Home Life Sciences Toward sustainable bioproducts from lignocellulosic biomass: Influence of chemical pretreatments on liquefied walnut shells
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Toward sustainable bioproducts from lignocellulosic biomass: Influence of chemical pretreatments on liquefied walnut shells

  • Nives Jovičić , Tajana Krička , Alan Antonović EMAIL logo , Božidar Matin , Lato Pezo , Ivan Brandić and Ana Matin
Published/Copyright: January 13, 2025
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Open Agriculture
From the journal Open Agriculture Volume 10 Issue 1

Abstract

The depletion of resources and the generation of significant waste pose considerable environmental challenges. Post-utilization of walnut kernels leaves behind substantial amounts of shells as the unused residue. Walnut shells find application in various production processes, offering an opportunity to mitigate environmental impacts through resource utilization. This study investigates the influence of chemical pretreatment on the properties of liquefied lignocellulosic biomass, specifically focusing on walnut shells as a prominent lignocellulosic material. The results reveal that samples subjected to alcohol pretreatment exhibited the lowest degree of liquefaction (85.00% at 120°C for 45 min), while the highest degree of liquefaction was observed in samples after alkaline pretreatment (90.76% at 90°C for 15 min). Analyzing functional groups in liquefied walnut shell biomass, formed during the addition of polyhydric alcohols, glycols, and organic acids, underscores its potential for diverse bioproducts. Pretreatment significantly increases the hydroxyl (OH) number, irrespective of the type, temperature, and duration of chemical pretreatment. Compared to the untreated sample, alkali pretreatment produces the highest OH number (1288.03 KOH/g), surpassing mean values after acid and alcohol pretreatment. The results highlight the efficacy of chemical pretreatment in tailoring the properties of liquefied walnut shell biomass, addressing the challenges associated with resource depletion and waste accumulation.

Keywords: walnut shell utilization; lignocellulosic biomass; resource efficiency; chemical pretreatment; liquefied biomass

1 Introduction

The escalating global demand for energy and chemicals, coupled with the rapid depletion of fossil fuel reserves, presents an urgent challenge. Traditional fossil fuel-based materials not only contribute to environmental degradation but also are subject to volatile market conditions with diminishing availability. This has led to growing interest in exploring renewable resources, particularly agricultural biomass, as sustainable alternatives. Biomass offers a renewable source of raw materials for creating environmentally friendly fuels, chemicals, and materials, offering a potential solution to the over-reliance on fossil fuels and the environmental concerns they create [1].

Agricultural biomass stands out as a vital source of renewable raw materials, offering substantial production potential. The utilization of biomass as a raw material continues to burgeon across Europe, emphasizing its pivotal role in sustainable resource management [2,3]. A significant proportion of agricultural waste comprises lignocellulosic material, distinguished by its attributes of strength, flammability, biodegradability, and reactivity [4]. The versatility of agricultural biomass extends to its application as a raw material for natural fiber production, presenting an alternative source of materials independent of oil derivatives [5,6].

Despite the potential of lignocellulosic biomass, its industrial application faces several challenges, particularly in the pretreatment and liquefaction processes required for converting it into valuable products like biocomposites [6]. Current methods, such as acid hydrolysis and alkaline pretreatment, often suffer from high costs, inefficiencies, and environmental concerns. This research aims to address these limitations by developing novel, more sustainable approaches to biomass pretreatment and liquefaction, contributing to the shift toward greener materials. Global warming and nature conservation have focused attention on testing the properties of natural fibers from biomass. This process resulted in the expansion of the use of natural fibers and the replacement of synthetic fibers in various applications. Many studies have confirmed that the production of biocomposites by adding natural lignocellulosic fibers to synthetic polymer matrices has less harmful impact on the environment than petroleum-based composites [7,8]. Natural fibers can be used as reinforcement in polyurethane resins, which are synthetic polymers that have urethane groups in their structure. One of the methods of obtaining polyols on a natural basis is the liquefaction process of biomass. This method relies on turning the entire biomass into a liquid by reacting the biomass with a suitable solvent and catalyst. The general interest in obtaining polyols from renewable raw materials is caused by the reduction of oil resources and the constant growth of gas and oil prices as well as concern for the environment [7]. Lignocellulosic biomass liquefaction is a process that involves converting complex biomass materials, such as wood, agricultural residues, or dedicated energy crops, into liquid biofuels or valuable chemicals. The liquefaction of lignocellulosic biomass is a promising technology for converting biomass into liquid valuable products through complex chemical and physical reactions, and liquefied biomass represents a possible alternative raw material for polymer synthesis. Polyols obtained by liquefaction of biomass can be used for various materials. With the increase in the level of ecological awareness, biomass and natural fibers in polymer composites are used more and more and represent a sustainable alternative to synthetic fibers [9]. Therefore, as one of the solutions to reduce the impact on the environment, the possibility of replacing polymer materials with biocomposites is increasingly being studied because they are more environmentally friendly compared to traditional polymer materials [8].

Lignocellulosic materials, such as walnut shells, are difficult to degrade due to the crystalline structure of cellulose and the complex structural organization of cellulose, hemicellulose, and lignin, so it is necessary to carry out appropriate pretreatment procedures [10]. By pretreatment, it is possible to improve the decomposition of the raw material, the removal of lignin, and thus enable the partial or complete hydrolysis of hemicellulose and the reduction of the amount of the crystalline fraction of cellulose [11]. In short, lignocellulosic biomass is translated from its natural form, which is inaccessible to cellulases, to a form where enzymatic hydrolysis is possible [4].

