Copper Ion Removal by Adsorption Using Fly Ash-Based Geopolymers: Process Optimization Insights from Taguchi and ANOVA Statistical Methods

The present study aimed to use geopolymer materials synthesized from different fly ashes, which are promising for the adsorption of copper ions from aqueous solutions. The characterization of fly ashes and prepared adsorbents was performed by energy-dispersive X-ray spectroscopy (EDS) analysis, Brunauer–Emmett–Teller (BET) surface area analysis, and Scanning Electron Microscopy (SEM). Taguchi and ANOVA methods were used to predict the effect of different working parameters on copper ion removal by prepared geopolymers. Based on data obtained by the Taguchi method, it was found that the factor most influencing the adsorption process is the type of adsorbent used, followed by the solution pH, the reaction time, the adsorbent dose, and the initial copper ion concentration. The ANOVA results agree with the Taguchi method. The optimal conditions of the adsorption process were: fly ash C modified by direct activation with 2 M NaOH, at 70 °C for 4 h, solution pH of 5, initial pollutant concentration of 300 mg/L, 40 g/L adsorbent dose, and 120 min of reaction time. Copper ion removal efficiency was determined experimentally under optimal conditions, achieving a value of 99.71%.


Introduction
Water remediation is recommended for the safety of human health and the environment.The type of material plays an important role in the adsorption process.It was underlined that the cost of adsorbents and the ability of adsorbents to be reused for a number of adsorption/desorption cycles are key parameters for their practical applications in wastewater treatment [1,2].Apart from many materials, geopolymer materials obtained from wastes are a viable alternative for wastewater treatment.For example, for the removal of cobalt, lead, nickel, and cadmium ions, adsorbents based on pyrophyllite mine waste-based geopolymer [3] and a geopolymer from dolochar ash [4] were proposed.Nayak and co-workers [5] prepared hydroxyapatite synthesized from egg shells and used it for fluoride removal.
Copper is the third most utilized metal in the world [6,7], being used in various industries [8].These industries must treat their effluent before discharging it [9].Although copper is an essential element for organisms [10], when consumed in excess, it shows deleterious effects such as irritation of eyes, nose, and mouth, stomachache, lung cancer, and neurotoxicity [11].
Liu and co-workers [6] provided a review focusing on methods used for the removal of copper ions from wastewaters, such as physicochemical techniques (e.g., membrane sep-aration, ion exchange, chemical precipitation, electrochemistry, adsorption) and biological techniques (e.g., biosorption, bioprecipitation, biomineralization).
For example, Mužek and co-workers [37] prepared an adsorbent for copper ion removal using a type F fly ash mixed with NaOH and Na 2 SiO 3 solutions.Four initial concentrations and three temperature values were investigated.The results demonstrate that the prepared geopolymer adsorbent shows excellent ability for copper ion removal.Al-Harahsheh and collaborators [38] synthesized a fly ash-based geopolymer using the alkali activator NaOH.Their data demonstrated that at a pH of 6 and 25 • C, the maximum adsorption capacity of the prepared material was 96.8 mg/g.By increasing the temperature to 45 • C, the maximum adsorption capacity increased to 152 mg/g.The study performed by obtaining glassy ceramic materials and co-workers [33] offered information regarding the preparation of geopolymers using a fly ash collected from Indonesia, treated with four alkali reagents, as adsorbents for copper ion removal.The results show that the treated geopolymers have enhanced adsorption capacities compared to unmodified fly ash.Purbasari and collaborators activated fly ash with 10 N NaOH solution and Na-silicate solution [39]; according to the results, the material is suitable for copper ion removal.
Roviello and co-workers have applied, for the first time, hybrid geopolymeric foams for the removal of different ions, such as Pb 2+ , Cd 2+ , Cu 2+ , and Zn 2+ ; the results show that the materials are effective in the adsorption of the targeted ions [40].
Harja and collaborators [41] conducted a study focused on the treatment of coppercontaminated waters by geopolymer materials derived from fly ash.All the developed adsorbents were obtained by treating a locally sourced fly ash with NaOH using different synthesis conditions and synthesis methods at mild temperatures (<100 • C) for a short period of time.The new products have a significant influence on the reduction of the negative impact of fly ash on the environment (related to its storage).In addition, coppercontaminated waters were treated.The study revealed that the prepared adsorbents show good removal efficiencies.The mentioned study was continued with a principal research objective of establishing the best experimental process conditions in order to obtain higher removal efficiency.Thus, to optimize the working parameters used for copper ion removal (i.e., pH, adsorbent dosage, contact time), our research team studied the neuro-evolutionary method, which incorporates neural modeling and genetic algorithm optimization [42].
The scientific literature indicates several optimization methods for the removal of metal ions from wastewater by adsorption, such as the Taguchi approach, Plackett-Burman Design, and Response Surface Methodology (based on three-level full factorial design, Box-Behnken design, central composite design, or Doehlert design) [43][44][45].The optimization of copper ion removal was investigated by Response surface methodology [44], and the optimization of lead ion removal was investigated by Box-Behnken design [45].Bayuo and co-workers [43] present in their critical literature review the Response surface optimization and modeling in heavy metal removal from wastewater.The optimal conditions for the copper adsorption process can be established by using the Taguchi method [46,47].This method is applied to optimize various processes in a wide range of areas, being able to find an optimized design configuration for multifactorial conditions.Compared with other optimization methods, the approach of ranking the controllable factors that influence the analyzed process allows a better visualization of the optimal conditions and requires much less experimental data.The major advantages of the Taguchi method are: (i) keeping the experimental cost to a minimum because a small number of trials are carried out; and (ii) reducing the time of experimental studies and establishing the most effective parameter that influences the process [48][49][50].
The Taguchi method is based on the realization of an orthogonal matrix that distributes the variables in a balanced way, and the experimental results are converted into a signal/noise ratio (S/N), which describes the level of dispersion and the degree of optimization in relation to the desired value [51].The term 'signal' represents the desired value (mean) for the output characteristic, while the term 'noise' represents the undesired value (standard deviation) [48,49,52].For example, if there are six controllable factors at three levels, a fully classical factorial design must use a number of 36, i.e., 729 experiments, to establish optimal conditions that characterize the process.In this case, the Taguchi method uses an L27 orthogonal matrix, which reduces the number of experiments to 27 [49,53].
The study highlights the possibility of using fly ash-based geopolymer materials for copper ion removal and gives detailed information regarding the process optimization insights from Taguchi and ANOVA statistical methods.The Taguchi method was utilized to establish the optimal conditions for the copper ion removal process by adsorption technique [54,55].The ANOVA analysis (general linear model) was used to determine the contribution of each working parameter on the removal efficiency [56,57].Five process variables were considered for the present research: type of adsorbent, solution pH, adsorbent dose, initial copper concentration, and reaction time.To the best of our knowledge, there are no data regarding the optimization conditions of copper ion removal using Taguchi and ANOVA methods for geopolymer materials synthesized from two different fly ash sources.
The following aspects are the strong points that make this study novel for a wide public: (1) the starting material, fly ash, does not involve any costs for purchase; (2) the inexpensive and simple preparation technique (the materials do not require more chemicals and energy); (3) a one-step process for the synthesis of fly-ash-based geopolymers is foreseen, which ensures nearly 100% recovery.

Materials and Reagents
For fly ash-based geopolymer synthesis, two types of fly ash were used: local Romanian fly ash, Holboca Iasi, (Cen1) and imported Czech Republic fly ash (Cen2).The two types of fly ash were selected for chemical reasons.The Cen1 fly ash is type F (the sum of silicon, aluminum, and iron oxides is over 75%), while Cen2 fly ash is type C, which means it contains more than 7% calcium oxide.NaOH was purchased from Chemical Company, Lasi, Romania (ACS reagent, ≥97.0%, pellets).

