# Sustainable valorization of spruce bark via hydrothermal carbonization for the production of furanic compounds

Taher Selmi, Eric Masson, Alain Celzard, Vanessa Fierro

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Taher Selmi, Eric Masson, Alain Celzard, Vanessa Fierro. Sustainable valorization of spruce bark via hydrothermal carbonization for the production of furanic compounds. Chemical Engineering Journal Advances, 2025, 24, pp.100881. 10.1016/j.ceja.2025.100881. hal-05287268

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# Sustainable valorization of spruce bark via hydrothermal carbonization for the production of furanic compounds

Taher Selmi<sup>a,b,*</sup>, Eric Masson<sup>b</sup>, Alain Celzard<sup>a,c,✉</sup>, Vanessa Fierro<sup>a,*✉</sup>

## ARTICLE INFO

**Keywords:**

Bark valorization

Hydrothermal carbonization

Severity factor

5-HMF

Furfural

5-MF

## ABSTRACT

This study aimed to sustainably valorize of *Picea abies* bark (PB), a low value residue, by extracting three furanic compounds, namely 5-hydroxymethylfurfural (5-HMF), furfural (F) and 5-methylfurfural (5-MF) through hydrothermal carbonization (HTC). Using response surface methodology (RSM) with a full factorial design (FFD) of experiments, the effects of temperature (160-240°C), reaction time (1-24 h) and particle size (50-1000 µm) on furanic compound production were evaluated. Statistical analysis revealed that temperature was the most influential factor, followed by reaction time, while particle size had no significant effect, despite differences in composition: small sizes are richer in cellulose and lignin, and large sizes are richer in hemicelluloses and extractives. Kinetic modeling confirmed that 5-HMF, F, and 5-MF act as intermediates in the transformation of PB-derived sugars to humins. The pH profile during HTC correlated with furanic compound evolution, initially decreasing due to acid formation, then increasing as less acidic byproducts, likely phenolics, formed. Optimal individual yields were obtained at 180°C for 5-HMF and 160°C for both F and 5-MF, with reaction times between 7 and 8 h. Multiple optimizations identified 170°C and 9.35 h as the best conditions for maximizing all three compounds simultaneously. These findings contribute to the sustainable valorization of lignocellulosic biomass and demonstrate the potential of HTC for efficient furanic compound recovery.

## 1. Introduction

Agricultural by-products and agro-industrial wastes are potential raw materials for the production of high-added value chemicals, but their use for this purpose is still minor. Bark, the main by-product of the wood industry, is mainly used to produce energy [1]. However, the decomposition of cellulose and hemicelluloses under the effect of heat, oxygen and acids generate furanic compounds. Thus, corn cobs and switchgrass [2], banana waste [3], sugarcane bagasse [4], black bamboo [5] or corncob [6] have been used to produce furanic compounds and acids. The bark of various trees, which is a source of tannins that can be used in a variety of applications [7–12], can also be used to produce furanic compounds [1,13–16].

According to 2019 statistics from the Forêt Cellulose Bois-construction Ameublement (FCBA) technological institute, 1.2 million hectares in France are covered by common spruce (*Picea abies*) forests. Studies show that 24 % of *Picea abies* bark components are soluble in organic solvents and water. The holocellulose yield in *Picea abies* bark is

36 %, and the most important extractable components are polyphenols, especially tannins. In the Vosges department, conifers are the most common forest plantations with spruce and fir. The yield of spruce bark (12 %) is higher than that of beech (10 %) [1]. However, despite its abundance in European forests and its significant content in hemicelluloses, cellulose and extractives, spruce bark (*Picea abies*) has been scarcely investigated for furanic compounds production. Previous studies have mainly focused on agricultural residues or other tree barks, leaving a knowledge gap regarding this feedstock. For these advantages, *Picea abies* was selected in this study to produce 5-hydroxymethylfurfural (5-HMF), furfural (F) and 5-methylfurfural (5-MF).

Furanic compounds are indeed of significant economic interest worldwide, as they are widely used as precursors to produce value-added chemicals such as solvents, surfactants, furfuryl alcohol, biofuels, etc. used in the chemical and agro-industry [17,18]. Furfural, for example, is the most important compound thanks to its high solubility and ease of recycling. It is used to dissolve aromatic compounds, as a solvent for lubricants refining, motor oil and phenolic resin production,

<footnote><sup>a</sup> Université de Lorraine, CNRS, IJL, Epinal F-88000, France</footnote>

<footnote><sup>b</sup> CRITT Bois, 27 rue Philippe Seguin, Epinal F-88000, France</footnote>

<footnote><sup>c</sup> Institut Universitaire de France (IUF), Paris F-75231, France</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: taher.selmi@univ-lorraine.fr (T. Selmi), vanessa.fierro@univ-lorraine.fr (V. Fierro).</footnote>
and as a reactive solvent for the manufacture of compounds for the automotive industries.

According to the latest report published by Grand View Research [19], the global furfural market grew by 11.9 % from 2014 to 2020, representing $ 1.2 trillion. Global furfural market demand was 300 kilotons in 2013, and it is expected to reach 652.5 kilotons in 2020. In 2017, China dominated global furfural consumption (75 %) and dominated furfural production (over 80 %) due to its high production of corn cobs, the main commodity used for furfural production worldwide. Furfural can also be produced from barks such as *Pinus patula* [20], *Platanus × acerifolia* [21] and *Torreya grandis* [22,23].

Various methods have been used for the production of furanic compounds, such as catalytic hydrothermal pretreatment [6], microwave-assisted reaction [24], steam explosion [25], hydrothermal liquefaction [2], hydrothermal gasification [26,27] or hydrothermal carbonization [4]. The hydrothermal process takes place in liquid water at moderate to high temperature (130°C - 400°C) and self-generated steam pressure (10 bar - 220 bar). It can be classified as carbonization, gasification and hydrothermal liquefaction, depending on temperature and pressure. Hydrothermal carbonization and gasification dominate in the 130°C - 300°C temperature range and at pressures below 150 bar, producing gases and carbonaceous solids. At high temperature (300°C - 400°C) and pressure (150 bar - 200 bar), hydrothermal liquefaction produces liquids such as bio-oils [26].

Here, we focus on hydrothermal carbonization (HTC), which has been known since 1915 [28], but is gaining increasing interest as a pretreatment to produce novel carbonaceous materials in a solvent (usually water) at moderate temperature (130°C - 250°C) and under self-generated pressure in an autoclave reactor [29]. In addition to carbonaceous materials, HTC can produce high value-added molecules [30] and carbon nanodots [31].