Chemical pretreatment of lignocellulosic materials involves the application of chemicals, such as ozone, acids, alkalis, and peroxides, to alter the crystalline structure of cellulose and remove hemicellulose and lignin. This process enhances the contact surface and pores of the lignocellulosic material, leading to partial lignin removal (delignification), reduced lignin polymerization, and partial or complete hydrolysis of hemicellulose [12]. The chemical treatment process of lignocellulosic biomass aims to degrade lignin from the material, with its effectiveness dependent on the lignin proportion [13]. Catalyzed by enzymes, metal salts, acids, or alkalis [14], hydrolysis breaks chemical bonds by inserting water molecules between atoms, transforming large molecules into smaller ones [15,16]. The process involves treatment of lignocellulosic raw material under specific pressure and temperature conditions [14,15,17], hydrolyzing hemicellulose and cellulose into simple compounds [18,19]. Previous studies show hydrolysis as an effective treatment, significantly reducing the lignin content, removing hemicellulose, and disrupting the crystalline structure of cellulose [4,20].

Inorganic acids, predominantly sulfuric or hydrochloric acid, are commonly used, with hydrolysis with dilute sulfuric acid being a successful method that accelerates subsequent cellulose hydrolysis [21]. However, the costs associated with dilute acid pretreatment are higher due to the need for corrosion-resistant and high-pressure reactor materials [22,23]. There are two main pretreatment types: continuous with low acid concentration at high temperatures and discontinuous with concentrated acid at low temperatures, resulting in cellulose hydrolysis and hemicellulose conversion to monosaccharides [24]. Acid procedures, particularly with diluted sulfuric acid, are commonly used but are more expensive due to equipment corrosion resistance requirements. After acid pretreatment, pH neutralization and salt extraction are necessary, further increasing costs [25].

Alkaline pretreatment stands as a widely adopted technique for the solubilization of lignin, achieved by diminishing the degree of polymerization and disrupting the bonds between lignin and other polymers using different bases [26]. The reagents employed for alkali pretreatment encompass sodium hydroxide, potassium hydroxide, ammonium hydroxide, and aqueous ammonia, among others [21,27]. Being effective, especially for hardwood and agricultural residues, it enhances lignocellulosic raw material porosity, enabling lignin removal and hemicellulose modification [18,28].

Organic solvent pretreatment involves the utilization of organic or aqueous organic solvent mixtures, sometimes in conjunction with inorganic acid catalysts, for delignification. Several organic solvent combinations, including triethylene glycol, methanol, ethanol, ethylene glycol, tetrahydrofurfuryl alcohol, and acetone, have been applied at defined temperatures and pressures, often in the presence of specific catalysts. This organosolv pretreatment method aims to achieve high-quality lignin extraction, producing a value-added product [21].

2 Materials and methods

In this study, walnut shell biomass from the domestic cultivar Šejnovo (Sisak-Moslavina County, Croatia) served as the primary research material. Following the separation of walnut shells from the kernels, the shells underwent natural air-drying. Subsequently, the air-dried samples were milled to various particle sizes using a Fritsch–Pulverisette 19 knife-mill. Sieving was conducted using a laboratory electromagnetic sieve shaker Cisa RP–08 (shaking time: t = 15 ± 1 min) following TAPPI T 264 cm-97 standards. After crushing and sieving the walnut shell samples, particles between 600 μm and 1.25 mm were selected, since the uniformity module showed that medium particles predominate in samples that have been pulverized and sieved several times. Initial chemical analysis was made on the selected sample (initial – “raw sample”), and the chopped walnut shells (600 μm–1.25 mm) were subjected to chemical pretreatment.

2.1 Chemical pretreatment

After the initial analysis of the walnut shell, the lignocellulosic biomass was subjected to chemical pretreatment. Hydrolysis was carried out in a laboratory autoclave (WR2 reactor, Indianapolis, USA) in three ways:

  • acid pretreatment (dilute H2SO4 5%),

  • alkali pretreatment (dilute KOH 5%),

  • alcohol pretreatment (ethanol)

under the following conditions:

  • time (t1 – 15′, t2 – 30′, and t3 – 45′),

  • temperature (T1 – 80°C, T2 – 90°C, T3 – 100°C, T4 – 110°C, and T5 – 120°C), and

  • 4 bar pressure.

The indicated temperatures are lower than those in previous studies, and they were chosen for reasons of energy saving; therefore, the duration of the pretreatment is longer than in previous literature values.

2.2 Liquefied biomass preparation

Liquefied biomass was prepared following a methodology (Figure 1) outlined by Antonović et al. [29].

Figure 1 Liquefaction procedure and analytical methods for liquefied biomass characterization [30].
Figure 1

Liquefaction procedure and analytical methods for liquefied biomass characterization [30].

Smaller samples, obtained without prior chemical treatment, underwent liquefaction using a mixture of glycerol and sulfuric acid (H2SO4) as an acid catalyst (biomass/glycerol = 1:5, sulfuric acid at a concentration of 3 wt%) for 120 min at 150°C. The evaluation of polymer properties for the liquefied biomass involved determining the percentage of undissolved residue (UR), wood liquefaction degree (LD), and hydroxyl number (OH number). These parameters were crucial for identifying optimal liquefaction conditions.

2.3 LD and UR

After the liquefaction process, the UR in the liquefied biomass was assessed using a dioxane/water mixture, recognized as a universal diluent for liquefied biomasses. Specifically, 1 g of liquefied biomass was mixed with a dioxane/water solution in an 8/2 ratio and agitated on a magnetic stirrer for 60 min. Subsequently, the mixture underwent filtration through a glass-fiber filter B2 under vacuum conditions. The residue was subjected to repetitive rinsing with the same diluent until a colorless filtrate was obtained, and the UR was then dried in an oven at 105 ± 2°C until a constant weight was achieved. The percentage of UR was determined using the following equation according to Antonović et al. [29]:

UR = weight of UR ( g ) weight of liquefied wood ( g ) × 100 ( % ) .

