Pilot scale demineralization study on coal flotation tailings and optimization of the operational parameters with modeling

International Journal of Mineral Processing 145 (2015) 23–31

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International Journal of Mineral Processing journal homepage: www.elsevier.com/locate/ijminpro

Pilot scale demineralization study on coal flotation tailings and optimization of the operational parameters with modeling A. Suresh ⁎,1, R.K. Lingam, S.K. Sriramoju, A. Bodewar, T. Ray, P.S. Dash R&D, Tata Steel Ltd., Jamshedpur, Jharkhand 831001, India

a r t i c l e

i n f o

Article history: Received 31 October 2014 Received in revised form 17 September 2015 Accepted 9 November 2015 Available online 11 November 2015 Keywords: Demineralization Ash reduction Coal Chemical leaching Flotation tailings Optimization

a b s t r a c t Chemical beneficiation route for improvement of coal quality was demonstrated in pilot scale to produce low ash (8–10%) clean coal with a yield more than 70% by demineralization of high ash content flotation tailings obtained in a typical Indian coal washery. A Box–Behnken based design of experiment (DOE) method was used to generate data for development of a model. This model has been used to portray the effect of variables on reduction of ash content of coal tailings subjected to chemical leaching process. In this study, the functional relationship between process parameters (reaction temperature and time as well as reagent concentration) and the ash content of the final product was mapped using a second order non-linear model equation. The regression analysis was carried out using STATISTICA software for evolving appropriate equation to develop a commensurate model. The model predicted values of product coal ash content were in good agreement with the experiment values (R2 = 0.9671). Moreover, the main effects, two-way interaction effects as well as sensitivity of process parameters were investigated with the help of the model. Finally, model based optimization was carried out to identify the operating parameters (reaction temperature = 160 °C; reaction time = 60.6 min; reagent concentration = 17.6%) at which the product coal with minimum ash content could be produced. © 2015 Elsevier B.V. All rights reserved.

1. Introduction One single property that influences most the quality and market value of coal is undoubtedly the ash content. The lower the coal ash, the higher the energy content and more potential for different applications. Even though the Indian coal washeries aspired to produce coal with low ash content, the finely disseminated ash bearing inorganic minerals in the coal matrix (Sharma and Gihar, 1991; Choudhury et al., 2007) becomes the real bottleneck, as it brings down clean coal yield to an adverse uneconomical value. Because of this fact, in general, Indian coal washeries are forced to operate to produce coal with 14–17% ash content with a yield as low as 35–45% (Dash et al., 2015). The typical physical beneficiation circuit of an Indian coal washery employing 2-stage dense medium cyclone (DMC) (primary and secondary DMCs) for coarse coal and flotation cell for fine coal treatment is shown in Fig. 1. Usually, with present circuit as illustrated in Fig. 1, it is possible to produce clean coal with an ash content of 15% at only 39% yield. The remaining portion of the coal is being generated as 40% middling coal and 10% flotation tailings, leaving aside 11% dense media cyclone ⁎ Corresponding author. E-mail addresses: [email protected] (A. Suresh), [email protected] (R.K. Lingam), [email protected] (S.K. Sriramoju), [email protected] (A. Bodewar), [email protected] (T. Ray), [email protected] (P.S. Dash). 1 Mailing address: Coal & Coke Making Research Group, R&D, Tata Steel, Jamshedpur, Jharkhand 831001, India.

http://dx.doi.org/10.1016/j.minpro.2015.11.004 0301-7516/© 2015 Elsevier B.V. All rights reserved.

