# Optimizing activated carbon production from chontaduro seeds via physical activation with steam for ibuprofen removal: A factorial design and multi-attribute utility theory approach

L Angel, Audrey Villot, M Rodriguez Susa, J Plazas-Tuttle, Yves Andres, Claire Gerente

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L Angel, Audrey Villot, M Rodriguez Susa, J Plazas-Tuttle, Yves Andres, et al.. Optimizing activated carbon production from chontaduro seeds via physical activation with steam for ibuprofen removal: A factorial design and multi-attribute utility theory approach. Journal of Water Process Engineering, 2025, 70, pp.107020. 10.1016/j.jwpe.2025.107020. hal-05086786

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# Optimizing activated carbon production from chontaduro seeds via physical activation with steam for ibuprofen removal: A factorial design and multi-attribute utility theory approach

L. Angel<sup>a,b</sup>, A. Villot<sup>a,*</sup>, M. Rodriguez Susa<sup>b</sup>, J. Plazas-Tuttle<sup>c</sup>, Y. Andres<sup>a</sup>, C. Gerente<sup>a</sup>

## ARTICLE INFO

Editor: Gamze Varank

Keywords:

Biomass valorization
Ibuprofen adsorption
Multi-attribute optimization
Physical activation
Sustainable approach

## ABSTRACT

The primary goal of this research is to optimally produce activated carbon (AC) from chontaduro seeds (CS) in a sustainable way and evaluate its effectiveness in removing ibuprofen (IBU) from water. Chontaduro is a fruit from tropical regions in the Americas, especially Colombia. This initiative addresses two existing challenges: the lack of circular economy strategies for CS in this country and the increasing contamination of surface waters with IBU. Direct physical activation with steam was chosen as activation method because it saves water, time, and energy; enables better development of specific surface area and reduces process costs and related pollutants. Four variables (holding time, maximum temperature, temperature ramp and steam flow) were selected to find out the optimal set of parameters to produce AC through a 2<sup>k</sup> factorial experimental design, leading to an experiment with 16 runs. Multi-Attribute Utility Theory (MAUT) was applied to optimize the production of AC by maximizing yield, surface area, mesopore volume and net energy ratio (NER). Optimized AC (AC11) exhibits a surface area of 1105 ± 12 m<sup>2</sup>·g<sup>-1</sup> with a mesopore volume of 0.180 ± 0.013 m<sup>2</sup>·g<sup>-1</sup>. AC11 was tested for IBU removal in deionized water (DIW) and soft synthetic water (SW), showing remarkable adsorption capacities of 204.8 mg·g<sup>-1</sup> and 240.7 mg·g<sup>-1</sup>, respectively, after a pseudo-equilibrium time of 3 days. AC11 performance was compared to four others ACs produced in the experimental design, confirming that AC11 is indeed the optimal choice and validating the power of factorial design and MAUT. No competition in the adsorption mechanism was observed between IBU and natural organic matter (NOM). CS demonstrated to be a remarkable precursor for the production of AC.

## 1. Introduction

Around 140 billion tons of biomass residues are generated worldwide annually [1] and it is estimated that this amount will continue to increase in the coming years. This scenario necessitates urgent measures to reuse these waste materials in production processes [2], creating value and reducing the amount of waste disposed, marking a sharp shift from traditional linear production models. Activated carbon (AC) is a material produced from carbon-rich sources. Traditionally, AC has been produced from coal [3,4] or peat [5,6], but they are considered non-renewable sources [7]. Therefore, in response to the environmental concerns and in pursuit of embracing sustainability objectives, there has been a significant exploration of AC production from renewable

precursors with promising results, particularly from residual biomass [8–11]. Currently, the most commonly used materials for AC production, after coal (42 %), include coconut shell (22 %), wood (18 %) and other biomass residues (18 %) [12]. Coconut shell is a frequently employed residual biomass for industrial AC production [13], while wood serves as a versatile material for industrial applications [14].

AC is renowned for its remarkable adsorption capacity [15–17] due to its large surface area (500–1500 m<sup>2</sup>·g<sup>-1</sup>) and high porosity [10]. Mesoporous carbons have larger pores sizes (2–50 nm), which enables the adsorption of larger molecules [18] compared to microporous carbons (< 2 nm) [19–21]. Granular AC (particle size between 0.2 and 5 mm [22]) is favored for continuous use and regeneration [23]. To produce AC sustainably, it is crucial that the precursor is readily available

<footnote><sup>a</sup> IMT Atlantique, GEPEA UMR CNRS 6144, F-44307 Nantes, France</footnote>

<footnote><sup>b</sup> Universidad de los Andes, Environmental Engineering Research Center, Bogota, D.C., Colombia</footnote>

<footnote><sup>c</sup> Universidad de los Andes, Water Distribution and Sewerage Systems Research Center, Bogota, D.C., Colombia</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: luis.angel@imt-atlantique.fr (L. Angel), audrey.villot@imt-atlantique.fr (A. Villot), manuel-r@uniandes.edu.co (M. Rodriguez Susa), jplazas@uniandes.edu.co (J. Plazas-Tuttle), yves.andres@imt-atlantique.fr (Y. Andres), claire.gerente@imt-atlantique.fr (C. Gerente).</footnote>
locally. In the Pacific and Amazon regions of Colombia, the annual production of chontaduro (Bactris gasipaes), a fruit indigenous to the area and with high nutritional value [24], exceeds 40,000 tons, becoming the fifth fruit with the highest production in these regions [25]. The absence of a waste disposal strategy for chontaduro residues (> 20 % w.t.), comprising shells and seeds [26], demands sustainable alternatives. Chontaduro seeds (CS) are preferred for AC production due to their high fixed carbon content, low moisture [27], and mechanical resistance. Additionally, there is a notable absence of documented uses of CS to produce AC in the literature.

The production of AC is a two-step process: carbonization (or pyrolysis) and activation. Pyrolysis is a thermal decomposition of organic matter under inert atmosphere, releasing most of the non-carbon elements in gas phases and tars [28]. Operating conditions [29] and chemical composition of precursors has direct influence on this process [28]. Char produced from pyrolysis presents rudimentary surface area (< 100 m²·g⁻¹) [28]. Activation consists in a reaction between the precursor and an activating agent at high temperatures that results in surface area increasing. Activation can be physical or chemical [30], but physical activation is preferred because it enables improved control over microporosity development, does not require chemical neutralization, reduce process costs and pollutants, and is associated with greater physical strength [23]. Physical activation occurs at 700–1000 °C in the presence of oxidizing agents, such as CO₂ or steam [11]. Steam is preferred over CO₂ because it increases the pore depth and diameter, creating not only microporosity (as it mainly occurs with CO₂), but also mesopores and macropores. In addition, steam has a smaller critical molecular dimension and lower molecular weight, allowing an easier diffusion and faster reaction rate [28]. Also, high-density materials like seeds are better activated with steam [23]. Moreover, pyrolysis and activation can be carried out in a one-step methodology, known as direct physical activation [28], saving water, time, unit operations and energy [31]. This consists of starting the activation process immediately after carbonization by injecting the water into the reactor after reaching the desired temperature.

However, the optimal values for the operating parameters of pyrolysis and activation are usually not transferable from one biomass to another [23]. Therefore, AC production from CS requires a careful experimental design to determine optimal conditions [32]. Previous studies aimed to maximize yield [33,34], surface area [33,35], or removal efficiency for specific pollutants [36,37]. For this study, surface area, pore distribution and carbon content will be considered as response variables from a technical perspective since they are related to the adsorptive capacity of the AC, while yield and Net Energy Ratio (NER) will be used as sustainability indicators. NER represents the ratio between the energy contained in the by-products of the process and the energy consumed [38], giving a new perspective about the potential for valorization of CS. In this context, the use of a 2k factorial experimental design emerges as a powerful approach for exploring the factors influencing AC production [39]. Simultaneously, Multi-Attribute Utility Theory (MAUT) can be used to optimize the response variables by using a single indicator that contains the information from all of them [40]. Regarding the operating parameters, a previous study identified holding time, maximum temperature, and steam flow as crucial factors of direct physical activation to produce AC from peats using a factorial experimental design [32]. Therefore, these three factors will be analyzed, along with temperature ramp due to its potential effect on NER.