LD percentage was calculated according to the following equation:

LD = 100 – UR ( % ) .

2.4 OH number

A weight of 1.5–2.5 g of liquefied biomass was precisely weighed into two 250 mL Erlenmeyer flasks, and the process was initiated with the addition of 10 mL of a reagent mixture containing pyridine and phthalic acid anhydride. A third flask, serving as the blank solution, received only the reagent. Each flask, equipped with a condenser and a magnetic stirrer serving as a heater and an oil bath, maintained a constant temperature at 115°C, ensuring the condensation of the reagent. The mixture in the flask was heated for precisely 1 h from the point of the first drop condensation. Following that, 50 mL of pyridine was introduced into the cooled mixture, and titration was carried out using a 0.5 M sodium hydroxide solution in the presence of phenolphthalein until reaching the equivalent point (ensuring the absence of bright red staining for at least 30 s). The OH number for the liquefied biomass sample, expressed as mg KOH/g, was determined (following the approach of Antonović et al [29]) using the following equation:

OH number = ( B − A ) × c NaOH × 56.1 m ( mg KOH / g ) ,

where A is the volume of the NaOH solution used for sample titration (ml); B is the volume of the NaOH solution used for blank solution titration; C NaOH is the normality of the NaOH solution (M); m is the weight of the liquefied biomass sample.

The titration, following the ASTM D4274-05 standard, was continued until the solution turned pink. However, in the titration of the liquefied walnut shell biomass sample, a very dark red color appeared instead of the expected pink color, as in the case of the blank, so the method was modified.

The dark color was likely a result of the fact that the addition of the sample to the esterification reagent already produced a dark brown hue, stemming from the natural color of the liquefied walnut shell biomass. Since it is difficult to recognize the moment of neutralization or the equivalent point based on the color change, the method was modified in this part. To address this, the pH was continuously measured during titration to accurately identify the equivalent point and determine the precise consumption of sodium hydroxide.

The dry matter content of different particle sizes was determined by drying a sample in an oven, at two temperatures (102 and 150°C), until a constant weight was obtained.

2.5 Statistical analysis

Statistical analysis was performed using the TIBCO STATISTICA 13.3.0 software package (StatSoft TIBCO Software Inc.). Data presentation includes mean values accompanied by standard deviation. A random block design incorporating a time variable (15, 30, and 45 min) and five temperature regimes (80, 90, 100, 110, and 120°C) was employed. The statistical analysis involved calculating mean values and standard deviations for observed samples across five different temperatures and three distinct time intervals for each temperature regime. Analysis of variance (ANOVA) was conducted, followed by Tukey’s post hoc HSD. Additionally, principal component analysis (PCA), also known as Karhunen–Loève expansion and Karhunen–Loève transformation in the literature, was employed as a method of multivariate statistics.

3 Results

The properties of the liquefied walnut shell biomass were initially investigated before pretreatment to establish a baseline for comparison with the LD and UR, and the dry matter and OH number of the liquefied biomass were examined after pretreatment with acid, alkali, and alcohol depending on the following parameters: time (15, 30, and 45 min) and temperature (80, 90, 100, 110, and 120°C).

3.1 LD and UR

The LD of walnut shell biomass before chemical pretreatment was 90.58%, dry matter was 56.1%, and the OH number of the untreated sample was 385.67 mg KOH/g. Univariate ANOVA of data on the LD of all analyzed samples depending on the temperature, time, and type of pretreatment is shown in Table 1.

Table 1

Univariate analysis of the change in the LD and undissolved walnut shells with respect to the pretreatment type (solvent, temperature, and duration of the process)

DF Acid pretreatment Alkali pretreatment Alcohol pretreatment
SS
LD (%) UR (%) LD (%) UR (%) LD (%) UR (%)
Temperature 4 47.37n.s. 47.37n.s. 39.01n.s. 43.87n.s. 84.90n.s. 85.38n.s.
Time 2 4.91n.s. 4.91n.s. 15.40n.s. 18.81n.s. 2.72n.s. 2.64n.s.
Temp. × Time 8 23.07n.s. 23.07n.s. 40.69n.s. 49.78n.s. 15.28n.s. 15.60n.s.
Error 30 8.72n.s. 8.72n.s. 6.15n.s. 3.92n.s. 4.28n.s. 4.33n.s.

LD – liquefaction degree; UR – undissolved residue; n.s. – not significant; DF – degree of freedom; SS – sum of squares; Error – model error; P – probability of acceptance of the null hypothesis.

The influences of the linear coefficients of temperature and time on the product (interaction) of time and temperature are not statistically significant in any of the LD models. Similarly, the impact of error is also not statistically significant in one model of liquefaction.

The LD and UR of walnut shell biomass after pretreatment depending on the type, duration, and temperature of pretreatment are shown in Table 2.

