ABSTRACT
The utilization of phosphorus-rich aquaculture wastewater (AQW) offers a promising avenue for the recovery of valuable compounds, particularly hydroxyapatite (HAp) materials. The aim of this study was to use Response Surface Methodology (RSM) to explore the potential of recycling AQW directly to produce HAp compounds due to its considerable commercial value. Using the Central Composite Design (CCD) within the RSM, about three parameters, such as pH (between 9 and 11), contact time (between 36 and 60 h), and stirring rate (between 120 and 240 rpm) were optimized. The response of the CCD, crystallinity, was evaluated by X-ray diffraction analysis. The optimized conditions, pH of 10, contact time of 48 h, and stirring rate of 180 rpm resulted in HAp crystals characterized according to the Joint Committee on Powder Diffraction Standard, JCPDS (9,003,549), with a crystallite size of 23.6 nm. Remarkably, about 89.97% of the phosphate was removed from the AQW under these conditions, emphasizing the potential of this approach to reduce waste while producing a commercially viable product. The study highlights the viability of developing a zero-waste industry, and the utilization of calcium apatite-based products derived from AQW offers a promising socio-economic strategy for the future.
HIGHLIGHTS
Aquaculture wastewater acts as a resource for the recovery of valuable compounds.
Potential calcium apatite-based compounds were produced under different conditions.
Modeling from RSM was acheived to optimize the possibility of parameter efficiency to achieve the objective.
Optimum conditions for the highest recovery of phosphorus from aquaculture wastewater were studied.
The zero-waste approach in industry has long-term benefits in the future.
INTRODUCTION
Aquaculture plays a significant role in the economies of countries, contributing significantly to gross domestic product (GDP) through local seafood supply and exporting valuable commodities (Kurniawan et al. 2021). By 2020, the cumulative aquaculture production reached 400,017 metric tons (mt), with a wholesale value of RM3,114,731. A freshwater aquaculture system is a technique for cultivating freshwater aquaculture in various environments such as fishponds, fish pens, reservoirs, former mining ponds, rice fields, or canvas tanks. Species such as tilapia, catfish, and carp are usually farmed in freshwater lakes, ponds, rivers, or reservoirs for commercial purposes (Mat Zain & Lee 2022). However, in the pre-harvest phase, wastewater produced is often contaminated with particles and nutrients from sources such as fertilizers, spilt feed, and reared animal waste (Nizam et al. 2020).
Phosphorus (P) is one of the most important elements and nutrients required for the growth of aquatic plants and also as food for other microorganisms living freely in water bodies and rivers (Anawar & Chowdhury 2020; Tiwari & Pal 2022). However, excessive phosphorus levels in water bodies can trigger excessive algae growth, indicating eutrophication. A low P concentration of 0.02 mg/L is sufficient to trigger eutrophication and disrupt aquatic life by reducing dissolved oxygen content and hindering sunlight, eventually leading to depopulation and development of underwater dead zones (Jin et al. 2019; Yang et al. 2022; Dadi et al. 2023). Although nitrogen is also a contributing element to eutrophication, the role of phosphorus plays a major role (Schindler et al. 2016).
The recovery of P is a major challenge. Since the demand for P in the industry itself is not too impressive, most of the P is preferably recovered as fertilizer and other valuable compounds. In particular, the recovery of phosphate from agricultural wastewater in the form of hydroxyapatite (HAp) by chemical precipitation is of utmost practical importance for completing the P cycle (Cichy et al. 2019). Former studies suggested that P recovery involves self-crystallization between calcium and phosphorus, toward the amorphous calcium phosphate precursor and subsequent growth of HAp. The pH will change during the calcium–phosphate crystallization interaction, indicating the involvement of proton release mechanism (Li et al. 2022). HAp [Ca10(PO4)6OH2] is a bioceramic material which has been used over the decades. It has durable thermal and chemical properties which have been used widely in medicine, wastewater treatment, and various industries (Akpan et al. 2021). HAp is synthesized by different methods depending on the structure and apparent properties (Sadat-Shojai et al. 2013). The simplest and cheapest method is the wet precipitation method. The wet precipitation method allows the size and morphology of nanoparticles to be regulated, leading to the formation of nanoparticles under certain conditions (Sajid & Płotka-Wasylka 2020; Shrestha et al. 2020).
