Optimization of alkali treatment of rice husk ash for producing an alternative alkali activator via response surface methodology

Abstract

The geopolymer field is flourishing, as researchers work to lessen the reliance on conventional cements as building materials. However, high price and energy-intensity of  commercial alkaline activators hamper the usage of geopolymers. Bio-based waste materials are efficacious in mitigating the environmental concerns for alkaline activators, but the synthesis process of this alternative alkali activator requires further research, especially on the optimization for RHA dissolution parameters. The objective of this study was to optimize the alkali treatment process on rice husk ash (RHA) using response surface methodology. The effect of five factors, i.e., RHA quantity, sodium hydroxide concentration, stirring speed, heating temperature, and heating time on two output responses, i.e., activator alkalinity and silica composition of alternative activators, were investigated. Central composite design provided the combination for an optimum alternative activator, which was 162.5 g of RHA quantity, 3M of NaOH concentration, 350 RPM of stirring speed, 75 °C of heating temperature, and 3.5 h of heating time with R² > 0.91 of reduced quadratic models. The activator alkalinity (pH) and silica composition (wt%) for alkali treatment processed activator were 12.1 and 20.3, respectively.

Full Article

Optimization of Alkali Treatment of Rice Husk Ash for Producing an Alternative Alkali Activator via Response Surface Methodology

Herry Prabowo  ,^a,b,* Idawati binti Ismail  ,^a,* Md Rezaur Rahman  ,^a,* Azizah binti Abdul Nassir  ,^a Mohammad Abdul Mannan  , ^c Maha G. Batterjee  ,^d K. S. Alblawi  ,^e Jehan Y. Al-Humaidi  ,^f and Mohammed Muzibur Rahman  ,^g

The geopolymer field is flourishing, as researchers work to lessen the reliance on conventional cements as building materials. However, high price and energy-intensity of commercial alkaline activators hamper the usage of geopolymers. Bio-based waste materials are efficacious in mitigating the environmental concerns for alkaline activators, but the synthesis process of this alternative alkali activator requires further research, especially on the optimization for RHA dissolution parameters. The objective of this study was to optimize the alkali treatment process on rice husk ash (RHA) using response surface methodology. The effect of five factors, i.e., RHA quantity, sodium hydroxide concentration, stirring speed, heating temperature, and heating time on two output responses, i.e., activator alkalinity and silica composition of alternative activators, were investigated. Central composite design provided the combination for an optimum alternative activator, which was 162.5 g of RHA quantity, 3M of NaOH concentration, 350 RPM of stirring speed, 75 °C of heating temperature, and 3.5 h of heating time with R² > 0.91 of reduced quadratic models. The activator alkalinity (pH) and silica composition (wt%) for alkali treatment processed activator were 12.1 and 20.3 respectively.

DOI: 10.15376/biores.21.3.7516-7540

Keywords: Alkali treatment; Alternative activator; Rice husk ash; Response surface methodology

Contact information: a: Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia; b: Department of Architectural Engineering, Politeknik Negeri Pontianak, Jl. Jenderal Ahmad Yani, Kota Pontianak, Kalimantan Barat 78124, Indonesia; c: School of Civil Engineering, Faculty of Engineering, Papua New Guinea University of Technology, LAE411, Morobe Province, Papua New Guinea; d: Department of Chemistry, College of Sciences & Arts, King Abdulaziz University, Rabigh, Saudi Arabia; e: Chemistry Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia; f: Department of Chemistry, College of Science, Princess Nourah bint Abdulrahman University, P.O. BOX 84428, Riyadh 11671, Saudi Arabia; g: Center of Excellence for Advanced Materials Research & Chemistry Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;

* Corresponding author: iidawati@unimas.my; rmrezaur@unimas.my; herryprabowo@polnep.ac.id

Graphical Abstract

INTRODUCTION

Geopolymer concrete has emerged as a promising alternative construction material compared to Ordinary Portland Cement (OPC) (Farooq et al. 2026). The manufacturing process of OPC is known to have significant ecological consequences, primarily due to the substantial emission of greenhouse gases (GHG) (Neupane 2022). On the contrary, geopolymers are estimated to reduce global warming potential by 7% to 50% compared to OPC (Cong et al. 2024). Despite geopolymers having a lower GHG output, their utilization still leads to environmental issues due to chemical alkali activators. Alkali activator is one of the main constituent materials of geopolymer that are costly and energy demanding (Ricciotti et al. 2025).

Researchers have studied the feasibility of employing silica-rich resources from agricultural waste to synthesize alternative alkali activators. Rice husk is one of the popular agro-wastes that contribute significantly to global waste generation (150 million tons annually) (Kordi et al. 2024). Traditionally, rice husk was disposed of through practices such as burning, resulting rice husk ash (RHA) (Al-Alwan et al. 2024). This RHA is particularly useful because they contain soluble silicates and alkali metal ions, which are essential for the polymerization process in geopolymer concrete (Barragán-Ramírez et al. 2024). Previous studies showed that the synthesis of alternative sodium silicates from these resources is viable and yields an activator comparable to commercial ones (Alnahhal et al. 2021). Despite the promising potential to develop waste-derived alkaline activators, the properties of activators synthesized from RHA have not been fully understood in terms of the effects of the extraction process on the alkalinity and silica composition.

