Abstract
In this study, activated carbon (ACs) adsorbent was synthesized using the lignocellulosic waste (LCB) seed from Adansonia digitata L. (BSP) using two steps of hydrothermal carbonization (HTC) followed by activation. The hydrothermally produced char of BSP was activated to produce porous activated carbon BSPAC, where K2CO3 was used as a chemical activating agent. Box Behnken Design was used to optimize the input variables of pyrolysis temperature (A1), residence time (B1), and ratio (C1) for the pyrolysis process. Removal percentage (β1), percentage carbon yield (β2), and fixed carbon (β3) percentage were chosen as output responses. The analysis of variance was utilized to generate appropriate mathematical models with subsequent statistical analysis. Physiochemical characterizations were carried out for the hydrothermally carbonized sample (BSPC) and the optimized activated sample (BSPAC). Langmuir, Freundlich, and Temkin models were employed to estimate the isotherm model parameters. The results demonstrated that HTC with subsequent mild activation using K2CO3 can be considered as a greener route to obtain better-quality porous carbon having surface area of 599 m2/gm for removal of Cu(II) cations from wastewater.
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Two Step Synthesis and Application of Porous Carbon for Removal of Copper (II) from Wastewater: Statistical Optimization and Equilibrium Isotherm Analysis
Shobana Sinniha,a Zaira Zaman Chowdhury,a,* Ahmad Ibn Ibrahimy,b Mostak Ahmed,a Mohd. Rafie Bin Johan,a Mayeen Uddin Khandaker,c Irfan Anjum Badaruddin,d Sarfaraz Kamangar,d and Mohamed Hussien e
In this study, activated carbon (ACs) adsorbent was synthesized using the lignocellulosic waste (LCB) seed from Adansonia digitata L. (BSP) using two steps of hydrothermal carbonization (HTC) followed by activation. The hydrothermally produced char of BSP was activated to produce porous activated carbon BSPAC, where K2CO3 was used as a chemical activating agent. Box Behnken Design was used to optimize the input variables of pyrolysis temperature (A1), residence time (B1), and ratio (C1) for the pyrolysis process. Removal percentage (β1), percentage carbon yield (β2), and fixed carbon (β3) percentage were chosen as output responses. The analysis of variance was utilized to generate appropriate mathematical models with subsequent statistical analysis. Physiochemical characterizations were carried out for the hydrothermally carbonized sample (BSPC) and the optimized activated sample (BSPAC). Langmuir, Freundlich, and Temkin models were employed to estimate the isotherm model parameters. The results demonstrated that HTC with subsequent mild activation using K2CO3 can be considered as a greener route to obtain better-quality porous carbon having surface area of 599 m2/gm for removal of Cu(II) cations from wastewater.
DOI: 10.15376/biores.19.2.3699-3724
Keywords: Hydrothermal carbonization; Adsorption; Box Behnken Design; Analysis of variance; Isotherm
Contact information: a: Nanotechnology and Catalysis Research Center, University of Malaya, 50603 Malaysia; b: Department of Economics, Faculty of Business and Economics, University of Malaya, 50603 Malaysia; c: Applied Physics and Radiation Technologies Group CCDCU, School of Engineering and Technology, Sunway University, Bandar Sunway, 47500 Selangor, Malaysia; Faculty of Graduate Studies, Daffodil International University, Daffodil Smart City, Birulia, Savar Dhaka, 1216, Bangladesh; d: Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia; e: Department of Chemistry, King Khalid University, Abha, 61421, Saudi Arabia; *Corresponding author: dr.zaira.chowdhury@um.edu.my; Zaira.chowdhury76@gmail.com
INTRODUCTION
