Analysis of the Pelletability of Vegetable Crop Foliage Using a Commercial Flat Die Pellet Mill

Abstract

Agricultural residues serve as a vast yet underutilized biomass resource with significant potential for bioenergy and biomaterial applications. Converting these residues into densified biomass pellets enhances energy density, handling efficiency, and transportability, offering a sustainable alternative to conventional feedstocks. While extensive research has focused on woody biomass, studies on the pelletization of vegetable crop foliage remain limited. This study examines the pelletability of foliage from corn, soybean, tomato, eggplant, cucumber, and summer squash, assessing their physical properties, bulk durability, bulk density, and energy consumption during pelletization. Results demonstrated that variation in biomass composition significantly influences pellet quality, with lignin content improving durability and ash content affecting moisture uptake and combustion properties. Cucumber had the highest pellet density (691.2 kg/m³) and durability (97.9%), making it suitable for long-term storage and transport. Sawdust exhibited the lowest moisture absorption (16–18% db), which is attributed to its highest lignin content. Pelletization energy requirements varied significantly, with cucumber (21.8 kWh/t) and summer squash (18.7 kWh/t) requiring the lowest energy input, whereas soybean (49.6 kWh/t) and sawdust (47.3 kWh/t) exhibited the highest energy demands due to greater resistance to densification. A predictive model was developed to correlate single pellet density and durability with bulk pellet properties—yielding high predictive accuracy, with R² = 0.936 for bulk density (BD_e) and R² = 0.861 for bulk durability (BD_u)—thereby facilitating process optimization for large-scale pellet production. This study demonstrated that foliage residues from greenhouse crops, such as cucumber and summer squash, can be effectively pelletized with low energy input and high physical integrity. These outcomes suggest that such underutilized agricultural residues hold promise as a densified intermediate feedstock, supporting future applications in bioenergy systems and advancing circular resource use in controlled-environment agriculture.

1. Introduction

Biomass utilization for energy production has gained significant traction in recent decades as a sustainable alternative to fossil fuels. With the increasing demand for renewable energy sources, agricultural residues are being explored as potential feedstocks for pelletization due to their abundance and lower cost compared to traditional woody biomass. Agricultural residues such as corn, soybean, and tomato often go unutilized, leading to environmental concerns such as air pollution from open-field burning [1] and methane emissions from decomposition [2]. Pelletization transforms these residues into a high-density, energy-efficient form suitable for combustion and bioenergy applications, contributing to waste valorization and a circular bioeconomy [3,4,5].

Pelletization is a widely adopted technique that enhances the physical properties of biomass by increasing its density, improving its transportability, and optimizing its combustion characteristics [6,7,8]. This process involves compressing biomass under heat and pressure to produce uniform cylindrical pellets with improved energy content and mechanical properties. Key factors influencing pelletization include moisture content, particle size, chemical composition, and process parameters, such as die pressure and retention time [9,10,11]. While extensive studies have been conducted on woody biomass pelletization [12,13,14,15], limited research exists on the properties of agricultural residues and their behavior during the densification process on a large scale.

The majority of biomass pelletization research has focused on single-pellet experiments, which provide insights into fundamental densification mechanisms. However, batch-scale studies are essential for bridging the gap between laboratory research and industrial applications. Batch processes introduce additional variables such as feed rate fluctuations [16], power consumption, and operational efficiency, which significantly impact pellet quality and energy requirements. Understanding these parameters is crucial for optimizing large-scale pellet production and ensuring consistency in pellet characteristics.

Several factors influence the quality of pellets produced in batch-scale processes. The moisture content of the biomass plays a critical role, as an optimal moisture level (typically 8–12%) enhances bonding and reduces friction in the pellet mill [17,18,19]. Additionally, the chemical composition of biomass, particularly its lignin, cellulose, and hemicellulose content, affects pellet durability and combustion efficiency. Lignin, a natural binder, enhances pellet mechanical strength when exposed to heat [11,15], while excessive ash content can lower combustion efficiency and increase emissions [20].

Particle size and shape also have a significant impact on pelletization efficiency and final pellet quality. Smaller particles provide a greater surface area for bonding, improving pellet density and mechanical strength [21,22]. However, excessive fines can lead to die clogging and increased energy consumption during pelletization. Studies have shown that an optimal aspect ratio and circularity contribute to improved pellet integrity and durability [21,23].