rejects. The low yield is mainly contributed by poor liberation characteristics of Indian coal due to their drift origin. The existing washing techniques highly depend on the liberation characteristics as well as surface properties. Further, washeries are forced to operate on the basis of tradeoff between ash content and yield. Hence it is the inherent nature of Indian coal that both low ash and high yield cannot be attained at a time through physical beneficiation route. Nevertheless, physical beneficiation process cannot be replaced by any other alternative methods or chemical beneficiation techniques as it has huge cost competitive edge over others. In this context, not as a substitute for existing physical beneficiation method but as a complementary technique to treat the poorly liberated middling and tailings (obtained from physical beneficiation), the chemical beneficiation (leaching) route (Sharma and Gihar, 1991; Choudhury and Bhaktavatsalam, 1997; Chandaliya et al., 2012) has been explored in the present study. In this method, the mineral matters that are finely distributed in the coal matrix are selectively removed by sequential leaching with sodium hydroxide followed by hydrochloric acid (Waugh and Bowling, 1984; Yang et al., 1985; Dash et al., 2013, 2014a, 2015; Sriramoju et al., 2014) without any significant loss of carbonaceous matter. During alkali treatment stage, silica and alumina compounds present in the coal react with sodium hydroxide and form sodium silicate, sodium aluminate and sodium alumino-silicate (sodalite). The sodium silicate and sodium aluminate go to the liquid phase whereas the sodalite remains in the coal cake. Subsequently, sodalite compound present in coal reacts with hydrochloric acid and

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model was used to investigate the effect of operational parameters such as reaction temperature, reaction time and reagent concentration upon the extent of ash reduction of coal tailings. Finally, the model equation was solved (for optimization) to identify the optimized process conditions at which the maximum demineralization (i.e. minimum product coal ash) can be achieved. 2. Experimental work 2.1. Input material

Fig. 1. Typical physical beneficiation circuit of Indian washery.

dissolves the ash bearing Si and Al compounds into the liquid phase. Thus, coal obtained after acid treatment contains less ash bearing minerals that makes it low ash product. Beneficiation of poorly liberated middling/tailings with high ash content through chemical leaching route promises to produce low ash coal with yield much higher than the level that could be obtained through physical beneficiation method (Dash et al., 2015). Hence it can be added to the existing washing circuit as a complementary method to increase the overall yield and thereby conservation of mine life. Even though substantial amount of experimental work has been published in this area of chemical demineralization of coal, the research study on sequential alkali and acid leaching treatment of flotation coal tailings, having poor liberation characteristics and high ash content N30%, was not reported elsewhere in the literature. Moreover, development of a mathematical model relating the coal ash content and leaching process conditions was also not reported. Under this scenario, development of a model is imperative to acquire more insight on coal leaching process and also useful in optimization of process conditions. However the complex heterogeneous chemical reactions involved (Steel and Patrick, 2001, 2003) in the coal demineralization process makes it difficult to comprehend the effect of process parameter on ash reduction. Consequently, the option of development of first principle-based mathematical model was not attempted in this work. In this connection, data driven regression models were found useful not only in understanding the complex processes but also handy in quantifying the effects of process parameters (Naik et al., 2005; Suresh et al., 2009, 2012). However, the performance of these kinds of regression models heavily depends on the quality of data used for parametric estimation. Often DOEs are used to plan and design the experiments efficiently so that with minimum number of trials, maximum information could be obtained. This approach could very well minimize the time, cost and man hours that may be required for conducting the experiments. The successful applications of DOEs in understanding the various mineral processing systems are evident from the literature (Aslan and Cebeci, 2007; Tripathy and Rama Murthy, 2012). In the present work, chemical leaching treatment of flotation tailing coal sample has been investigated at pilot scale. The main theme of this article has been categorized under three parts. The first part of the paper presents the experimentation procedure and data generation method adopted through a three-factor three-level Box–Behnken based DOE for generation of data for different sets of process conditions. In the second part, the experimental data sets generated in part-1 were utilized to develop a nonlinear regression model. Subsequently, the developed