The utilization of lignocellulosic waste (LCB) to generate sustainable bio-based products (carbon, bio-oil, and biogas) is essential for promoting the practice of a circular economy. LCB consists of three major types of biopolymers: cellulose, hemicellulose, and lignin. Porous activated carbons (ACs) produced from LCB can generate adsorbent materials to effectively remove copper(II) from wastewater. The transition metal of copper can exist as a zero valent copper (metal), monovalent cuprous ions-Cu(I), and divalent cupric ions-Cu(II). Due to their bioaccumulation tendency, divalent cations of Cu(II) may cause severe toxicity in living organisms (Ali et al. 2023). Copper is mainly used for manufacturing semiconductors, electronic devices, and electro-plating processes. Long-term exposure to copper through polluted water can lead to diarrhea, nausea, and kidney and liver failures (Alcaraz et al. 2020; Mulungulungu et al. 2021). According to the EPA, USA, in drinking water, the copper level should not exceed to 1300 µ/L. Numerous approaches have been implemented to eliminate metallic pollutants from wastewater to comply with environmental regulations. There are several methods for treating wastewater, including electrochemical treatment (Ho et al. 2021), ion exchange (Virolainen et al. 2021), adsorption (Jin et al. 2018; Ali et al. 2023), and chemical precipitation (Tsai et al. 2020). In addition to its many advantages, adsorption is also recognized for its ease of implementation. Carbonaceous adsorbent can be regenerated and recycled after water treatment, which improves efficiency and reduces overall operating costs. Because of their substantial porous structure, large surface area containing functional groups, and resilience to harsh environmental conditions, carbon materials are widely used in water treatment (Chowdhury et al. 2016a; Adebisi et al. 2017). Regardless of extensive research carried out on developing new porous materials containing metal/organic frameworks (MOFs) (Rego et al. 2021) and covalent-organic frameworks (COFs) (Jiang et al. 2019) for the adsorptive removal of pollutants, such products are difficult to apply practically because of their high costs and complicated preparation methods. To address these obstacles, low-cost, effective, and sustainable carbonaceous materials must be developed from renewable sources of LCB residues (Chowdhury et al. 2012). Several biomass wastes, mainly containing lignocellulosic biopolymers (cellulose, hemicellulose, and lignin), have been used to produce activated carbon (ACs). Researchers have used physical, chemical, and physiochemical activation techniques to produce ACs from rice straw (Fierro et al. 2010), shell of palm and cocoa (Kundu et al. 2015; Saucier et al. 2015), frond of banana (Foo et al. 2013), peel of pomegranate and rambutan (Ahmad et al. 2014; Njoku et al. 2014), date stones (Abbas and Ahmed 2016), microalgae (Ferrera-Lorenzo et al. 2014), weeds of Crofton (Zheng et al. 2014), pulp mill sludge (Namazi et al. 2015), macadamia fruits’ endocarp (Pezoti Junior et al. 2013), lignin waste (Maldhure and Ekhi 2011), corn stover (Zhu et al. 2015), etc.
Thermochemical conversion of LCB has gained attention from researchers because it can provide abundant, renewable feed stock, including biofuels, bioplastics, and carbonaceous materials, for versatile applications. The LCB is generated from forest and agricultural residues, solid organic matter from processing industries, wood, paper, and pulp. It is a carbon-neutral and renewable feedstock to produce useful chemicals (Adebisi et al. 2016). In general, LCB consists of 20% to 40% hemicellulose, 40% to 60% cellulose, and 10% to 24% lignin (Rajesh Banu et al. 2021). The global yearly production of LCB residues is around 181.5 billion tons, and approximately 8.2 billion tons are utilized for various applications (Singh et al. 2022). This illustrates the potential availability of LCB residues for conversion to yield a diverse spectrum of valuable chemical products. Transformation of LCB is paving avenues for its recycling and production of sustainable, eco-friendly products for energy, and environmental applications. It can be readily converted to value-added products using biochemical (microbial/enzymatic treatment) and thermochemical (pyrolysis, hydrothermal carbonization, gasification, combustion, and liquefication) conversion processes (Wang and Wu 2023; Singh et al. 2022). The processes of carbonization and pyrolysis are used to produce carbonaceous materials, biofuel, and syngas from LCB (cellulose, hemicellulose, and lignin) residues (Patel and Shah 2021). Careless disposal of this LCB waste can adversely affect the ecosystem by contaminating water and soil resources (Yu et al. 2021). It is possible to extend the usefulness of LCB wastes even further through recycling and reprocessing. The potential of a material is not wasted but is further utilized rather than dumped.