In modern research practice, the integration of computer-aided methods has become a cornerstone for the further development of scientific investigations (Deng et al. 2023, 2024; Li et al. 2024). Through the use of computational modeling and analysis, researchers can explore complicated systems, fine-tune experimental parameters, and gain insights beyond the scope of conventional experimental methods (Singh 2021, 2023; Singh & Samsher 2021, 2022). The present study utilized RSM, a computational tool, to investigate the recovery of HAp from aquaculture wastewater (AQW) under specific parameter conditions. The materials obtained by RSM optimization were then analyzed and compared with commercial HAp to evaluate the physicochemical properties. This holistic approach not only increases the efficiency and precision of the present study but also creates a robust framework for addressing the challenges associated with sustainable resource management and waste reduction.
The development of aquaculture industries on a commercial scale shows that considerable amounts of phosphorus and nitrogen waste are produced. The abundance of these nutrients represents a promising opportunity for wastewater treatment aimed at zero waste, environmental friendliness and economic benefits. Proper management of this wastewater by converting pollutants into useful compounds for various applications supports a circular economy that reduces environmental impact while creating economic value and is a reliable new pathway to environmentally conscious and economically viable solutions. Thus, exploring the potential of using phosphorus-rich AQW for the recovery of valuable compounds, particularly HAp materials, opens up new avenues for resource recovery. Consequently, by understanding the potential impact of aquaculture on the aquatic environment and understanding the measures can be considered to minimize the environmental impact and it would be beneficial for the long-term sustainability of the sector. Phosphorus recovery and reuse are highly desirable. Therefore, this study focuses on the recovery of the element phosphorus from AQW to produce HAp, a valuable material. This approach can, therefore, reduce phosphorus pollution and increase the production of mineral phosphate in useful material.
METHODS
Materials and sample collection
Experimental design
. | Parameter . | −ꭤ | − 1 . | 0 . | + 1 . | + ꭤ |
---|---|---|---|---|---|---|
A | pH | 8 | 9 | 10 | 11 | 12 |
B | Contact time | 28 | 36 | 48 | 60 | 68 |
C | Stirring rate | 80 | 120 | 180 | 240 | 280 |
. | Parameter . | −ꭤ | − 1 . | 0 . | + 1 . | + ꭤ |
---|---|---|---|---|---|---|
A | pH | 8 | 9 | 10 | 11 | 12 |
B | Contact time | 28 | 36 | 48 | 60 | 68 |
C | Stirring rate | 80 | 120 | 180 | 240 | 280 |
The CCD generated a total of 20 runs. The wet precipitate method was used to synthesize HAp and the AQW was the source of phosphate and at the same time can be a good medium for phosphate recovery. The pH, contact time, and stirring rate have ranges of 9–11, 36–60, and 120–240, respectively. In total, there were 20 conditions generated based on the CCD and the range of the parameter itself.
Analysis of physicochemical properties of calcium apatite-based compounds
The samples were characterized using a Rigaku MiniFlex Benchtop X-Ray Diffractometer (XRD) with Cu Kα radiation (λ¼1.5406 Å). It was carried out for 20 sets of samples that were generated by RSM to determine the crystallinity and recorded over the range 2θ = 10–80° by step counting at 0.03° with an interval of 0.6 s per data point. The samples were arranged in clumps at the center of a glass sample container and were examined at a high-tension generator set at 30 kV, 15 mA. To examine the crystallinity of materials, the XRD spectrum was analyzed using the computer program PDXL software. It was able to identify the crystallinity which can be measured via crystalline to the total area, including the amorphous phase. Measurement of the ratio between the total area covering both amorphous and crystalline phases enables the identification of their respective proportions. The crystalline area is determined by adding up the integrated intensities scattered over a suitable angular interval by the crystalline phase.