Materials Preparation
A number of five adsorbents were synthesized using NaOH.The synthesis conditions are listed in Table 1.The synthesis of CenNa1, CenNa2, and CenNa3 was completed at room temperature (20 • C).The CenNa1 adsorbent was synthesized by treating Cen1 fly ash with 2 M NaOH solution, for 168 h of contact time.The Cen1 to NaOH ratio was 1:3.The CenNa2 adsorbent was synthesized by ultrasound method for 1 h of contact time using a Cen1 to NaOH ratio of 1:3 and a NaOH concentration of 2 M. The CenNa3 was obtained by treating Cen2 fly ash with 2 M NaOH solution by ultrasound method for 1 h of contact time.For CenNa4 and CenNa5 adsorbents, the direct activation method (70 • C) for a contact time of 4 h was used: CenNa4 was prepared by mixing Cen1 with NaOH, 2 M (ratio of 1:3), while the CenNa5 product was synthesized by mixing Cen2 with NaOH, 2 M (ratio of 1:3).After preparation, the adsorbents were kept in closed laboratory bottles.
The specific surface area BET was determined with Autosorb 1 MP-Adsorption System (Quantachrome Instruments, Boynton Beach, FL, USA).

Copper Ion Adsorption
The copper ion adsorption process was performed using a batch technique at room temperature with stirring.A stock solution of copper sulphate (1000 mg/L) was prepared.The working solutions of 300 mg/L, 500 mg/L, and 700 mg/L were obtained by diluting the stock solution.Distilled water was used in all experiments.For each type of adsorbent, the influence of the solution pH (2-5), adsorbent dose (10-40 mg/L), initial concentration, and reaction time was recorded.Thus, the influence of the type of adsorbent on the adsorption of copper ions was established.The maximum value of pH was selected at 5 to avoid copper ion precipitation.The adsorbents had good stability in the 2-5 pH range, a fact demonstrated in our previous paper [32].
The collected samples after the adsorption process were filtered using filter paper and the filtrate was analyzed spectrophotometrically using a Shimadzu UV-2450 DR UV-Vis spectrophotometer at λ = 390 nm, rubeanic acid.
The removal efficiency was determined by Equation (1) below: where C 0 and C f are the initial and final copper ion concentrations (mg/L).

Optimizing the Adsorption Process
The aim of the optimization studies was to maximize the efficiency of the adsorption process.Therefore, to process experimental data, the calculation 'larger is better' (Equation ( 2)) was considered to determine the S N ratio [48,58].
where: n is the number of repetitions under the same experimental conditions and y i is the experimental response.
A Taguchi mixed experimental design (18 different experiments-L18) was used to optimize the adsorption process [59].The effect of five controllable factors on the adsorption efficiency was studied: adsorbent type at 6 levels, pH, adsorbent dose, initial concentration, and contact time at three levels.For a fully classical factorial design, 486 (6 × 34) experiments would have been required.
ANOVA (general linear model) analysis of variance was used to evaluate the results obtained by the Taguchi method and to determine the percentage contribution of each factor on the adsorption efficiency.All mathematical calculations required for the Taguchi method and ANOVA analysis were performed using Minitab 19 statistical software.

Materials Characterization
Table 2 shows the measured BET surface areas of the modified materials.Furthermore, it was determined that the pore volumes ranged from 0.0233 to 1.35 cm 3 /g, representing an 8.0-fold increase over the initial fly ash material.The BET investigation shows that the surface area of CenNa1 adsorbent increased 1.29 times the surface area of Cen1 (5.8 m 2 /g).According to Table 2, CenNa1-CenNa5 have higher BET surfaces than Cen1 or Cen2 (7 m 2 /g); the increase can reach up to 10 times.
Large modification by alkaline attack is observed in SEM images, Figure 1.On the surface of spherical particles, new crystalline phases are growing; CenNa4 and CenNa5 have the most significant new phases, a fact confirmed by EDS analysis.The EDS analysis was performed to prove that fly ash-based geopolymer materials were successfully synthesized.The data for the local (Cen1) and imported fly ashes (Cen2) are included as well.

= −𝑙𝑜𝑔 ∑
(2) where: n is the number of repetitions under the same experimental conditions and  is the experimental response.A Taguchi mixed experimental design (18 different experiments-L18) was used to optimize the adsorption process [59].The effect of five controllable factors on the adsorption efficiency was studied: adsorbent type at 6 levels, pH, adsorbent dose, initial concentration, and contact time at three levels.For a fully classical factorial design, 486 (6 × 34) experiments would have been required.
ANOVA (general linear model) analysis of variance was used to evaluate the results obtained by the Taguchi method and to determine the percentage contribution of each factor on the adsorption efficiency.All mathematical calculations required for the Taguchi method and ANOVA analysis were performed using Minitab 19 statistical software.