On the other hand, the rapid development of civilization and industry brings with it a significant increase in the generation of consumer's by-products that affect the environmental matrices as water bodies [8]. Pharmaceuticals constitute an increasing proportion of these emerging contaminants in natural aquatic environments [41] and have been recently detected in surface water [42,43]. These compounds often display characteristics such as non-biodegradability, persistence, stability, and hydrophilicity, and are predominantly inclined to bioaccumulate in humans and animals [44]. Ibuprofen [2-(4-

isobutylphenyl) propanoic acid], is one of the most consumed non-steroidal anti-inflammatory drugs (NSAIDs), used as painkiller in headache, arthritis, muscle pain, etc. [45]. It has been shown that conventional wastewater treatments are not effective to remove ibuprofen (IBU) [41], which leads to its appearance in surface water bodies. Last years, studies have detected the presence of IBU in surface water in Colombia [46,47], but also in other countries around the world, including USA [48], South Africa [49], Portugal [50], France [43], China [51], and Costa Rica [52], showing that IBU is a globally widespread pollutant. Therefore, IBU becomes a contaminant of special interest and requires the search for sustainable solutions for its removal. AC has the property of retaining this type of organic compounds due to its polar nature, making them pass from the aqueous to the solid phase during the adsorption process [53]. Results from previous studies about IBU removal with AC show promising results [44,54,55].

Thus, considering the high availability of CS, the lack of solutions for their sustainable use, the promising composition of these residues and the emerging presence of IBU in surface water, the right conditions are in place to establish the main objective of this research, which is to optimally produce AC from CS through direct physical activation with steam in a sustainable way and evaluate its effectiveness in removing IBU from water. To achieve this, characterization of AC was carried out to confirm its suitability as precursor. Then, a standardized procedure for AC production, combined with factorial experimental design and MAUT, was employed to determine the optimal activation conditions after characterizing the ACs through some key variables. Finally, adsorption kinetics and isotherms were studied to evaluate the AC's adsorption capacity for IBU, and competition with a standard natural organic matter (NOM) was analyzed.

## 2. Materials and methods

### 2.1. Preparation and characterization of CS

To get the raw material, the chontaduro fruits were peeled and boiled; the pulps were removed, and the resulting seeds were washed. The material was dried at 105 °C until constant weight, crushed, and sieved to obtain particles between 2.36 and 4.75 mm. For characterization analyses, particles were ground into powder to ensure homogeneity.

For the determination of ash content in CS, a methodology adapted from ASTM D-1102 was followed. Approximately 5 g of dried and crushed seeds were placed in a crucible and heated in a muffle furnace (Nabertherm P330), using a ramp of 10 °C·min⁻¹ until reaching 550 °C. The sample was held at this temperature until constant weight was achieved. The procedure was performed by triplicated.

Proximate analysis aimed to determine moisture, volatile solids, and fixed carbon following an adapted procedure from ASTM D-7582. Approximately 20 mg of crushed and dried CS sample were placed in a SETSYS Evolution Thermogravimetric Analyzer (TGA) with a controlled nitrogen flow (20 mL·min⁻¹). An initial temperature of 105 °C was set and an isotherm of 15 min was applied. At a ramp of 10 °C·min⁻¹, the temperature was raised to 950 °C and an isotherm was applied for 50 min. At a ramp of -10 °C·min⁻¹, the temperature was lowered to 550 °C, the gas was changed to oxygen with a flow of 20 mL·min⁻¹ and the temperature was kept constant for 50 min. Finally, the equipment was allowed to cool to room temperature. The microbalance measured the mass in each step, allowing to calculate moisture, volatile solids, and fixed carbon (using the results from ash content). The analysis was performed by duplicated.

For the lignocellulosic content analysis, approximately 20 mg of crushed and dried CS sample underwent a series of temperature steps under a nitrogen flow (20 mL·min⁻¹) using TGA. The process started with an initial temperature of 105 °C, maintained for 15 min. Subsequently, a ramp of 10 °C·min⁻¹ raised the temperature to 250 °C and it was kept for 50 min. This sequence was repeated at 300 °C and 500 °C.
After completing the temperature profile, the equipment was allowed to cool to room temperature. Hemicellulose, cellulose, and lignin were calculated, and the mass was measured by the microbalance in each step. The procedure was performed by duplicated.

Ultimate analysis of CS was conducted using a Thermo Finnigan AE1112 Series Flash Elemental Analyzer. 0.5 to 1.0 mg of the sample was placed in a tin (CHNS analysis) or silver (O analysis) capsule [11]. To determine CHNS, flash combustion of the samples was performed at 920 °C in the presence of oxygen, while for O determination samples were pyrolyzed under helium atmosphere at 1000 °C. The gases produced passed through a chromatographic column, which allowed to determine the initial quantities of these elements. For each case, the method was performed by triplicated. All results from this section are presented in dry basis.

## 2.2. Optimization of AC production using 2<sup>k</sup> factorial design and multi-attribute utility theory

The transformation of CS into AC involves several parameters that can be controlled. To optimize the direct physical activation process, a comprehensive 2<sup>k</sup> factorial experiment was designed. The experiment explored four key factors, and each one was evaluated at two levels: low (-) and high (+). Holding time (code: A, levels: 0.75 h - 2 h), maximum temperature (code: B, levels: 850 °C - 950 °C), temperature ramp (code: C, levels: 5 °C·min<sup>-1</sup>-10 °C·min<sup>-1</sup>), and steam flow (code: D, levels: 0.5 mL·min<sup>-1</sup>-1 mL·min<sup>-1</sup>) were selected, resulting in a total of 16 experimental runs. Relevant factors and levels were obtained from literature for similar activation processes with biomass.

Seven response variables were selected to analyze the performance of the produced ACs: yield, carbon content, surface area, micropore volume, mesopore volume, total pore volume and NER.

The evaluation of the factors influencing the response variables was carried out by analysis of variance (ANOVA) using Minitab®. Factors were evaluated individually and their second order interactions. Each response variable was subjected to individual scrutiny. A 5 % significance level was chosen.

To simultaneously analyze all the relevant response variables, MAUT was applied. MAUT is a decision-making tool that combines multiple criteria into a single global utility score (labeled as GUS in this research for simplicity) [56]. GUS was used to enable a comprehensive assessment of the production process of AC from CS. The first step was selecting the response variables that were the most relevant in the study. Then, to compute all relevant response variables, each of them were normalized [57]. Minimum and maximum values of each indicator were assigned to 0 % and 100 % in the normalized scale, respectively, and the remaining values were assigned proportionally to that scale. Therefore, the normalized values are in the same scale and can be operated. The indicator is calculated with Eq. 1.

$$GUS = \sum_{j=1}^{n} x_j \bullet p_j \qquad (1)$$

Where GUS is the MAUT indicator (global utility score), x is the weight (or importance) of the response variable j in the process, and p is the normalized score of the response variable j. The indicator allows to select the best configuration according to the chosen criteria and allows to generate a ranking among the combinations.

## 2.3. Direct physical activation with steam

Direct physical activation process starts placing 100 g of dried and crushed CS with a particle size between 2.36 and 4.75 mm into a semirotating quartz tube, which is inserted into an electric furnace (Carbolite HTR 11/150). Direct activation process with steam begins with the pyrolysis step, in which the selected temperature ramp (C) is used until the desired maximum temperature (B) is reached; and follows with the activation phase, in which that temperature is kept constant during the

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 1.** [a] Temperature profile of direct physical activation with steam. [b] Experimental setup for direct physical activation with steam. [c] Composition of syngas measured by MicroGC (Run 9).</figcaption></figure>

holding time (A), and steam is injected (D). Temperature profile is illustrated in Fig. 1 [a]. Nitrogen flow (0.75 L·min<sup>-1</sup>) serves as a sweeping gas during activation, directing the produced gases out of the reactor. Water is injected when the maximum temperature is reached. Condensation of gases occurs through an ice-cooled condenser, collecting the condensable fraction in an oil collector. Gas microchromatography is used for real-time analysis of non-condensable gases. A schema of the process is shown in Fig. 1 [b].
## 2.4. Characterization of produced ACs

Yield, NER, ash content, elemental composition, and porosity properties were studied in the whole matrix of produced ACs. Yield represents the percentage of the obtained AC in relation to the initial mass of CS. A higher yield is related to a more efficient transformation of the precursor. Yield is obtained weighting initial and final mass in the process (Eq. 2).