Energy efficiency is a critical consideration in large-scale pellet production. Pellet mills require substantial energy input for compression, and understanding energy consumption patterns is essential for process optimization. The net energy required for pelletization is determined by measuring the total energy input during pellet production and subtracting the no-load energy consumption of the mill [24]. By analyzing the specific energy consumption per ton of pellets produced, researchers can develop strategies to enhance process efficiency and reduce operational costs.

Over-reliance on a single biomass feedstock, such as wood, poses sustainability risks and supply chain challenges [25,26]. Diversifying biomass sources to include agricultural residues promotes environmental conservation, enhances feedstock availability, and supports rural economies [19,27]. By investigating the pelletization potential of various vegetable crop residues, this study aims to expand the range of viable feedstocks for bioenergy production. In particular, greenhouse crops such as tomato, cucumber, eggplant, and summer squash are cultivated extensively in Canadian greenhouses [28], which generates substantial yet underutilized post-harvest foliage [29]. These materials offer significant bioenergy potential, particularly for localized use in greenhouse heating systems.

Recent studies have increasingly validated the practical application of greenhouse crop residues as a sustainable energy source for farm and greenhouse operations. Jadischke and Lubitz (2025) evaluated several strategies for managing vegetable production waste, reinforcing the value of biomass conversion [30]. Faidallah et al. (2022) demonstrated that using okra and cotton stalks in a biomass heating system not only maintained optimal greenhouse conditions but also enhanced crop yield and enabled biochar-based carbon sequestration [31]. Huang et al. (2020) showed that a biomass-fueled gas–soil heat exchanger effectively increased greenhouse air and soil temperatures, proving both effective and economically viable [32]. Perron (2023) further advanced this concept by evaluating a biomass polygeneration system in Québec, which recovered nearly 50% of waste heat annually, emphasizing energy efficiency through thermal storage [33]. Finally, Reinoso Moreno et al. (2019) enhanced the fuel quality of greenhouse residues in Spain through drying, contamination control, and blending, enabling their efficient use in biomass boilers and contributing to a closed-loop system by recycling CO₂ for greenhouse enrichment [34]. Together, these studies confirm the viability and sustainability of converting greenhouse residues into renewable thermal energy.

2. Objectives

Despite advancements in biomass pelletization research, significant knowledge gaps remain regarding the densification of agricultural residues in batch scale. Existing studies predominantly focus on woody biomass, leaving the unique characteristics of agricultural residues underexplored. Moreover, the relationship between single and batch-scale pelletization properties remains unclear, hindering industrial-scale adoption. This research seeks to comprehensively analyze the pelletization properties of vegetable crop residues, including tomato, eggplant, cucumber, summer squash, corn, and soybean, under a controlled condition batch process, evaluate the factors influencing pellet quality and energy consumption, and develop predictive models for optimizing the pelletization process. By evaluating the pelletization potential of vegetable crop residues, this study addresses environmental challenges by promoting sustainable waste management and renewable energy generation. Through the conversion of biomass into high-density pellets, it enhances resource circularity and supports the advancement of bioenergy technologies, contributing to the broader goal of sustainable agricultural waste utilization.

3. Material and Methods

3.1. Sample Preparation and Infeed Material Characterization

The horticultural crops eggplant and tomato were initially grown as seedlings in a greenhouse for six weeks before being transplanted to the field. Cucumber and summer squash, on the other hand, were directly seeded in the field. The agricultural crops corn and soybean were cultivated at the Lods Agronomy Research Centre, while tomato, eggplant, cucumber, and summer squash were grown at the Horticultural Centre on the Macdonald Campus of McGill University, Canada. Corn and soybean were included as conventional open-field crops that are extensively cultivated and well-documented in biomass research, thus serving as agricultural benchmarks for comparison. Additionally, a sawdust sample consisting of a 50/50 blend of lodgepole pine and white spruce was obtained from Premium Pellet Co., British Columbia, to serve as a commercial reference material for comparative analysis. This selection of biomasses also aligns with the previous study by Gholami B. et al., 2025, which examined the single pellet densification of these same feedstocks [35]. This study scales up to a batch pelletization, addressing applied challenges in large-scale biomass utilization. The foliage samples contained a moisture content of approximately 5–10% (wet basis) upon arrival. Prior to further processing, all samples were ground using a knife mill (Model SM100, Retsch Inc., Newtown, PA, USA) fitted with a 4 mm perforated screen. The ground samples were then stored in air-tight containers to prevent moisture absorption.