Materials Characterization
Table 2 shows the measured BET surface areas of the modified materials.Furthermore, it was determined that the pore volumes ranged from 0.0233 to 1.35 cm 3 /g, representing an 8.0-fold increase over the initial fly ash material.The BET investigation shows that the surface area of CenNa1 adsorbent increased 1.29 times the surface area of Cen1 (5.8 m 2 /g).According to Table 2, CenNa1-CenNa5 have higher BET surfaces than Cen1 or Cen2 (7 m 2 /g); the increase can reach up to 10 times.
Large modification by alkaline attack is observed in SEM images, Figure 1.On the surface of spherical particles, new crystalline phases are growing; CenNa4 and CenNa5 have the most significant new phases, a fact confirmed by EDS analysis.The EDS analysis was performed to prove that fly ash-based geopolymer materials were successfully synthesized.The data for the local (Cen1) and imported fly ashes (Cen2) are included as well.The EDS characterization, Table 3, proves that, regardless of the source, both fly ash samples contain O, Na, Mg, Al, Si, K, Ca, Ti, and Fe.Regarding carbon content, a value of 6.72% was found in Cen1, and only 1.25% in Cen2, this depending on burning conditions.Sulfur was not found in source materials.By analyzing the data, it can be noted that Cen2 contains a higher calcium content (6.85%), while Cen1 shows 1.15% calcium.In the synthesized adsorbents, the same components as in the starting material (Cen1 and Cen2) were found, but in different quantities, and the increased sodium content demonstrates the successful modification.Thus, Na content increased for CenNa1, CenNa2, and The EDS characterization, Table 3, proves that, regardless of the source, both fly ash samples contain O, Na, Mg, Al, Si, K, Ca, Ti, and Fe.Regarding carbon content, a value of 6.72% was found in Cen1, and only 1.25% in Cen2, this depending on burning conditions.Sulfur was not found in source materials.By analyzing the data, it can be noted that Cen2 contains a higher calcium content (6.85%), while Cen1 shows 1.15% calcium.
In the synthesized adsorbents, the same components as in the starting material (Cen1 and Cen2) were found, but in different quantities, and the increased sodium content demonstrates the successful modification.Thus, Na content increased for CenNa1, CenNa2, and CenNa4 materials to 3.45%, 2.82%, and 6.71%, respectively, compared to Na content of Cen1 (0.79%) [60].Regarding the CenNa3 and CenNa5 materials, the Na content was 3.92% and 5.67% compared to Na content of Cen2 (0.603%).It can be noted that the lower Na content is present in fly ash-based geopolymer materials prepared using the ultrasound method for 1 h of contact time.The clear increases in geopolymers' BET surface area and pore volume as compared to the original ashes have further shown the latter material's potentially improved capacities for copper adsorption.Superior results in terms of adsorption capacity for CenNa4 and CenNa5 can be explained by the advanced degree of modification, wide surface area, and high sodium concentration.