Table 2

LD after pretreatment

Acid pretreatment Alkali pretreatment Alcohol pretreatment
No. Temp (°C) Time (min) LD (%) UR (%) LD (%) UR (%) LD (%) UR (%)
T1 80 15 87.74 ± 0.72bcde 12.26 ± 0.72cdef 86.74 ± 0.19b 13.26 ± 0.19g 90.15 ± 0.41e 9.85 ± 0.41ab
T2 30 87.17 ± 0.38b 12.83 ± 0.38f 89.42 ± 0.14defgh 10.58 ± 0.14bcd 88.03 ± 0.28cd 11.97 ± 0.28cd
T3 45 85.32 ± 0.36a 14.68 ± 0.36g 89.34 ± 0.52defg 10.66 ± 0.52bcd 90.03 ± 0.35e 9.97 ± 0.35ab
T4 90 15 88.38 ± 0.26bcdef 11.62 ± 0.26bcdef 90.76 ± 0.13h 9.24 ± 0.13a 89.27 ± 0.61e 10.73 ± 0.61ab
T5 30 89.80 ± 0.69fg 10.20 ± 0.69ab 90.26 ± 0.39gh 9.74 ± 0.39ab 90.10 ± 0.09e 9.83 ± 0.10a
T6 45 89.80 ± 0.54fg 10.2 ± 0.54ab 89.43 ± 0.33defgh 10.57 ± 0.33bcd 89.68 ± 0.29e 10.32 ± 0.29ab
T7 100 15 89.07 ± 0.14cdefg 10.93 ± 0.14abcde 84.76 ± 1.11a 15.91 ± 0.34h 89.64 ± 0.55e 10.36 ± 0.55ab
T8 30 90.58 ± 1.23g 9.42 ± 1.23a 89.34 ± 0.38defg 10.66 ± 0.38bcd 89.44 ± 0.16e 10.56 ± 0.16ab
T9 45 88.99 ± 0.5cdefg 11.01 ± 0.5abcde 88.88 ± 0.28cdef 11.12 ± 0.28cde 89.54 ± 0.67e 10.46 ± 0.67ab
T10 110 15 88.38 ± 0.04bcdef 11.62 ± 0.04bcdef 88.35 ± 0.3cde 11.65 ± 0.3def 89.78 ± 0.05e 10.22 ± 0.05ab
T11 30 87.50 ± 0.53bc 12.5 ± 0.53ef 88.17 ± 0.36cd 11.83 ± 0.36ef 89.85 ± 0.18e 10.12 ± 0.24ab
T12 45 87.59 ± 0.4bcd 12.41 ± 0.4def 87.67 ± 0.46bc 12.33 ± 0.46fg 89.02 ± 0.45de 10.98 ± 0.45bc
T13 120 15 87.19 ± 0.31b 12.81 ± 0.31f 89.28 ± 0.63defg 10.72 ± 0.63bcd 87.18 ± 0.12bc 12.82 ± 0.12de
T14 30 89.27 ± 0.52efg 10.73 ± 0.52abc 89.53 ± 0.33efgh 10.47 ± 0.33bc 86.17 ± 0.45b 13.83 ± 0.45e
T15 45 89.15 ± 0.37defg 10.85 ± 0.37abcd 89.86 ± 0.29fgh 10.14 ± 0.29abc 85.00 ± 0.26a 15.00 ± 0.26f

T1–T15 denotes interaction of samples shown in Figure 2 (PCA) in relation to the changes in the variables studied; LD – liquefaction degree; UR – undissolved residue. The values with different letters in the column indicate a statistically significant difference (p ≤ 0,05) according to Tukey’s HSD test.

Figure 2 LD and UR of walnut shell biomass after pretreatment. (a) LD after acid pretreatment. (b) UR after acid pretreatment. (c) LD after alkali pretreatment. (d) UR after alkali pretreatment. (e) LD after alcohol pretreatment. (f) UR after alcohol pretreatment.
Figure 2

LD and UR of walnut shell biomass after pretreatment. (a) LD after acid pretreatment. (b) UR after acid pretreatment. (c) LD after alkali pretreatment. (d) UR after alkali pretreatment. (e) LD after alcohol pretreatment. (f) UR after alcohol pretreatment.

The statistical analysis determined the mean values and standard deviation of the observed samples of the LD and the UR at five different temperatures and three different times for each temperature regime after pretreatment with acid, alkali, and alcohol. Different mean values in the same column with a different letter in the exponent represent a statistical difference (p ≤ 0.05) according to Tukey’s post hoc test. It was found that the LD in the T4 treatment (alkali, 90°C, and 15 min) had the highest mean value (90.76%), while the lowest average value was 84.76% for the T7 treatment (alkali, 100°C, and 15 min).

Figure 2 graphically presents the data from Table 2, illustrating the relationships between pretreatment type, temperature, and duration.

By applying PCA, the parameters that have a significant influence on the distribution of samples based on the obtained data were analyzed using the first principal component, which explains 44.88% of all interactions in the observed data set. Figure 2 shows the PCA and the grouping of the samples in the direction of the vectors (variables) in relation to the type, temperature, and time duration of pretreatment (Figure 3).

Figure 3 PCA of the LD and UR.
Figure 3

PCA of the LD and UR.

As shown on the upper right side of the PCA diagram, the highest value of LD was observed for treatment T8 after acid pretreatment, treatment T4 after alkaline pretreatment, and treatment T1 after alcohol pretreatment.

3.2 Dry matter content

Univariate ANOVA of data on the dry matter of all analyzed samples depending on the temperature, time, and type of pretreatment is shown in Table 3.

Table 3

Univariate analysis of the change in dry matter of liquified walnut shells with respect to the pretreatment type

Acid pretreatment Alkali pretreatment Alcohol pretreatment
SS
DF DM (%) DM (%) DM (%)
Temperature 4 40.81n.s. 68.13n.s. 181.09n.s.
Time 2 4.30n.s. 8.60* 30.25n.s.
Temp. × Time 8 39.48n.s. 71.87n.s. 34.49n.s.
Error 30 25.46n.s. 25.67n.s. 12.53n.s.

DM – Dry matter; DF – number of degrees of freedom of variability source; SS – sum of square values; Error – model error; P – probability of acceptance of the null hypothesis; statistical significance: *p < 0.05; n.s. – not statistically significant.