The Fourier Transform Infrared (FTIR) spectroscopy (Shimadzu IRTracer-100 model, Japan) was used to analyze the functional group of the sample product. The sample was powdered before analysis. The functional group of compounds was recorded in the region of 650–4,000 cm−1 of wavelength and resolution of 2 cm−1 as most of the molecules’ functional group side chains and crosslink absorb light in this infrared range of the electromagnetic spectrum.
RESULTS AND DISCUSSION
Evaluation of RSM analysis
The determination of crystallinity of HAp under different conditions involving 20 runs include six central points with different combinations of parameters as presented in Table 2. The predicted values were obtained by a model fitting technique via software Design Expert ver. 13 and significantly correlated with the observed (actual) values. Table 2 presents the CCD analysis and Run 2 is considered as the most optimum condition.
Run . | A pH . | B Contact time (h) . | C Stirring rate (rpm) . | Crystallinity (%) . | Compound identification . | Joint Committee on Powder Diffraction Standard (JCPDS) No. . | |
---|---|---|---|---|---|---|---|
Actual . | Predicted . | ||||||
1 | 9 | 60 | 240 | 69.14 | 69.44 | Carbonate hydroxyapatite | 9,003,553 |
2 | 10 | 48 | 180 | 69.40 | 68.83 | Hydroxyapatite | 9,011,093 |
3 | 10 | 48 | 180 | 68.58 | 68.83 | Hydroxyapatite | 9,002,216 |
4 | 9 | 36 | 120 | 63.17 | 63.52 | Carbonate hydroxyapatite | 9,003,552 |
5 | 11 | 36 | 120 | 65.31 | 65.19 | Hydroxyapatite | 9,002,215 |
6 | 9 | 36 | 240 | 69.06 | 69.21 | Carbonate hydroxyapatite | 9,003,551 |
7 | 8 | 48 | 180 | 66.42 | 66.07 | Hydroxylellestadite | 9,009,375 |
8 | 10 | 28 | 180 | 66.73 | 66.52 | Chlorapatite | 9,001,263 |
9 | 9 | 60 | 120 | 64.79 | 64.86 | Carbonate hydroxyapatite | 9,003,555 |
10 | 11 | 60 | 120 | 66.07 | 66.10 | Hydroxyapatite-Dental | 9,002,218 |
11 | 11 | 60 | 240 | 70.02 | 69.85 | Hydroxyapatite-Dental | 9,002,224 |
12 | 10 | 48 | 180 | 68.64 | 68.83 | Hydroxyapatite | 9,003,548 |
13 | 10 | 48 | 280 | 71.63 | 71.48 | Carbonate Hydroxyapatite | 9,003,555 |
14 | 10 | 48 | 180 | 68.53 | 68.83 | Hydroxylellestadite | 9,009,375 |
15 | 10 | 68 | 180 | 67.52 | 67.47 | Hydroxyapatite-Dental | 9,002,226 |
16 | 12 | 48 | 180 | 67.98 | 68.15 | Apatite (CaOH) | 9,010,050 |
17 | 11 | 36 | 240 | 69.93 | 70.04 | Hydroxyapatite-Dental | 9,002,223 |
18 | 10 | 48 | 80 | 63.71 | 63.61 | Carbonate Hydroxyapatite | 9,003,550 |
19 | 10 | 48 | 180 | 70.01 | 68.83 | Hydroxyapatite | 9,002,214 |
20 | 10 | 48 | 180 | 67.85 | 68.83 | Hydroxyapatite | 9,003,549 |
Run . | A pH . | B Contact time (h) . | C Stirring rate (rpm) . | Crystallinity (%) . | Compound identification . | Joint Committee on Powder Diffraction Standard (JCPDS) No. . | |
---|---|---|---|---|---|---|---|
Actual . | Predicted . | ||||||
1 | 9 | 60 | 240 | 69.14 | 69.44 | Carbonate hydroxyapatite | 9,003,553 |
2 | 10 | 48 | 180 | 69.40 | 68.83 | Hydroxyapatite | 9,011,093 |
3 | 10 | 48 | 180 | 68.58 | 68.83 | Hydroxyapatite | 9,002,216 |
4 | 9 | 36 | 120 | 63.17 | 63.52 | Carbonate hydroxyapatite | 9,003,552 |
5 | 11 | 36 | 120 | 65.31 | 65.19 | Hydroxyapatite | 9,002,215 |