Taguchi Model and Statistical Analysis
Table 4 reports the controllable factors and their levels used in the Taguchi mixed design.For this study, Cen1 and all five synthesized fly ash-based geopolymer products were considered.According to the Taguchi design used, 18 different experiments were performed using the L18 orthogonal matrix, and the adsorption efficiency values and S/N ratios corresponding to each iteration were determined (Table 5).Table 6 shows the S/N ratio for each controllable factor along with the delta values (differences between the highest and lowest mean response values for each factor).The ranking levels of the S/N ratios established based on the delta values are also shown in Table 6.The optimal conditions to obtain the highest adsorption efficiency correspond to the highest value of the S/N ratio for each controllable factor.These values are marked in Table 6 with an '*' symbol.The variations of the S/N ratios for each controllable factor in the pollutant adsorption process are illustrated in Figure 2. Analyzing the data presented, it can be found that the factor that most influences the adsorption process is the type of adsorbent material used, followed by the solution pH, the reaction time, the adsorbent dose, and the initial concentration of the pollutant.
The correlation of these data with those in Table 1 indicates the optimal conditions of the adsorption process: CenNa5 adsorbent, solution pH of 5, initial concentration of the pollutant of 700 mg/L, 40 g/L adsorbent dose and 120 min. of reaction time.Copper ion removal efficiency was determined experimentally under optimal conditions, obtaining a value of 99.71%.
The results of the ANOVA analysis confirm the results obtained by the Taguchi method, indicating the same order of influence of the controllable factors on the adsorption process.The percentage contribution of each controllable factor on pollutant removal efficiency, determined by ANOVA analysis of variance, is shown in Figure 3. Table 6 and Figure 3 do not provide the values of pH or other controllable factors, but the values for the S/N ratio obtained from the Taguchi design analysis, for each individual level.
The variations of the S/N ratios for each controllable factor in the pollutant adsorption process are illustrated in Figure 2. Analyzing the data presented, it can be found that the factor that most influences the adsorption process is the type of adsorbent material used followed by the solution pH, the reaction time, the adsorbent dose, and the initial concen tration of the pollutant.The correlation of these data with those in Table 1 indicates the optimal conditions of the adsorption process: CenNa5 adsorbent, solution pH of 5, initial concentration of the pollutant of 700 mg/L, 40 g/L adsorbent dose and 120 min. of reaction time.Copper ion removal efficiency was determined experimentally under optimal conditions, obtaining a value of 99.71%.
The results of the ANOVA analysis confirm the results obtained by the Taguchi method, indicating the same order of influence of the controllable factors on the adsorption process.The percentage contribution of each controllable factor on pollutant removal efficiency, determined by ANOVA analysis of variance, is shown in Figure 3. Table 6 and Figure 3 do not provide the values of pH or other controllable factors, but the values for the S/N ratio obtained from the Taguchi design analysis, for each individual level.To establish the accuracy of the results provided by the Taguchi method, the predicted values of the pollutant removal efficiency and those determined experimentally were correlated (Figure 4).A good correlation between the values was found, with the value of the coefficient of determination R 2 being very good, indicating good accuracy in the prediction of the optimization method.
A comparison with the existing literature shows that the dominant controllable factor influencing copper ion adsorption depends on the specific adsorbent material used.Svilović and collaborators [47] studied the adsorption of copper ions on fly ash-based geopolymer, modified fly ash-based geopolymer with Pb ions, and zeolite NaX.After Taguchi optimization, it was concluded that the factor with the greatest influence was the copper initial concentration, followed by the adsorbent type and contact time.Another research conducted by Zarandi and collaborators [61] involved the adsorption of copper ions utilizing magnetic nanoparticles attached to activated carbon.Following Taguchi s approach, it was determined that the most influential controllable factor was the adsorbent dose, followed by pH and copper initial concentration.To establish the accuracy of the results provided by the Taguchi method, the predicted values of the pollutant removal efficiency and those determined experimentally were correlated (Figure 4).A good correlation between the values was found, with the value of the coefficient of determination R 2 being very good, indicating good accuracy in the prediction of the optimization method.To better visualize the influence of the controllable factors on the adsorption efficiency, the dependencies between the main factors that influence the adsorption process and the process efficiency (i.e., optimal conditions established by the Taguchi method-pH 5, 500 mg/L initial copper concentration, and 40 g/L adsorbent dosage) were graphically represented with the STATISTICA 7.1 software using the distance-weighted least squares fitting method.Graphical representations of these dependencies are shown in   A comparison with the existing literature shows that the dominant controllable factor influencing copper ion adsorption depends on the specific adsorbent material used.Svilović and collaborators [47] studied the adsorption of copper ions on fly ash-based geopolymer, modified fly ash-based geopolymer with Pb ions, and zeolite NaX.After Taguchi optimization, it was concluded that the factor with the greatest influence was the copper initial concentration, followed by the adsorbent type and contact time.Another research conducted by Zarandi and collaborators [61] involved the adsorption of copper ions utilizing magnetic nanoparticles attached to activated carbon.Following Taguchi's approach, it was determined that the most influential controllable factor was the adsorbent dose, followed by pH and copper initial concentration.
