Performance of Horizontal Roughing Filter for Colour Removal of Palm Oil Mill Euent Using Natural Adsorbent

Arezoo Fereidonian Dashti (  [email protected] ) Universiti Sains Malaysia Hamidi Abdul Aziz Universiti Sains Malaysia - Engineering Campus Seri Ampangan: Universiti Sains Malaysia - Kampus Kejuruteraan Seri Ampangan Mohd Nordin Adlan Universiti Sains Malaysia - Engineering Campus Seri Ampangan: Universiti Sains Malaysia - Kampus Kejuruteraan Seri Ampangan Ali Huddin Ibrahim Universiti Sains Malaysia - Engineering Campus Seri Ampangan: Universiti Sains Malaysia - Kampus Kejuruteraan Seri Ampangan

Research Article

Keywords: Calcination, Raw Limestone, Central Composite Design, Isotherm study, Palm Oil Mill Euent

Posted Date: April 26th, 2021

DOI: https://doi.org/10.21203/rs.3.rs-252985/v1

License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 2 3 Performance of Horizontal Roughing Filter for Colour Removal of Palm Oil Mill Effluent 4 Using Natural Adsorbent

Arezoo Fereidonian Dashti1*, Hamidi Abdul Aziz1, Mohd Nordin Adlan2, Ali Huddin Ibrahim1

1School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia 2Solid Waste Management Cluster, Science and Technology Research Centre, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia

5 6 *Corresponding author at: School of Civil Engineering, Universiti Sains Malaysia, Nibong Tebal, Seberang Perai 7 Selatan, Pulau Pinang, 14300, Malaysia. E-mail address: [email protected] 8 9 10 Abstract 11 12 Palm oil wastewater treatment was investigated via a filtration process using raw and calcinated limestone. The 13 column studies were conducted using different limestone sizes (4, 12, and 20 mm), calcination temperature (800 14 °C and 600 °C) and various filtration rates (20, 60, and 100 mL/min) and a comparison was made with raw limestone 15 under similar conditions. The response surface methodology using central composite design was employed to 16 optimize the process parameter. The experimental data was analyzed via the analysis of variance to identify the 17 interaction between the parameters and the dependent parameter. The results showed that the colour removal 18 increased with an increase in temperature (800 ºC) for filtration rate of 20 mL/min, the retention time of 317 min 19 and the smaller size (4 mm) of limestone and decreased with an increase in the filtration rate and size of raw 20 limestone. Based on the achieved results, the optimum condition for colour removal was at temperature 800 °C (61%), 21 600 oC (56%) and raw limestone (49%) respectively with the same experimental setup (flow rate of 20 mL/min and 22 limestone size of 4 mm). According to the statistical analysis, quadratic models demonstrated significant values 23 (0.000) for the response (colour). Freundlich and Langmuir isotherms also provided good correlation coefficient for 24 the colour removal. The data conforms to the Langmuir isotherm with the best fit model (R2=0.7). 25 26 27 28 Keywords: Calcination; Raw Limestone, Central Composite Design, Isotherm study, Palm Oil Mill Effluent 29 30 31 1. Introduction 32 Palm oil industry is considered as an agro-based industry that provides positive economic impact worldwide 33 and to Malaysia specifically (Nasrullah et al. 2017; Bello and Raman 2017). It is reported that the worldwide 34 production of palm oil was over 70 million metric tonnes in 2018, and it is anticipated to exceed 75 million metric 35 tonnes in 2020 (Hayawin et al. 2020). The production of one tonne of crude palm oil needs 5-7.5 tonnes of water and 36 more than half of it is discharged as wastewater called palm oil mill effluent (POME) (Dashti et al. 2020; Sani et al. 37 2020; Bello et al. 2017). Palm Oil Mill Effluent (POME) is characterized as a thick liquid that contains high organic 38 contents with brownish colour and strong smell. It also contains Biochemical Oxygen Demand (BOD), Oil and Grease 39 (O&G), high concentration of Chemical Oxygen Demand (COD), Suspended Solid (SS) and total solids. The 40 discharge of POME without proper treatment imposes serious pollution to the environment (Hossain et al. 2019;

1

41 Bashir et al. 2019; Fereidonian et al. 2018). The colour is formed by refractory compounds of natural organic matter, 42 such as tannins, phenolic compounds, and melanoidin (generated by heating organic oil from the extraction process) 43 (Limkhuansuwan and Chaiprasert 2010). The discharge of brownish colour wastewater into the water bodies can cause 44 adverse effects to aquatic lives by filtering the light passing through which results in less photosynthesis activity and 45 less dissolved oxygen in the water (Ratpukdi 2012). The most usual technology to treat POME is the biological 46 treatments using aerobic or an anaerobic ponding process, due to its low operating cost (Lawal et al. 2020). On the 47 other hand, the biological treatment is a long process, since it requires an extended treatment time to degrade the 48 organic particles (Kim et al. 2020; Chung et al.,2018; Lek et al. 2018). Moreover, lack of regulative control on 49 emission of greenhouse gases caused by biological treatment process is the main drawback of the available and 50 existing POME treatment process (Khadaroo et al. 2019). The process of biological wastewater treatment is ranked as 51 the second-largest producer of greenhouse gases in Malaysia. Gases produced by the biological treatment process are 52 odorous and corrosive as they contain ammonia and hydrogen sulfide (Mat et al. 2020; Hossain et al. 2019; Bakar et 53 al. 2018). The ponding system is also influenced because of the excessive organic load and low pH in addition to the 54 colloidal nature of the suspended solids in POME (Nahrul et al. 2017). Consequently, COD, residual BOD, turbidity 55 and SS concentration in treated POME do not meet the standard discharge constraints for industrial effluents provided 56 by Department of Environment (DOE), Malaysia (Jagaba et al. 2020; Bashir et al. 2019; Dashti et al. 2019). Alternative 57 methods were applied on POME treatment such as membrane, membrane bioreactor, physicochemical, ozonation, and 58 aerobic systems. However, these systems were not implemented in large scale due to operational problems such as 59 scum formation, sludge flotation, or high-energy requirements from the treatment system (Saeed et al. 2015). In this 60 research, we used POME from a polishing pond. After measuring the parameters, it was confirmed that physical 61 treatment could be used because the amount of BOD over COD was less than 0.1. 62 63 The features such as being chemical-free, being high solid retention capacity, having simple management and 64 low-cost maintenance are the highlighted benefits of Roughing Filter (RF) (Khazaei et al.,2016). Usually, RFs are 65 filled with the media of diameter ranging from 4 mm to 30 mm and operate at low hydraulic load varying from 0.3 m 66 h−1 to 1.5 m h−1 (Zeng et al. 2018; Khezri et al. 2015). During RF process, particles are removed if they are smaller 67 than the filter media. The particle removal is efficient when particles are successfully transported and attached on the 68 surface area of the media (or collector). This is defined by the approach to design deep-bed filters and is an estimate 69 using colloid filtration theory (CFT) (Watanabe et al 2002). When a particle is carried by gravitational settling from 70 its fluid streamline to a collector surface, sedimentation (ŋG) takes place. Sedimentation can be calculated by (1) 71 (Watanabe et al. 2002): 72 73 ŋ = (1) ʋ 74 In (1), the fluid density, particle density, gravitational constant and the average particle diameter are presented by 2 75 휌 (gm/푐푚 ), 휌 (gm/푐푚 ), g (cm/ s ) and 푑(cm) respectively. µ (gm/cm/s) is the fluid dynamic viscosity and v 76 (cm/s) is the fluid approach velocity. 77 Due to porosity, various physicochemical properties and particularly surface area of the filter media plays an important 78 role on RFs process (Lin et al. 2008). Various materials have been used as filter media, such as broken brick, various 79 gravel, charcoal (Ochieng et al. 2006), sand (Lin et al.,2008) and plastic (Nkwonta 2010). Limestone is one of the 80 most well-known media utilized in RF. The physical structure of limestone deteriorates quickly when it is calcinated 81 at the temperature of 600 °C. Under mentioned condition, both the peak stress and the coefficient of elasticity decrease 82 quickly which results in more cracks, porosities and enlargement of the surface area (Dashti et al. 2019; Zhang et al. 83 2017), and finally, leads to more absorption ability. This research aims to reduce the colour in polishing pond of 84 POME using calcinated limestone. Hence, the conditions for calcinating various sizes of limestone in different flow 85 rates were optimized using Central Composite Design (CCD) and Response Surface Methodology (RSM). The novelty 86 of this research lies in the use of the calcinated limestone to improve adsorption capabilities. Filter performance was