The influences of the linear coefficients of temperature and time and the product (interaction) of time and temperature are not statistically significant in the models of dry matter content in samples treated with acid and alcohol. The influence of the linear coefficient of time in the model of dry matter content, in samples treated with alkali, proved to be the most influential, statistically significant at the p ≤ 0.05 level, while the effects of the linear coefficient of temperature and the product (interaction) of time and temperature were not statistically significant. The influence of error is not statistically significant in any model of dry matter content.

The dry matter content depending on the type of pretreatment, duration, and temperature of pretreatment is shown in Table 4. Different mean values in the same column with different letters in the exponent represent a statistical difference (p ≤ 0.05) according to Tukey’s post hoc test.

Table 4

Dry matter content after pretreatment

Acid pretreatment Alkali pretreatment Alcohol pretreatment
Treatment Temp (°C) Time (min) DM (%) DM (%) DM (%)
T1 80 15 55.71 ± 0.75abcde 57.48 ± 0.59de 51.81 ± 0.42a
T2 30 56.50 ± 0.75bcde 54.99 ± 0.08abcd 55.98 ± 1.50de
T3 45 58.08 ± 0.37e 54.82 ± 2.09abcd 54.18 ± 0.27bcd
T4 90 15 55.11 ± 0.97abcd 52.31 ± 1.24a 52.82 ± 0.29abc
T5 30 53.59 ± 1.37a 55.04 ± 0.37abcd 53.69 ± 0.44abc
T6 45 53.97 ± 0.43abc 55.65 ± 1.06bcd 53.61 ± 0.32abc
T7 100 15 54.52 ± 1.10abcd 58.87 ± 0.44e 52.46 ± 0.25ab
T8 30 53.84 ± 1.07ab 54.23 ± 0.32abc 54.51 ± 0.04cd
T9 45 55.07 ± 0.84abcd 54.61 ± 1.68abc 53.86 ± 1.09bc
T10 110 15 55.44 ± 0.23abcde 55.94 ± 0.27cd 52.78 ± 0.75abc
T11 30 56.64 ± 0.40cde 54.55 ± 0.97abc 54.44 ± 0.67cd
T12 45 54.82 ± 0.29abcd 56.87 ± 0.44cde 53.64 ± 0.41abc
T13 120 15 56.83 ± 1.26de 52.50 ± 0.30a 57.56 ± 0.89e
T14 30 53.27 ± 0.70a 52.94 ± 0.91ab 57.30 ± 0.26e
T15 45 53.49 ± 1.75a 52.76 ± 0.30a 61.06 ± 0.31f

T1–T15 denote interactions of single samples in Figure 3 (PCA) in relation to the changes in the variables studied; DM – dry matter; the values with different letters in the column indicate a statistically significant difference (p ≤ 0,05) according to Tukey’s HSD test.

Statistical analysis determined the mean values and standard deviation of the dry matter content of the observed samples at five different temperatures and three different times for each temperature regime after pretreatment with acid, alkali, and alcohol. It was found that the dry matter content in the T1 treatment (80°C, 15 min) had the lowest mean value (51.81%) in the samples treated with alcohol, while the highest mean value was 61.06% for the T15 treatment (120°C, 45 min) for samples also treated with alcohol.

Figure 4 shows the PCA and the grouping of the samples in the direction of the vectors in relation to the type of pretreatment for the dry matter content.

Figure 4 PCA for dry matter.
Figure 4

PCA for dry matter.

Parameters that have a significant influence on the distribution of samples based on the obtained data were analyzed in the first main component, which explains 55.97% of all interactions in the observed data set.

On the left side of the PCA diagram, treatment T15 has the highest value of the dry matter content after pretreatment with alcohol (52.46%), while treatment T7 has the highest value after pretreatment with alkali (58.87%), and treatment T3 showed the highest dry matter content after acid pretreatment (58.08%).

3.3 OH number

Univariate ANOVA of data on the OH number of all analyzed samples depending on the temperature, time, and type of pretreatment is shown in Table 5.

Table 5

Univariate analysis of the change in the OH number of liquified walnut shells with respect to the pretreatment type

Acid pretreatment Alkali pretreatment Alcohol pretreatment
SS
DF OH number (mg KOH/g) OH number (mg KOH/g) OH number (mg KOH/g)
Temperature 4 186,572.88n.s. 115,541.33n.s. 204,769.29n.s.
Time 2 7,498.43n.s. 76,375.67n.s. 6,183.08n.s.
Temp. × Time 8 125,759.90n.s. 175,593.60n.s. 40,739.04n.s.
Error 30 1.71n.s. 1.43n.s. 1.95n.s.

Statistical significance: *p < 0.05; n.s. – not statistically significant; DF – number of degrees of freedom of the variability source; SS – sum of square values; Error – model error; P – the probability of acceptance of the null hypothesis.

The influences of the linear coefficients of temperature and time and the product (interaction) of time and temperature are not statistically significant in any model regarding the OH number content.

The OH number was determined for all samples of liquefied walnut shell biomass after hydrolysis with acid, alkali, and alcohol, depending on the temperature and duration of pretreatment. The mean values with standard deviation are shown in Table 6.