6 | 9 | 36 | 240 | 69.06 | 69.21 | Carbonate hydroxyapatite | 9,003,551 |
7 | 8 | 48 | 180 | 66.42 | 66.07 | Hydroxylellestadite | 9,009,375 |
8 | 10 | 28 | 180 | 66.73 | 66.52 | Chlorapatite | 9,001,263 |
9 | 9 | 60 | 120 | 64.79 | 64.86 | Carbonate hydroxyapatite | 9,003,555 |
10 | 11 | 60 | 120 | 66.07 | 66.10 | Hydroxyapatite-Dental | 9,002,218 |
11 | 11 | 60 | 240 | 70.02 | 69.85 | Hydroxyapatite-Dental | 9,002,224 |
12 | 10 | 48 | 180 | 68.64 | 68.83 | Hydroxyapatite | 9,003,548 |
13 | 10 | 48 | 280 | 71.63 | 71.48 | Carbonate Hydroxyapatite | 9,003,555 |
14 | 10 | 48 | 180 | 68.53 | 68.83 | Hydroxylellestadite | 9,009,375 |
15 | 10 | 68 | 180 | 67.52 | 67.47 | Hydroxyapatite-Dental | 9,002,226 |
16 | 12 | 48 | 180 | 67.98 | 68.15 | Apatite (CaOH) | 9,010,050 |
17 | 11 | 36 | 240 | 69.93 | 70.04 | Hydroxyapatite-Dental | 9,002,223 |
18 | 10 | 48 | 80 | 63.71 | 63.61 | Carbonate Hydroxyapatite | 9,003,550 |
19 | 10 | 48 | 180 | 70.01 | 68.83 | Hydroxyapatite | 9,002,214 |
20 | 10 | 48 | 180 | 67.85 | 68.83 | Hydroxyapatite | 9,003,549 |
The reliability of a model can be evaluated using ANOVA which considers uncertainties in the structural reliability of a model. The theoretical framework for calculating the probability of failure is known as the reliability index of structural systems (Chelladurai et al. 2020). Hence, the significance of the quadratic polynomial model was validated by performing the ANOVA test result as shown in Table 3.
Source . | Sum of squares . | df . | Mean square . | F-value . | p-value . | F-crit . |
---|---|---|---|---|---|---|
Model | 92.88 | 9 | 10.32 | 30.40 | <0.0001 | Significant |
A, pH | 4.30 | 1 | 4.30 | 12.65 | 0.0052 | |
B, contact time | 1.10 | 1 | 1.10 | 3.25 | 0.1016 | |
C, stirring rate | 75.59 | 1 | 75.59 | 222.69 | <0.0001 | |
AB | 0.0903 | 1 | 0.0903 | 0.2661 | 0.6172 | |
AC | 0.3486 | 1 | 0.3486 | 1.03 | 0.3347 | |
BC | 0.6105 | 1 | 0.6105 | 1.80 | 0.2095 | |
A² | 4.81 | 1 | 4.81 | 14.16 | 0.0037 | |
B² | 6.02 | 1 | 6.02 | 17.74 | 0.0018 | |
C² | 2.98 | 1 | 2.98 | 8.77 | 0.0143 | |
Residual | 3.39 | 10 | 0.3394 | |||
Lack of fit | 0.5281 | 5 | 0.1056 | 0.1843 | 0.9565 | not significant |
Pure error | 2.87 | 5 | 0.5732 | |||
Cor total | 96.28 | 19 |
Source . | Sum of squares . | df . | Mean square . | F-value . | p-value . | F-crit . |
---|---|---|---|---|---|---|
Model | 92.88 | 9 | 10.32 | 30.40 | <0.0001 | Significant |
A, pH | 4.30 | 1 | 4.30 | 12.65 | 0.0052 | |
B, contact time | 1.10 | 1 | 1.10 | 3.25 | 0.1016 | |
C, stirring rate | 75.59 | 1 | 75.59 | 222.69 | <0.0001 | |
AB | 0.0903 | 1 | 0.0903 | 0.2661 | 0.6172 | |
AC | 0.3486 | 1 | 0.3486 | 1.03 | 0.3347 | |
BC | 0.6105 | 1 | 0.6105 | 1.80 | 0.2095 | |
A² | 4.81 | 1 | 4.81 | 14.16 | 0.0037 | |
B² | 6.02 | 1 | 6.02 | 17.74 | 0.0018 | |
C² | 2.98 | 1 | 2.98 | 8.77 | 0.0143 | |
Residual | 3.39 | 10 | 0.3394 | |||
Lack of fit | 0.5281 | 5 | 0.1056 | 0.1843 | 0.9565 | not significant |
Pure error | 2.87 | 5 | 0.5732 | |||
Cor total | 96.28 | 19 |