The sample of coal tailings was obtained from the flotation circuit of Tata Steel's coal washing plant for the current study. The materials were analyzed at R&D, Tata Steel, Jamshedpur. Table 1 shows that the average ash content of the received coal sample is nearly 31%, although the ash level of tailings is usually around 35–40% in the washery circuit. This could happen during collection of certain stratified bed. The size distribution of coal sample is determined by standard sieve analysis. The ash content of coal sample is estimated by following the standard ASTM (D3174) testing procedure. The particle size distribution and size-wise ash analysis are given in Table 1. Further, the result of proximate analysis and ultimate analysis of coal sample is indicated in Table 2. The mineralogical composition of coal is shown in Table 3. The coal sample used for the present study mainly consists of inorganic impurities like silica, alumina and oxides of iron, calcium, phosphorous, magnesium, sodium, potassium, etc. The majority of coal ash is contributed by the presence of silica (55–60%) and alumina (20–25%). Commercial grade chemical reagents such as aqueous sodium hydroxide (NaOH) lye and hydrochloric acid (HCl) were used for this study. 2.2. Experimental procedure Experiments were conducted at the pilot plant facility, indigenously designed and commissioned by Tata Steel India, having a capacity to treat 500 kg of coal per batch. This pilot plant facility mainly includes coal preparation section, reagent preparation area and production zone with two reactors and one filter each for alkali and acid leaching. The chemical leaching pilot plant for coal was designed to operate using PLC based semi-automated control system (Dash et al., 2014b). The chemical leaching experiment for coal mainly involved alkali leaching in the first step and subsequently acid leaching in the second stage. The schematic diagram of experimental procedure followed for chemical demineralization of coal is illustrated in Fig. 2. During alkali leaching, coal slurry is prepared by mixing 500 kg of coal with the desired concentration of sodium hydroxide solution maintaining 1:5 coal-to-liquid ratio (w/w) inside the high pressure reactor. The optimum size of feed coal suitable for the chemical leaching process is 0.5 mm (500 μm). The coals with other size fractions such as middling and heavy media cyclone rejects, may also be treated using leaching process provided that it is ground to the required 0.5 mm size in the

Table 1 Size-wise ash analysis of tailings. Particle size range (μm)

wt.% retained

Cum. over size %

Cum. under size %

Ash %

+500 −500 to +212 −212 to +150 −150 to +120 −120 to +100 −100 to +75 −75

36.8 46.0 5.85 1.82 3.90 1.22 4.31 100

36.81 82.89 88.74 90.56 94.46 95.69 100.0

63.19 17.11 11.26 9.44 5.54 4.31 0

30.6 29.2 30.2 30.6 31.6 32.7 33.5 30.1

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Table 2 Proximate and ultimate analyses of tailings. Analysis

Value

1.1.1.1 Proximate analysis (db), % Ash Volatile matter Inherent moisture Fixed carbon 1.1.1.2 Ultimate analysis (ash free basis), % Carbon Hydrogen Nitrogen Sulfur Oxygen

31.0 23.2 2.5 43.3 80.7 5.1 1.3 1.1 11.8

grinding circuit before feeding to the leaching process. The operating pressure is maintained at around 10 bar, by injecting compressed air inside the high pressure reactor. The reactor is equipped with an agitator for accelerating the solid–liquid mass transfer kinetics. Steam jacket is provided for achieving the desired reaction temperature. Further, operating conditions such as reagent concentration, reaction temperature and reaction time varied on the basis of the DOE as mentioned in the subsequent section. After the preparation of the coal slurry, the reactor content is heated to the desired temperature and maintained all through the reaction time, set as per DOE. In the next step, the coal slurry is transferred to filter in order to separate the filtrate (spent reagent) and the coal cake. The coal cake, remaining in the filter, is then squeezed and washed with water to recover as much residual alkali as possible. The spent alkali loaded with inorganic impurities is then sent for regeneration process from where the regenerated alkali solution is recycled back into the process. A photograph of pilot plant facility used for carrying out the coal demineralization experiments is shown in Fig. 3. In the acid leaching stage, the coal cake (intermediate product) obtained from the alkali leaching step is mixed with the required amount of hydrochloric acid (HCl) to make-up 5% (w/w) concentration. Also coal-to-liquid ratio is maintained at 1:5. The acid leaching reaction is carried out at atmospheric pressure and room temperature. After the acid treatment, coal slurry is filtered and then washed with water to produce the low ash clean coal as a final product.