Table 6

OH number of liquefied biomass after pretreatment

Temperature (°C) Time (min) OH number after acid pretreatment (mg KOH/g) OH number after alkali pretreatment (mg KOH/g) OH number after alcohol pretreatment (mg KOH/g)
T1 80 15 1104.80 ± 0.2e 1059.97 ± 0.15b 1178.90 ± 0.26h
T2 30 1071.13 ± 0.32b 1222.10 ± 0.36k 1069.93 ± 0.31d
T3 45 928.13 ± 0.15a 1205.13 ± 0.32i 1204.03 ± 0.06l
T4 90 15 1101.13 ± 0.32d 1277.13 ± 0.15n 1204.27 ± 0.25l
T5 30 1192.07 ± 0.21h 1288.03 ± 0.06o 1217.03 ± 0.25n
T6 45 1249.27 ± 0.25n 1201.07 ± 0.06g 1193.10 ± 0.1j
T7 100 15 1203.07 ± 0.12i 922.03 ± 0.15a 1181.33 ± 0.31i
T8 30 1242.07 ± 0.31m 1260.37 ± 0.4m 1159.07 ± 0.12f
T9 45 1235.17 ± 0.15l 1182.27 ± 0.23f 1095.23 ± 0.25e
T10 110 15 1212.83 ± 0.38j 1151.07 ± 0.12e 1202.43 ± 0.45k
T11 30 1139.33 ± 0.31f 1125.17 ± 0.15d 1210.10 ± 0.17m
T12 45 1150.17 ± 0.15g 1119.13 ± 0.15c 1167.43 ± 0.38g
T13 120 15 1084.07 ± 0.12c 1210.13 ± 0.15j 1025.30 ± 0.26c
T14 30 1219.07 ± 0.21k 1227.07 ± 0.06l 1008.10 ± 0.17a
T15 45 1213.20 ± 0.17j 1204.37 ± 0.32h 1012.13 ± 0.15b

The values with different letters in the column indicate a statistically significant difference (p ≤ 0,05) according to Tukey’s HSD test.

Statistical analysis revealed that the OH number in the T7 treatment (100°C, 15 min) had the lowest value (922.03) in the samples after alkali hydrolysis, while the highest value observed was 1288.03 for the T5 treatment (90°C, 30 min) for samples treated with alkali.

4 Discussion

Biomass liquefaction technology breaks down high-molecular-weight components in biomass into low-molecular-weight gases, liquids, and solid residues [31]. This study examined the liquefaction properties of walnut shell biomass following various chemical pretreatments. The initial LD of untreated walnut shell biomass was 90.58%, with a dry matter content of 56.10% and an OH number of 385.67 mg KOH/g.

4.1 Effect of pretreatment on dry matter content

The dry matter content decreased following pretreatment. Specifically, alcohol pretreatment resulted in the lowest dry matter content compared to acid and alkali pretreatments. This suggests that alcohol pretreatment is more effective in reducing the dry matter content of walnut shells. The reduction in dry matter content across pretreatment methods indicates successful removal of certain biomass components, which is critical for subsequent processing [31].

4.2 Impact of pretreatment on LD

The LD of the untreated walnut shell biomass was 90.58%. The pretreatment processes did not significantly increase the LD, with alkali pretreatment at 90°C for 15 min, yielding the highest degree of 90.76%. In contrast, alcohol pretreatment consistently resulted in the lowest LDs compared to acid and alkali pretreatments.

This finding is consistent with the research of Domingos et al., who examined the influence of temperature, time, and biomass particle size on the LD of walnut shells [32]. They observed that over 70% of walnut shell biomass liquefied within the first 15 to 30 min, achieving a high liquefaction yield of 85% at 200°C. However, their results were lower than those obtained in this study. Domingos et al. also found that smaller granulation particles achieved higher liquefaction yields. Specifically, the LD increased slightly from 75 to 82% for particles of size from 0.420 to 0.250 mm. For even smaller particles (40–60, 60–80, and <80), the differences were not significant, with yields showing a tendency to increase up to 82% for 0.420–0.250 mm particle size and 80% for <0.177 mm particle size [32].

In their study, Domingos et al. used a solvent mixture of glycerol and ethylene glycol (1:1 ratio), catalyzed by sulfuric acid at 3% of the total mass, with a walnut shell/solvent ratio of 1:10. In contrast, this study employed glycerol as the liquefaction reagent and used sulfuric acid as the acidic catalyst (also at 3% of the total mass), but with a walnut shell/solvent ratio of 1:5 [32]. The highest LD observed by Domingos et al. was 85% at 200°C for 30 min, which is significantly lower than the degrees achieved in this study, where larger particles (0.63–1.25 mm) were analyzed.

4.3 Comparison with forest biomass

When compared to forest biomass, walnut shell biomass showed slightly lower LDs than hardwood species, which ranged from 88.47% for beech to 91.98% for common oak [29]. However, walnut shell biomass had higher LDs than softwood species, which ranged from 91.98% for common oak to 94.94% for spruce [29,33]. For example, Esteves et al. reported a LD of 80% for pine bark, and Yona et al. found LDs of 61–85% for cork oak, depending on the catalyst used [33,34]. The LD for Douglas fir bark, investigated by Esteves et al., was lower than the values observed for walnut shells in this study, with a maximum of 70% [35].

4.4 OH number analysis

The OH number, which reflects the concentration of OH groups, increased significantly following pretreatment. The untreated walnut shell sample had an OH number of 385.67 mg KOH/g. Pretreatment methods, particularly alkaline hydrolysis, led to a notable increase in the OH number. This enhancement is significant as the OH number is crucial for the application of liquefied biomass in various bioproducts. Antonović et al. reported higher OH numbers for hardwood, such as beech (798 mg KOH/g), compared to softwood, with spruce having the lowest OH number (544 mg KOH/g) [29]. Although the untreated walnut shells had a lower OH number compared to forest biomass, pretreatment markedly increased the OH number in all treated samples.

The bio-polyols derived from liquefied biomass, which include derivatives of carbohydrates, esters, ethers, glycols, and acids, exhibit a wide range of OH numbers from 77.8 to 800 mg KOH/g [28,36,37,38,39,40]. These bio-polyols are promising for the production of resinous materials such as polyurethane foams, adhesives, and epoxy resins. The high OH numbers observed in this study suggest that the liquefied biomass could be effectively used to produce materials with desirable thermal and mechanical properties, as evidenced by high thermal stability and performance of films prepared from liquefied polyol/starch/glycerol [41].