The p-value represents the degree of significance of each factor. Factors that have a p-value (Prob. > F) of less than 0.0500 (p < 0.05) imply that the model terms are significant with a confidence level of 0.95. The F-value of the ‘model’ obtained in this study was 30.40 with a p-value of 0.0001. This result proved that the model generated was statistically significant with only a 0.1% chance of noise occurrence. The variables of A, C, A2, B2, and C2 had significant effects on the crystallinity since the p-values were less than 0.05. However, other variables were indicated as insignificant due to being higher than 0.05 of p-value. For single variables, variable B was identified as a limiting factor toward the optimization of HAp crystallinity. Since there was no influence of model reduction in insignificant variables, those insignificant variables were retained. The F-value with ‘lack of fit’ obtained was 0.1843 and the p-value was 0.9565 which implied that the lack of fit was not significantly relative to the pure error. Therefore, the relationship between crystallinity and the model was well within the chosen range.
This study also observed that the crystallinity, the pH, and the stirring rate were significant. These factors affected the crystallinity since the p-value was <0.05, implying the good factors. In acidic conditions, the phosphate ion, P, and Ca ion cannot combine to form the HAp crystal. However, HAp itself can self-integrate very well in alkaline conditions and forms a chemical bond between P and Ca (Goh et al. 2021). The maximum reaction of HAp produced in an alkaline condition compared to the acidic condition showed that the crystallinity is dependent on the pH of HAp.
For the stirring rate, the agglomeration between the particles will form a crystal and the speed of the stirring rate influences the size of crystallite and crystallinity since the particle agglomeration during the precipitation and maturation step controls the sizes of aggregates (Tourbin et al. 2020). Most particles are small and in micron size, and will become a greater proportion of larger agglomerates. As the stirring rates increase, the particle has a narrow size distribution, with most of the particles on a volume basic and having size between 1 and 10 μm (Baig et al. 2020; Molenaar et al. 2021). The fit statistic obtained from RSM showed the coefficient of determination R2 was 0.9647, implying that 96.47% of the variability in the response can be explained by the quadratic model. Also, ought to be close to the value of 1 and considered significantly very high indicating that this model is highly reliable.
The difference between the predicted R2 and the adjusted R2 should be less than 0.2. In this study, the adjusted R2 was 0.9330 and the predicted R2 was 0.9094, the difference between these two values was only 0.02 which was less than 0.2 and, therefore, is an acceptable agreement. Adequate precision is a measurement of the experimental design signal-to-noise ratio which compares the range of predicted values at design points to average prediction error and a ratio that is >4 is desirable. Statistically, the value of adequate precision in this case was 19.3134, indicating an adequate signal that can be used to navigate the design space.