2.3. Selection of critical process variables The demineralization rate is mainly controlled by process parameters of alkali leaching than those of acid leaching. Based on a large number of trials conducted at the pilot plant, altogether nine variables — five in the case of alkali leaching and four in acid leaching, are found to influence the demineralization kinetics of coal as illustrated in Fig. 4. The five process variables involved in alkali leaching are (i) reaction temperature, (ii) reagent concentration, (iii) reaction time, (iv) coal–liquid ratio and (v) agitator speed. In the case of acid leaching four variables viz. (i) reagent concentration, (ii) reaction time, (iii) coal-liquid ratio and (iv) agitator speed are crucial for ash reduction in coal. Among the nine variables, it has been found that the alkali leaching process variables such as reaction temperature, reagent concentration and reaction time have a major contribution to ash reduction (demineralization) of product coal (Dash et al., 2013). The rest six numbers of

Table 3 Mineralogical composition of tailings coal. Minerals

SiO2

Al2O3

P

CaO

Fe(T)

K2O

Na2O

Others

wt.% in coal

17.26

7.7

0.203

0.720

2.08

0.58

0.22

0.99

Fig. 2. Schematic diagram for chemical leaching experiment.

process variables, shown in Table 4, are fixed based on the data and information obtained during preliminary experiments.

2.4. Design of experiments As discussed in the preceding section, the process variables such as reaction temperature, reagent concentration and reaction time during alkali leaching stage are found to be the most influencing variables and therefore need to be considered for investigation with the principal objective of achieving the optimum demineralization process condition. For this purpose, as mentioned earlier, three levels of the most influencing variables, in respect of alkali leaching have been selected as indicated in Table 5. In the present study, a three-factor three-level Box–Behnken design has been used to generate the experimental data to investigate the effect of chemical leaching process variables upon the ash reduction of flotation tailings. The range of process variables has been decided based on the knowledge gained through preliminary experimental study. It has been observed from the preliminary study that reagent concentration below 10% does not produce significant demineralization and concentration above 20% increases the solution viscosity (leads to slow filtration rate) and also consumes more water for cake washing with less benefit on demineralization. With respect to reaction temperature, it was observed that with temperature below 120 °C demineralization is poor and for temperature above 160 °C, increased system pressure is required to be maintained inside the reactor. Similarly, it has been found that for reaction time below 30 min insignificant demineralization was observed and for reaction time above 90 min, there is not much value addition in terms of demineralization percentage. Moreover higher reaction time would affect the commercial viability of the process as it is directly proportional to the capital cost of a plant and machinery. However, it is important to be noted that the synergy (i.e. interaction) among these three parameters plays a vital role in reducing the ash content of coal than the value of individual parameter alone. Altogether fifteen trials were designed, using Box–Behnken DOE. These are listed in Table 6.

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Fig. 3. Pilot plant facility for coal demineralization experiment.

3. Model development In this section, non-linear regression model equation is formulated by considering the product coal ash content as dependent variable with function of reaction temperature, reaction time and reagent concentration (i.e. independent variables) as shown in Eq. (1). Ash ð%Þ ¼ f ðtemp: ðT Þ; time ðt Þ; conc: ðxÞÞ

ð1Þ

where T is the reaction temperature, °C; t is the reaction time, min; x is the reagent concentration, wt.%; a to j are the coefficients that need to be estimated. 4. Results and discussion This section has been structured in such a way that the experimental results are discussed in the first part and the simulation and optimization results are reported in the subsequent part.

