HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 49(1) pp. 23–30 (2021) hjic.mk.uni-pannon.hu DOI: 10.33927/hjic-2021-04 OPTIMISATION OF THE PHYSICAL PROPERTIES OF RICE HUSK ASH IN CERAMIC MATERIALS USING THE RESPONSE SURFACE METHODOLOGY ASOTAH WISDOM*1 , UDOCHUKWU MARK1 , ELAKHAME ZEBERU2 , AND ABRAHAM ADELEKE3 1Department of Metallurgical and Materials Engineering, Federal University of Technology, Owerri, Imo State, NIGERIA 2Federal Institute of Industrial Research Oshodi, Lagos State, NIGERIA 3Department of Materials Science & Engineering, Obafemi Awolowo University, Ile-Ife, NIGERIA Optimisation of the physical properties of rice husk ash (RHA) in ceramic materials was carried out using Response Surface Methodology. The independent variables, namely the firing temperature and residue content, were statistically combined in a Central Composite Design with the effects on water absorption, linear shrinkage, bulk density, apparent porosity and apparent specific gravity determined. Physical and microstructural analyses were carried out to obtain infor- mation on the processes that occurred within the ceramic materials. The results obtained were analysed to determine the optimum physical properties of the ceramic materials within the range investigated. The residue content had a significant influence (at 95% confidence level) on the bulk density, water absorption, apparent porosity and apparent specific gravity but not on the linear shrinkage. The firing temperature had a more significant effect on the linear shrinkage than on the residue content, so that when elevated it contributed to an increase in linear shrinkage. The optimum residue content and firing temperature to enhance physical properties within the range investigated were 5.85% RHA and 1029.64 ◦C, respectively. These optimal conditions are expected to produce a ceramic material with a bulk density, linear shrinkage, apparent porosity, water absorption and apparent specific gravity of 1.64 g/cm3, 0.29%, 0.29 g/cm3, 18.26% and 2.11, respectively with a composite desirability of 100%. Keywords: Ceramics, Rice Husk Ash, Central Composite Design, Linear Shrinkage, Water Absorp- tion 1. Introduction Global concerns remain with regard to how best to man- age the ballooning quantity of waste materials generated on an annual basis. Waste is made up of different mate- rials and could be classified more specifically based on its physical state (solid, liquid, gas), source (agricultural, industrial, mining) or environmental impact (hazardous and non-hazardous waste) [1]. Agricultural (food) waste is common and has been on the rise globally, with an av- erage annual increase of between 5 and 10% [2]. Rice husk ash (RHA) is a residue generated when rice husk is burnt to ashes. Rice husk itself is a form of agri- cultural waste and a by-product of rice cultivation. Rice is a staple food that is widely cultivated and consumed globally. Therefore, waste generated from its production is abundant. The estimated global production from rice paddies is 600 million tonnes with 21 million tonnes of ash generated per year [3]. Open burning of the husk has a deleterious effect on the environment, polluting our air by the release of harmful gases, smoke and dust particles, *Correspondence: wisdomasotah@gmail.com which, when inhaled, can cause respiratory diseases [2]. This has led researchers to focus on managing this waste stream to reduce potential environmental concerns. Rice husk ash has been found to contain a very sig- nificant percentage of silica, namely about 85 − 90% [3, 4]. Given its high silica content, RHA is an excellent potential surrogate to replace quartz in triaxial ceramic bodies [5]. Research has shown that clay-based ceramics can tolerate the incorporation of waste materials in terms of their product formulation. This natural inclination has encouraged researchers to incorporate various industrial and agricultural