Various studies have shown promising results on silicate extraction from RHA (Rajan and Kathirvel 2021; Nzereogu et al. 2023; Trevizan Pelissaro et al. 2023). The dissolution of RHA in NaOH solution, which is an alkali treatment process, is a well-known extraction technique. The suspensions of RHA and NaOH were exposed to heating and stirring to induce the dissolution of silica from RHA into the NaOH solution. Parameters influencing the efficiency of this alkali treatment process have been investigated. However, the optimization for RHA dissolution (i.e., process temperature and duration, NaOH solution concentration, RHA quantity, and stirring speed) have not yet been confirmed.

Response surface methodology (RSM) is widely used to provide optimized condition by analyzing the effects of different independent variables (Veza et al. 2023). Many authors have claimed the suitability of using central composite design (CCD) of RSM (RSM-CCD) to optimize the independent variables in their research article (Pattanayak et al. 2024; Yao et al. 2024; Maaze et al. 2025). RSM-CCD is the choice to optimize the process parameters in this study as well as replace the conventional time-consuming methods. It efficiently models complex, non-linear relationships with a reduced number of experimental runs compared to a full factorial design. When dealing with 5 factors, CCD provides a robust, sequential, and rotatable framework that helps find the optimal point for production (Ghelich et al. 2019; Alawadhi et al. 2021).

The synthesis of alternative alkali activators has been the subject of extensive study. Despite this, there are very few studies on the optimization of alternative alkali activator parameters using RSM (Girish et al. 2026) based on the principles of design of experiments (DoE). Furthermore, the present research investigates simultaneously 5 parameters of alkali treatment, i.e., RHA quantity, NaOH concentration, stirring speed, heating temperature, and heating time. Among those independent variables, stirring speed is rarely investigated. The aim was to determine the optimum values of those 5 factors for alternative activators with maximum alkalinity and silica composition. Maximum alkalinity provides the necessary hydroxide ions (OH−) required to efficiently break down the siloxane bonds (Si-O-Si) (Wan et al. 2025). Meanwhile, high silica composition is crucial for providing the active silicate species needed to form the rigid geopolymer binding gel (Gao et al. 2022; Bezerra et al. 2023) during subsequent concrete applications. The findings contribute to the development of sustainable alternatives that potentially lower the overall environmental impact.

EXPERIMENTAL

Materials

Rice husk was collected from Parit Gadoh, a rice mill located at Kubu Raya, West Kalimantan, Indonesia. The as-received rice husk was burnt in a drum kiln without any pre-treatment. It was burnt at 500 °C for 12 h (Nair et al. 2008; Albert Daud et al. 2020) to become rice husk ash (RHA). Burning duration and temperatures are intended for maximizing the amorphous content of the RHA, as higher temperature and shorter times would induce the formation of crystalline SiO₂ (Hamidu et al. 2025). After burning, the RHA was placed in stainless steel tray and cooled down at ambient temperature for 24 h. Then, the RHA was sieved to pass through a 0.3 mm sieve. Next, the RHA was ground to achieve a mean particle size (d₅₀) of 31.38 μm, as per literature suggestions (Kamseu et al. 2017; Kim et al. 2025).

This study used RHA as a silica source to produce geopolymer’s alternative alkali activator (AA) solutions via alkali treatment. Laboratory grade sodium hydroxide (NaOH) pellets (98.0 % purity, Sigma-Aldrich, Germany) were used to prepare the solution for AA synthesis. The NaOH solutions based on various molarity were prepared with distilled water.

Response Surface Methodology (RSM)

RSM is an optimization tool based on statistical and mathematical techniques for optimizing processes and simultaneously establishing a relationship between the factors and responses. The Stat-Ease 360 version 25.0.6 (Stat-Ease, Inc., Minneapolis, MN) was used to design the experiment, examine and interpret the output model, and perform verification. Optimization for maximum alternative AA alkalinity and silica composition was employed using a CCD model. CCD was selected because it is highly efficient for fitting second-order surface models, allowing for the comprehensive evaluation of quadratic and interaction effects among multiple variables with a reduced number of experimental runs (Ghelich et al. 2019). Five numeric factors are included in the design: RHA quantity (A) in gram, NaOH concentration (B) in M, stirring speed (C) in rotation per minute (RPM), heating temperature (D) in °C, and heating time (E) in hour. The design generated a total of 50 experimental runs, including 32 factorial points (–1, +1), 10 axial points (–α, +α), and 8 center points (0) to properly evaluate pure experimental error. The independent variables were evaluated at five coded levels (–α, –1, 0, +1, +α), and all runs were fully randomized. The CCD model of factors in the form of coded and actual values is listed in Table 1.

The design was represented by a second-order polynomial regression model, as follows:

 (1)

where Y represents the responses (activator alkalinity and silica composition). The factors X_i, β₀, β_i, β_ii, β_ij, and ε in Eq. 1 are the process factors, offset coefficient, linear coefficients, quadratic coefficients, interaction coefficients, and residuals associated with the experiments, respectively.