$$ \text{Yield (\%)} = \frac{\text{Mass of produced AC}}{\text{Precursor mass}} \cdot 100\% \qquad (2) $$

NER assesses the energy recovery potential of the activation process. To determine Higher Heating Value (HHV) for solids, approximately 1 g of AC sample was burned in an oxygen bomb calorimeter (PARR 6100). For liquids, the procedure included the use of benzoic acid standard pellets with known heating value to burn approximately 1 g of oil. The energy potentially obtained from AC and oil was calculated by multiplying their respective masses by their HHV. Regarding syngas, its energy content was obtained from the gas composition results from Micro Gas Chromatograph (MicroGC) analysis. The molar composition of combustible gases and their Lower Heating Values (LHV) from literature data were used in the calculations. NER was calculated as the ratio between the energy contained in the by-products and the electric energy (EE) consumed by the furnace during the process [11]. The energy potential of AC is included in NER due to the possibility of reusing the material once it has fulfilled its application (Eq. 3).

$$ \text{NER} = \frac{\text{mass}_{\text{AC}} \times \text{HHV}_{\text{AC}} + \text{mass}_{\text{oil}} \times \text{HHV}_{\text{oil}} + \sum (\text{mass}_{\text{gas}_i} \times \text{LHV}_{\text{gas}_i})}{\text{EE}} \qquad (3) $$

Ash content of ACs was determined by following the methodology proposed for CS in section 2.1, modifying the initial mass for 2 g (as ashes are more concentrated in AC than in CS) and using 650 °C instead of 550 °C as heating temperature [28], since AC may contain more refractory minerals that require a higher temperature to be fully converted to ash. Ultimate analysis was conducted following the same procedure as for CS.

Porosity properties of ACs were analyzed through adsorption/desorption of nitrogen. Approximately 0.1 g of dried and uncrushed AC samples underwent a 48-h degassing process at 350 °C. Degassed samples were analyzed using a Micromeritics 3-Flex instrument in liquid nitrogen at -196 °C. Nitrogen gas pressure changes were used to measure adsorbed and desorbed nitrogen, and the resulting isotherms were analyzed with the Brunauer-Emmett-Teller (BET) method to determine specific surface area. The Horvath-Kawazoe (HK) and Barrett-Joyner-Halenda (BJH) methods were employed to calculate micropore and mesopore volumes, respectively.

Additionally, Gas Chromatography/Mass Spectrometry (GC/MS) techniques were employed to quantify the concentration of 16 typical Polycyclic Aromatic Hydrocarbons (PAH) compounds in some of the AC samples, following protocol UNE EN 17503. This aimed to verify if the ACs could release any toxic organic substances when used for water treatment purposes.

To better understand the interaction between the ACs surfaces and the adsorbates, pH zero-point charge (pH<sub>PZC</sub>) was determined for the optimized AC. 50 mL solutions of 10<sup>-2</sup> M NaCl were prepared in closed flasks. The pH of the solutions was adjusted between 2 and 12 using 0.1 M HCl or NaOH solutions. 0.15 g of AC was added to each solution, followed by a 5-day agitation period at 20 °C. pH<sub>PZC</sub> was then identified where the curve pH<sub>f</sub> vs. pH<sub>0</sub> crosses the line pH<sub>f</sub> = pH<sub>0</sub> [58].

Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) were employed simultaneously to examine the morphology of AC, determine its surface elemental composition and identify the presence of impurities.

## 2.5. Soft synthetic water, natural organic matter and ibuprofen adsorption tests

To reproduce typical surface waters quality for the adsorption tests and to maintain a uniform composition in the water samples used in the experiments, the use of a synthetic water recipe was proposed. Most of the surface waters in Colombia, for instance, are soft [59]. The inorganic soft water recipe used is based on previous research on the subject [60], whose characteristics and composition of proposed waters are like those found in Colombia's surface water bodies. Thus, the methodology proposed by the authors was followed. The ion composition of the resulting soft synthetic water (SW) contains anions and cations in balanced concentrations. The anions present in the SW recipe are Na<sup>+</sup>, Ca<sup>2+</sup>, Mg<sup>2+</sup> and K<sup>+</sup>, with concentrations of 250, 530, 120 and 25 µeq·L<sup>-1</sup>, respectively. Cations consist in Cl<sup>-</sup>, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and HCO<sub>3</sub><sup>-</sup>, with concentrations of 280, 30, 230 and 385 µeq·L<sup>-1</sup>, respectively. Both ions and cations total 925 µeq·L<sup>-1</sup> each [60].

IBU sodium salt supplied by Supelco® at a purity ≥98% was used. To accurately quantify IBU in water samples, calibration curves were prior constructed for deionized water (DIW) and SW using a range of IBU solutions with known concentrations. IBU concentrations were measured by UV-spectrophotometer at 222 nm [61] for DIW and SW. Quantification limits for DIW and SW were 1.8 and 0.3 mg·L<sup>-1</sup>, respectively.

To investigate adsorption kinetics, IBU solutions (950 mL) of known initial concentrations (60, 80 and 100 mg·L<sup>-1</sup> in DIW and 100 mg·L<sup>-1</sup> in SW) were prepared and placed into amber-colored reactors along with 400 mg of optimized AC. Stirring at 250 rpm and a constant temperature of 20 °C was kept. Supernatant samples were collected at specific times to monitor the remaining IBU concentration until no variations were found.

Experimental data was fitted to Pseudo-first order model (PFOM, Eq. 4), pseudo-second order model (PSOM, Eq. 5) and Elovich model (Eq. 6) using Minitab®. PFOM assumes that the rate of change of adsorbate is directly proportional to the difference between the saturation concentration and the amount of solid adsorbed, which can be applicable during the initial stage of the process. PFOM describes adequately the adsorption when it occurs through diffusion in the interface. On the other hand, the PSOM model assumes that the rate-limiting step is chemisorption. In this case, the adsorption rate depends on adsorption capacity rather than the concentration of adsorbate. It can predict behavior over the entire range of adsorption [62]. Regarding the Elovich model, it considers a heterogeneous surface, with no desorption or interactions between adsorbed molecules that could affect the adsorption process [34].

$$ q_t = q_e (1 - e^{-k_1 t}) \qquad (4) $$

$$ q_t = \frac{k_2 q_e^2 t}{1 + k_2 q_e t} \qquad (5) $$

$$ q_t = \frac{1}{\beta} \ln(\alpha\beta) + \frac{1}{\beta} \ln(t) \qquad (6) $$

Where q<sub>t</sub> (mg·g<sup>-1</sup>) represents the amount of IBU adsorbed after t (h), q<sub>e</sub> (mg·g<sup>-1</sup>) is the adsorbed IBU at equilibrium, k<sub>1</sub> (h<sup>-1</sup>) and k<sub>2</sub> (g·mg<sup>-1</sup>·h<sup>-1</sup>) are the rate constants for PFOM and PSOM, respectively, and α and β are the initial adsorption rate (m·g<sup>-1</sup>·h<sup>-1</sup>) and the desorption constant (g·mg<sup>-1</sup>) in the Elovich model, respectively. Initial adsorption rate in PSOM model can be calculated as k<sub>2</sub>·q<sub>e</sub><sup>2</sup>. Sum of squares error (SSE) was used as a goodness-of-fit indicator since linearization and R<sup>2</sup> may attenuate the high errors and SSE provides more realistic kinetic parameters [63].