Hydrothermal carbonization (HTC) is considered as one of the most efficient methods for carbonizing LCB to produce hydrochar, which can be converted to ACs for versatile applications. In this process, wet biomass is used, and energy-intensive pre-drying of biomass can be avoided (Benavente et al. 2015). Wet biomass is heated at relatively lower temperatures (120 to 350 °C) and under high pressure to obtain carbon-rich hydrochar. The characteristics of water change significantly under subcritical conditions. As the temperature increases from 100 to 350 °C, the pressure also increases, from 10 to 25 MPa. The dielectric constant of water decreases, and the strength of the hydrogen bonds also decreases in water. Water dissociates to form acidic hydronium ions (H3O+) and basic hydroxide (OH–) ions. The degree of coalification is dependent on the temperature and time of the HTC process. Hydrochar contains different functional groups and can be converted to ACs with superior qualities to treat wastewater (Chowdhury et al. 2018).
In most of the literature, strong Lewis’s acid-base catalysts of ZnCl2, H3PO4, or KOH are used as activating agents. Application of these harsh, corrosive chemicals can induce secondary pollution in the environment. Based on the literature, there is a lack of research on the HTC of BSP to produce hydrochar (BSPC) and subsequently convert it into porous activated carbons (BSPAC) using K2CO3. The porous texture and surface-active sites of carbonaceous adsorbent can be tuned by optimizing the activation conditions, which can ensure sufficient removal of pollutant molecules from wastewater. Process input variables were optimized to obtain maximum output responses using Response Surface Methodology (RSM). Temperature (A1), time (B1), and impregnation ratio (C1) were chosen as input variables using the Box Behnken Design (BBD). Three output responses/variables of percentage removal of copper, Cu(II) ions (β1), percentage yield (β2), and fixed carbon content (β3) were estimated. Under optimum conditions, the hydrochar BSPC was activated. Resultant activated carbon of BSPAC was used to remove Cu(II) cations using the adsorption process. Thus, herein K2CO3 was used as a mild activating agent to produce superior-quality activated carbon (ACs). In this study, the adsorption performance of the optimum BSPAC sample was evaluated by estimating the equilibrium isotherm parameters. Linear regression analysis was completed for the adsorption data using Langmuir, Freundlich, and Temkin isotherm models. The findings of this study indicate that hydrochar-BSPC has the potential to serve as an effective substrate for the synthesis of porous activated carbons (BSPAC), which can be successfully utilized for the removal of Cu(II) ions from wastewater.
EXPERIMENTAL
Materials
Adansonia digitata L. (baobab) seed was collected from Malaysian Agricultural Research and Development (MARDI). It was crushed to powder and washed with deionized water (DI). It was then dried at 60 °C for 24 h and stored in an airtight container. Potassium carbonate (K2CO3) was obtained from Sigma Aldrich, Malaysia. Analytical grade copper sulphate penta hydrate (CuSO4⋅5H2O) salt, HCl, and NaOH were obtained from Merck, Malaysia.
Methodology
Hydrothermal carbonization of Baobab seed powder (BSP)
The hydrochar was prepared from the washed and dried baobab seed powder (BSP). HTC was carried out in inert atmosphere by passing N2 gas to prevent sample burning and deionized water (DI) was used as solvent. About 10 g of BSP biomass sample was mixed with 100 mL DI water and placed inside the Teflon-lined autoclave. The temperature was kept at 180 ℃ for 120 min. The synthesized hydrochar was labelled as BSPC, washed with DI water until the pH reached 7, and dried under vacuum oven overnight at 50 °C. The BSPC sample was refluxed with preselected weight of K2CO3 based on Box Behnken Design (BBD) as provided by Tables 1 and 2. The samples of hydrochar were refluxed for 6 h at 60 ℃. It was then filtered to remove excess K2CO3. The samples were washed vigorously. It was dried in vacuum oven to prevent burning at 50 ℃ overnight. The samples were stored for activation under different experimental conditions of temperature ( and time ( as shown by the design matrix of Tables 1 and 2.