To better visualize the influence of the controllable factors on the adsorption efficiency, the dependencies between the main factors that influence the adsorption process and the process efficiency (i.e., optimal conditions established by the Taguchi method-pH 5, 500 mg/L initial copper concentration, and 40 g/L adsorbent dosage) were graphically represented with the STATISTICA 7.1 software using the distance-weighted least squares fitting method.Graphical representations of these dependencies are shown in Figures 5-14.
From Figure 5, it can be seen that the lowest removal efficiency is obtained at pH 2, and as the pH increases, the removal efficiency increases.Removal efficiencies close to 100% are obtained at pH 5 using CenNa4 and CenNa5 adsorbents.
Figure 6 demonstrates that by increasing the concentration of the initial copper solution from 300 mg/L to 700 mg/L, even at pH 5, the adsorption efficiency decreases, which can be explained by the saturation of the adsorption sites.
Under optimal conditions, the adsorbent dose has a positive impact on the adsorption process.Thus, at an adsorbent dose of 40 g/L, the removal efficiency values for unmodified fly ash and for synthesized materials are over 60% (Figure 7).
The reaction time visibly influences the removal efficiency.Operating at optimal reaction time brings significant time and energy savings.Thus, from Figure 8, it was found that for unmodified fly ash and for CenNa1 adsorbent, a removal efficiency above 90% can be reached after 120 min, while the same removal efficiencies can be obtained after a short contact time, i.e., 5-10 min, in the case of CenNa4 and CenNa5.
ciency, the dependencies between the main factors that influence the adsorption process and the process efficiency (i.e., optimal conditions established by the Taguchi method-pH 5, 500 mg/L initial copper concentration, and 40 g/L adsorbent dosage) were graphically represented with the STATISTICA 7.1 software using the distance-weighted least squares fitting method.Graphical representations of these dependencies are shown in                      From Figure 5, it can be seen that the lowest removal efficiency is obtained at pH 2, and as the pH increases, the removal efficiency increases.Removal efficiencies close to 100% are obtained at pH 5 using CenNa4 and CenNa5 adsorbents.
Figure 6 demonstrates that by increasing the concentration of the initial copper solu- As observed from the optimization data, the highest removal efficiency values were obtained for CenNa5.Therefore, the influence of each parameter was analyzed for this adsorbent, Figures 9-14.However, it can be seen that the differences of CenNa5 when compared to CenNa4 are not significant, so the results can be extrapolated to CenNa4.Initial pollutant concentrations of 300 mg/L at pH 5 lead to removal efficiencies higher than 95%.The fact that the pH producing the highest efficiencies is closer to neutral pH is beneficial.Although the modified fly ashes are slightly basic (the modification is carried out with NaOH solution), in water treatment plants, neutralization is practiced (mixing acidic and basic waters) so the pH of industrial waters is close to the optimal pH values arising from experimental research.
For CenNa5 at pH 5 and initial concentrations of 300 mg/L, the removal efficiency is above 80% even after 10 min of reaction time.
Experimental data for wastewater with an initial concentration of 300 mg/L revealed removal capacity values of 23.5 mg/g and 5.65 mg/g for the CenNa2 material using adsorbent dosages of 10 mg/L and 40 mg/L, respectively, while for CenNa3 the values obtained were 17.98 mg/g and 5.17 mg/g at the same dosages.Regarding CenNa4 and CenNa5, removal capacity decreased with adsorbent dosage, being 26.9 mg/g for the adsorbent dosage of 10 mg/L, 13 mg/g for the dosage of 20 mg/L, and 6.92 mg/g for the dosage of 40 mg/L.For all studied materials, removal capacities ranged from 6.9 mg/g to 27.25 mg/g.
The synthesized materials have removal efficiency, at the value of pH 5, in accord with the literature; a comparison with other materials is presented in Table 7.According to Table 7, the material prepared in this study showed good adsorption capacity.
An important aspect that must be treated with special attention is the safe disposal of the loaded adsorbent.A previous recommendation is made by Maiti and co-workers [69].The authors proposed to use the material after adsorption of heavy metals in cement matrix.The adsorbent materials used are derived from fly ash and therefore have a high SiO 2 content.The exhausted adsorbent material, resulting from the adsorption process, can be used to obtain vitreous matrices, which have the advantage of fixing the adsorbed heavy metal ions very well, without the danger of them reaching the environment.There are several studies in which some of the authors of this article were involved that certify these statements [70,71].