The normal probability plot was closely accumulated at a certain point. These indicated an accurate model along with good agreement throughout the operating variables range as the residuals were distributed in a linear line (Regti et al. 2017). The normal distribution between the experimental and predicted values (Figure 1) indicated that the predicted result and the experimental result are highly similar and correlated strongly with each other. Although there were some points that showed slight deviation, most of the points were scattered in straight lines proving that there was great correspondence between the actual and predicted values (Bayuo et al. 2022). These plots (Figure 2(a) and 2(b)) were considered a good fit for the quadratic models.
Identification of the type of HAp compounds produced
Table 2 presents the identification of compounds produced by RSM and was evaluated by the XRD analysis using PDXL software to match the spectra of the Joint Committee on Powder Diffraction Standard, JCPDS. From the XRD pattern search analysis, six of 20 experiment runs showed the formation of significant chemical structure of HAp compounds, indicating the suitable condition for phosphorus recovery from freshwater AQW. About four runs produced carbonated hydroxyapatite which indicated that the lower pH (below 10) is not enough for Ca2+ and crystallization process.
In addition, the CCD of the synthesized HAp exhibited a low-intensity peak which was considered low crystallinity since the calcination temperature was only at 100 °C but was still amorphous. However, low crystalline HAp exhibited a high biodegradation rate and was more metabolically active compared to high crystalline HAp (Alparslan 2017). The HAp CCD crystallite size was calculated using the Scherrer equation and the size obtained was 23.6 nm (Mohd Pu'ad et al. 2019).
The peak at 3,311.58 cm−1 was assigned to the stretching mode of the hydroxyl group (OH−). The band is relatively sideways from the broad peak for the O–H stretch vibration of hydrogen-bonded OH group (Poralan et al. 2015).
Recovery of phosphate ion in AQW
The AQW originally contained phosphorus concentrations between 17 and 23 ppm, which were added to achieve an optimal Ca/P ratio of 1.67 to produce HAp. After the synthesis of HAp, the phosphate concentration was measured and an impressive removal rate of 89.97% was observed, indicating successful optimization using RSM and CCD. Despite this success, a major challenge remains in optimizing the HAp production process for practical applications in wastewater treatment. Factors such as cost and availability of materials, and variations in wastewater composition are obstacles to large-scale implementation. To overcome these limitations, further research is required to assess scalability, cost efficiency, and operational robustness to ensure the feasibility and sustainability of the technology for practical use.
CONCLUSIONS
The goal for the present study, i.e. the optimization and characterization of HAp via RSM, was successfully achieved. This finding also proved that freshwater AQW can be recycled, with phosphorus being the most significant element that can be recovered. By chemical treatment with calcium as an adsorbent for phosphorus, the phosphate content in wastewater can be effectively reduced and converted into a valuable form. By running 12 experiment sets, the optimized conditions to produce HAp selected by RSM analysis were as follows: pH of 10, 48 h, and stirring rate of 180 rpm through a wet precipitate method. Statistically, RSM through CCD for optimization HAp showed that the quadratic model is significant. The FTIR result showed all the functional groups consisted of HAp while XRD showed the intensity and peaks of the commercial Hap, proving that this study was able to get almost similar HAp to the standard HAp. At the same time, this study managed to recover the uptake phosphate, and the phosphate recovery was around 89.97% which is considered as a good performance. For future research, the method could be extended to a larger industrial scale. Investigating alternative methods of phosphate recovery from wastewater and conducting thorough long-term environmental impact assessments are essential next steps. These assessments will be critical to making informed decisions and developing policies for the widespread implementation of such wastewater treatment and resource recovery methods.
ACKNOWLEDGEMENTS
The authors thank Universiti Malaysia Terengganu and the Ministry of Higher Education (MOHE) for providing the laboratory facilities and financial support, respectively. This work was supported by the Fundamental Research Grant Scheme (FRGS) (FRGS/1/2020/TK0/UMT/01/1).
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.