When evaluating the OH numbers across different pretreatment methods, it is evident that alkaline hydrolysis consistently yields the highest OH numbers compared to those of acid and alcohol pretreatments. The untreated walnut shell biomass had an average OH number of 385.67 mg KOH/g. Pretreatment significantly elevated the OH number across all methods, underscoring the effectiveness of these processes in enhancing the biomass potential for various applications.

Comparative analysis of LDs among various hardwood species reveals that softwood generally exhibits a lower content of UR and a higher LD than hardwood. However, hardwood demonstrates substantially higher OH numbers. Antonović et al. reported the highest OH number in beech biomass (798 mg KOH/g) and the lowest in spruce (544 mg KOH/g) [29]. In contrast, the untreated walnut shell sample in this study had a lower OH number compared to that of forest biomass. Nevertheless, pretreatment markedly increased the OH number, with all treated samples showing a significant rise regardless of the pretreatment type, temperature, or duration.

4.5 Implications for bio-polyols

The liquefaction process yields bio-polyol, a product rich in OH groups, derived from the degradation of cellulose, hemicellulose, and lignin [35,42]. These bio-polyols, which include derivatives such as carbohydrates, esters, ethers, glycols, and acids, exhibit a wide range of OH numbers from 77.8 to 800 mg KOH/g [28,36,37,38,39,40]. This diversity highlights the potential of bio-polyols as valuable resources for the production of resinous materials. They are suitable for applications in polyurethane foams [42,43], polyurethane adhesives [39], urea-urethane elastomers [40], and epoxy resins [28]. Furthermore, polymer composites incorporating polyethylene and liquefied biomass demonstrate exceptional thermal stability [41]. Films composed of liquefied polyol/starch/glycerol also exhibit superior thermal, dynamic, and mechanical properties [36].

In summary, these findings underscore the critical role of pretreatment in optimizing the functional properties of biomass-derived polyols, thus enhancing their potential for diverse industrial and environmental applications. This study provides insights into the effects of chemical pretreatment on the LD and OH number of walnut shell biomass. While pretreatments did not significantly enhance the LD beyond the untreated biomass, they notably increased the OH number, especially with alkaline hydrolysis. These findings underscore the potential of using pretreatment methods to improve the quality and application of bio-polyols derived from biomass.

5 Conclusions

This study underscores the environmental challenges associated with unsustainable resource depletion and substantial waste generation. The utilization of walnut shells, often discarded as unused residue post-kernel extraction, emerges as a promising solution to these issues. The growing interest in liquefied biomass for environmentally friendly applications underscores the potential of walnut shells due to their favorable properties. With lower hygroscopic and higher hydrophobic components compared to forest biomass, walnut shell fillers offer significant commercial advantages in the production of polymer-based composite materials, particularly for outdoor products requiring high resistance. Moreover, walnut shells prove to be a versatile and valuable biomass resource employed in various production processes. The investigation into the OH number further reveals the positive impact of pretreatment, especially through alkaline hydrolysis, resulting in a substantial increase in the OH number. The exceptionally high OH number values observed in walnut shell biomass after pretreatment, irrespective of the solvent, temperature regime, or duration, position it as a promising candidate for diverse polymerization applications. This, coupled with favorable percentages of UR and LD, underscores the potential of walnut shell biomass in contributing to sustainable and versatile solutions in the realm of materials science and production.

  1. Funding information: This research was funded by the European Regional Development Fund, under the Operational program competitiveness and cohesion 2014–2022, project no. KK 01.2.1.02.0286, “Development of innovative pellets from forest and/or agricultural biomass – INOPELET.”

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results and approved the final version of the manuscript. Conceptualization: N.J., A.A., and T.K.; methodology: A.A., L.P., and I.B.; software: I.B. and A.M.; formal analysis: B.M.; investigation: N.J. and A.A.; writing – original draft preparation: N.J.; writing – review and editing: A.M., L.P., T.K., and A.A.; visualization: I.B. and N.J. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The data sets generated during the current study are available from the corresponding author on reasonable request.

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Received: 2024-01-19
Revised: 2024-10-04
Accepted: 2024-10-08
Published Online: 2025-01-13

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/opag-2022-0382
Keywords for this article
walnut shell utilization; lignocellulosic biomass; resource efficiency; chemical pretreatment; liquefied biomass
Creative Commons
BY 4.0