Hydrothermal carbonization of cellulose and hemicelluloses produces wood sugar molecules such as xylose, pentosans and others. These sugars transform under the effect of temperature to produce furanic compounds (5-HMF, F and 5-MF). The amounts of furanic compounds obtained varies according to operating conditions: temperature, HTC duration, biomass particle size, solvent nature, pH, presence of catalysts and various other factors [32]. Moreover, most optimization studies relied either on experimental parameter screening or on statistical modeling alone, without incorporating with kinetic insights. The present work addresses these gaps by (i) evaluating spruce bark as a renewable feedstock for furanics, (ii) integrating a full factorial RSM design with kinetic modeling to reveal mechanistic insights into furanic formation and degradation, and (iii) in addition to HTC conditions, studying the effect of PB particle size fractions on the production of furanic. These findings highlight both the methodological novelty and the practical relevance of our study for biomass valorization.

The objective of this work was to maximize the production of 5-HMF, F and 5-MF by subjecting *Picea abies* bark (PB) to HTC. In order to determine the optimal conditions maximizing furanic compound production, a statistical study was applied using a full factorial design (FFD) within a response surface methodology (RSM). To build the design of experiments, PB particle size and HTC temperature and duration were used as factors, and concentrations of 5-HMF, F and 5-MF were used as responses.

The novelty of this study was not only to optimize the HTC conditions for the production of furanic compounds, but also to provide kinetic evidence of their role as intermediates towards humins and to assess the real influence of PB particle size. This dual approach aims to provide new insights into sustainable biomass valorization.

## 2. Material and methods

### 2.1. Raw material and chemical composition

*Picea abies* bark (PB) came from a sawmill located in the Vosges

department, Northeastern France. The bark was kiln-dried at 40°C and ground using a planetary ball mill (Retsch PM 100) to obtain a 50-1000 µm fraction called GPB (Ground Picea Bark). The particles were sieved with a horizontal sieving machine (Retsch AS 400) to obtain different fractions: size S (50-100 µm), size M (200-300 µm), size L (400-500 µm) and size XL (500-1000 µm). The fractions were placed in an oven at 75°C until a constant mass was obtained.

The chemicals used in the extraction process: petroleum ether (96 %), toluene (99.5 %), ethanol (96 %), sulfuric acid (95-97 %), sodium chlorite (80 %), acetic acid (99.8 %), sodium hydroxide (98.5 %), were purchased from Sigma Aldrich.

The chemical composition, moisture and ash content of GPB and the fractions S, M, L, and XL were determined in accordance with American Society for Testing and Materials (ASTM) standard procedures, as explained below. Successive extractions were used to determine extractives content using ASTM D1107-96 [33]. These extractives were removed with petroleum ether, an ethanol/toluene mixture (2/1, v/v) and water using a Soxhlet apparatus for 6 to 8 h with four cycles per h. The extractives content was determined as a percentage of the anhydrous biomass weight.

The holocellulose content was determined by applying the protocol detailed elsewhere [34], which removes lignin by depolymerization and solubilization in a mixture of acetic acid and sodium chlorite. The suspension was placed in an oven at 75°C for 24 h. Holocellulose was obtained after filtration of the suspension through a sintered glass funnel. The holocellulose (containing both hemicelluloses and cellulose) was dried and weighed. The cellulose and hemicelluloses content was determined by treating the holocellulose according to the protocol described in [34]. Treating holocellulose with 17.5 % NaOH followed by acetic acid removed the hemicelluloses and left the cellulose, which was then filtered, washed with distilled water and dried. Assuming that holocellulose is composed solely of cellulose and hemicelluloses, and that the latter constitute the soluble fraction, the hemicelluloses content was deduced from the holocellulose and cellulose content.

Following ASTM E1721-01 protocol [35], lignin was collected after solubilizing holocellulose with sulfuric acid (72 %). The remaining insoluble residue was considered as lignin. Finally, the ash content was determined according to the standard ASTM E1755-0 [36]: the samples were heated to 575°C under air for 3 h, then weighed after being placed in desiccators until cooled to room temperature.

All composition analyses were carried out in triplicate, and standard deviations are included in the Supplementary Information (Table SI 4).

### 2.2. Hydrothermal carbonization (HTC)

HTC experiments were carried out in 200 mL autoclaves (Anton Parr). In a typical experiment [29], 4 g of each PB fraction (S, M, L and XL) were introduced into a glass vial containing 40 g of distilled water, which was then introduced into a 200 mL Teflon-lined autoclave. The autoclave was hermetically sealed and placed in a ventilated oven preheated to the set temperature (160°C, 180°C, 220°C or 240°C). After the required reaction time (from 1 to 24 h), the autoclave was removed from the oven and allowed to cool to room temperature.

The reaction ordinate, R₀, based on the combination of the parameters that most modify the HTC results: temperature T (°C), and residence time t (h), is described by the following Eq.:

$$R_0 = t \text{Exp} \left( \frac{T - 100}{14.75} \right) \quad (1)$$

where 14.75 (°C) is an empirical constant linking temperature to the activation energy for breaking glycosidic bonds in polysaccharides during hydrothermal treatment. It means that each 14.75°C rise in temperature increases the reaction rate by about tenfold. Overend and Chornet [37] proposed the severity factor (log R₀) to combine time and temperature into a single number, so that experiments can be compared
more easily. When they derived the severity formula, they assumed that: (i) The reaction behaves roughly like a first-order reaction; (ii) The main step is breaking glycosidic bonds, which link sugars in cellulose and hemicellulose.

## 2.3. Separation and analysis of solid/liquid fractions

After the HTC reaction, vacuum filtration was used to separate the liquid fraction from the solid fraction. The volume of the liquid fraction was measured, then made up to 50 mL with distilled water to normalize all samples for reproducible concentration determination. The diluted liquid fractions were stored in a freezer until analysis. The liquid fraction was analyzed by Ultimate 3000 high-performance liquid chromatography (HPLC) to determine the main furanic compounds, using a Pinnacle DB Biphenyl column (250 × 4.6 mm, 5 µm). UV absorption wavelengths of 254 and 280 nm were used to quantify F, 5-HMF and 5-MF. The mobile phases used were A (water) and B (acetonitrile/methanol (50/50 v/v) mixture) with a flow rate of 1 mL.min⁻¹.

## 2.4. Kinetic of furanic compounds generation

The conventional reaction pathway for the production and degradation of furanic compounds is shown in Fig. 1. Cellulosic sugars are rapidly converted into their derivatives (fructose, glucose, pentose...), which are then dehydrated to 5-HMF, F and 5-MF (R₁F), then degraded (R₁D) to final products such as humins. Indeed, humins were formed gradually during storage of the filtered solution, which was initially free of solids, at room temperature. In this study, humins were not separated or quantified independently from the hydrochar. However, their presence is indirectly supported by the kinetic model, which identifies furanics as intermediates leading to humin formation. Sugar derivatives and furanic compounds are simultaneously polymerized to secondary products (R₂F and R₂D) [38,39]. R₁F, R₂F, R₁D, and R₂D are the reaction rates of the four reactions detailed in Fig. 1.