The product ash percentage is related to the process variables in the form of second order quadratic Eq. (2),

4.1. Experimental results

Ash ð%Þ ¼ a þ b  T þ c  T 2 þ d  t þ e  t 2 þ f  x þ g  x2 þ h  Tt þ i  tx þ j  Tx ð2Þ

The chemical demineralization experiments were conducted at pilot plant as per the DOE, using the flotation coal tailing sample received

Fig. 4. Critical process parameters for chemical demineralization of coal.

A. Suresh et al. / International Journal of Mineral Processing 145 (2015) 23–31 Table 4 Fixed experimental parameters.

Table 6 Design of experiments.

Alkali leaching

Run number Reaction temp. (T), °C Reaction time (t), min Reagent conc. (x), %

Coal slurry ratio Agitator speed

1:5 120 rpm

Acid leaching Coal slurry ratio Reaction time Acid conc. Agitator speed

1:5 30 min 5% 200 rpm

from the coal washery. The product coal ash and yield results obtained are shown in Table 7 against each trial run. It can be observed from Table 7 that trial corresponding to number 14 yields the product coal with a minimum ash content of 8.8% (on dry basis) among all the 15 numbers of experiments conducted. This corresponds to the process conditions: 160 °C reaction temperature, 60 min reaction time and 20% reagent concentration. The percentage of demineralization (mentioned in Table 7) is calculated using the following relation,

Dimineralization ð%Þ ¼

27

  Ash ð%ÞFeed −Ash ð%ÞProduct  100: Ash ð%ÞFeed

From the above results, it can be inferred that the leaching process adopted for the tailing sample could yield demineralization as high as 72% from the feed coal at this process condition. Since there is insignificant loss of carbonaceous matter, the yield of product coal mainly depends on the percentage of demineralization. In other words, the product coal yield is directly proportional to the product coal ash content. The product coal yield achieved at the maximum ash reduction experiment (trial #14) is around 72%. Sink and float analysis was carried out using the coal tailing (feed) sample in laboratory to measure the yield of product coal (with 9% ash) achievable through physical beneficiation process. As anticipated, the yield of product coal obtained through this method is very low (only 10%) due to poor liberation characteristics. It can be inferred from these results that leaching route has the potential to produce almost 7 times higher yield than physical route at the same ash level of product coal. Further, the combustible recovery is estimated as 95.65% (same for all experimental runs) after considering the loss of small portion of carbonaceous material with the filtrate solution of alkali and acid leaching circuit. The mineralogical content of product coal (obtained from trial #14) in comparison with feed (Table 8) reveals that the alumina to silica ratio is reduced from 0.45 to 0.22. This reduction of alumina to silica ratio in metallurgical coal can substantially bring down the blast furnace operating temperature. Additionally, the reduction in phosphorus content from 0.20% to 0.022% would greatly enhance the efficiency of steel making process. Further, it is also interesting to note that there is no increment of sodium content in product coal after chemical leaching treatment. The proximate analysis (Fig. 5) result clearly shows remarkable improvement in the fixed carbon percentage of product coal due to substantial decrease in the ash content. The overall ash reduction in the blast furnace coal is undoubtedly beneficial in reducing the slag volume and improving the blast furnace productivity. This will also result Table 5 Levels of process parameters. S. no.

Variables

Low (−1)

Medium (0)

High (1)

1 2 3

Reaction temperature, °C (T) Reaction time, min (t) Reagent concentration, % (x)