Ash content was determined following the procedure outlined by Sluiter et al. (2008) [36]. The analysis was performed in quadruplicate by subjecting the ground samples to dry oxidation at 550–600 °C in a muffle furnace. The residual mass post-combustion was recorded as the ash content and expressed as a percentage of the initial biomass weight [36]. The energy content of the biomass was evaluated by measuring the Gross Calorific Value (GCV) using an oxygen bomb calorimeter (Model 6100, Parr Instrument Company, Moline, IL, USA), following the ASTM standard D-2015. Each measurement was conducted in four replicates to ensure accuracy and reproducibility. Extractive content was determined on a dry basis using a Soxhlet extraction unit [37]. The lignin content was assessed through the Klason lignin method, which quantifies the acid-insoluble fraction of lignin in the biomass [38]. All extractions and lignin analyses were repeated four times to confirm data consistency. Elemental composition (carbon, hydrogen, nitrogen, and oxygen) of the ground biomass was assessed using an Elemental Analyzer (FISONS EA 1108—Thermo Fisher Scientific, Waltham, MA, USA). The analysis was performed in quadruplicate to ensure precision.

The true density (TD) of the biomass samples was determined using a gas pycnometer (Model MVP-D160-E, Quantachrome Instruments, Boynton Beach, FL, USA), with five replicates performed per sample to enhance result reproducibility. The bulk density (BD) was measured by filling a container (internal dimensions: 100 × 100 × 100 mm) with the ground biomass and recording its mass. Particle size distribution was assessed through mechanical sieving following the ANSI/ASAE standard S319.4 (ASAE, 2008b), utilizing a Ro-Tap shaker (Tyler Industrial Products, Cleveland, OH, USA). The analysis was carried out in four replicates. Shape parameters of biomass particles were determined through image analysis using ImageJ software (Version 1.53K, National Institutes of Health, Bethesda, MD, USA). Over 1000 randomly selected particles were analyzed to extract width (W), length (L), area (A), and perimeter (P). The aspect ratio (AR) and equivalent particle diameter ($D_{eq}$) were calculated using the following equations [39,40]:

$$AR={\displaystyle \frac{W}{L}}$$

(1)

$$D_{eq}=\sqrt{{\displaystyle \frac{4A}{\pi }}}$$

(2)

where AR is the aspect ratio (dimensionless), W is the maximum width, and L is the maximum length of each particle. Additionally, A is the projected two-dimensional area of the particle yielding an equivalent diameter ($D_{eq}$) in units of length.

3.2. Pelletization

A flat die pellet mill (GEMCO Energy-ZLSP 300B, Anyang GEMCO Energy Machinery Co., Anyang, China) was used to pelletize all the samples (Figure 1). The pellet die had 6 mm diameter holes, a counterbore design, with a total thickness of 26 mm, an effective thickness of 20 mm, and a relief depth of 3 mm. To ensure a uniform pelletization setup, the gap between the die and roller was precisely adjusted to approximately 1 mm using the adjustment nut, optimizing compression efficiency and pellet formation consistency. Prior to pelletization, the moisture content of all biomass samples was measured and adjusted to 11.50–12.50% through controlled water spraying. To achieve homogeneous moisture distribution, the biomass materials were conditioned in a cement mixer for a minimum of 60 min. Furthermore, a 30 min warm-up phase was necessary to optimize the performance of the pellet mill. During this stage, a preconditioning mixture comprising sawdust, fine white sand, and engine oil in a ratio of 0.5/0.3/0.2 was utilized to elevate the temperature of the die and roller while simultaneously filling residual voids within the pelletization chamber and roller gears, ensuring process stability.