Regardless of the formation of secondary products (R₂F and R₂D), the evolution of furanic compound (C₇) concentration with time was represented by the following differential Eq.:

$$ \frac{dC_F}{dt} = R_{1F} - R_{1D} \qquad (2) $$

where the reaction rate Eq.s read:

$$ R_{1F} = k_{1F}C_s \qquad (3) $$

$$ R_{1D} = k_{1D}C_F \qquad (4) $$

C₇ is the concentration of 5-HMF, F or 5-MF, and C₈ is the concentration of sugar derivatives. The solution of the differential Eq. is the 1st-order kinetic model, called Model A in the literature and given by the following Eq. [40]:

$$ C_F = C_{S,0} \left( \frac{k_{1F}}{k_{1D} - k_{1F}} \right) \left[ \exp(-k_{1F} t) - \exp(-k_{1D} t) \right] \qquad (5) $$

where C₅₀ is the initial concentration of sugar derivatives. The kinetic constants (k₁F and k₁D) were investigated as a function of temperature using the Arrhenius Eq.:

$$ k_i = k_{0,i} \exp\left(-\frac{E_{a,i}}{RT}\right) \qquad (6) $$

where T is the temperature and R is the ideal gas constant (R = 8.314 J. mol⁻¹. K⁻¹). The pre-exponential factor (k₀), activation energy (Eₐ) and kinetic constants (k₁F and k₁D) were obtained by non-linear fitting of F, 5-HMF and 5-MF concentrations as a function of HTC time, using OriginPro 2018 software by applying the Levenberg-Marquart algorithm.

## 2.5. Experimental design and statistical analysis: Response Surface Methodology (RSM)

A full factorial design (FFD) under response surface methodology (RSM) was applied to determine optimal conditions under which furanic compound production is maximal, and HTC time and temperature are minimal. In this study, three factors were varied with different levels for each: HTC temperature (A), HTC duration (B) and PB particle size (C), as shown in Table 1. A total of 64 sets of experiments were carried out. Responses expressed as dependent variables were 5-HMF (Y₁), F (Y₂) and 5-MF (Y₃) contents. The compounds content, Yₖ (mg.g⁻¹), was calculated as follows:

$$ Y_k = C_k \times \frac{V_T}{m_{PB}} \qquad (7) $$

where Cₖ (mg.L⁻¹) is the concentration of compound k (F, 5-HMF or 5-MF) given by HPLC analysis, V_T is the total volume of solution after solid/liquid separation (V_T = 50 mL) and mₚₖ is the mass of PB on dry basis.

The experimental design factors and RSM response ratios are summarized in Table SI 1. The results of the experimental design were analyzed using Design-Expert 11 statistical software, applying a cubic model based on higher-order polynomials [41,42]:

$$ \begin{aligned} Y_k = & \beta_0 + \beta_1 A + \beta_2 B + \beta_3 C + \beta_{1,1} A^2 + \beta_{2,2} B^2 + \beta_{1,2} AB + \beta_{1,3} AC + \beta_{2,3} BC \\ & + \beta_{1,2,3} ABC + \beta_{1,1,2} A^2 B + \beta_{1,1,3} A^2 C + \beta_{1,2,2} AB^2 + \beta_{3,2,2} CB^2 \\ & + \beta_{1,1,1} A^3 + \beta_{2,2,2} B^3 \end{aligned} \qquad (8) $$

where Yₖ is the response variable corresponding to one of the three furanic compounds (F, 5-HMF and 5-MF). The model coefficients are the intercept β₀, while βᵢ corresponds to the linear effects of the individual factors (HTC temperature, HTC duration and PB particle size), βᵢⱼ corresponds to the two-factor interaction terms, and βᵢⱼ₁ represents the three-factor (cubic) interaction terms. These coefficients were determined by fitting a cubic polynomial model to the experimental data.

Parameter C is a categorical factor, so there is no quadratic or cubic term (C², C³) in the model applied. In the absence of HTC reaction, i.e., A

<figure><img src="image_3.png" /><figcaption>Fig. 1. Reaction pathway for the production and degradation of furanic compounds from cellulosic sugars.</figcaption></figure>
<table><caption>**Table 1**
Experimental parameters and their coded names and levels used in the FFD.</caption><thead><tr><th>Type</th><th>Factors</th><th>Code</th><th>Unit</th><th>Levels</th><th>Level 1</th><th>Level 2</th><th>Level 3</th><th>Level 4</th></tr></thead><tbody><tr><td>Numerical</td><td>Temperature</td><td>A</td><td>°C</td><td>4</td><td>160</td><td>180</td><td>220</td><td>240</td></tr><tr><td>Numerical</td><td>Duration</td><td>B</td><td>h</td><td>4</td><td>1</td><td>3</td><td>6</td><td>24</td></tr><tr><td>Categorical</td><td>Particle size</td><td>C</td><td>µm</td><td>4</td><td>S: (50-100)</td><td>M: (200-300)</td><td>L: (400-500)</td><td>XL: (500-1000)</td></tr></tbody></table>

B = C = 0, there is no production of furanic compounds, so $Y_k = 0$. Therefore, $\beta_0 = 0$ and the applied model becomes:

$$Y_k = \beta_1 A + \beta_2 B + \beta_3 C + \beta_{1,1} A^2 + \beta_{2,2} B^2 + \beta_{1,2} AB + \beta_{1,3} AC + \beta_{2,3} BC \\ + \beta_{1,2,3} ABC + \beta_{1,1,2} A^2 B + \beta_{1,1,3} A^2 C + \beta_{1,2,2} AB^2 + \beta_{3,2,2} CB^2 \\ + \beta_{1,1,1} A^3 + \beta_{2,2,2} B^3 \quad (9)$$

The significant factor was selected using a stepwise method, with a threshold p-value of 0.05 for adding or removing terms. A model and/or its factors are insignificant if the p-value is greater than 0.05 at a 95.0 % confidence level. The desirability function was used to determine optimal response conditions [43,44].

## 3. Results and discussion

### 3.1. Chemical composition

The chemical composition, humidity and ash content of ground *Picea abies* bark (GPB), along with its fractions of sizes S, M, L and XL are shown in Fig. 2. Hydrophobic parts such as resins and fatty acids, extracted with petroleum ether, accounted for 3.5 %, 2.1 %, and 2.3 % for size S, size XL and GPB, respectively. The same result was found for raw bark [1]. Ethanol/toluene solvent extraction indicates that GPB and size S are less rich in tannins, flavonoids and stilbenes (17.9 % and 18.8 %, respectively) than size XL (27.5 %). The tannin, flavonoid and stilbene contents of GPB are comparable to those of the inner bark of *Picea abies* studied elsewhere [1]. Water extraction yields a biomass free of proteins/alkaloids, carbohydrates and inorganics. GPB and fractions of sizes S and XL have almost a comparable amount of extractives when obtained by extraction only with water (8.5 %, 8.7 %, and 7.7 %, respectively).