To investigate potential competition between natural organic matter (NOM) and IBU during adsorption onto AC, a NOM standard from surface water (Suwannee River, Georgia, United States) was supplied by the
<table><caption>**Table 1**
Summary of energy balance, response variables and MAUT analysis.</caption><thead><tr><th rowspan="2">Sample</th><th colspan="4">Factors levels</th><th colspan="5">Energy balance</th><th colspan="5">Response variables (values before normalization)</th><th colspan="5">MAUT optimization (normalized values)</th></tr><tr><th>A (Hold Time)</th><th>B (Max. Temp.)</th><th>C (Temp. Ramp)</th><th>D (Steam flow)</th><th>Final solid mass (g)</th><th>AC HHV (cal·g<sup>-1</sup>)</th><th>Final liquid mass (g)</th><th>Oil HHV (cal·g<sup>-1</sup>)</th><th>Syngas energy (KW·h)</th><th>Energy consumption (KW·h)</th><th>Yield (%)</th><th>Net Energy Ratio (%)</th><th>BET surface area (m<sup>2</sup>·g<sup>-1</sup>)</th><th>Carbon content (%)</th><th>Micropore volume (cm<sup>3</sup>·g<sup>-1</sup>)</th><th>Mesopore volume (cm<sup>3</sup>·g<sup>-1</sup>)</th><th>Total pore volume (cm<sup>3</sup>·g<sup>-1</sup>)</th><th>Yield (%)</th><th>Net Energy Ratio (%)</th><th>Surface Area</th><th>Mesopore volume</th><th>GUS</th></tr></thead><tbody><tr><td>AC1</td><td>—</td><td>—</td><td>—</td><td>—</td><td>24.0</td><td>6701 ± 10</td><td>36.2</td><td>5209 ± 168</td><td>0.055</td><td>2.735</td><td>24.0</td><td>16.9 ± 0.3</td><td>600 ± 23</td><td>79.0 ± 1.6</td><td>0.252 ± 0.014</td><td>0.063 ± 0.001</td><td>0.299 ± 0.015</td><td>100 %</td><td>69 %</td><td>7 %</td><td>21 %</td><td>49 %</td></tr><tr><td>AC2</td><td>+</td><td>—</td><td>—</td><td>—</td><td>19.2</td><td>6775 ± 26</td><td>52.9</td><td>2149 ± 51</td><td>0.079</td><td>3.844</td><td>19.2</td><td>9.4 ± 0.1</td><td>700 ± 7</td><td>77.9 ± 1.6</td><td>0.296 ± 0.004</td><td>0.085 ± 0.011</td><td>0.365 ± 0.015</td><td>64 %</td><td>16 %</td><td>21 %</td><td>34 %</td><td>34 %</td></tr><tr><td>AC3</td><td>—</td><td>+</td><td>—</td><td>—</td><td>22.0</td><td>6742 ± 293</td><td>33.2</td><td>5005 ± 109</td><td>0.046</td><td>3.429</td><td>22.0</td><td>12.0 ± 0.3</td><td>583 ± 28</td><td>83.9 ± 0.4</td><td>0.246 ± 0.010</td><td>0.061 ± 0.004</td><td>0.292 ± 0.015</td><td>85 %</td><td>35 %</td><td>5 %</td><td>20 %</td><td>36 %</td></tr><tr><td>AC4</td><td>+</td><td>+</td><td>—</td><td>—</td><td>13.6</td><td>6953 ± 54</td><td>38.7</td><td>3098 ± 61</td><td>0.063</td><td>4.391</td><td>13.6</td><td>7.1 ± 0.1</td><td>1120 ± 32</td><td>78.3 ± 0.3</td><td>0.473 ± 0.014</td><td>0.159 ± 0.005</td><td>0.603 ± 0.022</td><td>22 %</td><td>0 %</td><td>81 %</td><td>79 %</td><td>45 %</td></tr><tr><td>AC5</td><td>—</td><td>—</td><td>+</td><td>—</td><td>22.3</td><td>7211 ± 48</td><td>36.0</td><td>2201 ± 31</td><td>0.093</td><td>2.295</td><td>22.3</td><td>16.2 ± 0.1</td><td>548 ± 12</td><td>84.4 ± 0.6</td><td>0.233 ± 0.006</td><td>0.029 ± 0.005</td><td>0.252 ± 0.009</td><td>87 %</td><td>64 %</td><td>0 %</td><td>0 %</td><td>38 %</td></tr><tr><td>AC6</td><td>+</td><td>—</td><td>+</td><td>—</td><td>16.8</td><td>6834 ± 205</td><td>46.7</td><td>2387 ± 14</td><td>0.144</td><td>3.382</td><td>16.8</td><td>12.0 ± 0.1</td><td>879 ± 28</td><td>83.7 ± 1.6</td><td>0.366 ± 0.012</td><td>0.074 ± 0.009</td><td>0.421 ± 0.023</td><td>45 %</td><td>35 %</td><td>47 %</td><td>27 %</td><td>39 %</td></tr><tr><td>AC7</td><td>—</td><td>+</td><td>+</td><td>—</td><td>20.2</td><td>6896 ± 342</td><td>34.3</td><td>5675 ± 10</td><td>0.116</td><td>2.783</td><td>20.2</td><td>18.1 ± 0.3</td><td>587 ± 5</td><td>83.0 ± 0.2</td><td>0.247 ± 0.004</td><td>0.049 ± 0.011</td><td>0.281 ± 0.009</td><td>71 %</td><td>78 %</td><td>5 %</td><td>12 %</td><td>42 %</td></tr><tr><td>AC8</td><td>+</td><td>+</td><td>+</td><td>—</td><td>12.3</td><td>6842 ± 250</td><td>45.7</td><td>3674 ± 194</td><td>0.190</td><td>4.157</td><td>12.3</td><td>11.6 ± 0.3</td><td>1256 ± 82</td><td>80.2 ± 0.6</td><td>0.530 ± 0.038</td><td>0.171 ± 0.017</td><td>0.655 ± 0.038</td><td>11 %</td><td>32 %</td><td>100 %</td><td>86 %</td><td>57 %</td></tr><tr><td>AC9</td><td>—</td><td>—</td><td>—</td><td>+</td><td>21.7</td><td>7132 ± 103</td><td>50.1</td><td>1911 ± 43</td><td>0.112</td><td>2.723</td><td>21.7</td><td>14.8 ± 0.1</td><td>605 ± 21</td><td>81.7 ± 0.9</td><td>0.254 ± 0.007</td><td>0.046 ± 0.001</td><td>0.283 ± 0.008</td><td>83 %</td><td>54 %</td><td>8 %</td><td>10 %</td><td>39 %</td></tr><tr><td>AC10</td><td>+</td><td>—</td><td>—</td><td>+</td><td>13.8</td><td>6797 ± 28</td><td>102.8</td><td>940 ± 17</td><td>0.168</td><td>3.843</td><td>13.8</td><td>10.1 ± 0.1</td><td>1186 ± 17</td><td>75.5 ± 0.1</td><td>0.492 ± 0.003</td><td>0.167 ± 0.040</td><td>0.635 ± 0.032</td><td>23 %</td><td>21 %</td><td>90 %</td><td>83 %</td><td>54 %</td></tr><tr><td>AC11</td><td>—</td><td>+</td><td>—</td><td>+</td><td>15.3</td><td>6837 ± 253</td><td>49.4</td><td>2265 ± 169</td><td>0.215</td><td>3.440</td><td>15.3</td><td>13.6 ± 0.3</td><td>1105 ± 12</td><td>77.4 ± 0.1</td><td>0.464 ± 0.006</td><td>0.180 ± 0.013</td><td>0.610 ± 0.016</td><td>34 %</td><td>46 %</td><td>79 %</td><td>91 %</td><td>62 %</td></tr><tr><td>AC12</td><td>+</td><td>+</td><td>—</td><td>+</td><td>2.2*</td><td></td><td>52.2</td><td>1142 ± 72</td><td>0.283</td><td>4.761</td><td>2.2*</td><td>7.4 ± 0.1</td><td></td><td>80.2 ± 1.7</td><td>0.269 ± 0.014</td><td>0.044 ± 0.014</td><td>0.301 ± 0.023</td><td>79 %</td><td>100 %</td><td>14 %</td><td>9 %</td><td>50 %</td></tr><tr><td>AC13</td><td>—</td><td>—</td><td>+</td><td>+</td><td>21.2</td><td>6876 ± 71</td><td>61.8</td><td>2607 ± 140</td><td>0.125</td><td>2.269</td><td>21.2</td><td>21.2 ± 0.5</td><td>646 ± 32</td><td>77.4 ± 1.6</td><td>0.498 ± 0.002</td><td>0.195 ± 0.024</td><td>0.673 ± 0.027</td><td>0 %</td><td>37 %</td><td>91 %</td><td>100 %</td><td>57 %</td></tr><tr><td>AC14</td><td>+</td><td>—</td><td>+</td><td>+</td><td>10.8</td><td>6672 ± 96</td><td>96.1</td><td>1289 ± 8</td><td>0.195</td><td>3.433</td><td>10.8</td><td>12.3 ± 0</td><td>1193 ± 5</td><td>79.5 ± 2.4</td><td>0.437 ± 0.011</td><td>0.131 ± 0.005</td><td>0.543 ± 0.012</td><td>29 %</td><td>65 %</td><td>71 %</td><td>62 %</td><td>57 %</td></tr><tr><td>AC15</td><td>—</td><td>+</td><td>+</td><td>+</td><td>14.6</td><td>6883 ± 90</td><td>59.8</td><td>2727 ± 176</td><td>0.189</td><td>2.837</td><td>14.6</td><td>16.3 ± 0.4</td><td>1048 ± 29</td><td>2.4</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>AC16</td><td>+</td><td>+</td><td>+</td><td>+</td><td>2.2*</td><td></td><td>79.9</td><td>1121 ± 64</td><td>0.356</td><td>4.248</td><td>2.2*</td><td>10.8 ± 0.1</td><td></td><td>80.2 ± 1.7</td><td>0.269 ± 0.014</td><td>0.044 ± 0.014</td><td>0.301 ± 0.023</td><td>79 %</td><td>100 %</td><td>14 %</td><td>9 %</td><td>50 %</td></tr></tbody></table>