by-products into ceramic bodies, poten- tially reducing environmental pollution [5–8]. The introduction of RHA as a substitute for quartz in triaxial ceramic bodies has been found to reduce the ther- mal expansion and maturing temperature as well as in- crease the glassy phase while marginally improving their strength. This reduction in the maturing temperature will lead to cost savings in terms of energy consumption, low- ering overall production costs [9]. However, the introduc- tion of this type of residue in compositions used to pro- duce ceramic tiles needs to be further analysed. One way https://doi.org/10.33927/hjic-2021-04 mailto:wisdomasotah@gmail.com 24 WISDOM, MARK, ZEBERU, AND ADELEKE Table 1: Analysed parameters: firing temperature (FT), residue content (RC) – their levels and coded values. Factor Coded Levels −α Low Medium High +α −1.414 −1 0 1 1.414 FT (◦C) 1030 1040 1065 1090 1100 RC (%RHA) 5.85 10 20 30 34.12 of analysing the effects of replacing rice husk ash in tri- axial ceramic bodies is through the statistical design of experiments, which has many advantages over the one- factor-at-a-time method. The factorial design of the experiment allows for the simultaneous investigation of the effects (direct and in- teractive) of two or more independent variables on the dependent variable [10]. Response surface methodology is a powerful statistical tool that allows researchers to de- velop a second-order polynomial model to determine the optimum condition for an improved response [11]. This study has dual objectives. he first is to create a mathematical model that describes the physical prop- erties (dependent variables) as a function of the firing temperature (FT) and the residue content (RC, in unit %RHA), the independent variables. This modelling is based on the central composite design of experiments and regression analysis, an efficient statistical technique, to determine the regression coefficients that ensure the best fit of the predictive polynomial. Data analysis would in- volve evaluating the experimental design using various estimates of the regression matrix and model analysis. The second objective was to determine the optimum con- ditions (FT, RC) that improve its physical properties. 2. Experimental The following raw materials were used in this study: rice husk collected within Epe, a town in Lagos State; as well as feldspar and kaolin sourced in the vicinity of Ogijo and Shagamu in Ogun State. To evaluate the effect of the FT and RC on the physical properties of the mate- rial, Response Surface Methodology combined with Cen- tral Composite Design were used. The Central Compos- ite Design method consisted of two factors at two levels, namely 22, which yielded 4 cube points with 4 axial (star) points and 5 center points. Minitab 19 Statistical Software (Minitab Inc., USA) was used to design the experiment and analyse the results. Table 1 presents the factors as well as their levels and coded values. Initially, the kaolin was beneficiated by being soaked in water for 24 hours and then sieved through the mesh of a 150 µm sieve (particles with diameters of less than 150µm down to submicron and nanoparticles passed through). Particles larger than 150µm (predominantly sand, silt and debris) were extracted. The slurry was left to stand and the excess water decanted. The wet clay was air-dried and milled using a ball mill (Model 87002 Figure 1: Fired ceramic samples. Limongs – France A50 – 43). Feldspar rock was also milled to particle sizes of less than 150µm. The rice husk ash was dried and sieved to particle sizes of less than 150µm. All the sieving was conducted using a BS 410 sieve to eliminate ions and other debris. The dry powders were measured in the right pro- portions according to the experimental design using the coded and uncoded values presented in Table 2 as well as a metro weighing scale with a sensitivity of 0.001 mg. Water was