Approximately 2 kg of biomass was utilized for each pelletization trial. Each biomass type was tested in replicated trials under identical operating conditions. All equipment- related and process-related parameters, including die and roller gap, feed rate, conditioning time, and target moisture content, were held constant throughout the study to ensure a controlled experimental environment. The objective was to isolate the effect of inherent biomass characteristics on pelletization performance. As this study involved no independent treatment variables, statistical comparisons were conducted using mean comparison methods across replications. The least squares mean (LS) method at p = 0.05 was used to assess significant differences in pellet quality and energy consumption parameters among biomass types. To maintain a uniform feed rate, which is a critical parameter influencing pelletization efficiency [16,41], an in-house-built hopper and feeder system equipped with inverters was employed. Both the hopper and feeder were operated at a 20 Hz frequency to enhance material flowability and facilitate consistent feeding into the pelletization chamber. Following the pelletization process, the produced pellets were cooled and subsequently screened to remove fine particles and unpelletized material. The final pellet samples were stored in air-tight containers to prevent moisture reabsorption and ensure consistency for subsequent quality assessments.

3.3. Pelletization Energy Consumption

The energy consumption during the pelletization process was monitored and quantified according to [24] using a digital three-phase power data logger (AEMC 8336 PowerPad III, Dover, NH, USA), which was connected to measure the input currents supplied to the flat die pellet mill. Since the operational energy demand of the pellet mill is influenced by its mechanical configuration, it was essential to distinguish between the baseline energy requirement of the equipment and the actual energy needed for pelletization [24].

To determine the net energy consumption specific to each biomass type, the energy required to run the pellet mill under no-load conditions, referred to as pellet mill energy consumption (PMEC), was first measured. This baseline value was subtracted from the total pelletization energy consumption (TPEC) recorded during biomass pelletization to obtain the net pelletization energy consumption (NPEC), according to Equation (3). The net specific pelletization energy consumption (NSPEC) for each biomass was then calculated by normalizing the NPEC to the mass of the processed biomass in tons (Equation (4)) [24]. This approach provides an accurate measure of the energy efficiency of the pelletization process for different biomass types.

$$NPEC\left(\mathrm{kWh}\right)=TPEC-PMEC$$

(3)

$$NSPEC\left(\mathrm{kWh}/\mathrm{t}\right)={\displaystyle \frac{NPEC\left(\mathrm{kWh}\right)}{infeed material mass \left(\mathrm{t}\right)}}$$

(4)

3.4. Pellet Color Index

The color characteristics of the biomass pellets were evaluated using a spectrophotometer (CM5-Konica Minolta, Konica Minolta, Tokyo, Japan), operating in reflectance mode. Prior to each measurement, the instrument was calibrated using a standard white reference plate to ensure accuracy and consistency. Color assessment was based on the CIE Lab (CIELAB) color space, where L* represents lightness (ranging from 0 = black to 100 = white), a* corresponds to the red–green axis (positive values indicating red, negative values indicating green), and b* denotes the yellow–blue axis (positive values indicating yellow, negative values indicating blue). To minimize variability, each sample was measured five times, and the mean values of L*, a*, and b* were recorded for subsequent analysis [42]. The overall color variation (∆E) between the raw biomass and the pelletized samples was determined using the following equation:

$${∆E}_i=\sqrt{{\left(a_i-a_0\right)}^2+{\left(b_i-b_0\right)}^2+{\left(L_i-L_0\right)}^2}$$

(5)

where i represents the color index of the biomass, and 0 corresponds to the color index of the pelletized sample.

3.5. Bulk Density

The bulk density (BD_e) of the pelletized biomass was determined by measuring the mass of the pellets contained within a standardized container with inner dimensions of 100 × 100 × 100 mm. The pellets were carefully poured into the container without compaction, ensuring a natural settling condition. The total mass of the biomass within the fixed volume was recorded, and the bulk density was calculated as the ratio of the mass (g) to the container volume (cm³) in 5 replications. This method provides a reliable estimation of the bulk density, which is a crucial parameter for evaluating the storage, handling, and transport efficiency of the pelletized biomass.