120 30 10

140 60 15

160 90 20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

140 120 160 120 160 140 140 140 140 140 120 140 120 160 160

60 60 90 30 30 90 60 30 90 30 90 60 60 60 60

15 10 15 15 15 10 15 20 20 10 15 15 20 20 10

in better coke rate, thereby improving operating cost of iron making and consequently in subsequent process streams. No significant change has been observed in the percentage of volatile matter content of coal after the chemical leaching treatment. Hence, it can be inferred that the leaching reactions do not disturb the hydrocarbon chains with low molecular weight. Further, the gross calorific value (GCV) of product coal is significantly improved from 5650 kcal/kg to 7010 kcal/kg, which is almost 24%, after the chemical treatment. Also, experiments were conducted to study the effect of multi-stage leaching on the demineralization of feed coal sample. The process conditions maintained at both stages of multistage leaching were 160 °C reaction temperature, 60 min reaction time and 20% reagent concentration. The product coal ash of multi-stage leaching experiments has been reported in Fig. 6. It is interesting to note that the major percentage of demineralization (almost 72%) takes place in the single stage of leaching (i.e. one round of alkali and acid leaching) itself. The ash content of product coal at the end of single, double and triple stages was obtained as 8.8%, 5.6% and 4.9%, respectively. It has been observed that no significant demineralization is possible after the second stage of leaching.

4.2. Regression model and simulation The experimental data sets, generated as per the Box–Behnken DOE were analyzed with the help of STATISTICA (version 10.0) software. The non-linear regression analysis was carried out to estimate the coefficients (a–j) of Eq. (2). The resultant non-linear model equation i.e. the

Table 7 Product coal ash and yield obtained in pilot plant. Run number

Product coal ash (%)

Product coal yield (%)

Demineralization (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

11.60 17.10 11.10 21.50 11.40 16.50 11.60 17.40 14.80 23.30 18.00 11.60 17.40 8.80 13.20

74.66 79.61 74.24 84.08 74.49 79.04 74.66 79.90 77.46 86.05 80.49 74.66 79.90 72.37 76.04

62.58 44.84 64.19 30.65 63.23 46.77 62.58 43.87 52.26 24.84 41.94 62.58 43.87 71.61 57.42

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Table 8 Comparison of mineralogical composition between product and feed coal. Minerals

SiO2

Al2O3

P

CaO

Fe(T)

K2 O

Na2O

Others

wt.% in feed wt.% in product

17.26 6.15

7.7 1.35

0.203 0.022

0.720 0.11

2.08 1.05

0.58 0.249

0.22 0.201

0.99 0.73

product ash content as a function of process variables is shown in the following Eq. (3), Ash ð%Þ ¼ 76:7−:0969T þ 0:0000312T 2 −0:86t þ 0:00432t 2 −2:083x þ 0:1005x2

þ 0:00133Tt−0:01175Tx þ 0:007tx: ð3Þ

The resultant model Eq. (3) has been used to study the effect of leaching process parameters such as reaction temperature, reagent concentration and reaction time on the ash content of product coal. It can be inferred from Eq. (3) that the product coal ash is a linear function of reaction temperature as the coefficient of T2 (0.0000312) has almost negligible value. It is also noted from the coefficients of t2 and x2 that the effect of reagent concentration on ash reduction is more nonlinear than the effect of reaction time. Further from the values of cross product coefficients (Tt, Tx, tx) the interaction effect between the process parameters can be explained. It was noted that the interaction between reaction temperature and reagent concentration is more dominant than the interaction between temperature and reaction time as well as reaction time and reagent concentration. The predicted product coal ash (using the model) is plotted against the experimentally measured ash content, as shown in Fig. 7. The scatter plot presents the relation between experimental and predicted values of product ash. From the value of correlation coefficient (R2 = 0.9671) and root mean square error (RMSE =0.7817), the model performance is found satisfactory. The standard deviation of prediction has been estimated as 0.7364. 4.2.1. Sensitivity analysis The sensitivity of ash content with respect to change in process variables has been ascertained by partial derivatives of Eq. (3). By differentiating Eq. (3) with respect to T, t and x, the following first order partial differential Eqs. (4)–(6) are obtained. ∂ðAshÞ ¼ −0:0969 þ 0:0000624T þ 0:00133t−0:01175x ∂T

ð4Þ

∂ðAshÞ ¼ −0:86 þ 0:00864t þ 0:00133T þ 0:007x ∂t

ð5Þ

Fig. 5. Proximate analysis of feed and product coal.

Fig. 6. Product coal ash at different levels of multistage trial.