added and the mixture homogenized to make it workable. A quantity of the ceramic composite was put into a metal mould and placed on a press die’s mantel- piece. A minimum pressure of 300 kN/m2 was applied uniaxially upon the sample in the press die. The mould was constantly lubricated to facilitate the easy removal of the composite. The pressed specimens were stored overnight before being dried at 90 ± 100 ◦C for 48 hrs in an oven (Memmert GmbH, Germany). The dried spec- imens were fired in a laboratory electric furnace (Ther- molyne 46200) at a rate of 5◦C/min between 1000 ◦C and 1100 ◦C according to the phase change corresponding to the composition of the mixture. The chosen parameters for the firing cycle on a laboratory scale were adapted from the parameters used in industry. The following physical properties were evaluated: water absorption (WA), bulk density (BD), apparent porosity (AP), apparent specific gravity (AP-SG), and lin- ear shrinkage (LS). The LS was determined from the vari- ation in the linear dimension of the specimen. According to Archimedes’ method, WA and BD were determined using water at room temperature as the immersion fluid. The volume of open pores was determined by the AP, while the AP-SG evaluated how impervious the ceramic is to water. The crystalline phases of the fired samples were anal- ysed by the X-ray diffraction (XRD) technique using a Rigaku MiniFlex Benchtop X-ray Diffractometer. The fracture surfaces of the samples were morphologically characterised by Scanning Electron Microscopy (SEM) using a Phenom ProX SEM and Energy Dispersive X-ray (EDX) analysis determined the elemental composition of the samples. The fired samples are presented in Fig. 1. Hungarian Journal of Industry and Chemistry OPTIMISATION OF THE PHYSICAL PROPERTIES OF RICE HUSK ASH IN CERAMIC MATERIALS 25 3. Results and Discussion Table 3 describes the elemental composition of the ce- ramic samples. By analysing the morphology and ele- mental composition of the various structures, information on the processes that occurred within the ceramic body when subjected to independent variables and on the na- ture of the minerals was obtained. EDX measurements showed that the ceramic samples were mainly composed of primary metals such as Na, K, Ca, Mg, Fe, Ag and Ti as well as non-metals like P and S – the contents of which vary between samples and even within individual samples depending on the composition of the investigated area. The high concentrations of Si and Al are related to the raw materials, that is, rice husk ash and kaolin. Silica, an oxide of Si, is commonly found in several mineralogical clay-rich and clay-deficient phases such as kaolin, mica, feldspar and quartz. Alumina, an oxide of Al, is usu- ally associated with some of these mineralogical phases. Potassium (K2O) and sodium (Na2O) oxides generally originate from feldspars, hence the presence of P and Na in the EDX data. The low content of Fe is essential to pro- duce white ceramics since it can lead to the development of a reddish colour during sintering [6, 12]. Values of the dependent variables (observed and pre- dicted), namely LS, WA, AP, AP-SG, and BD, from the ceramic samples are listed in Table 4. All values were cal- culated using the experimental planning matrix produced by Minitab 19 software. The results of the regression analysis, namely the values of the polynomial equation for the dependent variables, are shown in Eqs. 1–5 and the corresponding coefficients of the regression analysis, that is, R-squared (R2) and adjusted R-squared (R2(adj.)), are presented in Table 5. BD(g/cm 3 ) = −28.92 + 0.015 RC + 0.0572 FT− 0.000337 RC2 − 0.000027 FT2 (1) WA(%) = −25.44 + 0.0188 RC + 0.04789 FT− 0.000295 RC2 − 0.000022 FT2 − 0.000006 RC×FT (2) APSG = −388.1 + 0.233 RC + 0.730 FT− 0.003957 RC2 − 0.000341 FT2 − 0.000062 RC×FT (3) AP(g/cm 3 ) = −52.50 + 0.0348 RC + 0.0987 FT −0.000596 RC2−0.000046 FT2−0.000009 