3.6. Bulk Durability Index

The bulk durability (BD_u) of the pellets was assessed using the tumbler (Figure 2), following the ANSI/ASAE Standard S269.4 (ASAE, 2007). In this method, approximately 500 g of pellets was placed in a durability testing chamber, which consists of a rotating drum with internal baffles. The drum was rotated at 50 rpm for 10 min, subjecting the pellets to controlled impact and abrasion forces. After tumbling, the pellets were carefully removed and sieved using a standard 3.15 mm mesh with round perforations to separate intact pellets from broken fragments and fines [43]. The pellet durability index (PDI) was then calculated as the percentage ratio of the mass of intact pellets to the initial mass before tumbling according to Equation (6). This standardized approach ensures a consistent evaluation of pellet durability, which is a critical parameter for assessing resistance to mechanical degradation during handling, storage, and transportation, and the equation is shown as follows:

$${BD}_u\left(\%\right)={\displaystyle \frac{M_f}{M_i}}\times 100$$

(6)

where $M_f$ is the mass of pellets after tumbling and screening, and $M_i$ is the initial mass of the pellets, which is 500 g based on the standard.

3.7. Pellet Moisture Uptake

The moisture uptake performance of the pellets was evaluated using a humidity chamber (Memmert HCP50, FRG, Germany) to simulate real-world storage and transportation conditions. The chamber was set to 40% relative humidity at 90 °C, providing an optimal environment for assessing the moisture absorption capacity of the pellets. The experiment was conducted over a 30-day period in three replicates to ensure that the pellets reached moisture equilibrium. Monitoring moisture uptake is essential, as it directly impacts pellet stability, mechanical integrity, and combustion efficiency during storage and transport. This parameter is particularly critical for biomass pellets shipped overseas, where exposure to fluctuating humidity levels can lead to swelling, microbial degradation, and reduced energy value, potentially compromising their quality and usability.

4. Results and Discussion

Pellets were successfully produced in batch scale using a flat die pellet mill, from six different types of biomasses, including corn, soybean, tomato, eggplant, cucumber, and summer squash stalks, each exhibiting distinct characteristics (Figure 3). The variations in biomass composition significantly influenced the physical properties of the pellets, with noticeable differences in color, highlighting the impact of chemical composition during the pelletization process [35,44,45]. Despite these variations, the batch production method maintained consistency in pellet formation, demonstrating its effectiveness in handling diverse biomass sources. This section explores the relationship between biomass properties and pellet quality in a scaled-up production setting, providing insights into optimizing feedstock selection and process parameters for improved efficiency and uniformity in biomass pellet production.

4.1. Color Index Parameters

The results of the color parameter analysis are shown is

Table 1. The mean comparison analysis of color index of various pellets.

Pellets	Color Parameters (-)
	L*	a*	b*	${∆\mathit{E}}_{\mathit{i}}$
Corn	49.0 c	4.6 a	17.5 b	13.3 bc
Soybean	52.5 b	4.5 a	17.6 b	13.6 bc
Tomato	43.0 d	3.3 b	15.9 c	18.8 a
Eggplant	41.8 de	4.3 a	16.9 bc	12.6 c
Cucumber	41.1 e	3.3 b	15.6 c	19.8 a
Summer squash	38.9 f	2.5 b	13.1 d	14.8 b
Sawdust	69.2 a	4.6 a	21.0 a	5.8 d
Pr > F (Model)	<0.0001	<0.0001	<0.0001	<0.0001
Significant	Yes	Yes	Yes	Yes

Note: Values followed by different letters (a to f) within a column show significant differences in each parameter among various samples (p < 0.05) based on a least-square means comparisons using Tukey’s HSD test.

. The results show significant variations in the color parameters (L*, a*, and b*) among different pellet types, indicating diverse responses to the pelletization process. Sawdust exhibited the highest lightness (L* = 69.2), maintaining its original brightness with minimal color transformation (ΔE = 5.8), making it the most visually stable biomass. In contrast, summer squash (L* = 38.9) and cucumber (L* = 41.1) exhibited the lowest lightness values, suggesting significant darkening due to pelletization. The red–green parameter (a*) varied slightly across biomass types, with sawdust, corn, and soybean showing the highest red component (a* $~$ 4.5), while summer squash and cucumber had the lowest values, indicating a greener tint (a* $~$ 3). The blue–yellow component (b*) was highest in sawdust (b* = 21.0), making it the most yellowish, whereas summer squash (b* = 13.1) had the lowest, indicating less yellow saturation.