∂ðAshÞ ¼ −2:083 þ 0:201x−0:01175T þ 0:007t ∂x

ð6Þ

The sensitivities (change in product ash with respect to variation in process parameter) of each variable are estimated, using Eqs. (4)–(6) by fixing the other two at the center level and the resultant values are reported in Table 9. From Table 9, it can be concluded that at a fixed reaction time and reagent concentration, the increase in reaction temperature decreases the product ash content as evident from the negative slope. The equal slopes at three different levels of temperature, ascertained its linear functionality with product ash content. Further useful inference can be drawn from the magnitude of slope that every 1 °C raise in reaction temperature would bring down the product ash by 0.186%. In other words, the reduction of 1% product ash could be accomplished by raising the reaction temperature by 5.4 °C. This above statement is valid only within the temperature range (i.e. 120–160 °C) of experimentation. In the case of reaction time (Table 9), the negative slope at the initial (30 min) and middle (60 min) levels indicates the decreasing trend of ash content with time. However, the positive slope at higher level (90 min) indicates the reverse trend of demineralization. Similar trend

Fig. 7. Relation between experimental and predicted product ash.

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Table 9 Sensitivity analysis results. Derivative

ð∂ðAshÞ Þt¼60; ∂T

x¼15

ÞT¼140; ð∂ðAshÞ ∂t

x¼15

ð∂ðAshÞ Þ ∂x T¼140;

t¼60

Parameter level

Sensitivity

T = 120 °C T = 140 °C T = 160 °C t = 30 min t = 60 min t = 90 min x = 10% x = 15% x = 20%

−0.186 −0.185 −0.183 −0.310 −0.050 0.209 −1.298 −0.293 0.712

was also observed with respect to reagent concentration. From the slopes (Table 9) and also from coefficients of Eq. (3), the degree of nonlinearity of individual parameters on the demineralization can be sorted out in the descending order as follows: Reagent concentration N reaction time N reaction temperature.

4.2.2. Two variable effects on demineralization The two-variable effect of process parameters upon the product coal ash can be visualized clearly from three-dimensional (3D) surface plots, generated using the model Eq. (3) as shown in Fig. 8. Fig. 8(a) shows the effect of reaction time and reaction temperature on product ash at the center level of reagent concentration (x = 15%). It can be inferred from the plot that at any fixed temperature, the product ash decreases with an increase in reaction time till 60 min and thereafter the increasing trend in ash content is observed. At the same time for any fixed reaction time, an increase in the reaction temperature linearly decreases the ash content of product coal. The effect of reaction temperature and reagent concentration upon ash content at the center level of reaction time (t = 60 min) is shown in Fig. 8(b). It is interesting to observe that at low temperature region, the maximum demineralization occurs at the medium range of reagent concentration (i.e. around 15%); but the trend changes while moving towards higher temperature. It shows that at higher temperature range, the maximum demineralization point (i.e. minimum product ash) is shifted towards slightly higher reagent concentration (i.e. around 17%). The effect of reagent concentration and reaction time upon product ash at the center level of reaction temperature (T = 140 °C) is shown in Fig. 8(c). The demineralization capacity increases with an increase of both reaction time and reagent concentration till a certain point (around a concentration of 17% and reaction time of 60 min) beyond which an increase of reaction time and the reagent concentration has a negative impact on ash reduction. Further, the contour plot (as shown in Fig. 9) displays the product ash zones with respect to change in two process variables at a time, while fixing the third variable at constant value. Fig. 9(a)–(c) shows the effect of reaction temperature and reaction time at different levels of reagent concentration: 10%, 15% and 20%, respectively. When increasing the concentration from 10% to 15%, the iso-ash line of any particular ash content gets shifted towards the low temperature zone. It clearly indicates that the increase in reagent concentration yields better demineralization rate at given reaction temperature. However, further increase in concentration to 20% does not have any significant impact on ash reduction (Fig. 9(c)). Fig. 9(d)–(f) shows the effect of reaction temperature and reagent concentration at different levels of reaction time: 30 min, 60 min and 90 min, respectively. The appearance of low ash zone at the upper-right corner of plot area implies that higher reaction temperature and concentration are conducive for producing low ash coal. Fig. 9(g)–(i) shows the effect of reaction time and reagent concentration at different levels of reaction temperatures: 120 °C, 140 °C and 160 °C, respectively. The upward shifting of low ash zone when increasing the temperature indicates that the low ash