Analyses include factors significantly affecting the responses using the analysis of variance (ANOVA) and coefficient of determination (R²), 3D RSM plots, and contour plots based on the interactions between parameters. The ANOVA results show P-values and model F-values. The P-values less than 0.05 indicate model terms are significant, and the higher F-values implied model significance and less chance that the values could be due to noise.

Most importantly, the software suggests optimum factor conditions for specific product yields, reflecting the sensitivity of the variable factors. The R² values (close to 1), adjusted R², and predicted R² values were analyzed to optimize process parameters. The difference between predicted and adjusted R² is supposed to be less than 0.2 in well-fit models. Also, a good precision value higher than 4 reflects a desirable signal-to-noise ratio. The diagnostic tools present in the software helped ensure the model helps predict the target responses.

Table 2 lists the values of the studied factors and results from the experiment. The five factors, RHA quantity, NaOH concentration, stirring speed, heating temperature, and heating time have lower and higher value inputs ranging from 75 to 250 g, 0.5 to 5 M, 100 to 600 RPM, 50 to 90 °C, and 1 to 5 h, respectively. During ANOVA model analysis, the values in Table 2 were adjusted to accommodate the reduction of insignificant terms and noise in the model. It was described in the effect of variables subsection. RHA quantity ranges were selected to establish a solid-to-liquid ratio around 0.5 to 0.2 that provides sufficient silica (Luukkonen et al. 2018; Font et al. 2020; Alnahhal et al. 2024). The NaOH concentrations were fixed based on the necessary alkalinity to break down siloxane bonds efficiently (He et al. 2013; Tong et al. 2018). Meanwhile, heating temperature and heating time inputs were obtained from previous studies which indicated that elevated heat and extended durations enhance dissolution up to a specific threshold (Tong et al. 2018; Font et al. 2020; Kallamalayil Nassar and Kathirvel 2023; Alnahhal et al. 2024). The stirring speed range was specifically selected to ensure adequate suspension of RHA particles.

Table 1. Variables and Coded Levels

Synthesis of Alternative Activator

An alkali treatment process was used to synthesize alternative AA by dissolving rice husk ash (RHA) powder in a NaOH solution under continuous magnetic stirring while maintaining heat. The alkali treatment was processed using magnetic stirring hot plate Corning PC-620D, NY, USA. This process was commonly used by previous researchers to produce alternative alkali activator solutions (Tchakouté et al. 2016; Alnahhal et al. 2024).

The NaOH pellets at first were mixed with 500 mL distilled water in a glass beaker and then cooled to ambient temperature. RHA was then added to the sodium hydroxide solution in a 1000 mL glass beaker, which was placed on a hot plate magnetic stirrer. The solid to liquid ratio ranged from 0.5 to 0.2. The alkali treatment was initiated, which involves continuous stirring of the prepared mixture at a previously mentioned temperature and duration ranges with the aid of a hot plate magnetic stirrer. During the treatment, the beaker was covered with a laboratory film to minimize evaporative losses. The temperature of the mixture was monitored using a thermometer submerged directly in the suspension, and was maintained at the target value with a tolerance of ± 2 °C.

The next step was the filtration process after allowing the mixture to cool to room temperature. The filtration was performed under atmospheric pressure using Whatman No. 42 filter paper (pore size 2.5 μm) to separate solid particles from liquid solutions. The primary objective of the filtration was to remove any undissolved particles that might have remained in the solution after synthesis. Once the filtration process was completed, the resulting solution was carefully transferred into bottles, and the final volume of the recovered filtrate was recorded for subsequent analyses.

Table 2. Design of Experiment and Results of the Central Composite Design

Test Methods to Characterize Materials

Particle size analyzer

Particle size distribution (PSD) of ground RHA was determined with a laser diffraction particle size analyzer using CILAS 1090 equipment (Ariane group, France). The PSD was measured by using RHA suspension in water and detecting particle sizes by the laser-beam reflection, recalculating the distribution of the particles through Fourier transform methods. The RHA suspension in water was prepared to obtain obscuration around 15% and ultrasonicated for 5 minutes prior to measurement to prevent particle agglomeration.

Scanning electron microscopy

This study used scanning electron microscopic (SEM) analysis to investigate the particle shape and surface texture of RHA. SEM characterization was carried out using the SEM Hitachi TM4000 model, Japan. SEM was operating at an accelerating voltage of 15 kV. Prior to imaging, the RHA samples were sputter-coated with a thin layer of gold to enhance electrical conductivity and prevent charge accumulation.

