influence on breakage probability, bypass and fish hook.

AC C

EP T

ED

MA

NU

SC

RI

• Amine-glycol-polyol mixture was found to be the most effective.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Figure 12

Figure 13

Figure 14

Figure 15

Figure 16

Figure 17

Figure 18

Figure 19