Fig. 8. Three dimensional surface plots showing the effect of two variables on product ash. (a) Reaction time vs reaction temperature; (b) Reaction temperature vs reagent concentration; (c) Reaction time vs reagent concentration.

is not achievable below certain level of reagent concentration, irrespective of maintaining at any reaction time. The observations and interpretations obtained from the 3D surface plots and contour plots are more of qualitative than quantitative as

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Fig. 9. Contour plot: a–c) Reaction time vs reaction temp.; d–f) Reagent conc. vs reaction temp.; g-i) Reagent conc. vs reaction time.

A. Suresh et al. / International Journal of Mineral Processing 145 (2015) 23–31

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the peaks cannot be located easily from the graphical format. Hence, the model based optimization technique is employed to locate the global minimum at which the process conditions are optimal to yield the product coal with minimum ash.

Efforts can be further extended to optimize the operating cost of demineralization process by solving the cost function.

4.3. Optimization of process condition

The authors would like to thank Mr. Radhakrishna Adhari of Phoenix Power Products for his contribution towards laboratory analysis and data collection. The support and co-operation of Mr. S. Majumdar in arranging the required resources for conducting experiments are highly appreciated. Last but not the least we convey our sincere thanks to Dr. T.K. Roy for his synergic help and cooperation in preparation of this paper. Authors are also thankful to the management of Tata Steel Limited (OPS/FY15/637) for their support and motivation towards publishing this work.

The optimization of chemical leaching process involves the minimization of product coal ash with respect to the process operating condition. The objective function can be formulated as, Minimize : Ash ð%Þ ¼ 76:7−:0969T þ 0:312  10−4 T 2 −0:86t þ 0:00432t 2 −2:083x þ 0:1005x2 þ 0:00133Tt −0:01175Tx þ 0:007tx:

ð7Þ Eq. (7) is subjected to the following constraints: Reaction temperature, 120 °C ≤ T ≤ 160 °C; Reaction time, 30 min ≤ t ≤ 90 min; Reagent concentration, 10% ≤ x ≤ 20%; Ash % ≥ 0% (non-negative constraint). The constraints are fixed on the basis of minimum and maximum process conditions at which experiments were conducted. Beyond the minimum or maximum limit the model will not be valid. In this study, Microsoft Excel “SOLVER” routine has been used to solve the optimization problem. Consequently, the optimal operating conditions are estimated as: reaction temperature = 160 °C; reaction time = 60.6 min; reagent concentration = 17.6%, at which the product coal with a minimum ash content of 7.5% could be produced within the range of model validity. 5. Conclusion The experimental results showed that the minimum ash content of 8.8% is achievable by maintaining the reaction temperature at 160 °C, reagent concentration at 20% and reaction time at 60 min duration. Overall 72% demineralization has been achieved at the said process conditions. Apart from the benefit of reduction in ash content, the mineralogical content of the product coal reveals that the alumina to silica ratio got reduced from 0.45 to 0.22. This reduction of alumina to silica ratio in metallurgical coal can substantially bring down the blast furnace operating temperature, improvement in blast furnace operation, reduction in slag volume and hence better productivity, etc. Subsequently, the nonlinear regression model equation was developed to study the effect of leaching process variables on the product coal ash content. This model was found useful in getting deeper insights and understanding about the chemical leaching process of coal. Finally, the optimization was carried out to identify the operating parameters at which the product coal with minimum ash content can be produced.

Acknowledgments

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