<footnote>* Value obtained from muffle furnace.</footnote>

<footnote>** Unable to measure due to total gasification of the sample.</footnote>

<footnote>*** Excluded from the optimization process.</footnote>
International Humic Substances Society – IHSS (Suwannee River NOM - 2R101N). Three solutions of 950 mL were prepared. One contained 5 mg·L⁻¹ of NOM; another with 150 mg·L⁻¹ of IBU, and the third with 150 mg·L⁻¹ of IBU and 5 mg·L⁻¹ of NOM. Initial NOM concentration (5 mg·L⁻¹, C = 50.7 % w/w) was chosen based on typical range for COT in surface waters (1–3 mg·L⁻¹). The solutions were placed into amber-colored reactors along with 400 mg of optimized AC. Stirring at 250 rpm and a constant temperature of 20 °C were kept. Total organic carbon (TOC) monitoring was conducted. The TOC analysis employed a Shimadzu TOC – L analyzer to measure both Total Carbon (TC) and Inorganic Carbon (IC). The TOC content was calculated by subtracting IC from TC.

Adsorption isotherms were constructed to study the adsorption capacity of the optimized AC. IBU solutions of 200 mL with varying concentrations ([30–500] mg·L⁻¹) were prepared and placed into amber-colored reactors with 80 mg of optimized AC. After stirring at 250 rpm for three days (pseudo-equilibrium time found in kinetics tests), the remaining IBU concentrations were measured with UV-spectrophotometry.

To analyze the data, Freundlich (Eq. 7) and Langmuir (Eq. 8) isotherm models were fitted with linearization. The Langmuir model is based on the assumptions that the surface of the material is homogeneous (all adsorption sites are energetically equivalent), the adsorption process is reversible, and the adsorption occurs in a monolayer, where each site can host only one molecule without interactions between them. In contrast, the Freundlich model assumes a heterogeneous surface and adsorption sites with varying binding energies, describing a multilayer mechanism [62].

$$q_e = K_F \cdot C_e^n \rightarrow lnq_e = lnK_F + \frac{1}{n}lnC_e \quad (7)$$

$$q_e = \frac{q_{max} \cdot K_L C_e}{1 + K_L C_e} \rightarrow \frac{C_e}{q_e} = \frac{1}{q_{max} \cdot K_L} + \frac{1}{q_{max}} C_e \quad (8)$$

Where $K_F$ (g·L⁻¹) and n are Freundlich model's constants, $C_e$ (mg·L⁻¹) is the IBU concentration in the equilibrium, $q_{max}$ (mg·g⁻¹) is the maximum adsorption capacity and $K_L$ is Langmuir model's constant.

To compare the performance of the optimized AC obtained from the MAUT analysis, three other configurations from the original 16 runs were also used to construct their adsorption isotherms.

## 3. Results and discussion

This section presents the results and discussion of the experiments performed. It covers the preparation and characterization of CS, the optimized production of AC by direct physical activation with steam using a factorial experimental design and MAUT, and the examination of the properties of the produced ACs, including yield, net energy ratio, proximate and ultimate compositions was tested and compared to those of other ACs from the experimental matrix.

### 3.1. Chemical characterization of CS

Low ash content (2.2 % ± 0.2 %) was obtained from muffle furnace, which is in the desirable range to avoid pore obstruction during activation [23]. Values for volatile solids (79.1 % ± 0.4 %) and fixed carbon (18.7 % ± 0.4 %) were obtained from proximate analysis. High value for volatile solids promotes greater porosity in the produced AC [23].

Hemicellulose (29.0 % ± 3.7 %), cellulose (40.5 % ± 5.3 %) and lignin (10.1 % ± 0.6 %) were obtained from lignocellulosic content analysis. Lignin is the major contributor of the AC yield and its value is in the typical range for other lignocellulosic biomasses used to produce AC [2,28,64]. C (53.2 % ± 1.0 %) and O (36.3 % ± 0.4 %) are the most abundant elements in the CS, followed by H (8.2 % ± 0.3 %). These values are in the typical range for agricultural biomasses [28,29,65],

and the initial value for C content is a promising result to consider CS as AC precursor. Also, the presence of O favors the pore development in the AC matrix. Finally, low values for N (1.4 ± 0.2) and S (< LoD) were obtained. These values are in the typical range for lignocellulosic biomasses [28,66–68], which is a desirable condition due to the issues related to harmful gases emissions attached to nitrogen and sulfur oxides [65].

### 3.2. Direct physical activation

The thermochemical conversion process during the direct physical activation of CS can be divided into several stages. The evolution of the composition of the syngas during the direct physical activation reveals the processes occurring in the reactor and it is represented in Fig. 1 [c] (example for AC9). During the carbonization step, even with limited presence of oxygen in the air flow, the CO and CO₂ appear, which means that the oxygen contained in the CS is consumed to produce the combustion, enriching the carbon content in the char. However, when the oxygen runs out, CO and CO₂ levels decline since combustion stops. Likewise, CH₄ is also produced during carbonization because volatile hydrocarbons such as aldehydes, ketones and alcohols generated from the thermal decomposition of biomass components can undergo hydrogenation reactions, where the hydrogen available in the biomass binds to the carbon atoms, forming methane.

With the injection of steam, the activation starts. As a result, CO and CO₂ are released again and H₂ is produced in the oxidation reaction. Finally, when the steam supply ends, the production of these gases decreases until they disappear from the outflow at the end of the cooling phase. This sequence highlights the critical role of steam in enhancing the activation and pore development.

### 3.3. Resolving the experimental matrix: Response variables and AC properties

Sixteen runs of AC production from CS were performed to evaluate all possible combinations between low (–) and high (+) factor conditions of the factorial design. ACs produced from each run are labeled with the respective number (e.g. AC from run 1 is AC1). Results for the response variables are summarized in Table 1. The first outcome that jumps out is that for runs 12 and 16, the gasification of the CS was complete and only ashes were collected at the end of the activation process. This is explained by the fact that these runs had the high (+) values of holding time, maximum temperature, and steam flow, which favors the reaction of the carbon present in the precursor, to such an extent that all the carbon is consumed. Considering this, runs 12 and 16 are discarded in the optimization process, since no AC was produced in them. Then, results for yield of these runs are assigned from muffle furnace essays (ashes value), while textural results (surface area and pore distribution) and carbon content were not possible to measure since there were not samples to analyze.

Some results of interest can be extracted from Table 1, leaving aside runs 12 and 16. In terms of yield, AC1 obtained the highest results (24.0 %), what corresponds to a short process at the low temperature and steam flow, while AC14 exhibited the minimum value (10.8 %), which had the opposite parameters. These results are in line with expectations, showing that maximum temperature [69], holding time and steam flow directly influence the degree of precursor reaction.