Cellulose content (30.8 %) and lignin content (24.5 %) are significant for GPB, which accounts for almost a third of the total composition in the case of cellulose, and a quarter in the case of lignin. These results are consistent with those reported elsewhere (29.0 % of cellulose and 24.5 % of lignin) when *Picea abies* bark was used to extract phenolic content

[13].

By increasing the size from S to XL, the cellulose content decreases from 36.3 % to 28.9 %, and the lignin content decreases from 23.7 % to 17.5 %. On the other hand, hemicelluloses content is higher for XL size (16.3 %) than for S size (9.0 %), showing that as size increases, so does the hemicelluloses content. This is confirmed by the hemicelluloses content of ground but not sieved PB, 16.1 %. The higher hemicellulose content of larger particles (size XL) can be attributed to their origin from inner bark tissues, which are naturally richer in hemicelluloses and extractives [1]. By contrast, finer fractions (size S) derive mainly from the outer layers, which are richer in cellulose and lignin, reflecting the anatomical heterogeneity of bark.

The ash content of GPB is 4.4 %, which almost corresponds to the average ash content of the four sizes S, M, L and XL, which is about 4.6 %, indicating that GPB and the sizes considered have a significant ash content.

### 3.2. Furanic compound production and kinetics

#### 3.2.1. Changes in pH

The final pH of the liquid recovered after HTC reaction and filtration shows some variation with temperature and depends on the different particle sizes used. As shown in Fig. 3a, most curves exhibit an initial decrease in pH during the first hours of the HTC process, followed by either stabilization or a progressive increase over time. However, this trend is not universal: pH evolution differs according to particle size and temperature. Moreover, for all sizes used and at the HTC temperature of 160°C, the final pH decreases with increasing HTC time, which may be due to the production of a significant amount of acid molecules. These are probably formic acid, furoic acid, levulinic acid and others. These molecules are indeed produced after degradation of furanic compounds [38]. At HTC temperature of 180, 220 and 240°C, the final pH decreases with HTC time up to 3 h, then increases. This may be due to the degradation of the acid molecules mentioned above or the production of basic or at least less acidic molecules, which may be phenolic compounds.

To investigate the two factors, HTC time and temperature, we have represented the final pH as a function of severity factor and fitted the data with a second-order polynomial (Eq. 10) as shown in Fig. 3b. Similar trends in the variation of final pH with HTC time and with severity factor were reported by Borrero-López et al. [40], in their study on the extraction of furanic and phenolic compounds from cellulose, hemicellulose and lignin using hydrothermal carbonization.

$$\text{pH}_{\text{Final}} = \text{p}_1 + \text{p}_2(\log R_0) + \text{p}_3(\log R_0)^2 \quad (10)$$

where p1, p2, and p3 are adjustable parameters and are gathered in Table SI 2. The final pH (Fig. 3b) for the four particle sizes used has the same behavior. By increasing the severity factor, the final pH decreases to a critical severity factor (4.91) for all sizes. Beyond this critical severity factor, the final pH increases, due to secondaries reactions [40].

#### 3.2.2. Effect of severity factor (log R₀)

The effect of severity factor on furanic compound production (5-HMF, F and 5-MF) is shown in Fig. 3 (c, d and e). Although not obvious from Fig. 3 alone, averaging the overall production of all compounds (F, 5-HMF and 5-MF) at all severity factors shows that the highest percentage of furan compounds is obtained with size XL, followed by size L.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Chemical composition of ground but not sieved PB (GPB), and its fractions with sizes S, M, L and XL.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Final pH of the HTC solutions as a function of (a) HTC time for different temperatures and particle sizes (experimental data connected by line plot using a modified Bezier interpolation), and (b) severity factor for different particle sizes, fitted by a second order polynomial. Production of (c) 5-HMF, (d) F and (e) 5-MF as a function of severity factor for the different particle sizes used. The dark pink band represents the 95% confidence interval of the model, and the light pink band represents the 95% prediction interval.</figcaption></figure>
This is to be expected, since sizes L and XL are the richest in cellulose and hemicelluloses, which are responsible for furanic compound production. The degradation of hemicelluloses indeed produces furfural [45], and the decomposition of cellulose forms 5-HMF and 5-MF [46,45]. Size S is the least rich in hemicelluloses, but the percentage of furanic compounds is not negligible compared with other sizes. This may be due to the high lignin content of size S, as it has been shown in the literature that the presence of lignin improves the stability of furanic compounds, especially furfural [47–49].

The production of 5-HMF is always higher than that of the other compounds (F and 5-MF), due to the cellulose-rich composition of PB. It is also due to the stability of 5-HMF, which appears to be more stable than F and 5-MF [50,51]. Indeed, F and 5-MF can be directly converted to acids (acetic, formic, furoic and levulinic acids, among others) [52, 53]. The experimental peak production of furanic compounds lies within a severity factor interval of 4.32 to 4.91. Experimental F, 5-MF and 5-HMF production peaks were indeed reached at severity factors of 4.32 (160°C, 6 h), 4.61 (180°C, 3 h) and 4.91 (180°C, 6 h), respectively. In fact, furfural production peaks first, followed by 5-MF and then 5-HMF. Hence, when 5-HMF production peaks, the production of the other compounds begins to decline due to degradation. This confirms that, at a high severity factor, furanic compounds react to produce other products such as the humins and acids mentioned above [53].

### 3.2.3. Kinetic modeling of furanic compound production

Following the reaction pathway presented in Fig. 1, we established a kinetic model of formation and decomposition reaction, presented by Eq. 5 and called Model A, to fit the kinetic data and determine the reaction rates of formation ($k_{1F}$) and degradation ($k_{1D}$). The kinetic modeling data for furanic compounds are shown in Fig. 4, and the parameters of model A are summarized in Table SI 3. It can be seen that the determination coefficient $R^2$ of Model A is significant at high temperature (240°C), indicating that furanic compounds are intermediates between sugars and final products. A similar result was obtained for furfural production from a Kraft-based hardwood pre-hydrolysis liquor [54]. Despite the fact that the determination coefficients were comparatively low under certain conditions (see Table SI 3), this can be attributed to the exclusion of secondary reaction pathways. Such pathways include the formation of secondary products directly from cellulosic sugar ($R_{2F}$) and the degradation of furanics to these secondary products ($R_{2D}$) (see Fig. 1 and Eq. 2). Nevertheless, the model is appropriate for capturing the main trends of furan formation and degradation, and for providing comparative activation energies, as has also been observed in related studies.

For all particle sizes and temperatures, the formation rate constant ($k_{1F}$) of furanic compounds is consistently higher than their degradation rate constant ($k_{1D}$) (Fig. 5), implying that the formation of these compounds is favored over the formation of degradation products.