RC×FT (4) LS = −32.7 + 0.0420 RC + 0.0600 FT+ 0.000104 RC2 − 0.000027 FT2 − 0.000042 RC×FT (5) Table 6 shows the regression coefficients obtained when the observed values of LS in Table 4 were fitted to the quadratic model. The statistical significance of each independent variable on this fitting was determined us- ing the P Value, F Statistic on its linear and quadratic terms as well as the interaction between regression co- efficients. The smaller the P Value, the more significant the corresponding regression coefficient; P Value < 0.05 indicated that the regression coefficient was statistically significant at 95% confidence interval. As can be seen in Table 6, only the FT exhibited statistical significance over the range investigated. The main regression coefficients (linear and quadratic) for the RC did not show any signif- icance. Therefore, the FT had a more significant effect on changing the LS than the RC. The LS determines the degree of compaction and den- sification by analysing the linear variation with regard to the length of the sample after firing, which is essential for Table 2: Parameters with coded and uncoded values. Run/Sample Coded Form of Independent Variables Uncoded Form of Independent Variables X1 X2 X1 X2 1 0 0 1065 20 2 0 −1.414 1065 5.8579 3 1 −1 1090 10 4 −1.414 0 1030 20 5 0 0 1065 20 6 1 1 1090 30 7 0 0 1065 20 8 1.414 0 1100 20 9 0 1.414 1065 34.1421 10 0 0 1065 20 11 −1 −1 1040 10 12 0 0 1065 20 13 −1 1 1040 30 X1 = firing temperature (celsius); X2 = residue content (%RHA) 49(1) pp. 23–30 (2021) 26 WISDOM, MARK, ZEBERU, AND ADELEKE Table 3: EDX data of ceramic samples. Element (Atomic Concentration %) Sample 9 11 4 2 13 3 6 8 7 Si 62.15 57.98 62.44 59.97 58.85 56.15 68.07 58.03 54.70 Al 21.66 27.71 17.12 26.02 24.19 29.44 18.62 28.46 29.07 Fe 3.20 2.78 3.15 2.9 4.51 3.43 1.99 3.32 3.52 K 2.48 1.65 2.72 1.57 2.33 1.75 2.15 1.48 2.19 Na 0.53 0.58 1.43 0.67 0.55 0.54 0.31 0.79 0.39 Table 4: Observed and predicted values of the linear shrinkage (LS), water absorption (WA), bulk density (BD), AP-SG (AP-SG) and apparent porosity (AP). LS BD (g/cm3) AP AP-SG WA Observed Predicted Observed Predicted Observed Predicted Observed Predicted Observed Predicted 0.086 0.086 0.086 0.086 0.499 0.499 3.525 3.525 0.283 0.283 0.054 0.087 1.687 1.675 0.371 0.361 2.684 2.605 0.22 0.216 0.135 0.962 1.683 1.701 0.384 1.403 2.732 2.868 0.228 0.236 0.026 0.023 1.72 1.726 0.421 0.436 2.968 3.059 0.245 0.253 0.086 0.086 0.086 0.086 0.499 0.499 3.525 3.525 0.283 0.283 0.114 0.103 1.722 1.731 0.421 0.425 2.972 3.018 0.244 0.245 0.086 0.086 0.086 0.086 0.499 0.499 3.525 3.525 0.283 0.283 0.051 0.081 1.752 1.735 0.462 0.446 3.259 3.137 0.264 0.257 0.133 0.127 1.717 1.718 0.389 0.399 2.814 2.862 0.227 0.232 0.086 0.086 0.086 0.086 0.499 0.499 3.525 3.525 0.283 0.283 0.05 0.034 1.694 1.696 0.395 0.391 2.798 2.782 0.233 0.231 0.086 0.086 0.086 0.086 0.499 0.499 3.525 3.525 0.283 0.283 0.071 0.083 1.733 1.726 0.441 0.423 3.1 2.995 0.255 0.246 Table 5: Relevant statistics for the analysis of variance of the mathematical models that describe the variables BD, AP, AP-SG, WA and LS. Variable Model F Statistic P Value R2 R2(adj.) BD Quadratic 15.78 0.001 0.92 0.86 AP Quadratic 29.38 0 0.95 0.92 AP-SG Quadratic 29.58 0 0.95 0.92 WA Quadratic 30.06 0 0.96 0.92 LS Quadratic 2.95 0.096 0.68 0.45 controlling the dimensions of the finished ceramic prod- uct [6]. The model yielded a positive value (0.06) for the regression coefficient of the FT (Eq. 5), suggesting that the FT is directly dependent on the LS. It is expected that as the temperature rises, the degree of LS increases. As the temperature rises during firing, a more significant amount of the liquid phase is produced and its viscos- ity decreases. This facilitates the elimination of pores, thereby increasing the extent of LS. The adequacy of the model was determined by the correlation coefficient R, which was calculated as 