2

87 also systematically studied with the aim of developing an effective new treatment technology for POME polishing 88 pond. 89 90 91 2. Materials and Methods 92 2.1 POME Sampling, Analysis and Preservation 93 The final discharge effluent of an open anaerobic pond (polishing pond), belonging to MALPOM Industries 94 located in Malaysia, was selected to be used in this research. Hanna HI8314 portable pH meter was used to measure 95 the pH of the POME on-the-location of MALPLM. During the process of sampling and transporting POME to the 96 laboratory, in order to keep it away from light exposure, 25-L black and air-tight container was used. In the laboratory, 97 the POME was immediately stored in cooling room (4 ºC) to be preserved for future biological activities. The COD 98 removal was evaluated based on APHA Standard Method 5220D, program 435 COD HR, HACH DR/ 2800 99 spectrophotometer while turbidity was measured by the method 2130B (APHA 2005) using HACH 2100Q 100 Turbidimeter. The colour was determined by the method 2120C (APHA 2005), Platinum-Cobalt Standard. Ammonia 101 nitrogen-Nessler method coupled with HACH DR/ 2800 spectrophotometer were used to measure Ammonia nitrogen. 102 103 104 2.2 Filter Design and Operation 105 The horizontal filter is an acrylic column which is 75 cm wide, 20 cm long, and 9 cm high (Table 1). Raw and 106 calcinated limestones with various sizes are used as a filter media during the process. The column study was run by 107 passing the POME through the horizontal filter (Fig. 1a) with 3 different filtration rates of 100, 60 and 20 mL/min. In 108 order to create different flow rates, Masterflex pump with capacity of 2000 mL/min was used to carry POME from 109 the tank to the horizontal roughing filter. The filtration process was carried out by taking samples from the inlet and 110 the outlet both prior to and after the retention time for different filter media sizes and flow rates over seven successive 111 days. 112 Colour removal efficiency percentage from POME was calculate by the following equation: 113 114 P = × 100 (2) 115 116 where C0 (PtCo) and C1(PtCo) represent the concentration of colour in influent and effluent of POME, respectively. 117 118 119 2.3 Calcination Limestone Process 120 The natural limestone used in the experiment was produced by a marble factory in Ipoh, Malaysia. The limestone 121 is crushed into 3 different small sizes of 20, 12 and 4 mm. After that, they were sieved to be uniform in terms of size, 122 between 4 mm and 20 mm using a sieve. They were washed to remove any impurities and then dried at room 123 temperature for 24 hours. By means of a stainless-steel furnace with inner length and diameter of 1.5 cm and 2.5 cm, 124 respectively, and outer length of 50 cm. The furnace was equipped with 2 different gauges. One for adjusting gas flow 125 and the other one for controlling the temperature (Fig. 1b). A Teflon tube piped the furnace to the gas tank. In the 126 course of the calcination process, nitrogen gas (Well Gas Company) with the flowrate of 200 cm3/min was flowing to 127 the tank. A scanning electron microscope (SEM) (Carl Zeiss Supra -35 VP, Germany) was used to measure 128 morphology of the surface area for both raw and calcinated limestone. Moreover, to evaluate the total surface area 129 metrics such as pore volume and pore size, the Brunauer-Emmett-Teller (BET) process (ASTM D3037) was applied 130 for the three media. X-ray fluorescence (XRF) was also used to measure the elemental composition of all three media. 131

132

3

133 134 (a)

135 136 (b) 137 Fig. 1. Horizontal roughing filter schematic (a) and calcination experimental diagram (b). 138 139 2.4 Data Analysis 140 RSM was used to analyse the colour removal efficiency of POME. Two independent variables namely, the flow 141 rate (20-100 mL/min) and the limestone size (4-20 mm) were selected to obtain the colour removal efficiency. Each 142 experiment was run 13 times. Experimental range of each variable was between their minimum and maximum level 143 as requested by CCD. The colour removal efficiency was treated as the dependent response (Y). To recognize the 144 sufficiency of the models, both independent factors and dependent response were optimized using analysis of variance 145 (ANOVA). Similar to previous works, for quantifying the effects of two different factors, a second-order polynomial 146 quadratic model (3) was used (Fereidonian Dashti et al. 2018; Zhong et al. 2014; Hay et al. 2012). 147 148 ∑ ∑ ∑ ∑ (3) 푌 = 훽 + 훽 푋 + 훽 푋 + 훽 푋푋 + ⋯ + 푒 149

150 In (3), Y is the dependent parameter (colour) and 푋 and 푋 represent the independent parameters. Subscript 0 is a fixed 151 coefficient while the coefficients of higher order are denoted by i, ii, and ij.