It can be noted that an increase in the rate of furanic compound

<figure><img src="image_6.png" /><figcaption>Fig. 4. Concentrations of 5-HMF, F and 5-MF produced as a function of HTC time, and corresponding kinetic modeling fitted by Model A.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 5. Arrhenius fitting applied to the kinetic constants of furanic compounds for the different particle sizes used.</figcaption></figure>

formation (k₁F) occurred when the temperature increased from 160°C to 240°C. This normally implies an increase in the concentration C₅₀ of furanic compound with temperature. However, it was found to increase as the temperature increased from 160°C to 180°C, and then decrease as the temperature continued to increase towards 240°C. For size XL, for example, the C₅₀ value increases from 0.69 g.L⁻¹ to 0.78 g.L⁻¹, and then decreases to 0.11 g.L⁻¹ as the temperature rises from 160°C to 180°C, then to 240°C, respectively. This can be explained by the fact that the degradation rate of furanic compounds (k₁D) also increases with temperature until it becomes predominant. It can indeed be seen that 5-HMF concentrations are always higher than those of F and 5-MF for different particle sizes and at different temperatures. Similar results were obtained when studying the conversion of cellulose and lignocellulosic waste into furanic compounds using a microwave-assisted method [55]. Similar profiles of furanic compound evolution as a function of HTC time (1-24 h) and temperature (140-240°C), from lignin, cellulose and hemicellulose, were reported by Borrero-López et al. [40]. Production increases continuously over the entire time interval at low temperatures (140-160°C), while at higher temperatures it shows initial growth followed by a decrease at longer reaction times, confirming the degradation of furanic compounds under certain conditions.

We plotted the rate constants of Model A (k₁F and k₁D) against the HTC temperature to determine the pre-exponential factor (k₀ᵢ) and the activation energy (Eₐᵢ) by applying the Arrhenius equation. The parameters summarized in Table 2 were obtained by non-linear fitting of rate constants with temperature, as shown in Fig. 5. It can be seen that the activation energies of furanic compound formation (Eₐ₁F), reported in Table 2, are always higher than those of degradation (Eₐ₁D) for all particle sizes, with the exception of size XL. This indicates that the degradation of furanic compounds for sizes S, M and L is more favorable

<caption>Table 2</caption>

Parameters of the Arrhenius model for furanic compounds produced from all particle sizes.

<table><thead><tr><th></th><th>Size</th><th>S (50-100 µm)</th><th>M (200-300 µm)</th><th>L (400-500 µm)</th><th>XL (500-1000 µm)</th></tr></thead><tbody><tr><td colspan="6">5-HMF</td></tr><tr><td rowspan="3">k₁F</td><td>k₀₁F (h⁻¹)</td><td>1.039 E18</td><td>5.107 E22</td><td>5.066 E13</td><td>4.113 E4</td></tr><tr><td>Eₐ₁F (kJ. mol⁻¹)</td><td>180.817</td><td>217.733</td><td>132.470</td><td>47.062</td></tr><tr><td>R²</td><td>0.995</td><td>0.989</td><td>0.956</td><td>0.929</td></tr><tr><td rowspan="3">k₁D</td><td>k₀₁D (h⁻¹)</td><td>1.221 E9</td><td>1.276 E8</td><td>2.201 E10</td><td>5.712 E4</td></tr><tr><td>Eₐ₁D (kJ. mol⁻¹)</td><td>90.674</td><td>80.234</td><td>101.060</td><td>49.707</td></tr><tr><td>R²</td><td>0.963</td><td>0.995</td><td>0.994</td><td>0.930</td></tr><tr><td colspan="6">F</td></tr><tr><td rowspan="3">k₁F</td><td>k₀₁F (h⁻¹)</td><td>6.774 E13</td><td>6.444 E10</td><td>2.727 E7</td><td>6.912 E4</td></tr><tr><td>Eₐ₁F (kJ. mol⁻¹)</td><td>130.386</td><td>100.583</td><td>67.323</td><td>41.439</td></tr><tr><td>R²</td><td>0.989</td><td>0.995</td><td>0.985</td><td>0.490</td></tr><tr><td rowspan="3">k₁D</td><td>k₀₁D (h⁻¹)</td><td>1.129 E7</td><td>4.004 E9</td><td>2.428 E9</td><td>1.049 E4</td></tr><tr><td>Eₐ₁D (kJ. mol⁻¹)</td><td>69.089</td><td>94.774</td><td>92.754</td><td>41.291</td></tr><tr><td>R²</td><td>0.975</td><td>0.837</td><td>0.950</td><td>0.932</td></tr><tr><td colspan="6">5-MF</td></tr><tr><td rowspan="3">k₁F</td><td>k₀₁F (h⁻¹)</td><td>3.660 E5</td><td>2.698 E8</td><td>2.753 E5</td><td>*</td></tr><tr><td>Eₐ₁F (kJ. mol⁻¹)</td><td>48.181</td><td>76.668</td><td>47.055</td><td>*</td></tr><tr><td>R²</td><td>0.764</td><td>1.000</td><td>0.764</td><td>*</td></tr><tr><td rowspan="3">k₁D</td><td>k₀₁D (h⁻¹)</td><td>1.617 E4</td><td>2.697 E4</td><td>4.923 E4</td><td>*</td></tr><tr><td>Eₐ₁D (kJ. mol⁻¹)</td><td>42.612</td><td>45.977</td><td>47.010</td><td>*</td></tr><tr><td>R²</td><td>0.806</td><td>0.957</td><td>0.817</td><td>*</td></tr></tbody></table>

<footnote>* No data due to lack of 5-MF production under these conditions.</footnote>
than for size XL. Therefore, the content of furanic compounds for size XL is higher than for other sizes, as already concluded from Fig. 5. The Arrhenius parameters found in this work can be compared with the results from the literature [40,54,56,57], but for the pre-exponential factors (k₀,i), they are sometimes not comparable with the available data. This may be related to the difference in precursor biomass, temperature ranges, and/or the large difference in time ranges.

### 3.3. Statistical analysis of the production of furanic compounds

In order to determine the best conditions for maximizing the production of furanic compounds by HTC, we applied a cubic polynomial model using the Response Surface Methodology (RSM). The fit summary report produced by the Design-Expert software recommends the modified cubic model over the quadratic model for the three furanic compounds studied. Moreover, in this case, the quadratic model is not significant. The statistical study of 5-HMF, F and 5-MF was carried out by applying the stepwise selection method to select the significant factor, with selection or rejection of the factor based on the p-value parameters. A p-value less than 0.05 indicates that the model terms are significant, whereas a p-value greater than 0.05 indicates that the model terms are not significant. Occasionally, factors may have a p-value greater than 0.05, but these are selected in the model to maintain the hierarchical pattern. The experimental design and the observed and predicted responses obtained from 64 different combinations of HTC reaction conditions are summarized in Table SI 1.