Fixed bed pyrolysis to produce activated carbon BSPAC from hydrochar BSPC
Approximately 40 g of K2CO3 impregnated hydro-char (BSPC) was placed inside the fixed bed reactor and N2 gas flow was given at 100 mL/min to ensure inert atmosphere and then the gas cylinder was changed to CO2 gas flow. The gas flow rate for CO2 was maintained at 100 mL/min. The temperature and time were changed according to the design matrix provided by Tables 1 and 2. The N2 gas flow was changed to CO2 gas flow after the reactor reached a stable temperature as suggested by the design matrix of Table 2. Thus, the activation of hydrochar was carried out for specific temperature (A1) and duration (B1) after refluxing them with predetermined ratio of K2CO3 (C1) (Table 2). The synthesized ACs from 17 experimental runs (Table 2) were meticulously washed using hot DI water to eliminate the remaining chemical solution until the pH reached 7. The samples were dried under vacuum oven at 50 ℃ overnight. The samples were stored for further characterizations and the results obtained were tabulated in Table 2 for development of regression model, ANOVA analysis, and process optimization using numerical approach. Tables 1 and 2 illustrate the RSM-BBD design layout for activation conditions.
Table 1. Input/Independent Variables using Box Behnken Design for Activation of hydrochar BSPC
The experimental run number (N) required for three input variables can be expressed by Eq. 1,
N = 2k (k − 1) + C0 (1)
where k signifies the number of variables, C0 shows the number of central points, and where under same conditions, experiments are repeated.
Table 2. Box-Behnken Factorial Design for Preparation of BSPAC from BSPC
In this study, three input variables of activation temperature (A1), residence time (B1), and weight ratio (C1)between hydrochar (BSPC) and activating agent of K2CO3 were chosen. The desired responses/output variables were percentages removal of Cu(II) from wastewater (β1), yield percentages (β2), and fixed carbon content (β3). The lower, middle, and higher levels of each factor/variable were indicated as -1, 0, and +1, correspondingly (Adebesi et al. 2016; Pei et al. 2023). The experimental runs can be randomized, and the second order regression model can be expressed using the Eq. 2 (Chowdhury et al. 2016b; Ayed et al. 2019;),
(2)
where Y is the preselected response, constant coefficient is b0, the linear coefficient is bi, denotes the interaction coefficients, is the coefficient for quadratic terms. Here, and reflect the coded values of variables (Chowdhury et al. 2016b;). Five trials are needed at the center point for the three input variables, and overall, 17 experiments (N) are required (Table 2) (Adebesi et al. 2016; Pei et al. 2023).
Equilibrium adsorption studies
A precise amount of BSPAC sample prepared under optimum condition was placed inside the plastic bottles containing the adsorbate – Cu(II) solution, where the concentration ranges were varied from 50 to 100 mg/L. The samples were agitated at 250 rpm. To ensure true adsorption rather than precipitation, the pH was kept constant at pH 5.5. Removal percentages of Cu(II) cations were estimated using Eq. 3 and yield percentages were determined by Eq. 4:
(3)
(4)
Equations 5 and 6 were used to calculate amount of adsorbate (Cu(II) cations) ions, (mg/g) at different time t and (mg/g) after equilibrium contact time, t (min) (Adebesi et al. 2017a). Equations 5 and 6 are as follows,
(5)
(6)
where C0 is the initial concentration of the pollutant adsorbate of Cu(II) cations, is the remaining equilibrium concentration in liquid phase; both in mg/L, is the weight of adsorbent carbon in g and volume of adsorbate solution is in L (Akinpelu et al. 2021).