4

152 3 Results and Discussion 153 3.1 POME Properties 154 155 The POME used was characterized by 4000 Pt Co colour, 2750 mg/L COD, pH 8.3 and 652 mg/L SS, and 156 collected from United Palm Oil Mill Sdn. Bhd in Nibong Tebal, Pulau Pinang of polishing pond (last pond of 157 biological treatment) as presented in Table 3. Generally, raw palm mill wastewater is acidic (pH 3 - 4) and high in 158 temperature. COD and BOD of fresh POME are in the range of 50,000 mg/L and 25,000 mg/L, respectively. Moreover, 159 SS and Dissolved Solids (DS) are remarkably high value components of POME (Garritano et al. 2018). Hence, the 160 polishing pond characteristics in the current work was different from the conventional raw POME. All the parameters 161 evaluated in this work were measured following the Standard Methods for Water and Wastewater Examination (APHA 162 2005). The temperature and the pH were measured in the field while ammonia nitrogen, turbidity, BOD, COD, colour 163 and SS were analyzed via laboratory analyses. Measuring the mentioned parameters supported physical treatment due 164 to the amount of BOD over COD which was less than 0.1 ( < 0.1). The concentration of revealed the rate of 165 degradation and biodegradability of wastewater organic components. To determine proper strategies for the 166 wastewater treatment plan, stable zone, biodegradable area, toxic region and finally the zones proposed by 167 Samudro and Mangkoedihardjo (2010) were used. The amount of BOD and COD in the wastewater helps evaluate 168 the organic matter content in effluent, where an elevated ratio demonstrates the incidence of biodegradation 169 (Huang et al. 2017; Aziz 2011). 170 171 172 3.2 Media Properties 173 Chemical components shown in Table 4 belong to XRF results of raw and calcinated limestone. According to 174 Table 4, the two main components of limestone are calcium oxide and magnesium oxide (Dashti et al. 2019). It is 175 concluded that calcinated limestone has fewer impurities (iron, silica) in comparison with raw limestone. The findings 176 of this work agree with the results from previous works (Wang et al. 2016; Ramezani et al. 2017). 177 178 179 3.3 Morphology Analysis of the Surface Area 180 A previous study (Côté and Konrad 2009) showed that the mechanical and porous properties of limestone are 181 quickly changed when the temperature rises from 200 °C to 500 °C. This is because, in the mentioned temperature 182 range, decomposition and expansion occur in minerals which cause water evaporation inside the stone. This 183 evaporation results in physical and chemical changes among some minerals (Cai et al. 2017; Ozguven and Ozcelik 184 2014) and these changes cause new cracks in the stone which generate, propagate and coalesce pores. When the 185 temperature rises from 500 °C to 600 °C, almost all amount of moisture in the limestone evaporates. A huge number 186 of stomata, cracks and powder are created, and only few unbroken crystal ribbons remain both during and at the end 187 of calcination process in 800 °C temperature (Chen et al. 2009; Kavosh 2015) as shown in Fig. 2. Furthermore, Chen 188 et al. (2009) validated that the occurrence of coalescence during the calcination process converted the smaller pores 189 into larger pores which improved the adsorption capability of the limestone. The porosity of the limestone was 190 reduced, and its apparent weight was increased in the temperature of above 1000 °C as validated by Kilic (2014). Fig. 191 2 depicts the morphologies of the raw and calcinated limestone in the temperature of 600°C and 800°C. BET results 192 show that surface area and total pore volume in calcinated limestone were higher than raw limestone as shown in 193 Table 5. These findings agree with previous research (Fereidonian Dashti et al. 2018; Chen et al. 2017). 194 195 196

5

197 198 (a) (b) 199

200 201 (c) 202 Fig. 2. The SEM images of (a) raw limestone; (b) calcinated limestone at 600 °C; and (c) calcinated limestone at 203 800 °C 204 205 3.4 Process Optimization of Colour Removal 206 In this study, the quadratic model was used to measure colour removal efficiency. The results of the Analysis 207 of Variance (ANOVA) for POME colour removal under different flowrates and sizes for raw and calcinated limestone 208 are shown in Tables 6, 7 and 8, respectively. The mean squares were afforded by dividing the sum of squares of every 209 two variations by the error of variance shown by the Degrees of Freedom (DF). The F-value of the different models 210 was assessed by dividing the mean squared of models by residual mean. The R2 coefficient represents the ratio of the 211 regression sum of squares to the total sum of squares. A R2 value close to 1 is a favourable and agrees with previous 212 research (Ghafari et al. 2009). A desired model could be achieved by minimum R2 value of 0.80 (Dashti et al. 2020

6

213 and Fereidonian Dashti et al. 2018). Meanwhile, the Lack-of-Fit (LoF) was insignificant. An insignificant LoF is 214 preferable because the primary objective of this study is to create a model that fits the experimental data (Dashti et al. 215 2019; Pambi and Musonge 2016). In this research due to pure error, all three models were insignificant for both the 216 calcinated and raw limestone response. Tables 6 to 8 report that the Coefficient of Variance (CV) ratio is less than 217 10% for all three quadratic models. It should be kept in mind that a CV more than 10 percent imply a reproducible 218 model. Comparing the average prediction error to the range of the predicted values at the design points is called 219 Adequate Precision (AP) and ratios greater than 4 indicate an adequate model discrimination. Moreover, predicted 220 models can be used to navigate the design space defined by the CCD when AP values are higher than 4 for the achieved 221 responses. According to the findings and based on (4) to (6), A (raw and calcinated limestone (mm), B (flow rate 222 ml/min) , and A2 were significant for all three models. Actual factors of models and trace the effect of their changes 223 in the process on removal efficiency of the colour in POME can be calculated by (4) to (6). Model adequacy is normally 224 evaluated by applying diagnostic plots provided by Design Expert software, such as the predicted versus actual value 225 and the normal residual probability plots in Fig. 3. The expected colour removal efficiency values achieved from the 226 actual experimental data and that of the model were in good agreement with 3 models (Bhatti et al. 2011; Wang et al. 227 2014). Fig. 4 illustrates the plot of disruption of the comparative effects of two independent variables on the efficiency 228 of colour removal for 3 models. In Fig. 4, a sharp curvature for the flow rate (mL/min) (A) and limestone particle size 229 (mm) (B) is observed, showing that the colour removal efficiency response was slightly sensitive to both process 230 variables. 231 232 Colour Removal %= 53.52 - 0.07A - 0.52B - 1.74A2 - 0.02B2 + 3.90AB (4) 233 Colour Removal% =54.97 + 0.57A - 3.19B - 5.32A2 + 0.10B2 - 3.90AB (5) 234 Colour Removal %= 67.66 + 0.12A - 2.46B - 2.39A2 + 0.04B2 + 3.12AB (6) 235 236