Statistics analyses of the fit of the reduced polynomial cubic models for furanic compounds are recapitulated in Table 3. The predicted R² values for 5-HMF, F and 5-MF were 0.7643, 0.7510 and 0.6761, respectively. They are in reasonable agreement with the adjusted R² value (0.7995, 0.7744 and 0.7100, respectively), i.e. the difference is much less than 0.2, considered a common empirical threshold for validating a model. This is confirmed by the correlation between the predicted and observed data shown in Fig. SI 1 a, c, and e for 5-HMF, F and 5-MF, respectively. Therefore, the relatively low values of the predicted R² indicate that the suitability of these models is relatively limited. A similar result was obtained elsewhere [58]. To check the adequacy of the model, the coefficient of determination, R², was used, which shows the agreement between the prediction and the observed results. Acceptable R² values were obtained for the non-reduced cubic model (0.8763, 0.8310, and 0.8324 for 5-HMF, F and 5-MF, respectively). The coefficients of determination R² of the reduced cubic models for 5-HMF, F and 5-MF were 0.8313, 0.8026 and 0.7463, respectively. The reduced cubic model of 5-HMF and F gives also a good fit, with R² values greater than 0.80 [59].

The statistical parameters of the Analysis of Variance (ANOVA) for the three responses (5-HMF, F and 5-MF) are summarized in. The p-values and F-values indicate that A-Temperature (°C), B-Time (h), AB, AB², A³ (except for 5-HMF) and B³ are the significant factors for the three responses, and that A²B is significant for 5-HMF, and A³ is significant for F and for 5-MF. The terms A² and B² were added to maintain the hierarchy of the model. The size factor (C) has always a p-value greater than 0.05, indicating that it is not significant and has no substantial effect on the production of furanic compounds. These results are confirmed by the factors represented in the Pareto diagrams summarized in Table 4. The absence of the size factor in the Pareto diagram confirms that it is not an influencing factor. This contradicts the literature and the results found here when studying the chemical compounds (cellulose,

<table><caption>**Table 3**
Statistical parameters for fitting furanic compound responses.</caption><thead><tr><th>Fit Statistics</th><th>5-HMF</th><th>F</th><th>5MF</th></tr></thead><tbody><tr><td>R<sup>2</sup></td><td>0.8313</td><td>0.8026</td><td>0.7463</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.7995</td><td>0.7744</td><td>0.7100</td></tr><tr><td>Predicted R<sup>2</sup></td><td>0.7643</td><td>0.7510</td><td>0.6761</td></tr></tbody></table>

hemicellulose and lignin) of bark, which show that, for example, biomass rich in hemicelluloses produces more furfural. Furthermore, as we have shown that the different sizes used S, M, L and XL have a different percentage of hemicelluloses (Fig. 2), they should normally produce a different amount of furfural (which we have already seen experimentally). There should therefore be an influence of size, but the statistical study shows the opposite. This may be explained by the small difference in furanic compound production between the different sizes, which cannot be detected by the models applied. In this cubic model, statistical significance was determined at p < 0.05. Based on the experimental variability (Table SI 1), a difference of approximately 15–20% in furanic yield between particle size fractions would be required to reach this threshold. The differences actually observed were generally below this level, which explains why particle size appeared statistically insignificant. In some conditions, the yields approached this threshold, but the effect of particle size was masked by the much stronger influence of temperature and time, which dominate over size-related variations. The cubic model may underestimate subtle particle size effects because it treats particle size as a categorical factor. To enhance reliability, future work might use larger datasets, hierarchical or mixed models, or others continuous responses that can sensitively be influenced by the factor size.

The best regression equations representing the production of 5-HMF, F and 5-MF were obtained, and they are given below. They represent the reduced cubic model after the selection of influential and hierarchical factors:

$$5 - \text{HMF} = -0.0389 \times \text{A} + 6.4303 \times \text{B} - 0.0396 \times \text{AB} - 0.0002 \times \text{A}^2 - 0.1655 \times \text{B}^2 + 0.0001 \times \text{A}^2\text{B} + 0.0006 \times \text{AB}^2 + 0.0006 \times \text{B}^3 \quad (11)$$

$$F = -0.0270 \times A + 4.8600 \times B - 0.0226 \times AB + 0.0001 \times A^2 - 0.1938 \times B^2 + 0.0008 \times AB^2 + 3.9910 \times 10^{-07} \times A^3 + 0.0005 \times B^3 \quad (12)$$

$$5 - \text{MF} = -0.0043 \times A + 1.2424 \times B - 0.0058 \times AB - 8.6188 \times 10^{-06} \times A^2 - 0.0467 \times B^2 + 0.0002 \times AB^2 + 1.5727 \times 10^{-07} \times A^3 + 0.0043 \times 10^{-02} \times B^3 \quad (13)$$