The quantities of gas produced, as well as the calorific value of the gas mixture were calculated for each of the runs. Results for calorimetry essays, MicroGC analysis and electrical consumption of the furnace are shown in Table 1. HHV of the AC does not present significant changes between different runs. This is because the C content is similar among all the runs, so the combustion has similar amounts of combustible material. Regarding the oil, those runs with higher steam flow and holding time (and thus longer steam supply time), generate a higher amount of oil than their counterparts due to the water content present in the oil.
<figure><img src="image_7.png" /><figcaption>Fig. 2. H/C and O/C ratios for ACs produced in the experimental design.</figcaption></figure>

However, the higher the amount of water, the lower the HHV of the oil.

Regarding NER, AC13 (21.2 % ± 0.5 %) stood out above the others, followed by AC7. These ACs correspond to the short processes, so they need the lesser EE, leading to higher values of NER. NER is an important measurement for evaluating the energy efficiency of a process, as it provides an estimate of how much energy can be recovered in the form of byproducts compared to the total energy consumed. This result has important implications from economic and environmental perspectives. Economically, a high NER indicates efficient resource use, since a significant proportion of the energy invested in the process is recovered. From an environmental standpoint, a high NER suggests a lower carbon footprint and a sustainable process. It is interesting that despite the low

energy consumption of AC13, it still showed an acceptable value of surface area (646 m²·g⁻¹), which suggests that energy efficiency can be targeted without drastically compromising the quality of the AC. AC13 also exhibited a notable value of yield (21.2 %), but a low value of mesopore volume (0.044 cm³·g⁻¹). This is related to the limited extent of precursor degradation, which may hinder mesopores development and limit the AC adsorption capacity for large water contaminants. However, it presents an acceptable micropore volume, which makes it potentially suitable for other applications with smaller targeted molecules.

Ash content shows an increase for all 14 ACs, passing from 2.2 % in CS to [6.70 % - 16.4 %]. This increase is due to the partial release of the volatile solids because the reaction, and the variation in the ash content between the ACs is related to the extent of reaction in each configuration. Carbon content was obtained from elemental analysis. As expected, most of the remaining material in the AC samples corresponds to C, passing from 53.2 % in CS to 75.5 % - 84.4 %, while part of the O and H react in combustion reactions, decreasing from 36.3 % in CS to 5.2 % - 2.6 % and from 8.2 % to 1.2 % - 0.4 %, respectively. H/C and O/C ratios of the ACs are presented in Fig. 2. AC8 and AC11 exhibit the lowest O/C ratios, which suggests minimal polarity and high hydrophobicity as they are primarily composed of chemically stable fixed carbon aromatic ring. This is attributed to the aromaticity development in the AC structure due to the higher removal of H and O in the precursor [70]. Conversely, AC1, AC5, AC9 and AC13 present high values of this ratio. In these cases, the ACs increase their affinity for the water and the IBU adsorption is reduced [41]. A similar analysis can be done with H/C ratio, with AC3, AC4, AC7 and AC8 showing the lowest values and AC9 and AC13 showing the highest. The H/C ratio reflects the aromatic nature of the AC [71]. Low values of this ratio means high aromaticity, which is related to higher capacities to adsorb organic compounds [72].

To determine surface area and pore distribution, nitrogen adsorption-desorption tests were performed for each of the AC samples in duplicate. Typical microporous materials isotherms do not present hysteresis loops, while mesoporous ones exhibit isotherms with them, which indicates capillary condensation at the mesopore level and limited uptake at relative high pressure [28]. For these types of curves, at low relative pressure, the curve represents unrestricted monolayer adsorption, and when monolayer coverage is complete multilayer adsorption begins. The isotherms of AC5 (highest proportion of

<figure><img src="image_8.png" /><figcaption>Fig. 3. Nitrogen adsorption/desorption isotherms [a] AC5, [b] AC14.</figcaption></figure>

<figure><img src="image_9.png" /></figure>
<figure><img src="image_10.png" /><figcaption>Fig. 4. Standardized effects of the factors on the response variables. [a] yield, [b] NER, [c] surface area, [d] carbon content, [e] micropore volume, [f] mesopore volume, [g] total pore volume. Significance = 0.05.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Fig. 5. SEM observations of AC11 at [a] 500×, [b] 2000× and [c] 10,000×. [d] EDX spectrum for AC11 at 2000×. [e] Elemental composition mapping of AC11.</figcaption></figure>

micropores: 89 % micro - 11 % meso) and AC14 (the highest proportion of mesopores: 72 % micro - 28 % meso) are presented in Fig. 3 [a] and [b], respectively. As expected, AC with wider hysteresis loops corresponds to a higher proportion of mesopores.

Summary of porosity properties is presented in Table 1. For surface area, AC8 stood out with 1256 $m^2·g^{-1}$, which can be attributed to the specific conditions during activation, including a high holding time and maximum temperature. This high surface area indicates a greater availability of active sites for adsorption. AC10, AC14 and AC11 also scored significantly. Similarly, for mesoporous volume, both AC14 (0.195 $cm^3·g^{-1}$) and AC11 (0.180 $cm^3·g^{-1}$) achieved high scores. Mesopores provide additional sites within the material, allowing the adsorption of larger molecules. They also enable the access to micropores, facilitating diffusion processes. Interestingly, these ACs stand out in these response variables while maintaining yields comparable to other samples, unlike the complete precursor gasification observed in AC12 and AC16. This suggests that maximizing the quality of AC can be done without compromising the sustainability component.

Nitrogen adsorption-desorption tests show a strong influence of holding time, maximum temperature and steam flow into surface area, and micro, meso and total pore volume. This occurs because high values in these factors are intimately related to a higher development of porosity, which is directly linked to lower yields. Specifically, a longer holding time enhances the occurrence of chemical reactions and the formation of more extensive pore networks, contributing to increased

surface area and pore volume. Similarly, higher maximum temperature promotes greater carbonization and activation.

### 3.4. Statistical analysis to evaluate the impact of the factors on the response variables

The effects of the factors and their second order interactions into the response variables were evaluated. Analysis of variance (ANOVA) was performed for each one of the response variables (Fig. 4). Confirming what was stated before, ranked from highest to lowest, holding time, steam flow and maximum temperature have significative influence in yield, surface area and pore distribution. As was explained, this is because these factors are directly linked to the degradation of the carbonaceous material, so the higher these values are, the faster the available material will degrade (low yield) and the better the porosity will develop (high surface area, micro, meso and total pore volume). Moreover, the interactions between pairs of these three factors also impact the responses significantly, introducing new variations in the activation process. For instance, the interaction between holding time and steam flow can influence the rate of carbonaceous material degradation, leading to specific changes in surface area and pore distribution.

With respect to carbon content, the selected parameters have no influence on the carbonaceous composition of the AC produced. Regarding NER, holding time is the only factor which has a significant impact on it. This is due to the electric energy consumed, since the
<table><caption>**Table 2**
Fitting of adsorption kinetics models.</caption><thead><tr><th rowspan="2">Sample</th><th colspan="3">Pseudo-first order</th><th colspan="3">Pseudo-second order</th><th colspan="3">Elovich</th></tr><tr><th>q<sub>e</sub> (mg·g<sup>-1</sup>)</th><th>k<sub>1</sub> (1/h)</th><th>SSE</th><th>q<sub>e</sub> (mg·g<sup>-1</sup>)</th><th>k<sub>2</sub> (g·mg<sup>-1</sup>·h<sup>-1</sup>)</th><th>SSE</th><th>β (g·mg<sup>-1</sup>)</th><th>α (mg·g<sup>-1</sup>·h<sup>-1</sup>)</th><th>SSE</th></tr></thead><tbody><tr><td>C<sub>0</sub> = 60 mg·L<sup>-1</sup> DIW</td><td>96.6</td><td>0.140</td><td>752.3</td><td>109.8</td><td>0.00147</td><td>325.3</td><td>0.06330</td><td>82.6</td><td>1212.4</td></tr><tr><td>C<sub>0</sub> = 80 mg·L<sup>-1</sup> DIW</td><td>126.0</td><td>0.135</td><td>999.4</td><td>144.6</td><td>0.00104</td><td>492.4</td><td>0.04885</td><td>102.6</td><td>2872.6</td></tr><tr><td>C<sub>0</sub> = 100 mg·L<sup>-1</sup> DIW</td><td>129.6</td><td>0.160</td><td>1650.5</td><td>145.3</td><td>0.00133</td><td>688.7</td><td>0.04643</td><td>123.6</td><td>1638.3</td></tr><tr><td>C<sub>0</sub> = 100 mg·L<sup>-1</sup> SW</td><td>146.0</td><td>0.169</td><td>3705.6</td><td>162.0</td><td>0.00132</td><td>2082.3</td><td>0.04308</td><td>172.0</td><td>2333.8</td></tr></tbody></table>

maximum power consumption occurs during the time when the maximum temperature is kept constant, which corresponds to holding time definition.