Predicted vs. Actual Predicted vs. Actual

49.32 58.34

41.49 50.51

3 2 33.66 42.67 Predicted Predicted

25.83 34.84

18.00 27.00

18.00 25.83 33.66 41.49 49.32 27.00 34.84 42.67 50.51 58.34

237 Actual Actual 238 (a) (b) 7

Predicted vs. Actual

61.00

54.00

47.00 2 Predicted

40.00

33.00

33.00 40.00 47.00 54.00 61.00

239 Actual 240 (c) 241 Fig. 3. Actual predicted plot of colour removal using: (a) raw limestone; (b) calcinated limestone at 600 °C; and (c) 242 calcinated limestone at 800 °C 243 Perturbation Perturbation B 49 58

A B 41 51

B 34 43 Colour

Colour A B

26 A 35 A

18 27

-1.000 -0.500 0.000 0.500 1.000 -1.000 -0.500 0.000 0.500 1.000

244 Deviation from Reference Point Deviation from Reference Point 245 (a) (b)

8

Perturbation

61 B

54

A 47 Colour

B 40 A

33

-1.000 -0.500 0.000 0.500 1.000

246 Deviation from Reference Point 247 (c) 248 Fig. 4. Colour removal plot using: (a) raw limestone; (b) calcinated limestone at 600 °C; and (c) calcinated 249 limestone at 800 °C 250 251 252 3.5 Performance of Filters 253 Table 2 lists the results of 36 runs for the CCD experimental design of all 3 models. 3-D plots are created to 254 investigate the effect of two factors namely, size and flow rate on the colour removal efficiency. According to the 255 results and based on (4) to (6), both particle size of limestone and flow rate have a significant effect on response. Fig. 256 5 shows that the colour removal efficiency increased with the reduction of flow rate and limestone particle size, which 257 agrees with the results reported by Daee et al. (2019) and Maung (2006). The optimum colour removal efficiencies 258 obtained by applying roughing filter with particle size of 4 mm and flow rate of 20 mL/min were 49% for raw 259 limestone, and 58% at 600 °C and 61% C at 800 °C for calcinated limestone, respectively. Therefore, increasing the 260 pore volume, the surface area of limestone and the temperature improve the colour removal efficiency, as also 261 validated by Chen et al. (2009). In the course of the roughing filter process some suspended particles were settled 262 through the settling process due to their gravity. A particle requires less time to travel along the settling distance and 263 stick to or absorb onto the media layer if the flow rate is faster (Khezri et al. 2015; Dastanai et al. 2007). Nkwonta 264 (2010) pointed out that the flow rate has a significant influence on colour removal. Effective colour removal in 265 roughing filters are achieved with low flow rates because low flow rates are critical to retain particles that are 266 gravitationally deposited to the surface of the media. Affam and Adlan (2013) investigated the removal of colour from 267 leachate using vertical up-flow filtration technique by combination of three different media sizes (i.e., 4-8 mm, 8-12 268 mm and 12-18 mm) and five different filtration rates (100 mL/min, 80 mL/min, 60 mL/min, 40 mL/min and 20 269 mL/min). The filter media were stacked in descending sizes from bottom to top for all experiments. The results showed 270 that removal efficiency was improved from 36% to 62% for colour removal at flow rate of 20 mL/min. 271 272 273

9

e

49

42

34

26

18 Colour Colour

20 40 4 60 8 A: Flow rate 12 80 16 B: Limestone 20 100 274 275 276 (a) 277

ne

59

51

43

35

28 Colour Colour

4 20 8 40 12 60 B: Limestone 16 80 A: Flow rate 20 100 278 279 (b) 280

10

61

54

47

40

33 Colour Colour

4 100 8 80 12 60 B: Limestone 40 16 A: Flow rate 281 20 20 282 (c) 283 Fig. 5. Response surface for colour removal efficiency using: (a) raw limestone; (b) calcinated limestone at 600 °C; 284 and (c) calcinated limestone at 800 °C 285 286 287 3.6 Numerical Optimization 288 In this work, some parameters such as limestone size, temperature and flow rate have significant effects on 289 POME treatment and can improve colour removal efficiency for certain conditions. Therefore, in order to find the best 290 values of independent variables that present optimum values for colour removal, RSM was applied. Each of the 291 independent factors is exclusively tuned in an attempt to earn the optimum value for colour removal (Bashir et al. 292 2010; Hosseini 2012; Aziz et al. 2011; Ahmad et al. 2005). Table 9 shows the limitations of each variable and the 293 desired response. When desirability is 1 or close to 1, the best outcome of the experiment and the most optimum 294 conditions for each solution can be selected for further validation (Hosseini 2012). In this work, the range of flow rate 295 and size of limestone were from 20 to 100 mL/min and from 4 to 20 mm, respectively. As Table 9 reports, optimum 296 removal efficiency and desirability were achieved by the lower range of limestone size (4 mm) and flow rate (20 297 mL/min). Table 9 also reports that the responses of different model predictions are closely agreeing with the results 298 of laboratory experiment. 299 300 301 3.7 Adsorption Isotherms 302 Qualitative information of the special relation between the amount of adsorbate mass on the surface of 303 adsorbent and the concentration of adsorbent in addition to the kind of solute-surface interaction are described by 304 adsorption isotherms (Khandaker et al. 2020). The current work used two different isotherms, namely Freundlich and 305 Langmuir, to fit the equilibrium data acquired from the actual experiments. Theoretically, the assumption of the 306 Langmuir isotherm is that the adsorbate covers the homogeneous surface of adsorbent in a monolayer (Jawad et al. 307 2020). The Langmuir isotherm is presented in (7):

11

308 = + (7) / 309 310 In (7), the rate of adsorption, adsorption capacity, adsorbate equilibrium concentration and the quantity of adsorbate 311 are denoted by b (L/mg), Q (mg/g), Ce (mg/L) and x/m (qe) (mg/g), respectively. According to Table 10, the Langmuir 312 model sufficiently fits the equilibrium data. The equilibrium parameter (RL) (Balark et al. 2017) can also be used to 313 explain the Langmuir isotherm features (8): 314 푅 = (8) ( ) 315 316 In (8), the initial colour concentration and the Langmuir constant are denoted by C0 (mg/L) and b, respectively. 317 Isotherms can be categorized into three different categories based on the value of favorable (RL<1), linear (RL = 1), 318 and unfavorable (RL>1) (Isa et al. 2007). Table 10 shows favorable adsorption process for colour for all types of 319 limestone. The main underlying theory of the Freundlich isotherm model is the assumption that the adsorption process 320 occurs on heterogeneous surfaces and can be presented as the empirical (9) (Dashti et al. 2020): 321 322 323 푙표푔 푞 = 푙표푔 퐾 + 푙표푔 퐶 (9) 324