where, as mentioned, A and B are the temperature and time factors, respectively. This cubic model was fitted to the data obtained and was used to predict the response to any condition, even those not obtained in the design. The p-value was less than 0.0001 for all three reduced cubic models of the three responses 5-HMF, F and 5-MF with F-values of 26.41, 28.45 and 20.59, respectively. This means that the models are remarkably significant, and that there is only a 0.01 % chance that a high F-value is due to noise. The prediction equations of 5-HMF, F and 5-MF above (Eq. 11-13) show that the A-Temperature (°C), AB and B² terms have a negative sign for their coefficients, as does the A² term (except for furfural), while the B-Time (h), AB², A³ and/or B³ favors increased production of 5-HMF, F and 5-MF. On the other hand, the negative coefficients of the A-Temperature, AB, A² and/or B² terms indicate antagonistic effects, meaning that increasing these factors tends to reduce the production yields of furanic compounds. This suggests that higher temperatures or specific interaction effects between temperature and time favor the degradation of 5-HMF, furfural and 5-MF under the conditions studied. This result is in line with another study, where F and 5-HMF production was optimized from wheat straw using a microwave-assisted process [24].
<table><caption>**Table 4**
Analysis of variance (ANOVA) and Pareto diagrams for the response surface of the reduced cubic model.</caption><thead><tr><th>Source</th><th>P-value</th><th>F-value</th><th>Remarks</th><th>Pareto diagrams</th></tr></thead><tbody><tr><td colspan="5" style="text-align:center; font-weight:bold;">5-HMF</td></tr><tr><td>Model</td><td>&lt; 0.0001</td><td>26.41</td><td>Significant</td><td></td></tr><tr><td>A-Temperature</td><td>&lt; 0.0001</td><td>51.87</td><td>Significant</td><td></td></tr><tr><td>B-Time</td><td>&lt; 0.0001</td><td>46.45</td><td>Significant</td><td></td></tr><tr><td>AB</td><td>&lt; 0.0001</td><td>40.50</td><td>Significant</td><td></td></tr><tr><td>B<sup>3</sup></td><td>&lt; 0.0001</td><td>27.49</td><td>Significant</td><td></td></tr><tr><td>AB<sup>2</sup></td><td>&lt; 0.0001</td><td>26.91</td><td>Significant</td><td></td></tr><tr><td>A<sup>2</sup>B</td><td>&lt; 0.0001</td><td>19.37</td><td>Significant</td><td></td></tr><tr><td>B<sup>2</sup></td><td>0.0881</td><td>3.01</td><td>Non-significant</td><td></td></tr><tr><td>A<sup>2</sup></td><td>0.1321</td><td>2.34</td><td>Non-significant</td><td></td></tr><tr><td colspan="5" style="text-align:center; font-weight:bold;">F</td></tr><tr><td>Model</td><td>&lt; 0.0001</td><td>28.45</td><td>Significant</td><td></td></tr><tr><td>A-Temperature</td><td>&lt; 0.0001</td><td>79.81</td><td>Significant</td><td></td></tr><tr><td>AB<sup>2</sup></td><td>&lt; 0.0001</td><td>74.64</td><td>Significant</td><td></td></tr><tr><td>B-Time</td><td>&lt; 0.0001</td><td>28.98</td><td>Significant</td><td></td></tr><tr><td>B<sup>3</sup></td><td>&lt; 0.0001</td><td>22.16</td><td>Significant</td><td></td></tr><tr><td>AB</td><td>&lt; 0.0001</td><td>19.98</td><td>Significant</td><td></td></tr><tr><td>A<sup>3</sup></td><td>0.0250</td><td>5.30</td><td>Significant</td><td></td></tr><tr><td>B<sup>2</sup></td><td>0.1495</td><td>2.10</td><td>Non-significant</td><td></td></tr><tr><td>A<sup>2</sup></td><td>0.1531</td><td>2.14</td><td>Non-significant</td><td></td></tr><tr><td colspan="5" style="text-align:center; font-weight:bold;">5-MF</td></tr><tr><td>Model</td><td>&lt; 0.0001</td><td>20.59</td><td>Significant</td><td></td></tr><tr><td>A-Temperature</td><td>&lt; 0.0001</td><td>62.96</td><td>Significant</td><td></td></tr><tr><td>AB<sup>2</sup></td><td>&lt; 0.0001</td><td>48.32</td><td>Significant</td><td></td></tr><tr><td>B-Time</td><td>&lt; 0.0001</td><td>19.02</td><td>Significant</td><td></td></tr><tr><td>AB</td><td>0.0001</td><td>16.95</td><td>Significant</td><td></td></tr><tr><td>B<sup>3</sup></td><td>0.0003</td><td>15.27</td><td>Significant</td><td></td></tr><tr><td>A<sup>3</sup></td><td>0.0107</td><td>6.98</td><td>Significant</td><td></td></tr><tr><td>B<sup>2</sup></td><td>0.1588</td><td>2.04</td><td>Non-significant</td><td></td></tr><tr><td>A<sup>2</sup></td><td>0.2163</td><td>1.56</td><td>Non-significant</td><td></td></tr></tbody></table>

Pareto diagrams indicate that factors A-Temperature (°C), B-Time (h) and AB² are the most influential on the production of furanic compounds. On the other hand, A² and B² are the least influential factors on the production of 5-HMF, F and 5-MF. This study leads us to determine the best conditions for optimizing the production of 5-HMF, F and 5-MF, each individually, and then that of all three compounds at the same time.

### 3.4. Optimum condition for the production of furanic compounds

#### 3.4.1. Single response optimization

To evaluate the effect of HTC time and temperature factors on the responses of 5-HMF, F and 5-MF, 3D response surface plots were used. Fig. 6 represents the 3D variation of the responses as a function of time and temperature factors. Figures SI 1 b, d, and f are 2D projections of Fig. 6.

As previously indicated, the particle size factor (C) proved statistically non-significant. Consequently, in the 3D response surface plots (Fig. 6), the effect of size was neutralized by averaging over all size

levels. Each surface therefore represents the predicted mean response as a function of temperature and time, irrespective of particle size. The 3D figures clearly show that 5-HMF, F and 5-MF are extensively produced at low temperatures (160°C and 180°C). At these temperatures, the production of furanic compounds increases with the duration of HTC up to a certain optimum. A more detailed analysis shows that the highest 5-HMF content (6.29 mg.g⁻¹) was obtained at 180°C for 8.18 h of HTC. In contrast, extraction of F (4.65 mg.g⁻¹) and 5-MF (1.29 mg.g⁻¹) was highest at 160°C for 7.13 h and 7.41 h of HTC, respectively (see Fig. SI 2 and Table 5). As can be seen, the extraction of furanic compounds is confirmed to be highly dependent on the temperature and duration of HTC. At high temperatures (220°C and 240°C), it is clear that there is no production of furanic compounds over time, except at low HTC duration (1 h), but it remains low. Furanic compound production is also very low at low HTC times (1 h) for the most of temperatures.

Above 180°C, when the aforementioned time optima are exceeded, the production of furanic compounds decreases over time. This can be explained by the degradation of furanic compounds and the formation of various by-products such as acetic acid, levulinic acid and/or formic
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Response surfaces using a full factorial design obtained by plotting (a) 5-HMF, (b) F, and (c) 5-MF concentration against HTC temperature and time, averaged over all particle sizes.</figcaption></figure>

<table><caption>**Table 5**
Optimum conditions for maximizing furanic compound production.</caption><thead><tr><th>Compound</th><th></th><th>Temperature (°C)</th><th>Time (h)</th><th>Size (µm)</th><th>Predicted value (mg.g<sup>-1</sup>)</th><th>Actual value (mg.g<sup>-1</sup>)</th><th>Error of prediction %</th></tr></thead><tbody><tr><td rowspan="3">Single optimization</td><td>5-HMF</td><td>180.65</td><td>8.18</td><td>Average</td><td>6.29</td><td>6.37</td><td>1.27</td></tr><tr><td>F</td><td>160.00</td><td>7.13</td><td>Average</td><td>4.65</td><td>5.20</td><td>11.83</td></tr><tr><td>5-MF</td><td>160.00</td><td>7.41</td><td>Average</td><td>1.29</td><td>1.14</td><td>11.63</td></tr><tr><td rowspan="3">Multiple optimization</td><td>5-HMF</td><td>171.06</td><td>9.35</td><td>Average</td><td>6.41</td><td>7.32</td><td>14.20</td></tr><tr><td>F</td><td></td><td></td><td></td><td>4.65</td><td>3.85</td><td>17.20</td></tr><tr><td>5-MF</td><td></td><td></td><td></td><td>1.33</td><td>1.05</td><td>21.05</td></tr></tbody></table>

acid [55]. Specifically, 5-HMF can undergo rehydration to produce levulinic acid and formic acid. In addition, furanic compounds may condense into soluble polymers, or cross-polymerize with glucose, leading to the formation of insoluble humins [60]. This is in agreement with other results, which show that the stability of furanic compounds is very low, particularly furfural, which degrades rapidly at high temperatures [61].