Energy dispersive X-ray spectroscopy

The elemental composition of RHA was examined using the energy dispersive X-ray spectroscopy (EDX). EDX characterization was carried out using the Bruker EDX (Bruker, Germany) with ESPRIT software. To determine the silica composition (wt%) of the alternative activator, 5 mL of the synthesized solution was dried in an open container in a closed drying oven at 60 °C for 7 d. Instead of prolonged ambient drying, a fixed drying protocol was applied by storing the samples in a desiccator for 14 d to ensure consistent moisture removal. EDX was selected for this purpose because it provides a reliable semi-quantitative elemental profile of the dried solid residue. The reported silica composition for each run represents the average of three replicate EDX spot analyses.

pH Measurement

The alkalinity of the synthesized alternative alkali activator was quantified by pH measurement. The pH was determined with a Benchtop Fisher Scientific Accumet AB150 pH meter. Prior to use, the pH meter was calibrated using standard buffer solutions at pH 4.01, 7.00, and 10.01. All measurements were conducted at a controlled room temperature of 25 ± 1 °C, and the reported alkalinity for each experimental run was based on the average of three replicate measurements to ensure accuracy.

RESULTS AND DISCUSSION

Characterization of Materials

Particle size analyzer

The particle size vs. cumulative passing graph is shown in Fig. 1. The cumulative passing curve showed a median particle diameter (D₅₀) of 31.4 μm. Meanwhile, D₁₀ and D₉₀ were obtained as 5.97 μm and 63.3 μm respectively. Literature suggested RHA was milled to attain average particle size in the range 5 to 40 μm before it was dissolved in an NaOH solution (Adesanya et al. 2021). This approach is based on the rationale that particle size reduction increases the available specific surface area, thereby facilitating dissolution and improving reaction kinetics with NaOH.

Finer RHA particles have a higher specific surface area, which is crucial for enhancing the reactivity of the silica content. This is because a larger surface area allows for more interaction sites between the silica and the NaOH solution (Zulfiqar et al. 2015). Moreover, the reactivity of the amorphous silica in RHA is directly proportional to its specific surface area (Endale et al. 2023; Hamidu et al. 2025).

Scanning electron microscopy

SEM images, as shown in Fig. 2., revealed that RHA consisted of irregular and angular shaped particles. This morphology is frequently observed in amorphous silica found in RHA aligns with previous studies. Meanwhile, SEM alone cannot definitively confirm the non-crystalline nature of the silica content (Sompech et al. 2016; Sarma et al. 2024).

Fig. 1. Rice husk ash particle size distribution

Fig. 2. SEM of rice husk ash

Energy dispersive X-ray spectroscopy

Figure 3 presents the energy dispersive X-ray (EDX) spectrum and the corresponding elemental composition of the synthesized RHA. The spectrum reveals a distinct elemental profile characterized by sharp characteristic peaks for Silicon (Si) and Oxygen (O), alongside a significant peak for Carbon (C). These peaks indicate the elemental presence of silicon and oxygen, which is consistent with silica as the primary inorganic constituent, coexisting with carbon. However, EDX alone cannot definitively confirm exact chemical phases such as SiO₂ or establish the presence of a residual organic carbon matrix with certainty.

Fig. 3. EDX of rice husk ash

Effect of Variables on Alternative Alkaline Activator Alkalinity

Mathematical model for predicting alkalinity of alternative activator

RSM was used to develop mathematical models from the experimental results and predicted responses, where the output is influenced by several variables. This mathematical model is derived from the relationship between the input and output parameters. The factors RHA quantity, NaOH concentration, stirring speed, heating temperature, and heating time are represented as A, B, C, D and E, respectively (Table 1).

The investigated factors and their corresponding experimental results are presented in Table 2. Initially, the experimental matrix was generated using a Central Composite Design (CCD), comprising 50 randomized runs: 32 factorial points, 10 axial points, and 8 center points. Upon recording the measured responses for each run, a full quadratic regression model and Analysis of Variance (ANOVA) were formulated using the Stat-Ease software. Because the initial full model contained statistically insignificant terms that adversely affected the fit statistics, a term-reduction procedure utilizing backward elimination was applied to improve the Predicted R², Adjusted R², and Adequate Precision values. Furthermore, repeated identical response values were incorporated to enhance the model’s predictive power and interpretability. Later, the predictive accuracy and reliability of the reduced model were verified by conducting confirmatory experimental runs.

The activator pH ranged from 11.3 to 13.2. The highest pH value was 13.2 at of 75 g RHA, 3M NaOH concentration, 400 RPM stirring speed, 80 °C heating temperature, and 1.0 h heating time. Meanwhile, the lowest pH was 11.3 at 75 g RHA, 0.5M NaOH concentration, 400 RPM stirring speed, 80 °C heating temperature, and 4-h heating time.

The best-fitting model was found to be a reduced quadratic model. A term-reduction procedure was performed using backward elimination to remove statistically insignificant interaction terms (P > 0.05) from the full model. These interaction terms were excluded from the final reduced model to simplify the equation and improve its predictive capability without modeling noise. Equation 2 represents the mathematical model (in terms of actual factors) for predicting alternative activator alkalinity:

R1=+13.5499+0.0099A+1.7085B−0.0032C−0.0084D−1.9180E−5.0653e-05A²

−0.3071B²+4.3466e-06C² +0.4434E² (2)

The + sign in the equation denotes the synergistic effects of variables, and the antagonistic effects are indicated by the − sign in the equation (Niu et al. 2022). While the stirring speed (C), heating temperature (D), and heating time (E) had adverse effects on the activator alkalinity, the RHA quantity (A) and NaOH concentration (B) had a positive effect on the yield. Furthermore, the second-order term A² and B² posed unfavorable effects, while C² and E² were favorable.