325 where, qe = colour amount adsorbed per unit of mass adsorbent (mg/g); Ce = adsorbate equilibrium (mg/L); n = 326 adsorption intensity; and K = adsorption capacity (mg/g). A favorable adsorption mechanism ( < 1) increases 327 adsorption capacity while a weak adsorption capacity is the result of an unfavorable adsorption mechanism ( > 1) 328 (Aziz et al. 2008 and Hossain et al. 2019). R2 and K values for the Freundlich isotherm based on linear regression 329 correlation (9) are listed in Table 11 and are shown in Fig. 7. Findings of this work validated that the Langmuir 330 isotherm model fits the experimental data better than the other model (Fig 6). Another outcome is that the colour 331 coated the surface of the adsorbent in a monolayer fashion and homogenously. This is caused by the higher R2 value 332 acquired using Langmuir isotherm (0.735) compared with the Freundlich isotherm (0.622) (Fig. 6 and 7). Keong 333 (2012) achieved colour removal of leachate (Q) equal to 1.070 Pt Co/g and 0.065 Pt Co/g; and b values of 5.36×10-3 334 /Pt Co and 5.44×10-3 /Pt Co for calcinated limestone at 400 ˚C and 200 ˚C, respectively. The values of R2 for Freundlich 335 model were 0.751 and 0.605 in comparison to Langmuir model which were 0.502 and 0.376 for calcinated limestone 336 at 400 ˚C and 200 ˚C. Pala and Erden (2004) used lime for leachate treatment. They found out that Langmuir isotherm 337 was more satisfactory (R2=0.97) for colour removal compared to Freundlich model.

338

12

y = -794855x + 1442.6 R² = 0.6411 1180 1170 1160 1150 1140 1/qe 1130 1120 1110 1100 1090 1080 0.0000 0.0001 0.0002 0.0003 0.0004 0.0005

1/Ce 339

340 (a)

y = -493978x + 1103.8 y = -356460x + 888.74 R² = 0.7085 R² = 0.735 930 770 920 760 750 910 e e 740

900 1/q 1/q 730 890 720 880 710 870 700 860 690 850 680 0.0000 0.0002 0.0004 0.0006 0.0000 0.0002 0.0004 0.0006 1/Ce 1/Ce 341

342 (b) (c)

343 Fig 6. Langmuir Isotherm for Colour adsorption: (a) raw limestone, calcinated limestone at 600 ˚C; (b) 344 and calcinated limestone at 800 ˚C; (c)

345

13

y = -0.2486x - 2.2047 R² = 0.6053

-3.04 3.34 3.36 3.38 3.40 3.42 3.44 -3.04

-3.05 -3.05 Log x/m -3.06 -3.06

-3.07

-3.07 Log Ce 346

347 (a)

y = -0.1046x - 2.513 y = -0.2422x - 2.1351 R² = 0.6065 R² = 0.6229

-2.83 -2.93 3.00 3.10 3.20 3.30 3.40 3.50 3.30 3.35 3.40 3.45 -2.94 -2.84 -2.94 -2.85 -2.95 -2.86 -2.95 Log x/m Log x/m -2.87 -2.96

-2.88 -2.96

-2.97 -2.89

Log ce 348 Log ce

349 (b) (c)

350 Fig 7. Freundlich Isotherm for colour adsorption: (a) raw limestone, calcinated limestone at 600 ˚C; 351 (b) and calcinated limestone at 800 ˚C; (c)

352

353

14

354 4. Conclusions 355 This research focused on development of a low-cost, eco-friendly and functionalized material to remove colour 356 from palm oil mill effluent (POME). More specifically, the optimum conditions of colour removal from polished 357 POME were investigated using calcinated and raw limestone in a horizontal roughing filter process as filter media. 358 The interaction between the variables (flow rate and limestone size) and experimental parameter had been used to 359 enhance the colour removal using response surface methodology. Optimum variables, according to the model, 360 consisted of calcinated limestone at 800 ℃ with the size of 4 mm and a flow rate of 20 mL/min. The mentioned settings 361 led to 61% colour removal, while only 18% was achieved when using raw limestone with the size of 20 mm and flow 362 rate of 100 mL/min. A particle requires less time to travel along the settling distance and stick to or absorb onto the 363 media layer if the flow rate is fast. According to the achieved results, when the size of filter media is small and the 364 flow rate is low, more colour removal is achieved. Furthermore, the Langmuir isotherm yielded R2 of 0.735 and fitted 365 well the adsorption data in this study. 366 367 368 369 Acknowledgements 370 371 This research was awarded Universiti Sains Malaysia Short-Term Grant (Grant No. 304/ PAWAM/60311001). 372 which is belong to the Solid Waste Management group, Engineering Campus, USM. 373

374 Conflict of Interest 375 The authors notify that there are no conflicts of interest. 376 377 Availability of data and materials 378 All authors agreed that all data and materials as well as software support our published and comply with 379 field standards 380 381 Declarations 382

383 The authors declare that they have no known competing financial interests or personal relationships that 384 could have appeared to influence the work reported in this paper. The research leading to these results 385 received funding from Universiti Sains Malaysia Short-Term Grant under Grant Agreement No (304/ 386 PAWAM/60311001)

387 388 Authors' contributions 389 Formal analysis and investigation; Writing - original draft preparation: Dr. Arezoo Fereidonian Dashti 390 Supervisor and co-supervisor: Prof. Mohd Nordin Adlan; Prof. Hamidi Abdul Aziz 391 Methodology: Ali Huddin Ibrahim 392

15

393 394 Abbreviations and Chemical Symbols ANOVA Analysis of variance 395 AP Adequate Precision APHA American Public Health Association396 AWWA American Water Works Association397 BET Brunauer-Emmett-Teller BOD Biological oxygen demand 398 CaO Lime 399 CaCo3 Calcium Carbonate COD Chemical oxygen demand 400 ºC Degrees celsius CCD Central composite design 401 CL Calcinated limestone CV Coefficient of variance 402 DOE Department of Environment 403 Fe2O3 Ferric oxide Fig Figure 404 g Grams ha Hectare 405 HRF Horizontal roughing filter 406 HRT Hydraulic retention time Kg Kilogram 407 L Liters LOF Lack of Fit 408 LS Limestone 409 Mm Millimeters Mg/L Milligrams per liter 410 MgO Magnesium oxide m2/g Square meter per gram 411 % Percent 412 POME Palm oil mill effluent pH Potential hydrogen 413 RL Raw Limestone RSM Response Surface Methodology 414 SD Standard deviation 415 SEM Scanning electron microscopy SiO2 Silicon dioxide 416 SS Suspended Solid XRD X-Ray Diffraction 417 Cc/g Cubic centimeter per gram 418 419 420 421 422 423