### 3.5. MAUT analysis results highlighting optimal conditions and further characterization of optimized AC

To determine the optimal conditions for the activation process of CS, MAUT was applied and GUS was computed. Yield, surface area, mesopore volume and NER were selected as the variables of interest, according to the main objectives of this research. Yield is related to a more resource-efficient process, aligning with sustainability goals by maximizing resource utilization. NER reflects the ratio of energy output to energy input, providing insights into the overall sustainability performance. Surface area and mesopore volume were selected due to their direct impact on the adsorption capacity of ACs, particularly relevant for adsorbing larger soluble molecules like IBU. Each of these variables was normalized using the maximum and minimum values obtained from tests (excluding data regarding runs 12 and 16 as they do not produce

AC). The weighting was distributed equally among the factors (25 %). The corresponding results can be found in Table 1.

MAUT analysis suggests that the optimal configuration is found in AC11 (score = 62 %). This run has high values of maximum temperature and steam flow, alongside low values of temperature ramp and holding time. AC11 exhibits remarkable values in surface area (1105 ± 12 m²·g⁻¹) and mesopore volume (0.18 ± 0.013 cm³·g⁻¹) compared to ACs from other configurations, while maintaining acceptable values in yield and NER. It also shows a notable value of micropore volume (0.464 ± 0.006 cm³·g⁻¹), which contributes to the adsorption of pollutants. However, this AC properties not only outstand in the present study but also in a global comparison with other ACs [21,23,73]. The optimized AC (AC11) produced with this set of conditions was selected to continue to the next phase of the study regarding the removal of IBU from water. To compare the performance of AC11 with other configurations, ACs with difference in one parameter with respect to AC11 were selected. Thus, AC3 (difference in steam flow), AC9 (difference in maximum temperature) and AC15 (difference in temperature ramp) were also subjected to some subsequent analyses. AC1 was also included because it

<figure><img src="image_12.png" /><figcaption>**Fig. 6.** [a] Adsorption kinetic of IBU onto AC11 in DIW at C₀ = 60 mg·L⁻¹, 80 mg·L⁻¹ and 100 mg·L⁻¹ and in SW at C₀ = 100 mg·L⁻¹ and PSOM models, [b] TOC monitoring to analyze interaction between IBU and NOM in adsorption in SW.</figcaption></figure>
showed the highest yield with low levels in all parameters.

To determine if the materials were suitable for use in drinking water treatment, they were analyzed for PAH content. The analysis showed values below the limit of detection (< LoD) for all cases, except for the presence of 0.13 mg·kg⁻¹ of naphthalene in AC15. Therefore, this AC was discarded for further analyses due to the toxicological characteristics of the compound.

### 3.6. Further characterization of optimized AC: Morphology and surface elemental composition

Morphology of AC11 was observed with SEM and EDX at 500×, 2000× and 10,000× magnifications. Results are illustrated in Fig. 5 [a], [b] and [c], respectively, showcasing the morphological features of the AC11 derived from CS. At 500× magnification, the image reveals a detailed view of the porous structure, showing the distribution and size of larger pores on the AC surface. At 2000× magnification, a heterogeneous porous structure with relative uniformity in the pore sizes becomes evident, and solids covering the AC surface can also be seen. 10,000× magnification confirms the presence of macropores in the material, with the solids covering the surface being more clearly visible.

To identify the elements present in the sample, EDX was performed simultaneously with SEM observations. EDX spectrum and mapping at 2000× (Fig. 5 [d], [e], respectively) shows that AC11 surface is mainly composed of carbon (C = 80.70 % w.t.) an oxygen (O = 9.95 % w.t.), with significant traces of potassium (K = 3.98 % w.t.), phosphorus (P = 2.70 % w.t.), magnesium (Mg = 1.53 % w.t.), calcium (Ca = 0.86 % w.t.) and silicon (Si = 0.26 % w.t). These elements originate from the CS used in the AC production process and are common in biomasses-derived ACs [28]. The C content measured by SEM/EDX is slightly different from that obtained with ultimate analysis (%C = 77.4 ± 0.1 w.t.), while O values present a higher variation (%O = 2.6 ± 0.2 w.t.), likely due to the superficial nature of SEM/EDX measurements. Also, O is more likely to be present in the surface, while the inner matrix of the AC is mainly composed by C. Finally, the sum of metals detected in the surface (9.33 % w.t.) is smaller than the ashes measured with muffle furnace (%Ashes = 16.4 ± 2.4 w.t.), which can be related to the fact that these elements may volatilize easier when they are exposed in the surface.

### 3.7. Adsorption kinetics study for the optimal material

Table 2 illustrates the performance of the adsorption kinetics models used to fit the experimental data from the kinetics tests for known initial concentrations (60, 80 and 100 mg·L⁻¹ in DIW and 100 mg·L⁻¹ in SW). Pseudo-second order model (PSOM) represents better the observed data than pseudo-first order model (PFOM) and Elovich model for all the cases when SSE values are compared. This implies that chemisorption is the rate-limiting step [74], and the rate of adsorption depends on the adsorption capacity of the adsorbent rather than the concentration of the adsorbate [16]. Chemisorption generally involve electron transfer, valence change, π-π interaction and hydrogen bonding [75], which may create stronger, more stable and irreversible interactions [76]. Also, PSOM fitting suggests a dependence of the reaction rate on the square of remaining IBU concentration [77]. The value of k₂ remains similar in the four cases, which means that it is a parameter related to the adsorbent, confirming the role of the material in the chemisorption mechanism. Conversely, the initial adsorption rates were determined in 17.7, 21.7, 28.1 and 34.6 mg·g⁻¹·h⁻¹ for initial concentration of 60, 80 and 100 mg·L⁻¹ in DIW and 100 mg·L⁻¹ in SW, respectively, which indicates that higher adsorption capacities and the presence of ions lead to faster adsorption phenomena at the start.

Fig. 6 [a] shows the evolution of the amount of IBU adsorbed into the AC11 (adsorption capacity) over the time and the fitted PSOM. The adsorption process exhibited fast kinetics during the initial phase (9 h), after which there was a gradual occupation of the binding sites until reaching equilibrium. The initial rapid phase corresponds to the

saturation of surface-active sites, while the subsequent slower stage may represent the occupation of internal sites as the contaminant reaches them through diffusive processes. The pseudo-equilibrium time is reached after 72 h of contact between the AC11 and the IBU solutions for the three concentrations used in DIW and for 100 mg·L⁻¹ in SW. The long equilibration times can be explained by the granular nature of the AC, so it may take longer times for the IBU (with molecular dimensions 1.03 × 0.52 × 0.43 nm [44]) to reach the binding sites of the innermost pores compared to other AC presentations.

On the other hand, pH<sub>PZC</sub> = 10.9 was determined for AC11. This means that at a pH below this value, the acidic functional groups like carboxylic, phenolic, or lactonic [64] present on the surface of the AC will tend to protonate, resulting in a net positive charge. IBU exists as neutral species at pH < pKa, coexists as neutral and anionic species at pH ≈ pKa, and exists as IBU⁻ at pH > pKa [78]. Thus, H-bonding and electrostatic attraction may occurs between IBU (pKa = 4.9) and AC11 when the pH of the solution is between pKa<sub>IBU</sub> and pH<sub>PZC</sub> [44].

For DIW, it is observed that the adsorption capacity at equilibrium increases as the initial concentration of IBU in the water increases. This is logical because there is a greater amount of IBU molecules that can be adsorbed on the active sites of the AC11 as long as saturation is not reached. Also, adsorption capacity for C₀ = 100 mg·L⁻¹ increases from 145.3 mg·g⁻¹ to 162.0 mg·g⁻¹ when SW (initial pH = 7.4; final pH = 9.1) is used instead of DIW (initial pH = 5.0, final pH = 9.2). This means adsorption seems to be better in SW, and indicates that AC11 is increasing the pH of the solution. The presence of ions in SW causes the thickness of the diffuse layer around the AC11 particles to decrease, which promotes electrostatic attraction between the dominant deprotonated IBU⁻ and the positively charged AC11 surface, since pKa<sub>IBU</sub> < pH < pH<sub>PZC</sub>. In DIW, since there are no ions initially present in solution, the pH starts close to the pKa<sub>IBU</sub> and the thickness of the diffuse layer is larger, so the IBU⁻ concentration and the electric potential are lower than in SW, which results in that the forces of attraction are consequently weaker.