### 3.4.2. Multiple response optimization

Graphical optimization using overlaid contour plots was used to determine the best conditions for maximizing the production of all furanic compounds, but this method is limited to the number of responses and factors, which should not exceed two [43]. This is not the case here, since we have three responses (5-HMF, F and 5-MF content) to

optimize for two factors (time and temperature). To optimize multiple responses, the solution found in 1980 is to use of the desirability function developed by Derringer and Suich [62]. The variation of 5-HMF, F and 5-MF as a function of time and temperature was represented by the prediction profiler shown in Fig. SI 3. The optimum conditions for the simultaneous production of 5-HMF, F and 5-MF from *Picea abies* bark using hydrothermal carbonization are a temperature of 171°C and a duration of 9.35 h, as summarized in Table 5. Based on these results, we plotted the variation of 5-HMF, F and 5-MF contents as a function of time and temperature in the same figure. This type of figure, known as overlaid contour plots (as shown in Fig. 7), enables us to study the production ranges (green color) and non-production ranges (blue color) of the furanic compounds, and to select the position of optimum conditions (indicated in the white box). Comparing the optimum conditions
<figure><img src="image_9.png" /><figcaption>Fig. 7. Overlaid contour plots of optimal conditions for the simultaneous production of three compounds 5-HMF, F and 5-MF.</figcaption></figure>

of single and multiple optimizations, we note that the temperature (170°C) is almost the average between 5-HMF, 5-MF and F (180, 160 and 160°C, respectively), and that the time is a little longer by, almost, 1 h for 5-HMF and 2 h for F and 5-MF.

The calculated percentages of variation between model-predicted and experimental values for single and multiple optimizations are summarized in Table 5. Although the difference between predicted and experimental values is small, the percentage variation is high for F (17.20 %) and 5-MF (21.05 %) responses. This can be attributed to the low parameter values, making it difficult to obtain values that are more precise. The results indicate the robustness of the models in predicting experimental responses over the range of values studied, particularly under optimal conditions.

### 3.5. Literature comparison

The optimum hydrothermal carbonization (HTC) conditions reported in the literature for the production of furanic compounds from various biomasses and their comparison with the present work are reported in Table SI 5. Overall, the comparison highlights that temperature and residence time strongly depend on the type of biomass and its chemical composition. For example, orange peel waste showed maximum yields of furfural (1.5%) and 5-HMF (9.1%) under relatively mild conditions (180°C, 0.5 h), with longer treatments leading to huminization [63]. Similarly, olive stone fractions yielded high furfural concentrations (7.61 g/L, 19.9%) at 190°C and 4 h, which were mainly attributed to their high hemicellulose content [30]. Beech wood hemicellulose and microcrystalline cellulose also produced significant furanic amounts (6.8 g/L furfural and 1.8 g/L 5-HMF), with optimum residence times ranging from 3 to 4 h [40].

In contrast, studies on spruce bark have so far mainly focused on the solid fraction. Bejenari et al. [64] reported optimum HTC conditions at 280°C for 1 h, which favored the formation of a stable hydrochar (42.5% yield), but did not analyze liquid-phase furanics. This underlines the originality of the present work, which demonstrates that under milder conditions (160–180°C, 7–9 h), spruce bark can be efficiently converted to furanic compounds (up to 7.3 mg/g 5-HMF and 5.2 mg/g furfural), with particle size showing no statistical influence. Thus, our study complements existing knowledge by shifting the focus from hydrochar valorization to liquid-phase chemical production.

Other biomasses, such as rice straws and coffee residues, also revealed the importance of feedstock composition and experimental

design. Rice straws treated by HTC with a metal catalyst reached remarkable yields (22.5% furfural and 18.6% 5-HMF) under mild conditions (180–200°C, 1 h), but degraded under harsher conditions [65]. Coffee and peapod residues produced furfural and 5-HMF in the range of 0.6–2.2% at 180°C and 1–4 h, with higher severity required for coffee compared to peapod, reflecting the role of matrix recalcitrance [66].

An additional trend emerging from the literature is the interplay between temperature and residence time. These two factors appear to be inversely related once the optimum conditions are reached. In other words, beyond the optimum point, higher temperatures require shorter residence times to maintain satisfactory furanic yields. Conversely, prolonging the reaction time at high temperature leads to significant yield losses. All studies reviewed consistently reported a decline in furanic yield and/or concentration after the optimum conditions, confirming the onset of degradation pathways and the conversion of furans into humins. This highlights the narrow operational window for maximizing liquid-phase furanics and the importance of fine-tuning process severity to avoid excessive humin formation.

### 4. Conclusions

In this study, hydrothermal carbonization (HTC) conditions were optimized to maximize the extraction of furanic compounds (5-HMF, F and 5-MF) from *Picea abies* bark (PB). To this end, PB was first ground to obtain 0-1000 µm (GPB), and sieved into four different particle sizes called size S (50-100 µm), size M (200-300 µm), size L (400-500 µm) and size XL (500-1000 µm). Each size was impregnated with water and hydrothermally carbonized in an autoclave at varying temperatures (160°C, 180°C, 220°C and 240°C) and reaction times (1, 3, 6 and 24 h). The liquid fraction, obtained after HTC and filtration, was then analyzed by HPLC and the compositions of the biomass fractions were characterized using ASTM methods.

Results showed that smaller particles (especially size S) had higher ash, cellulose, and lignin content, while larger particles retained more hemicellulose and extractives. pH trends during HTC suggested initial acidification due to furan degradation, followed by a rise linked to the formation of phenolic compounds. Kinetic modeling revealed that furanic compound formation was dominant, with size XL showing the highest yields due to slower degradation. A response surface methodology using a full factorial design determined that temperature, time, and their interaction significantly influenced furan production, while particle size did not. Optimal single-compound yields were found at 160–180°C and 7–8 h, and multiple optimizations indicated ideal conditions for maximizing all three compounds at once were 170°C and 9.35 h. These findings demonstrate that spruce bark, currently an underutilized forestry residue, represents a promising renewable feedstock for the production of furanic platform chemicals. The insights into statistical optimization and kinetic modeling can guide future process scale-up and integration into biorefineries. Further studies could explore catalytic HTC taking account others factors (solvent nature, pH, presence of catalysts), the potential of PB pre-treatment, and valorization of the solid residue to achieve a more circular and sustainable biomass valorization strategy.

### Funding

The authors gratefully acknowledge the financial support of the ECOVALUE project by the Carnot Institute ICEEL and the European Union, through the Operational Programme FEDER-FSE Lorraine and Massif des Vosges 2014–2020.

### CRediT authorship contribution statement

Taher Selmi: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Eric Masson: Writing – review & editing, Project administration, Methodology,
**Funding acquisition, Formal analysis, Conceptualization.** **Alain Celzard:** Writing – review & editing, Formal analysis. **Vanessa Fierro:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Funding acquisition, Formal analysis, Conceptualization.

## 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.

## Acknowledgements

The authors gratefully acknowledge the financial support of the ECOVALUE project by the Carnot Institute ICEEL and the European Union, through the Operational Programme FEDER-FSE Lorraine and Massif des Vosges 2014–2020.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.cej.2025.100881.

## Data availability

Data will be made available on request.

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