0.68, moreover, the high P Value and low coefficient of determination are indicative of the model’s considerable degree of variability (Table 5). The data points in the normal percentage distribution curves shown in Fig. 2 lie close to the line indicating no signifi- cant deviation from normality nor any need for response Table 6: Statistics for the analysis of the variance of the model that describes linear shrinkage (LS). Model Term Regression Coeff. P Value F Statistic Intercept −32.7 - - RC 0.042 0.144 2.7 FT 0.06 0.047 5.78 RC2 0.000104 0.292 1.3 FT2 −0.000027 0.102 3.54 RC×FT −0.000042 0.411 0.76 transformation. The residual versus fitted plot should re- semble a scatter plot as shown in Fig. 3. If the plots do not present a random scattering of data as represented in Fig. 2, then any trends will indicate flaws in the assumptions [6, 11]. Regression coefficients obtained when the observed values of BD, WA, AP and AP-SG were fitted in the quadratic model are shown in Tables 7–10, respectively. The main independent variable, namely the FT, did not exhibit any significance over the range investigated. Among the interacting regression coefficients shown in Tables 7–10, interactions between the RC and FT were not found to be statistically significant. The WA capacity is directly related to the type of mi- crostructure that developed while the samples were sin- Hungarian Journal of Industry and Chemistry OPTIMISATION OF THE PHYSICAL PROPERTIES OF RICE HUSK ASH IN CERAMIC MATERIALS 27 Figure 2: Normal probability plot of the residual plot for linear shrinkage (LS). Figure 3: Residual plot against the fitted plot for linear shrinkage (LS). Figure 4: Normal probability plot of the standardized residual of the bulk density (BD). tering and its level of porosity. This is regarded as a sim- ple way to predict the technological properties of the final products [6, 13]. Within the range investigated, the RC was shown to be more dependent on the WA, BD, AP and AP-SG, which is in good agreement with the model prediction. The adequacy of this prediction is confirmed by the coefficient of determination, R2, values of BD, AP, AP- SG and WA which were calculated to be 0.92, 0.95, 0.95 and 0.96, respectively (Table 5). The high values of R2 given as 0.86, 0.92, 0.92 and 0.92 (Table 5) indicate that the adjusted models do not present a considerable degree of variability. This is also evident on the normal percent- Table 7: Statistics for analysis of variance of the model that describes the bulk density (BD). Model Term Regression Coeff. P Value F Statistic Intercept −28.92 - - RC 0.015 0.008 13.14 FT 0.0572 0.506 0.49 RC2 −0.000337 0 57.44 FT2 −0.000027 0.007 14.15 RC×FT 0 1 0 Table 8: Statistics for analysis of variance of the model that describes water absorption (WA). Model Term Regression Coeff. P Value F Statistic Intercept −25.44 - - RC 0.0188 0.043 6.08 FT 0.04789 0.593 0.31 RC2 −0.000295 0 128.25 FT2 −0.000022 0.001 28.88 RC×FT −0.000006 0.676 0.19 Table 9: Statistics for analysis of variance of the model that describes apparent specific gravity (AP-SG). Model Term Regression Coeff. P Value F Statistic Intercept −388.1 - - RC 0.233 0.031 7.23 FT 0.73 0.447 0.65 RC2 −0.003957 0 119.58 FT2 −0.000341 0.001 34.79 RC×FT 0.000062 0.755 0.11 Table 10: Statistics for analysis of variance of the model that describes apparent porosity (AP). Model Term Regression Coeff. P Value F Statistic Intercept −52.5 - - RC 0.0348 0.03 7.33 FT 0.0987 0.522 0.45 RC2 −0.000596 0 123.29 FT2 −0.000046 0.001 28.91 RC×FT −0.000009 0.76 0.1 age distribution curve and the residuals versus fits plot (Figs. 4 and 5, respectively), which show that the data set is normally distributed and falls within (−2,2) for BD. Furthermore, similar plots are produced for WA, AP and AP-SG with a statistical significance of 95% and less than 5% as outliers observed. Fig. 6 shows the XRD patterns of the ceramic samples with