Since in natural surface waters other compounds of organic nature may coexist, a competition analysis was performed to know the performance of the AC to remove IBU in the presence of NOM. TOC analysis was used because different organic species were added to the samples. Three solutions were prepared as described in section 2.5. The results of the TOC monitoring are shown in Fig. 6 [b]. It was observed that the shapes of the decay curves of IBU with and without NOM presence are identical, indicating that the presence of NOM at typical local Colombian surface water concentrations does not negatively affect the adsorption process, and there is no competition between NOM and IBU. Qualitatively, these NOM concentrations had less impact than ions from SW on IBU adsorption. The pseudo-equilibrium time remains unaffected. Additionally, a decay in the NOM curve over time was observed, suggesting that the AC can also remove NOM due to the organic characteristics of its components. This suggests that the AC has the potential to remove a wide range of organic contaminants.

### 3.8. Adsorption isotherms study

Isotherms are essential to understand the interaction between adsorbate and adsorbent. IBU adsorption phenomena was analyzed for different initial concentrations ([30–500] mg·L⁻¹) with a specific M<sub>AC</sub>/V ratio (0.4 g<sub>AC</sub>·L⁻¹) until the pseudo-equilibrium time (3 days). AC11 was tested in DIW and SW matrixes by duplicated. AC1, AC3 and AC9 were also tested in DIW (single time) for comparison and to validate the criteria which have led to select AC11. However, negligible adsorption was observed for AC9. This may be related to the fact that AC9 exhibits the lowest mesopore volume (0.046 ± 0.001 cm³·g⁻¹) and the lowest adsorption average pore diameter (1.872 ± 0.010 nm) between the tested materials, which turns out to be insufficient to capture IBU molecules at the initial concentrations of the experiment. Additionally, the high H/C and O/C ratios of AC9 restricted its capacity to adsorb IBU.
<table><caption>**Table 3**
Fitting adsorption isotherm models.</caption><thead><tr><th rowspan="2">Sample</th><th colspan="3">Langmuir</th><th colspan="3">Freundlich</th></tr><tr><th>q<sub>max</sub><br>(mg·g<sup>-1</sup>)</th><th>K<sub>L</sub><br>(L·mg<sup>-1</sup>)</th><th>R<sup>2</sup></th><th>n</th><th>K<sub>F</sub><br>(g·L<sup>-1</sup>)</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>AC3<br>DIW</td><td>73.4</td><td>0.01893</td><td>0.914</td><td>3.317</td><td>11.23</td><td>0.714</td></tr><tr><td>AC11<br>DIW</td><td>204.8</td><td>0.03517</td><td>0.981</td><td>4.149</td><td>47.23</td><td>0.953</td></tr><tr><td>AC11<br>SW</td><td>240.7</td><td>0.04831</td><td>0.991</td><td>4.416</td><td>63.71</td><td>0.954</td></tr></tbody></table>

Data were fitted to Langmuir and Freundlich isotherm models. Summary of parameters is presented in Table 3. Langmuir model fitter better for AC11 SW (R² = 0.991), AC11 DIW (R² = 0.981) and AC3 DIW (R² = 0.914), which means that monolayer adsorption is present in this case [58], where IBU molecules occupies specific binding sites and they do not interact between them. This model directly gives the parameter of maximum adsorption capacity (qmax), which allows to compare between different samples. For AC1, however, any of the models gives an acceptable value for R² and qe remains negligible as well, so it may be a similar case to AC9 in which sites are not enough to retain IBU molecules. Fitted curves and observed data is plotted in Fig. 7. Comparing qmax in AC11 DIW (204.8 mg·g⁻¹) and AC3 DIW (73.4 mg·g⁻¹) allows to confirm that the selected criteria of maximizing surface area and mesopore volume were adequate. As expected, higher values for these response variables represent a better adsorption capacity of the pollutant, since there are more sites with adequate size in the AC surface to host the IBU. Also, the presence of ions in AC11 SW (qmax = 240.7 mg·g⁻¹), enhances the adsorption capacity of the AC11, regardless of the initial adsorbate concentration. This confirms that adsorption is more

efficient in SW. This is a significant advantage if the application of the adsorption mechanism of this AC11 is performed with the inorganic ion composition of soft surface waters rather than the controlled ion conditions of laboratory DIW. Finally, AC11 from this study exhibits outstanding capacity to adsorb IBU compared to AC produced from other biomasses, e.g., Nauclea diderrichii waste (70.92 mg·g⁻¹ [79]), Lantana camara stalk (106.4 mg·g⁻¹ [80]), coconut shell (89.29 mg·g⁻¹, [81]; recycled textiles (53.9 mg·g⁻¹ [44]) or commercial granular ACs (185.2 mg·g⁻¹ [82], 120 mg·g⁻¹ [83]).

## 4. Conclusion

The production of AC from CS was meticulously investigated, with the aim of optimizing the direct physical activation process with steam and assessing the resulting material's efficiency implemented in water treatment. In this study, IBU was chosen to be the reference molecule due to this wide presence through the world. The experimental matrix explored the effects of holding time, maximum temperature, ramp temperature and steam flow on AC properties across 16 runs. ANOVA analysis revealed the significant influence of holding time, steam flow, and maximum temperature on yield, surface area, and pore distribution. These factors played a crucial role in the degradation of carbonaceous material, affecting yield and porosity development. Carbon content remained unaffected, while NER was only affected by holding time due to its impact on energy consumption. This factorial study shows that the optimum conditions correspond to run 11, characterized by high maximum temperature (950 °C) and steam flow (1 mL·min⁻¹, which means 0.01 mL·min⁻¹ per gram of CS), along with low temperature ramp (5 °C·min⁻¹) and holding time (0.75 h). This set of parameters leads to an AC (AC11) with a yield of 15.3 %, a surface area of 1105 m²·g⁻¹, a mesopore volume of 0.180 m³·g⁻¹, and a NER of 13.6 %. AC11 was subsequently studied through the adsorption kinetics. The results

<figure><img src="image_13.png" /><figcaption>**Fig. 7.** Adsorption isotherm curves of IBU onto different AC from the experimental design for DIW and onto AC11 for SW. LM: Langmuir model.</figcaption></figure>
obtained highlight the superiority of the PSOM to fit the data, with
equilibrium achieved after 72 h, indicating that IBU removal was gov-
erned by chemisorption. This result validates the power of 2k factorial
design and MAUT to optimize AC production and confirms that the
operating parameters were well chosen. Finally, the Langmuir model
fitting indicated monolayer adsorption of IBU on AC11 surface. AC11 in
SW exhibited enhanced adsorption capacity (240.7 mg·g⁻¹) compared
to DIW (204.8 mg·g⁻¹). No competition between IBU and NOM was
observed for the adsorption mechanism. Since a reduction in NOM
concentration was observed and NOM encompasses a wide range of
organic compounds, AC11 may have the potential to remove other
organic pollutants from water. This study opens new possibilities for
sustainable waste management in Colombia and provides a promising
solution to address pollution emerging challenges in surface waters.

CRediT authorship contribution statement

**L. Angel:** Writing – original draft, Investigation, Data curation, Conceptualization. **A. Villot:** Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. **M. Rodriguez Susa:** Supervision, Funding acquisition, Conceptualization. **J. Plazas-Tuttle:** Supervision, Investigation, Conceptualization. **Y. Andres:** Supervision, Methodology, Funding acquisition, Conceptualization. **C. Gerente:** Writing – review & editing, Supervision, Methodology, Conceptualization.

Funding sources

This work was supported by IMT Atlantique - GEPEA UMR CNRS 6144, France and the Environmental Engineering Research Center of Universidad de los Andes, Colombia.

Declaration of competing interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Data availability

Data will be made available on request.

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