ANOVA for the prediction quadratic model

Table 3 shows ANOVA results based on the RSM study produced by Stat-Ease 360 version 25.0.6. The P-value for the quadratic model of alternative activator alkalinity was significant (P < 0.0001), which shows that the results were significant, and the model was excellent for predicting the response (alternative activator pH). Furthermore, the model F-value of 109.6003 implies the model was significant, since there was only a 0.01% chance that an F-value this large could occur due to noise. In this case, A, B, D, E, A², B², C², and E² were significant model terms (note: A is RHA quantity; B is NaOH concentration; C is stirring speed; D is heating temperature; E is heating time). Therefore, the hypothesis of this study—the significant effect of factors—was confirmed. Values greater than 0.1000 indicate that certain of the model terms were not significant. The insignificant term, C, had a P-value of 0.3213. Statistical insignificance indicates that the study lacked sufficient evidence to prove that the variable being tested had any effect. Most importantly, the stirring speed (C) did not have significance in the model. In this model, the Stat-Ease software did not show the lack of fit value after backward elimination was used. It reduced the number of model terms, which changed the sum of squares. Although without lack of fit statistics, the model still can be evaluated by utilizing several parameters such as R², Adjusted R², Predicted R², and Adequate Precision. Moreover, predicted vs. actual plot and residual vs. predicted plot also can be used to validate the model (Chen and Chen 2025).

Table 3. ANOVA for Quadratic Model of Activator Alkalinity

The R² of 0.9610 achieved is indicative that the model produced was valid and closely fitted to the experimental data. This result was similar to one reported by Adamu et al. (2019). The predicted R² of 0.9497 was in reasonable agreement with the adjusted R² of 0.9523. The difference between predicted R² and adjusted R² was found to be 0.0026, which is within the required limit (not more than 0.2) for a good model. R² can improve by addition of more independent variables therefore goodness of fit cannot be concluded from value of R² alone. Conversely adjusted R² only increases with variables that have strong correlation with the dependent variable otherwise it decreases. On the other hand, predicted R² is an indication of how well the model developed produces responses for new observation and whether the model is complicated or not. Therefore, adjusted R² and predicted R² are more desirable properties for goodness of fit in statistics. In this model, R² was close to 1; thus, the data and the models produced were very significant.

The mean pH value of alternative activators was 12.7. The standard deviation in activator alkalinity was ± 0.0963, which is acceptable, while the coefficient of variation (CV %) in activator alkalinity was 0.757%, which is below the acceptable level of 10%, reflecting a good model. In addition, the signal-to-noise ratio (S/N ratio) was measured by adequate precision (AP). Therefore, when the AP value is greater than 4, the model discrimination is considered satisfactory (Rocha et al. 2023). Moreover, the predicted values’ ranges at the design point to the average prediction error are compared using AP (Khalid et al. 2024). Therefore, the AP obtained from the experimental values (45.3787) could be considered adequate, which is a good sign and implies that the model can be used to navigate the design space (Alkharisi and Dahish 2025). The descriptive statistics of the model, factors’ sum of the squares, and mean square values are shown in Table 3.

Diagnostics of actual versus predicted responses

Considering the positive note of the prediction model from the statistics, the Stat-Ease Software gives other diagnostic tools based on the experimental data and model for further analysis. The predicted versus actual plot and residual versus predicted are discussed here. These plots help to understand if there are any outliers in the experimental results (Fig. 4). Figure 4a shows a predicted plot of the actual responses which is roughly a straight line, which shows the model’s good quality in predicting the responses with R² value of 0.9610. The correlation between the predicted and actual activator alkalinity, and the linearity shows the model is useful for predicting the alkalinity efficiently. Meanwhile, the residual versus predicted plots, depicted in Fig. 4b, shows constant variants with no specific trends and no outliers (no points above the red line).

Fig. 4. a: Predicted vs. actual plot; and b: Residuals vs. predicted

Analysis of response surface plots of alternative activator alkalinity

Figure 5 represents the three-dimensional plots of the interaction between the dependent variables and independent variables for alternative activator alkalinity. Figures 5a and 5b show that the alkalinity of alternative activators tended to reach optimum as the RHA quantity and NaOH concentration was increased. After reaching optimum peak, the alkalinity decreased with increasing RHA quantity and NaOH concentration. Initially, increasing the RHA quantity provides more amorphous silica available to be dissolved by NaOH. This increases the silicate content in the solution, which is crucial for forming the geopolymer binding gel. The dissolution process is limited by the amount of NaOH available. Once the RHA quantity exceeds the capacity of the NaOH to dissolve it, saturation, the dissolution efficiency drops. The excess RHA remains as undissolved solid residue, e.g., unreacted particles rather than contributing to the active silicate species (Muñoz-Castillo et al. 2025).