16

424 References

425 Affam AC, Adlan MN (2013) Operational performance of vertical up flow roughing filter for pre-treatment of leachate 426 using limestone filter media. J. Urban. Environ. Eng 7(1):117-125. https://www.jstor.org/stable/26203395

427 APHA AWWA WPCF, Standard Methods for Examination of Water and Wastewater, Washington, DC (2005).

428 Ahmad A, Ismail S, Bhatia S (2005) Membrane treatment for palm oil mill effluent: effect of transmembrane pressure 429 and crossflow velocity. Desalination 179 (1-3): 245-255. https://doi.org/10.1016/j.desal.2004.11.071

430 Aziz SQ, Aziz HA, Yusoff MS, Bashir MJ (2011) Landfill leachate treatment using powdered activated carbon 431 augmented sequencing batch reactor (SBR) process: Optimization by response surface methodology. J. Hazard. Mater 432 189 (1-2): 404-413. https://doi.org/10.1016/j.jhazmat.2011.02.052

433 Aziz HA, Adlan MN, Ariffin KS (2008) Heavy metals (Cd, Pb, Zn, Ni, Cu and Cr (III)) removal from water in 434 Malaysia: Post treatment by high quality limestone Bioresour. Technol 99 (6): 1578-1583. 435 https://doi.org/10.1016/j.biortech.2007.04.007 436 Bakar SNHA, Hasan HA, Mohammad AW, Abdullah SRS, Haan TY, Ngteni R, Yusof KMM (2018) A review of 437 moving-bed biofilm reactor technology for palm oil mill effluent treatment. J. Clean. Prod 171: 1532-1545. 438 https://doi.org/10.1016/j.jclepro.2017.10.100 439 Balark D, Mostafapour FK, Azarpira H, Joghataei A (2017) Langmuir, Freundlich, Temkin and Dubinin– 440 radushkevich isotherms studies of equilibrium sorption of ampicilin unto montmorillonite nanoparticles. J. Pharm. 441 Res. Int 20 (2): 1-9. 10.9734/JPRI/2017/38056 442 443 Bashir MJ, Lim JH, Amr SSA, Wong LP, Sim YL (2019) Post treatment of palm oil mill effluent using electro- 444 coagulation-peroxidation (ECP)technique. J.Clean. Prod 208: 716-727. https://doi.org/10.1016/j.jclepro.2018.10.073 445 Bashir MJ, Aziz HA, Yusoff MS, Adlan MN (2010) Application of response surface methodology (RSM) for 446 optimization of ammoniacal nitrogen removal from semi-aerobic landfill leachate using ion exchange resin. 447 Desalination 254 (1-3): 154-161. https://doi.org/10.1016/j.desal.2009.12.002 448 Bello MM, Raman AAA (2017) Trend and current practices of palm oil mill effluent polishing: Application of 449 advanced oxidation processes and their future perspectives. J. Environ. Manage 198: 170-182. 450 https://doi.org/10.1016/j.jenvman.2017.04.050 451 Bhatti MS, Kapoor D, Kalia RK, Reddy AS, Thukral AK (2011) RSM and ANN modeling for electrocoagulation of 452 copper from simulated wastewater: Multi objective optimization using genetic algorithm 453 approach. Desalination 274(1-3): 74-80. https://doi.org/10.1016/j.desal.2011.01.083 454 Cai J, Wang S, Kuang C (2017) A modified random pore model for carbonation reaction of CaO-based limestone with 455 CO2 in different calcination-carbonation cycles. Energy Procedia 105:1924-1931. 456 https://doi.org/10.1016/j.egypro.2017.03.561

457 Chen LJ, Jun H, Chao JQ, Qin BD (2009) Swelling and breaking characteristics of limestone under high temperatures. 458 J Min Sci Technol (China) 19 (4): 503-507. https://doi.org/10.1016/S1674-5264(09)60094-6

459 Chen L, Wang C, Wang Z, Anthony EJ (2017) The kinetics and pore structure of sorbents during the simultaneous 460 calcination/sulfation of limestone in CFB. Fuel 208: 203-213. https://doi.org/10.1016/j.fuel.2017.07.018

17

461 Chung CY, Selvarajoo A, Sethu V, Koyande AK, Arputhan A, Lim ZC (2018) Treatment of palm oil mill effluent 462 (POME) by coagulation flocculation process using peanut–okra and wheat germ–okra. Clean Technol Environ 463 Policy 20(9):1951-1970.https://doi.org/10.1007/s10098-018-1619-y 464 465 Côté J, Konrad JM (2009) Assessment of structure effects on the thermal conductivity of two-phase porous 466 geomaterials. Int J Heat Mass Tran 52(3-4): 796-804. https://doi.org/10.1016/j.ijheatmasstransfer.2008.07.037 467 Dashti AF, Aziz HA, Ibrahim AH, Zahed MA (2020) Suspended Solid Removal of Palm Oil Mill Effluent Using 468 Horizontal Roughing Filter and Calcinated Limestone. Water Air Soil Pollut 231(8):1- 469 15. https://doi.org/10.1007/s11270-020-04755-z 470 Dashti AF, Aziz HA, Adlan MN, Ibrahim AH (2019) Calcined limestone horizontal roughing filter for treatment of 471 palm oil mill effluent polishing pond. Int J Environ Sci Technol 16(10): 6419-6430. https://doi.org/10.1007/s13762- 472 019-02467-4 473 Daee M, Gholipour A, Stefanakis AI (2019) Performance of pilot Horizontal Roughing Filter as polishing stage of 474 waste stabilization ponds in developing regions and modelling verification. Ecol. Eng 138: 8-18. 475 https://doi.org/10.1016/j.ecoleng.2019.07.007 476 Dastanaie AJ, Bidhendi GN, Nasrabadi T, Habibi R, Hoveidi H (2007) Use of horizontal flow roughing filtration in 477 drinking water treatment. Int J Environ Sci Technol 4(3): 379-382. https://doi.org/10.1007/BF03326297

478 Fereidonian Dashti A, Adlan MN, Abdul Aziz H, Ibrahim AH (2018) Application of response surface methodology 479 (RSM) for optimization of ammoniacal nitrogen removal from palm oil mill wastewater using limestone roughing 480 filter. J. appl. res. water wastewater 5(1): 411-416. 10.22126/ARWW.2018.892