Conclusions
This study explores the fly-ash geopolymers prepared from two fly ashes (local and imported types) in mild conditions of temperature as new potential low-cost and envi-ronmentally friendly materials for copper ion removal.The performance of synthesized materials was investigated at different operating parameters.To obtain the optimal conditions for the adsorption process, the Taguchi method was used, while the ANOVA analysis (general linear model) was used to determine the contribution of each working parameter on the removal efficiency.
The factor that most influences the adsorption process is the type of adsorbent used, followed by the solution pH, the reaction time, the adsorbent dose, and the initial copper ion concentration.The results of the ANOVA analysis confirm the results obtained by the Taguchi method, indicating the same order of influence of the controllable factors on the adsorption process.A good correlation coefficient between the values was found (R2 = 0.981), which indicates good accuracy for the prediction of the optimization method by Taguchi method.
Newly designed adsorbent, CenNa, can contribute to clean water having a high removal performance of 99.71%.Further, based on the promising results obtained in the present study, we are looking for in-depth research having the objectives: (i) a detailed characterization including other techniques, (ii) isotherms/kinetics/thermodynamics evaluation, (iii) characterization of the loaded material for the suggestion of an adsorption mechanism, (iv) regeneration for multiple cycles using various desorption agents.
For an industrial plant, the technological parameters and especially the controllable ones are the most important.The management and regulation of these parameters are essential for the proper development of the industrial process, while the pollutant retention mechanism became secondary.That is the reason why the optimization methods, used in industrial practice, focus on controllable parameters.
The adsorbents containing heavy metal ions can be easily regenerated, after which, due to the predominant mineral part, they can be used in the cement industry, or for obtaining glassy ceramic materials.Future studies will be focused on these aspects as well.
Therefore, the study can draw the attention of researchers and the removal of other types of pollutants from wastewaters can be investigated by using the findings and fly ash-based geopolymer materials synthesized by our proposed methods.