Figures 5c and 5d show that the alkalinity was almost constant when the stirring speed increased. On the other hand, alkalinity decreased with the increase of RHA quantity. Stirring speed primarily influences the mass transfer rate rather than the chemical equilibrium itself. While adequate stirring is necessary to keep the RHA particles suspended and ensure contact with the NaOH solution, increasing the speed beyond a certain point does not significantly change the final extent of dissolution or the resulting alkalinity. Once the system is well-mixed, the reaction is limited by other factors such as temperature or concentration, not by how fast it is stirred (Nanda et al. 2024). As more RHA is added to the NaOH solution, more silica dissolves. This dissolved silica reacts with the OH^– ions from the sodium hydroxide to form alkali activator. This reaction consumes the free hydroxide ions, thereby lowering the pH and the overall alkalinity of the solution. Essentially, the solution becomes more siliceous and less alkaline (Samuel Owoeye 2017).

Figures 5e and 5f demonstrated that alkalinity decreased as the heating temperature increased when RHA quantity was kept constant. Higher temperatures provide more kinetic energy to the system, which accelerates the endothermic reaction between the amorphous silica in the RHA and the NaOH solution. The chemical reaction that produces sodium silicate involves the consumption of hydroxide ions (OH^–). As the temperature rises, the reaction proceeds more completely, dissolving more silica. Since the reaction consumes the free sodium hydroxide to form silicate species, the concentration of free OH^– ions (alkalinity) in the final solution decreases (Handayani et al. 2022).

Moreover, Figs. 5g and 5h show that alkalinity tended to increase when heating time increased. As the heating duration was extended, more silica was dissolved from the ash into the solution. The extended time ensures that the reaction reaches a higher degree of completion, resulting in a higher concentration of dissolved silicate species and a stable alkaline solution. While alkalinity and silica yield increased with time, research indicated that the most significant gains occurred within the first three hours. Beyond this optimum duration, the increase in dissolution, and thus alkalinity, becomes marginal and may not justify the additional energy consumption (Tong et al. 2018).

Fig. 5. 3D RSM plots and contour plots of two factors on the activator alkalinity where the other three factors are kept constant

Effect of Variables on Silica Composition

Mathematical model for predicting silica composition of alternative activator

The best-fitting mathematical model from RSM was found to be a reduced quadratic model. A term-reduction procedure using backward elimination was applied to remove statistically insignificant interaction terms (P > 0.05). Consequently, no interaction terms were included in the final reduced model to avoid modeling noise and improve predictive accuracy. Equation 3 is a mathematical model based on actual factor terms for predicting silica composition.

R2 = +17.1531+0.6317A+0.3883B−0.0025C−0.1183D+6.6034E−0.0002A²

−0.2157B²−1.2724E² (3)

Equation 3 shows that the stirring speed (C) and heating temperature (D) had negative effects on the silica composition, meanwhile the RHA quantity (A), NaOH concentration (B), and heating time (E) had synergistic effects. In addition, the second-order term A², B², and E² posed unfavorable effects.

ANOVA for the predicted quadratic model

Table 4 shows ANOVA results based on the RSM study produced by Stat-Ease 360 version 25.0.6. The P-value for the quadratic model of alternative activator silica composition was significant (P < 0.0001), which shows that the results were significant, and the model was excellent for predicting the response (alternative activator silica composition). Furthermore, the model F-value of 55.8058 implies the model was significant since there was only a 0.01% chance that an F-value this large could occur due to noise. In this case, A, B, C, D, E, A², B², and E² were significant model terms (note: A is RHA quantity; B is NaOH concentration; C is stirring speed; D is heating temperature; E is heating time). Therefore, the hypothesis of this study—the significant effect of factors—was confirmed. Values greater than 0.1000 indicate the model terms were not significant. Statistical insignificance indicates that the study lacks sufficient evidence to prove that the variable being tested had any effect. Most importantly, A, B, C, D, E, A², B², and E² had P-values lower than 0.1000. It means that all those variables were significant in the model.

The R² of 0.9159 achieved is indicative that the model produced was valid and closely fitted to the experimental data. This result is similar to one reported by Adamu et al. (2019). The predicted R² of 0.8850 is in reasonable agreement with the adjusted R² of 0.8995. The difference between predicted R² and adjusted R² was found to be 0.0145, which is within the required limit (not more than 0.2) for a good model. R² can be improved by addition of more independent variables; therefore, goodness of fit cannot be concluded from value of R² alone. Conversely adjusted R² only increases with variables that have strong correlation with the dependent variable otherwise it decreases. On the other hand, predicted R² is an indication of how well the model developed produces responses for new observation and whether the model is complicated or not. Therefore, adjusted R² and predicted R² are more desirable properties for goodness of fit in statistics. In this model, the R² value was close to 1. The adequate precision value of 30.8293 shows that it is higher than 4 and, therefore, offers an adequate signal-to-noise ratio. The mean silica composition value of alternative activator was 16.5772. The standard deviation in activator silica composition was ± 0.3571, which is acceptable, while the coefficient of variation (CV %) in activator silica composition was 2.1541%, below the acceptable level of 10%, reflecting a good model. The descriptive statistics of the model, factors’ sum of the squares, and mean square values are shown in Table 4.