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  19. Simulation model to realize soybean self-sufficiency and food security in Indonesia: A system dynamic approach
  20. Gender, empowerment, and rural sustainable development: A case study of crab business integration
  21. Metagenomic and metabolomic analyses of bacterial communities in short mackerel (Rastrelliger brachysoma) under storage conditions and inoculation of the histamine-producing bacterium
  22. Fostering women’s engagement in good agricultural practices within oil palm smallholdings: Evaluating the role of partnerships
  23. Increasing nitrogen use efficiency by reducing ammonia and nitrate losses from tomato production in Kabul, Afghanistan
  24. Physiological activities and yield of yacon potato are affected by soil water availability
  25. Vulnerability context due to COVID-19 and El Nino: Case study of poultry farming in South Sulawesi, Indonesia
  26. Wheat freshness recognition leveraging Gramian angular field and attention-augmented resnet
  27. Suggestions for promoting SOC storage within the carbon farming framework: Analyzing the INFOSOLO database
  28. Optimization of hot foam applications for thermal weed control in perennial crops and open-field vegetables
  29. Toxicity evaluation of metsulfuron-methyl, nicosulfuron, and methoxyfenozide as pesticides in Indonesia
  30. Fermentation parameters and nutritional value of silages from fodder mallow (Malva verticillata L.), white sweet clover (Melilotus albus Medik.), and their mixtures
  31. Five models and ten predictors for energy costs on farms in the European Union
  32. Effect of silvopastoral systems with integrated forest species from the Peruvian tropics on the soil chemical properties
  33. Transforming food systems in Semarang City, Indonesia: A short food supply chain model
  34. Understanding farmers’ behavior toward risk management practices and financial access: Evidence from chili farms in West Java, Indonesia
  35. Optimization of mixed botanical insecticides from Azadirachta indica and Calophyllum soulattri against Spodoptera frugiperda using response surface methodology
  36. Mapping socio-economic vulnerability and conflict in oil palm cultivation: A case study from West Papua, Indonesia
  37. Exploring rice consumption patterns and carbohydrate source diversification among the Indonesian community in Hungary
  38. Determinants of rice consumer lexicographic preferences in South Sulawesi Province, Indonesia
  39. Effect on growth and meat quality of weaned piglets and finishing pigs when hops (Humulus lupulus) are added to their rations
  40. Healthy motivations for food consumption in 16 countries
  41. The agriculture specialization through the lens of PESTLE analysis
  42. Combined application of chitosan-boron and chitosan-silicon nano-fertilizers with soybean protein hydrolysate to enhance rice growth and yield
  43. Stability and adaptability analyses to identify suitable high-yielding maize hybrids using PBSTAT-GE
  44. Phosphate-solubilizing bacteria-mediated rock phosphate utilization with poultry manure enhances soil nutrient dynamics and maize growth in semi-arid soil
  45. Factors impacting on purchasing decision of organic food in developing countries: A systematic review
  46. Influence of flowering plants in maize crop on the interaction network of Tetragonula laeviceps colonies
  47. Bacillus subtilis 34 and water-retaining polymer reduce Meloidogyne javanica damage in tomato plants under water stress
  48. Vachellia tortilis leaf meal improves antioxidant activity and colour stability of broiler meat
  49. Evaluating the competitiveness of leading coffee-producing nations: A comparative advantage analysis across coffee product categories
  50. Application of Lactiplantibacillus plantarum LP5 in vacuum-packaged cooked ham as a bioprotective culture
  51. Evaluation of tomato hybrid lines adapted to lowland
  52. South African commercial livestock farmers’ adaptation and coping strategies for agricultural drought
  53. Spatial analysis of desertification-sensitive areas in arid conditions based on modified MEDALUS approach and geospatial techniques
  54. Meta-analysis of the effect garlic (Allium sativum) on productive performance, egg quality, and lipid profiles in laying quails
  55. Optimizing carrageenan–citric acid synergy in mango gummies using response surface methodology
  56. The strategic role of agricultural vocational training in sustainable local food systems
  57. Agricultural planning grounded in regional rainfall patterns in the Colombian Orinoquia: An essential step for advancing climate-adapted and sustainable agriculture
  58. Perspectives of master’s graduates on organic agriculture: A Portuguese case study
  59. Developing a behavioral model to predict eco-friendly packaging use among millennials
  60. Government support during COVID-19 for vulnerable households in Central Vietnam
  61. Citric acid–modified coconut shell biochar mitigates saline–alkaline stress in Solanum lycopersicum L. by modulating enzyme activity in the plant and soil
  62. Herbal extracts: For green control of citrus Huanglongbing
  63. Research on the impact of insurance policies on the welfare effects of pork producers and consumers: Evidence from China
  64. Investigating the susceptibility and resistance barley (Hordeum vulgare L.) cultivars against the Russian wheat aphid (Diuraphis noxia)
  65. Characterization of promising enterobacterial strains for silver nanoparticle synthesis and enhancement of product yields under optimal conditions
  66. Testing thawed rumen fluid to assess in vitro degradability and its link to phytochemical and fibre contents in selected herbs and spices
  67. Protein and iron enrichment on functional chicken sausage using plant-based natural resources
  68. Fruit and vegetable intake among Nigerian University students: patterns, preferences, and influencing factors
  69. Bioprospecting a plant growth-promoting and biocontrol bacterium isolated from wheat (Triticum turgidum subsp. durum) in the Yaqui Valley, Mexico: Paenibacillus sp. strain TSM33
  70. Quantifying urban expansion and agricultural land conversion using spatial indices: evidence from the Red River Delta, Vietnam
  71. LEADER approach and sustainability overview in European countries
  72. Influence of visible light wavelengths on bioactive compounds and GABA contents in barley sprouts
  73. Assessing Albania’s readiness for the European Union-aligned organic agriculture expansion: a mixed-methods SWOT analysis integrating policy, market, and farmer perspectives
  74. Genetically modified foods’ questionable contribution to food security: exploring South African consumers’ knowledge and familiarity
  75. The role of global actors in the sustainability of upstream–downstream integration in the silk agribusiness
  76. Multidimensional sustainability assessment of smallholder dairy cattle farming systems post-foot and mouth disease outbreak in East Java, Indonesia: a Rapdairy approach
  77. Enhancing azoxystrobin efficacy against Pythium aphanidermatum rot using agricultural adjuvants
  78. Review Articles
  79. Reference dietary patterns in Portugal: Mediterranean diet vs Atlantic diet
  80. Evaluating the nutritional, therapeutic, and economic potential of Tetragonia decumbens Mill.: A promising wild leafy vegetable for bio-saline agriculture in South Africa
  81. A review on apple cultivation in Morocco: Current situation and future prospects
  82. Quercus acorns as a component of human dietary patterns
  83. CRISPR/Cas-based detection systems – emerging tools for plant pathology
  84. Short Communications
  85. An analysis of consumer behavior regarding green product purchases in Semarang, Indonesia: The use of SEM-PLS and the AIDA model
  86. Effect of NaOH concentration on production of Na-CMC derived from pineapple waste collected from local society
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