The adsorption isotherm serves to establish a correlation between the concentration of a pollutant or adsorbate in a liquid medium and its adsorbed amount onto the solid adsorbent surface. Linear regression analyses for Langmuir, Freundlich, and Temkin isotherms were run. The suitability of the model was checked by estimating the values of R2 along with some model constants. An adsorption system is better represented by a model; with an R2 value near to unity (Adebesi et al. 2017b; Huang and Garcia-Bennett 2021).
According to the Langmuir adsorption isotherm, changes in pressure inside the equilibrium system affect the amounts of pollutants adsorbed. The observation illustrates the correlation between the number of active sites over the adsorbent and the pressure. A monolayer of pollutant can accumulate over the homogeneous surface of adsorbent (Doan et al. 2021; Mittal et al. 2021;). However, it should be noted that the Langmuir isotherm is applicable specifically at lower temperatures. Langmuir adsorption model can be represented by Eqs. 7, 8, and 9,
(7)
(8)
(9)
where equilibrium adsorbed amount is denoted by qe (mg/g), qm (mg/g) is the maximum monolayer adsorption capacity, Ce represents the concentration of the pollutant at equilibrium, and the energy of adsorption is defined by Langmuir adsorption constant KL. If Langmuir constant RL = 1; the process is linear, for RL> 1, the process is unfavourable; RL= 0, the process is irreversible, and for 0 <RL< 1, the process is favourable.
According to Freundlich isotherms, pollutants can form multilayer over the heterogeneous surface of the adsorbents (Akinpelu et al. 2021). The linear and nonlinear forms of Freundlich isotherms are given as Eqs. 10 and 11: 
(10)
(11)
Here, 
reflects sorption affinity and KF (mg/g) (L/mg)1/n denotes constant for Freundlich isotherm. Freundlich isotherm states that sorption is linear at low pressure but independent at high pressure.
The Temkin isotherm suggests a progressive decrease in the heat of sorption with increasing surface coverage. Additionally, during the sorption process, there are indirect interactions between the adsorbate and adsorbent (Chowdhury et al. 2015; Akinpelu et al. 2021). Linear and non-linear equations of Temkin model are:
(12)
(13)
where Temkin constant is 
, which correlates with the heat of sorption, and the Binding constant is KT (L/g). Solution absolute temperature is T (°K), and universal constant is R = 8.314 J/mol.K).
Characterizations
The surface morphological features showing texture of surface along with pore characteristics of starting biomass (BSP), hydrochar (BSPC), and their respective activated carbon (BSPAC) prepared under optimum conditions were observed using field emission scanning electron microscopy (FESEM- Fei Quanta Feg-450, Malaysia). Thermogravimetric/proximate analysis (TGA) of BSP, BSPC, and BSPAC were performed using an analyzer (TGA-Mettler Toledo TGA/SDTA 851e, USA). This would estimate the percentage fixed carbon, volatile materials, moisture, as well as ash proportion of the sample. The temperature was raised from 30 ℃ to 850 ℃ under air and nitrogen flow to calculate the weight loss of the samples in each stage. Elemental analyzer was used in (PerkinElmer- Series II 2400, Japan) to calculate the proportion of carbon, hydrogen, nitrogen, and others in the starting BSP, hydrochar (BSPC), and activated carbon (BSPAC) samples. Nitrogen gas adsorption-desorption analysis was carried out at 77 °K to measure the surface area, pore diameter, and pore volume of the synthesized samples. The synthesized samples were outgassed before Brunauer Emmett Teller (BET) analysis for 4 h at 300 ℃. After that, BET analysis (Tri-Star II Micrometrics surface area analyzer, USA) was carried out to analyze the pore size distribution of BSPC and BSPAC samples.
RESULTS AND DISCUSSION
Fitting of Regression Model and Statistical Analysis for Activation of Hydrochar (BSPC) to Produce Activated Carbon (BSPAC)
Regression models were developed for three responses of removal percentages of Cu(II) cations (β1), yield percentages (β2), and fixed carbon content (β3) percentages. The developed models were formulated using the highest degree of polynomial equations for all three responses, which included significant additional terms. Based on the sequential model sum of squares, it was determined that these models were not aliased. Quadratic models were proposed for copper removal percentages (β1) and yield percentages (β2). A linear model was suggested for fixed carbon content percentages (β3). The underlying empirical equations proposed by BBD design are represented by Eqs. 14, 15, and 16 as a function of coded variables after deleting unnecessary factors from the responses. The reliability of the designs can be verified by examining the plots of experimental versus predicted data points and can be represented by Figs. 1(a), 1(b), and 1(c).