Table 4. ANOVA for Quadratic Model of Silica Composition

Diagnostics of actual versus predicted responses

Considering the positive note of the prediction model from the statistics, the Stat-Ease Software gives other diagnostic tools based on the experimental data and model for further analysis. The predicted versus actual plot and residual versus predicted are discussed here. These graphs help to understand if there are any outliers in the experimental results (Fig. 6). The predicted plot of the actual responses is roughly a straight line, which shows the model’s good quality in predicting the responses (Fig. 6a). Since the R² value is 0.9159, 92% of the data points were around the fitted regression line, with only 8% of residues. The correlation between the predicted and actual activator silica composition, and the linearity shows the model is useful for predicting the silica composition efficiently. Meanwhile, the residual versus predicted plots (Fig. 6b) show constant variants with no specific trends and no outliers (no points above the red line).

Fig. 6. a: Predicted vs. actual plot; and b. Residuals vs. predicted

Analysis of response surface plots of silica composition

Figure 7 represents the three-dimensional response plots of the effects of two independent variables at a time on the alternative activator silica composition, while keeping the other variables constant. It reflects the absence of statistically significant interaction terms in the model. Figures 7a and 7b show that the silica composition of the alternative activator tends to reach an optimum as the RHA quantity and NaOH concentration increase. After attaining the optimum peak, the silica composition decreased with increasing RHA quantity and NaOH concentration. Firstly, increasing the RHA quantity and NaOH concentration improves silica composition because the chemical conditions promote the breakdown of the silica network. Higher NaOH concentrations increase the number of hydroxide ions (OH^–). These ions attack the siloxane bonds (Si-O-Si) in the amorphous silica of the RHA, breaking them down into soluble silicates. Increasing the RHA quantity simply provides more source material (silica) to be dissolved, which initially drives up the concentration of dissolved silica in the solution. After reaching the optimum peak, further increases lead to a decrease in the measured silica composition because the response is evaluated as the weight percentage (wt%) of silica within the dried solid residue. When the NaOH concentration exceeds the optimal ratio required for extraction, the excess unreacted NaOH remains in the filtrate. Upon drying, this excess NaOH contributes significantly to the total solid mass, thereby diluting the relative weight percentage of silica. Similarly, adding RHA beyond the solubility limit of the alkaline solution does not yield more dissolved silica, as the excess undissolved ash is simply removed during filtration (Muñoz-Castillo et al. 2025).

Figures 7c and 7d show that the silica composition decreased when the stirring speed increased and RHA quantity constant. Initially, stirring helps dissolve silica by reducing the boundary layer thickness around particles, which aids mass transfer. However, once the stirring speed is high enough to eliminate diffusion resistance, the reaction becomes controlled by the chemical reaction rate itself. Beyond a certain speed, increasing agitation does not significantly speed up the breakage of siloxane bonds (Si-O-Si). Excessively high stirring speeds can disturb the equilibrium or stability of the solution without adding benefit, leading to diminishing returns where the measured soluble silica might slightly decrease due to physical disturbances or precipitation issues (Al-Abboodi et al. 2020).

Figures 7e and 7f demonstrated that the silica composition decreased as the heating temperature increased when RHA quantity was kept constant. At temperatures above a certain threshold, the amorphous silica dissolved in the solution can begin to crystallize into less soluble forms, such as low-quartz or cristobalite. This crystallization removes silica from the liquid phase, effectively lowering the measured silica composition in the solution. While higher temperatures generally accelerate initial dissolution initially, excessive heat can lead to rapid saturation and the formation of a localized silica-rich gel layer around unreacted RHA particles, which impedes further extraction (Nzereogu et al. 2023). Furthermore, because the silica composition is determined as a weight percentage (wt%) of the dried solid residue using EDX, excessive heating temperatures increase water evaporation during synthesis. This evaporation concentrates the unreacted NaOH in the filtrate. Upon drying, this excess sodium hydroxide contributes a larger mass fraction to the solid residue, thereby diluting the relative weight percentage of the extracted silica (Setyawan et al. 2019). In some experimental setups, increasing the temperature beyond the optimum (e.g., 80 °C) provided no significant increase in silica yield. In fact, prolonged heating or excessive temperatures can lead to the formation of stable, insoluble silicate species that do not contribute to the active silica content in the activator (Tong et al. 2018).

Moreover, Figs. 7g and 7h show that silica composition of alternative activator tended to reach optimum values as the RHA quantity and heating time increased. Initially, extending the heating duration provided sufficient time for the hydroxide ions to attack the siloxane bonds (Si-O-Si) and extract more amorphous silica from the RHA. Simultaneously, an optimal RHA quantity ensured that enough source material was available to be dissolved. After reaching the optimum point, further increases in RHA and heating time led to a decrease in the measured silica composition. As observed with elevated temperatures, prolonged heating times exacerbated evaporative losses, which concentrated the unreacted NaOH in the solution and ultimately diluted the measured weight percentage of silica in the dried residue (Nanda et al. 2024).