481 Garritano AN, de Oliveira Faber M, De Sá LR, Ferreira-Leitão VS (2018) Palm oil mill effluent (POME) as raw 482 material for biohydrogen and methane production via dark fermentation. Renew. Sustain. Energy Rev 92: 676-684. 483 https://doi.org/10.1016/j.rser.2018.04.031 484 Ghafari S, Aziz HA, Isa MH, Zinatizadeh AA (2009) Application of response surface methodology (RSM) to optimize 485 coagulation–flocculation treatment of leachate using poly-aluminum chloride (PAC) and alum. J. Hazard. Mater 486 163(2-3): 650-656. https://doi.org/10.1016/j.jhazmat.2008.07.090 487 Hayawin ZN, Ibrahim MF, Faizah JN, Ropandi M, Astimar AA, Noorshamsiana AW, Abd-Aziz S (2020) Palm oil 488 mill final discharge treatment by a continuous adsorption system using oil palm kernel shell activated carbon produced 489 from two-in-one carbonization activation reactor system. J. Water Process. Eng 36: 101262. 490 https://doi.org/10.1016/j.jwpe.2020.101262 491 Hay JXW, Wu TY, Teh CY, Jahim JM (2012) Optimized growth of Rhodobacter sphaeroides using response surface 492 methodology (RSM). J Sci Ind Res 71 (2): 149–154. http://hdl.handle.net/123456789/13493 493 Hossain MS, Omar F, Asis AJ, Bachmann RT, Sarker MZI, Ab Kadir MO (2019) Effective treatment of palm oil mill 494 effluent using FeSO4. 7H2O waste from titanium oxide industry: Coagulation adsorption isotherm and kinetics 495 studies. J. Clean. Prod 219: 86-98. https://doi.org/10.1016/j.jclepro.2019.02.069 496 Huang D, Hu C, Zeng G, Cheng M, Xu P, Gong X, Xue W (2017) Combination of Fenton processes and biotreatment 497 for wastewater treatment and soil remediation. Sci. Total Environ 574: 1599-1610. 498 https://doi.org/10.1016/j.scitotenv.2016.08.199 499 500 Isa MH, Lang LS, Asaari FAH, Aziz HA, Ramli NA, Dhas JPA (2007) Low cost removal of disperse dyes from 501 aqueous solution using palm ash. J. Dyes Pigm 74(2): 446-453. https://doi.org/10.1016/j.dyepig.2006.02.025

18

502 Jagaba AH, Kutty SRM, Hayder G, Latiff AAA, Aziz NAA, Umaru I, Ghaleb AA, Abubakar S, Lawal IM, Nasara 503 MA (2020) Sustainable use of natural and chemical coagulants for contaminants removal from palm oil mill effluent: 504 A comparative analysis. Ain Shams Eng. J 11(4): 951-960. https://doi.org/10.1016/j.asej.2020.01.018 505 506 Jawad AH, Mubarak NSA, Abdulhameed AS (2020) Hybrid Crosslinked Chitosan-Epichlorohydrin/TiO 2 507 Nanocomposite for Reactive Red 120 Dye Adsorption: Kinetic, Isotherm, Thermodynamic, and Mechanism Study. J 508 Polym Environ 28(2): 624-637. https://doi.org/10.1007/s10924-019-01631-8 509 510 Kavosh M, Patchigolla K, Oakey JE, Anthony EJ, Champagne S, Hughes R (2015) Pressurised calcination– 511 atmospheric carbonation of limestone for cyclic CO2 capture from flue gases. Chem Eng Res Des 102: 116-123. 512 https://doi.org/10.1016/j.cherd.2015.06.024

513 Keong CW (2012) Treatment of Semi Aerobic Leachate by Calcinated Limestone. M.Sc, Universiiy sains Malaysia.

514 Khazaei M, Nabizadeh R, Mahvi AH, Izanloo H, Ansari Tadi R, Gharagazloo F (2016) Nitrogen and phosphorous 515 removal from aerated lagoon effluent using horizontal roughing filter (HRF). Desalination Water Treat 57(12): 5425- 516 5434. https://doi.org/10.1080/19443994.2014.1003100 517 518 Khezri SM, Majidi G, Jafari Mansoorian H, Ansari M, Atabi F, Tohidi Mogaddam T, Rashtchi N (2015) Efficiency 519 of horizontal roughing filter in removing nitrate, phosphate and chemical oxygen demand from effluent of waste 520 stabilization pond. Environ. Health Eng. Manag 2(2): 87-92. https://ssrn.com/abstract=2662795 521 Khandaker S, Toyohara Y, Saha GC, Awual MR, Kuba T (2020) Development of synthetic zeolites from bio-slag for 522 cesium adsorption: Kinetic, isotherm and thermodynamic studies. J. Water Process. Eng 33: 101055. 523 https://doi.org/10.1016/j.jwpe.2019.101055

524 Kilic O (2014) Application of ultrasonic velocity measurement and thermal analysis for determination of limestone 525 quality. J. Physicochem. Probl. Miner. Process. 50(2): 525-534. 10.5277/ppmp140209

526 Kim ITS, Sethu V, Arumugasamy SK, Selvarajoo A (2020) Fenugreek seeds and okra for the treatment of palm oil 527 mill effluent (POME)–Characterization studies and modeling with backpropagation feed forward neural network 528 (BFNN). J. Water Process. Eng 37: 101500. https://doi.org/10.1016/j.jwpe.2020.101500 529 530 Lawal AA, Hassan MA, Farid MAA, Yasim-Anuar TAT, Yusoff MZM, Zakaria MR, Roslan AM, Mokhtar MN, 531 Shirai Y (2020) Production of biochar from oil palm frond by steam pyrolysis for removal of residual contaminants 532 in palm oil mill effluent final discharge. J. Clean. Prod 265: 121643. https://doi.org/10.1016/j.jclepro.2020.121643 533 534 Lek BLC, Peter AP, Chong KHQ, Ragu P, Sethu V, Selvarajoo A, Arumugasamy SK (2018) Treatment of palm oil 535 mill effluent (POME) using chickpea (Cicer arietinum) as a natural coagulant and flocculant: Evaluation, process 536 optimization and characterization of chickpea powder. J. Environ. Chem. Eng 6(5): 6243-6255. 537 https://doi.org/10.1016/j.jece.2018.09.038 538 Limkhuansuwan V, Chaiprasert P (2010) Decolorization of molasses melanoidins and palm oil mill effluent phenolic 539 compounds by fermentative lactic acid bacteria. J Environ Sci 22(8): 1209-1217. https://doi.org/10.1016/S1001- 540 0742(09)60240-0 541 Lin E, Page D, Pavelic P (2008) A new method to evaluate polydisperse kaolinite clay particle removal in roughing 542 filtration using colloid filtration theory. Water Res 42(3): 669-676. https://doi.org/10.1016/j.watres.2007.08.018

19

543 Mat Yasin NMF, Hossain M, HPS AK, Zulkifli M, Al-Gheethi A, Asis AJ, Yahaya ANA (2020) Treatment of Palm 544 Oil Refinery Effluent Using Tannin as a Polymeric Coagulant: Isotherm, Kinetics, and Thermodynamics 545 Analyses. Polymer 12(10): 2353. https://doi.org/10.3390/polym12102353

546 Maung (2006) A Study on The Performance of Limestone Roughing Filter For the Removal of Turbidity, Suspended 547 Solids, Biochemical Oxygen Demand and Coliform Organisms Using Wastewater from The Inlet of Domestic 548 Wastewater Oxidation Pond. M.Sc, University Sains Malaysia.