RCs of 5.85% and 34.14%, that is, the lowest and highest RCs studied in the present work. According to the XRD patterns, all the ceramic samples consisted of simi- lar minerals, namely kaolinite [Al2Si2O5(OH)4], mont- morillonite [NaMgAlSiO2(OH)H2O], suessite [Fe3Si], and illite [KAl2Si3AlO10(OH)2]. With RCs of 34.14%, 49(1) pp. 23–30 (2021) 28 WISDOM, MARK, ZEBERU, AND ADELEKE Figure 5: Plot of the standardized residual against the fit- ted values of the bulk density (BD). (a) 5.86% RHA (b) 34.14% RHA Figure 6: XRD patterns of samples against their RCs (%RHA). orthoclase and synthesised quartz were present. Quartz exhibited the highest peak, which confirms the high con- centration of Si according to the EDX data. Little or no difference was detected between the intensities of the peaks. All ceramic samples contained clay minerals; quartz, kaolinite, illite and albite are the mineral phases of the raw materials used. The micrograph (Fig. 7) shows that the ceramic ma- trix of samples sintered at higher temperatures (1090 ◦C (a) 1030 ◦C (b) 1040 ◦C (c) 1065 ◦C (d) 1090 ◦C (e) 1100 ◦C Figure 7: SEM micrograph of fracture surfaces of ceramic samples against RC. and 1100 ◦C) was more finely dispersed and densely packed. A reduction in porosity was observed com- pared to samples sintered at lower temperatures (1030 ◦C, 1040 ◦C and 1065 ◦C) which were more porous and ir- regular as at higher sintering temperatures. As observed, increase in FT led to an increase in vitreous phase, facil- itating the elimination of pores and an increase in densi- fication. This hypothesis is in good agreement with other research [6, 12, 13]. 3.1 Response optimisation The optimised responses with regard to the physical prop- erties of ceramics in which rice husk ash is used as a sil- ica precursor and their criteria are presented in Table 11, moreover, the desired quality is shown in Fig. 8. From the optimisation results and plot presented in Fig. 8, the opti- mum RC and FT to achieve the desirable physical proper- ties are 5.85% and 1029.64 ◦C, respectively. These opti- mal conditions are expected to produce a ceramic product with a BD of 1.64 g/cm3, LS of 0.29, AP of 0.29 g/cm3, WA of 18.26%, and AP-SG of 2.11. The optimal combination of the factors shown in Fig. 8 effectively maximises the LS as well as minimises the Hungarian Journal of Industry and Chemistry OPTIMISATION OF THE PHYSICAL PROPERTIES OF RICE HUSK ASH IN CERAMIC MATERIALS 29 Figure 8: Optimisation plot of the variables. BD, WA, AP-SG, and AP. The composite desirability of 100% showed how the settings optimise all five quality responses when they are considered as objective response functions simultaneously [11]. 4. Conclusions The physical properties of the ceramic samples using the response surface methodology were optimised. The mod- elling was based on central composite design and it was Table 11: Criteria and results of the optimisation of the process conditions. Response Goal Response Desirability Linear Shrinkage (%) Minimum 0.29 100% Bulk Density (g/cm3) Minimum 1.64 100% Apparent Porosity (g/cm3) Minimum 0.29 100% Apparent Specific Gravity Minimum 2.11 100% Water Absorption (%) Minimum 18.26 100% Composite desirability – 100% possible to obtain significant mathematical models which correlate the factors FT and RC with the dependent vari- ables LS, WA, AP, AP-SG, and BD. The FT had a statis- tically significant effect on the LS so that raising the tem- perature enhanced the degree of shrinkage. 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Ceramic Soc., 2007, 27(16), 4649–4655 DOI: 10.1016/j.jeurceramsoc.2007.02.217 Hungarian Journal of Industry and Chemistry https://doi.org/10.1155/2015/695061 https://doi.org/10.17577/ijertv7is070117 https://doi.org/10.1016/j.jeurceramsoc.2017.07.018 https://doi.org/10.1016/j.jeurceramsoc.2007.02.217 Introduction Experimental Results and Discussion Response optimisation Conclusions