Optimization of Alternative Alkali Activator Synthesis

Figure 8 illustrates the numerical optimization results in terms of ramp graphs, which are used to clarify the results. Numerical optimization uses a ramp graph to clarify the results. Generally, depending on the aim of the research, the design factors and responses are set to achieve the study’s objective (Abdellatief et al. 2023). For this study, the responses, namely, activator alkalinity and silica composition, were set to the maximum option, and factors including RHA quantity, NaOH concentration, stirring speed, heating temperature, and heating time were set in the range option. Based on Fig. 8, the optimum conditions for maximum responses were RHA quantity of 162.5 g; NaOH concentration of 3M; stirring speed of 350 RPM; heating temperature of 75 °C; and heating time of 3.5 h. In addition, the desirability value was 0.692 for this response. Although moderate, this value indicated that the formulation was sufficiently desirable to fulfil the responses’ objectives (Raut et al. 2025).

Fig. 7. 3D RSM plots and contour plots of two factors on the silica composition where the other three factors are kept constant

Fig. 8. Ramp chart for statistically optimized factors and response for experimental design

Confirmatory Runs of the Predicted Optimal Response

Statistical literature and practitioners have long advocated the use of confirmation experiments as the final stage of a sequence of designed experiments. The confirmation is to verify that the optimal operating conditions identified as part of an RSM strategy are attainable. Moreover, it is also able to achieve the value of the response desired. Predicted values for confirmation tests were suggested by the Stat-Ease software. For each model, three experiments have been done.

The confirmation location for the confirmatory runs was at RHA quantity of 162.5 g; NaOH concentration of 3M; stirring speed of 350 RPM; heating temperature of 75 °C; and heating time of 3.5 h. The activator alkalinity (pH) for confirmation run 1, 2, and 3 was 12.0; 12.1; and 12.2, respectively. Meanwhile, confirmation experiments 1, 2, and 3 for silica composition (wt%) were 22.6; 25.8; and 12.5, correspondingly. The result of confirmation experiment is summarized in Table 5.

According to the validation criteria of the Stat-Ease software, the reported 95% prediction interval (PI) represents the expected range for the mean of the three confirmatory runs (n=3), rather than the broader interval expected for individual future observations. For activator alkalinity, both the individual runs and the data mean fell well within the 95% PI. For the silica composition, while the individual runs exhibited substantial variability and fell outside this narrow band, the overall data mean (20.3 wt%) successfully fell within the required 95% PI limits (19.6 to 21.2 wt%). Because the prediction interval for an average of multiple runs is mathematically narrower than that for a single run, it is statistically acceptable for individual data points to fall outside the boundaries provided the mean is successfully verified.

Nevertheless, this large dispersion among the individual silica composition runs indicates that while the mathematical model accurately predicts the average trend, the actual silica yield from batch to batch under these optimum conditions may vary considerably due to the complex nature of the extraction and drying procedures. Thus, operating at these settings should reliably produce the targeted alkalinity, but the silica yield should be interpreted as a scientifically validated average expectation rather than a precise batch-by-batch outcome.

Table 5. Summary of Confirmatory Runs

CONCLUSIONS

1. The application of Response Surface Methodology (RSM) with a Central Composite Design (CCD) proved highly effective in modeling the alkali treatment of rice husk ash (RHA). The developed reduced quadratic models accurately predicted the activator alkalinity and silica composition, revealing that RHA quantity, NaOH concentration, heating temperature, and heating time significantly dictate the extraction efficiency, whereas stirring speed showed no significant statistical impact.
2. The optimum conditions for maximizing both alkalinity and silica extraction were identified as an RHA quantity of 162.5 g, NaOH concentration of 3M, stirring speed of 350 RPM, heating temperature of 75 °C, and heating time of 3.5 h. These parameters establish a balanced thermochemical environment that provides sufficient hydroxide ions to break down siloxane bonds without causing excessive evaporation or unwanted silica saturation.
3. Confirmatory runs validated the optimization models, as the data means for both activator alkalinity (12.1) and silica composition (20.3 wt%) successfully fell within their respective 95% Prediction Intervals for the mean. While the alkalinity response was highly repeatable across individual runs, the silica composition exhibited substantial batch-to-batch variability. This indicates that while the mathematical model accurately predicts the average expected yield, the exact silica composition per batch may fluctuate. Furthermore, while this study successfully demonstrates a pathway to reduce reliance on commercial activators, future research should focus on refining the extraction and filtration controls to minimize this variability and subsequently evaluate the life-cycle and economic feasibility of scaling up this waste-derived activator for industrial geopolymer production.

ACKNOWLEDGMENTS

The authors would like to express the highest gratitude and appreciation to Universiti Malaysia Sarawak (UNIMAS) for providing the opportunity and support to conduct this research. This research is also funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R24).

Conflict of Interest

The authors declare that there is no conflict of interest regarding the publication of this manuscript.

Use of Generative AI

The authors affirm that there is no generative AI platforms that were used to create, draft, or revise the core scientific content. The conceptualization, data collection, analysis, and interpretation of findings were conducted solely by the researchers.

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Article submitted: February 9, 2026; Peer review completed: April 14, 2026; Revisions accepted: June 18, 2025; Published: June 26, 2026.

DOI: 10.15376/biores.21.3.7516-7540