549 Nkwonta O (2010) A comparison of horizontal roughing filters and vertical roughing filters in wastewater treatment 550 using gravel as a filter media. Phys. Sci. Int. J 5(8): 1240-1247. http://www.academicjournals.org/IJPS 551 Nasrullah M, Singh L, Mohamad Z, Norsita S, Krishnan S, Wahida N, Zularisam AW (2017) Treatment of palm oil 552 mill effluent by electrocoagulation with presence of hydrogen peroxide as oxidizing agent and polialuminum chloride 553 as coagulant-aid. Water Resour. Ind 17: 7-10. https://doi.org/10.1016/j.wri.2016.11.001 554 Nahrul HZ, Nor FJ, Ropandi M, Astimar AA (2017) A review on the development of palm oil mill effluent (POME) 555 final discharge polishing treatments. J. Oil Palm Res 29(4): 528-540. 10.21894/jopr.2017.00012 556 Ochieng GM, Otieno FAO (2006) Verification of Wegelin's design criteria for horizontal flow roughing filters (HRFs) 557 with alternative filter material. Water SA 32(1): 105-109. 10.4314/wsa.v32i1.5230 558 Ozguven A, Ozcelik Y (2014) Effects of high temperature on physico-mechanical properties of Turkish natural 559 building stones. Eng. Geol 183: 127-136. https://doi.org/10.1016/j.enggeo.2014.10.006 560 Pala A, Erden G (2004) Chemical pretreatment of landfill leachate discharged into municipal biological treatment 561 systems. Environ. Eng. Sci 21(5): 549-557. https://doi.org/10.1089/ees.2004.21.549 562 Pambi R, Musonge P (2016) Application of response surface methodology (RSM) in the treatment of final effluent 563 from the sugar industry using Chitosan. WIT Trans. Ecol. Environ 209: 209-219. 564 Ramezani M, Tremain P, Doroodchi E Moghtaderi B (2017) Determination of carbonation/calcination reaction 565 kinetics of a limestone sorbent in low CO2 partial pressures using TGA experiments. Energy Procedia 114: 259-270. 566 https://doi.org/10.1016/j.egypro.2017.03.1168

567 Ratpukdi T (2012) Decolorization of anaerobically treated palm oil mill wastewater using combined coagulation and 568 vacuum ultraviolet-hydrogen peroxide. Int. J. Chem. Eng 3(5), 333. DOI:10.7763/IJCEA

569 Saeed MO, Azizli K, Isa MH, Bashir MJ (2015) Application of CCD in RSM to obtain optimize treatment of POME 570 using Fenton oxidation process. J. Water Process. Eng 8: e7-e16. https://doi.org/10.1016/j.jwpe.2014.11.001

571 Samudro G, Mangkoedihardjo S (2010) Review on Bod, Cod And BOD/COD Ratio: A Triangle Zone for Toxic, 572 Biodegradable and Stable Levels. Int. J. Acad. Res 2(4).

573 Sani S, Dashti AF, Adnan R (2020) Applications of Fenton oxidation processes for decontamination of palm oil mill 574 effluent: A review. Arab. J. Chem 13(10): 7302-7323. https://doi.org/10.1016/j.arabjc.2020.08.009 575 Wang G, Wen Z, Lou G, Dou R, Li X, Su F (2016) Mathematical modeling and combustion characteristic evaluation 576 of a flue gas recirculation iron ore sintering process. Int. J. Heat Mass Transf 97: 964–974. 577 https://doi.org/10.1016/j.ijheatmasstransfer.2016.02.087

578 Wang J, Yang T, Tian J, Liu W, Jing F, Yao J, Lei Z (2014) Optimization of reaction conditions by RSM and structure 579 characterization of sulfated locust bean gum. Carbohydr. Polym 114: 375-383. 580 https://doi.org/10.1016/j.carbpol.2014.08.035

20

581 Watanabe Y, Kawamoto A, Matsuda K (2002) Particle size distributions in functionally graded materials fabricated 582 by the centrifugal solid-particle method. Composites science and Technology, 62(6), 881-888. 583 https://doi.org/10.1016/S0266-3538(02)00023-4 584 Zhang W, Sun Q, Zhu S, Wang B (2017) Experimental study on mechanical and porous characteristics of limestone 585 affected by high temperature. Appl. Therm. Eng 110: 356-362. https://doi.org/10.1016/j.applthermaleng.2016.08.194 586 Zeng J, Chen S, Wan K, Li J, Hu D, Zhang S, Yu X (2018) Study of biological up-flow roughing filters designed for 587 drinking water pretreatment in rural areas: using ceramic media as filter material. Environ. Technol 41(10): 1256- 588 1265 https://doi.org/10.1080/09593330.2018.1530304

589 Zhong J, Zhang X, Ren Y, Yang J, Tan H, Zhou J (2014) Optimization of Bacillus subtilis cell growth effecting jiean- 590 peptide production in fed batch fermentation using central composite design. Electron. J. Biotechnol 17: 132–136. 591 http://dx.doi.org/10.1016/j.ejbt

592

21

Figures

Figure 1

Horizontal roughing lter schematic (a) and calcination experimental diagram (b). Figure 2

The SEM images of (a) raw limestone; (b) calcinated limestone at 600 °C; and (c) calcinated limestone at 800 °C Figure 3

Actual predicted plot of colour removal using: (a) raw limestone; (b) calcinated limestone at 600 °C; and (c) calcinated limestone at 800 °C Figure 4

Colour removal plot using: (a) raw limestone; (b) calcinated limestone at 600 °C; and (c) calcinated limestone at 800 °C Figure 5

Response surface for colour removal eciency using: (a) raw limestone; (b) calcinated limestone at 600 °C; and (c) calcinated limestone at 800 °C Figure 6

Langmuir Isotherm for Colour adsorption: (a) raw limestone, calcinated limestone at 600 ˚C; (b) and calcinated limestone at 800 ˚C; (c) Figure 7

Freundlich Isotherm for colour adsorption: (a) raw limestone, calcinated limestone at 600 ˚C; (b) and calcinated limestone at 800 ˚C; (c)

Supplementary Files This is a list of supplementary les associated with this preprint. Click to download.

ListofTables.docx