(14)
(15)
(16)
For a single parameter of pyrolysis temperature (A1), time (B1), and ratio (C1), the coefficient was derived based on the contribution of that specific factor in determining the removal percentage (β1), carbon yield (β2), and fixed carbon content (β3). The second-order terms reflected the quadratic impact of that variable. For all the above equations of 14, 15, and 16; positive sign preceding the term implied a synergistic relationship, whereas the negative sign reflected an antagonistic impact (Chowdhury et al. 2012; Ayed et al. 2019). To assess the reliability of the developed model, the coefficient of determination, R2, and standard deviation (SD%) values should be analyzed. The R2 values statistically indicate how accurately a model correlates with the experimental data (Adebesi et al. 2016). The R2 values for Eqs. 14, 15, and 16 were 0.997, 0.990, and 0.881, respectively. The multiplication of coefficients of two variables confirmed the cumulative effect of two factors/variables.
Fig. 1. Actual/experimental versus predicted data points (a); percentage removal β1 (b); percentage yield β2 (c); and percentage fixed carbon β3 for BSPAC
The results obtained from ANOVA analysis are presented in Tables 3, 4, and 5 for removal percentages (β1), yield percentages (β2), and fixed carbon content percentages (β3), respectively. The F-values obtained here for removal percentages of Cu(II) cations (β1), percentage yield (β2), and percentage fixed carbon content (β3) were 257.58, 80.01, and 31.99, respectively. This reflects that the models developed here, were significant. The Prob > F values were below 0.05, suggesting that the model input variables chosen for the specified responses held statistical significance (Chowdhury et al. 2012).
Table 3. ANOVA and Statistical Analysis for Effect of Input Parameters/Variables on Percentage Removal (β1)
The variables of temperature (A1), residence time (B1), ratio (C1), and their quadratic terms A12 and C12 were significant for the percentage removal, β1. Compared to temperature (A1), other linear variables of time (B1), and ratio (C1) had moderate effect on the percentage removal (β1). The interaction between temperature and time (A1B1) had greater impact on the responses of percentage removal, β1, than the other interaction variables of B1C1 and C1A1 (Table 3).
For yield percentages (β2), linear terms of temperature (A1), time (B1), their interaction terms A1B1 and A1C1 and their quadratic terms 
, 
were significant. Temperature had the greatest influence on this response. The interaction between time and ratio (B1C1) had more prominent influence rather than the other interaction terms (C1A1 and A1B1) on percentage yield, β2 (Table 4). For fixed carbon content percentages, β3; temperature (A1), time (B1), and ratio (C1) were significant model terms (Table 5). The obtained standard deviations (SD%) and coefficient of variation (CV) were minimal, implying that the suggested models are dependable (Tables 3, 4, and 5) (Sellamuthu et al. 2023). The signal/noise ratio (s/n) achieved in this case exceeded 4, indicating sufficient precision for the proposed models (Sellamuthu et al. 2023). The adequate precision values obtained for removal percentages for β1, percentage yield β2, and fixed carbon content β3, were 55.03, 19.74, and 18.70, respectively. Therefore, the experimental data collected in this research held statistical importance for directing the design (Sellamuthu et al. 2023).
Table 4. ANOVA and Statistical Analysis for Effect of Input Parameters/Variables on Percentage Yield (β2)
The impact of processing variables on percentage removal (β1)
According to the F values reported in Table 3, it was concluded that the pyrolysis temperature () had the strongest influence on percentage removal (β1), and the duration (B1) and ratio (C1) had approximately lower effects on this response. When the ratio was adjusted at center/zero, the trend for sorption capacity was quantified and the combined impact of temperature (A1) and time (B1) was illustrated by 3D RSM mesh plot of Fig. 2(a). A schematic representation of the impact of ratio (C1) and time (B1) on the removal percentages (β1) is presented in Fig. 2(b), in which the temperature (A1) was maintained constant at zero level.