Figure 2 . 20 Figure 2 .
Figure 2. The variations of the S/N ratios for each controllable factor in the pollutant adsorption process on the adsorbent materials.

Figure 3 .
Figure 3. Percentage contribution of each controllable factor on pollutant removal efficiency calculated using general linear model (ANOVA) analysis of variance.

Figure 3 .
Figure 3. Percentage contribution of each controllable factor on pollutant removal efficiency calculated using general linear model (ANOVA) analysis of variance.

Figure 4 .
Figure 4. Correlation between predicted and experimentally determined values of pollutant removal efficiency.

Figure 5 .
Figure 5.The dependence between the type of adsorbent, solution pH and removal efficiency.

Figure 4 .
Figure 4. Correlation between predicted and experimentally determined values of pollutant removal efficiency.

Figure 5 .
Figure 5.The dependence between the type of adsorbent, solution pH and removal efficiency.Figure 5.The dependence between the type of adsorbent, solution pH and removal efficiency.

Figure 5 .
Figure 5.The dependence between the type of adsorbent, solution pH and removal efficiency.Figure 5.The dependence between the type of adsorbent, solution pH and removal efficiency.

Materials 2024 , 20 Figure 6 .
Figure 6.The dependence between type of adsorbent, initial concentration of the pollutant and removal efficiency.Figure 6.The dependence between type of adsorbent, initial concentration of the pollutant and removal efficiency.

Figure 6 .
Figure 6.The dependence between type of adsorbent, initial concentration of the pollutant and removal efficiency.Figure 6.The dependence between type of adsorbent, initial concentration of the pollutant and removal efficiency.

Figure 6 .
Figure 6.The dependence between type of adsorbent, initial concentration of the pollutant and removal efficiency.

Figure 7 .
Figure 7.The dependence between type of adsorbent, adsorbent dose and removal efficiency.Figure 7. The dependence between type of adsorbent, adsorbent dose and removal efficiency.