A lot of studies have shown that activation time, as well as temperature, can diminish the surface area and sorption capacity of the synthesized ACs if the limit of those two factors is extremely high or low. A prolonged pyrolysis time or excessive pyrolysis temperature could adversely affect the existing porous structure (Chowdhury et al. 2016c). Similarly, insufficient activation time and/or a reduced temperature of activation cannot improve the porosities of the carbonaceous adsorbent. Thus, process optimization is needed to maintain the quality of the carbon (Sellamuthu et al. 2023).
Table 5. ANOVA and Statistical Analysis for Effect of Input Parameters/Variables on Percentage Fixed Carbon (β3)
Fig. 2. 3D Response surface plots for percentage removal (β1): (a) influence of temperature (A1) and time (B1) when ratio (C1) was constant at zero/center points (1.25); (b) Influence of time (B1) and ratio (C1) when temperature (A1) was constant at zero/center points (650 °C)
The higher the temperature, the more likely it is that micropores walls will deform and eventually break, to form mesopores. This will facilitate the penetration of the pollutant adsorbate inside the porous region of the carbon matrix. Even with an extensive surface area, if the size of mesopores is excessively large, it will be ineffective in capturing and retaining the smaller metallic cations. This leads to a decrease in removal efficiency (Chowdhury et al. 2017).
A synergistic relationship between the three variables and the removal efficiencies (β1) was observed in this study. Gradually raising the temperature and allowing sufficient contact time for activation would increase the reaction rate between the hydrochar and K2CO3. Thus, high-quality ACs can be produced by increasing the activation time and temperature up to a certain level. Beyond that certain limit, the ACs samples will deteriorate.
It can be observed from Fig. 2(a) that, the removal percentages decreased slightly after exceeding the specific limit of time and temperature. With increasing K2CO3 ratio, as well as time, the removal percentages were increasing (Fig. 2b). In some cases, excessive temperature may result in degradation of certain functional groups as well as porous texture of carbon, leading to more ash formation. Although the impregnation ratio was important in the development of pores, it was not the only factor. The increase in K2CO3 enhanced the reaction rate, resulting in an increase in the number of pores in the carbon. High concentration of K2CO3 was not also favourable. In that case, additional chemical reactions between K2CO3 and hydrochar can cause the destruction of pores that had existed previously. This might reduce the amount of fixed carbon content and increase the formation of ash residues.
The impact of processing variables on percentage yield (β2)
The impact of process variables on activated carbon yield (β2) was demonstrated by 3D response surfaces plots, presented by Figs. 3(a and b).
Fig. 3. 3D Response surface plots for percentage yield (β2): (a) influence of temperature (A1) and time (B1) when ratio (C1) was constant at zero/center points (1.25); (b) influence of time (B1) and ratio (C1) when temperature (A1) was constant at zero/center points (650 °C)
Figure 3(a) represents the overall impact of temperature (A1) and pyrolysis time (B1) on yield percentages (β2) where the ratio (C1) between hydrochar (BSPC) and K2CO3 was kept at zero level (ratio = 1.25). The influence of pyrolysis time (B1) and impregnation ratio (C1) on activated carbon yield (β2) percentages are shown by Fig. 3(b), where the temperature was kept at zero level (650 °C). In general, it was discovered that the yield of carbon (β2) decreased with increasing reaction temperature (A1), time (B1), and ratio (C1). As observed from Figs. 3a and b, the temperature had a greater influence on the carbon yield. Carbon yield was minimum when the sample was pyrolyzed at temperature 800 °C for 2 h and the ratio was 1.25. In contrast, the impact of activation time on the yield percentages was moderate. A rise in temperature would increase volatile compound discharge due to increased dehydration and elimination processes during the activation. It would finally decrease the yield of BSPAC.