Figure 7 . 20 Figure 8 .
Figure 7.The dependence between type of adsorbent, adsorbent dose and removal efficiency.Figure 7. The dependence between type of adsorbent, adsorbent dose and removal efficiency.Materials 2024, 17, x FOR PEER REVIEW 12 of 20

Figure 8 .
Figure 8.The dependence between adsorbent type, reaction time and removal efficiency.Figure 8.The dependence between adsorbent type, reaction time and removal efficiency.

Figure 8 .
Figure 8.The dependence between adsorbent type, reaction time and removal efficiency.

Figure 9 .
Figure 9.The dependence between solution pH, initial pollutant concentration and removal efficiency for CenNa5 adsorbent.Figure 9.The dependence between solution pH, initial pollutant concentration and removal efficiency for CenNa5 adsorbent.

Figure 9 .
Figure 9.The dependence between solution pH, initial pollutant concentration and removal efficiency for CenNa5 adsorbent.Figure 9.The dependence between solution pH, initial pollutant concentration and removal efficiency for CenNa5 adsorbent.

Materials 2024 , 20 Figure 10 .
Figure 10.The dependence between solution pH, adsorbent dose and removal efficiency for CenNa5 adsorbent.Figure 10.The dependence between solution pH, adsorbent dose and removal efficiency for CenNa5 adsorbent.

Figure 10 .
Figure 10.The dependence between solution pH, adsorbent dose and removal efficiency for CenNa5 adsorbent.Figure 10.The dependence between solution pH, adsorbent dose and removal efficiency for CenNa5 adsorbent.

Figure 10 .
Figure 10.The dependence between solution pH, adsorbent dose and removal efficiency for CenNa5 adsorbent.

Figure 11 .
Figure 11.The dependence of solution pH, reaction time and removal efficiency for CenNa5 adsorbent.Figure 11.The dependence of solution pH, reaction time and removal efficiency for CenNa5 adsorbent.

Figure 11 .
Figure 11.The dependence of solution pH, reaction time and removal efficiency for CenNa5 adsorbent.Figure 11.The dependence of solution pH, reaction time and removal efficiency for CenNa5 adsorbent.

Materials 2024 , 20 Figure 12 .
Figure 12.The dependence between initial pollutant concentration, adsorbent dose and removal efficiency for CenNa5 adsorbent.Figure 12.The dependence between initial pollutant concentration, adsorbent dose and removal efficiency for CenNa5 adsorbent.

Figure 12 .
Figure 12.The dependence between initial pollutant concentration, adsorbent dose and removal efficiency for CenNa5 adsorbent.Figure 12.The dependence between initial pollutant concentration, adsorbent dose and removal efficiency for CenNa5 adsorbent.

Figure 12 .
Figure 12.The dependence between initial pollutant concentration, adsorbent dose and removal efficiency for CenNa5 adsorbent.

Figure 13 .
Figure 13.The dependence between initial pollutant concentration, reaction time and removal efficiency for CenNa5 adsorbent.Figure 13.The dependence between initial pollutant concentration, reaction time and removal efficiency for CenNa5 adsorbent.

Figure 13 . 20 Figure 14 .
Figure 13.The dependence between initial pollutant concentration, reaction time and removal efficiency for CenNa5 adsorbent.Figure 13.The dependence between initial pollutant concentration, reaction time and removal efficiency for CenNa5 adsorbent.Materials 2024, 17, x FOR PEER REVIEW 15 of 20

Figure 14 .
Figure 14.The dependence between adsorbent dose, reaction time and removal efficiency for CenNa5 adsorbent.

Table 2 .
Surface area for synthesized materials.

Table 2 .
Surface area for synthesized materials.

Table 3 .
Elemental composition of raw and synthesized materials.

Table 4 .
The controllable factors and their levels used in the Taguchi mixed design.

Table 5 .
Experimental results obtained for pollutant removal efficiency using Taguchi L18 orthogonal matrix.

Table 6 .
Response table for S/N ratio ('larger is better' option).

Table 7 .
Comparison of removal efficiency of various adsorbents for Cu 2+ .