Among all biomasses, cucumber (ΔE = 19.8) and tomato (ΔE = 18.8) experienced the most significant total color change in comparison to their ground biomass, implying greater sensitivity to heat and pressure during pelletization. Corn and soybean exhibited moderate transformations (ΔE $~$ 13.5), while eggplant (ΔE = 12.6) remained relatively stable. These color variations can be attributed to thermal degradation, oxidation, and chemical changes in lignin and hemicellulose during the densification process [44,45]. The significant differences (p < 0.0001) in color parameters indicate that pelletization influences the visual properties of biomass, with certain feedstocks undergoing more drastic alterations than others. Sawdust, due to its minimal ΔE, appears to be the most resistant to color change, whereas foliage from agricultural crops such as cucumber and tomato are more prone to noticeable shifts. Recent research by Gholami B. et al. 2025 showed that these variations in color are attributed to differences in inorganic elemental composition including P, S, Cl, K, Ca, Cu, and Zn, which can affect the ash, heating value, and thermal stability of these biomass pellets [35].

A detailed correlation analysis by Gholami B. et al. 2025 revealed that the inorganic composition of the biomass played a measurable role in influencing pellet color indices [35]. Specifically, higher concentrations of calcium (Ca), potassium (K), and zinc (Zn) were found to be negatively correlated with the L* value, indicating that increased levels of these elements contributed to darker pellet surfaces, likely due to enhanced light absorption or catalytic degradation reactions during densification [46,47]. Similarly, sulfur (S) and chlorine (Cl) exhibited negative correlations with both a* and b* values, suggesting a tendency toward greener and less yellow pellet tones, respectively, which is potentially linked to their involvement in thermochemical transformations and volatile compound release [48]. In contrast, copper (Cu) demonstrated a positive correlation with L*, indicating that its presence may be associated with increased surface brightness in the final pellet. These findings highlight the multifactorial nature of pellet color development and the potential of elemental profiling as a supplementary tool for assessing biomass transformation during pelletization. Understanding these color characteristics can provide insights into the combustion properties, processing behavior, and suitability of these biomass pellets for various applications.

4.2. Pelletization Energy

The results of pelletization performance parameters of various biomass samples are summarized in

Table 2. Mass in-flow, performance parameters, and energy consumption of flat die pellet mill for the pelletization of crop foliage.

Samples	MC (wb, %)	Infeed Material Mass (g)	Produced Pellet Mass (g)	Fine Particle (%)	Pelletization Performance (%)	Feeding Time (s)	Feed Rate (g/s)	NSPEC (kWh/t)
Corn	12.10	2058.3	2011.5	2.3	95.5	157	13.1	33.0
Soybean	11.80	2041.5	1961.9	5.9	90.4	150	13.6	49.6
Tomato	12.00	2150.0	2017.5	4.1	90.0	119	18.1	28.2
Eggplant	12.05	2085.0	1913.4	1.6	90.3	152	13.7	32.4
Cucumber	12.10	2152.8	2088.7	1.7	95.4	127	16.9	21.8
Summer squash	12.05	1987.8	1945.9	1.9	96.0	121	16.4	18.7
Sawdust	11.90	2167.6	2038.5	3.3	91.0	110	19.7	47.3

. The moisture content across all biomass samples remained almost constant, ranging between 11.80% and 12.10%, confirming effective moisture adjustment before pelletization. This consistency is essential, as variations in MC could significantly affect pellet quality, energy consumption, and overall pelletization efficiency [18,19]. Since the moisture content was well-regulated, the observed differences in pelletization performance, feeding rate, and net specific pelletization energy consumption (NSPEC) can be attributed to the intrinsic physical and chemical properties of each biomass rather than moisture-related effects.

Across all samples, the pellet mass was consistently lower than the infeed material mass, primarily due to material loss during pelletization, indicating that a portion of the material remains within the die and pelletization chamber. Another source of material loss is attributed to the fine particles and unpelletized biomass that are screened by sieving the pellets using a standardized 3.15 mm sieve with round perforations. These two sources of material loss affect the pelletization performance directly. Soybean (5.9%) and tomato (4.1%) exhibited the highest fine particle content, indicating higher material loss, which contributed to their lower pelletization performance. In contrast, biomasses like corn (1.6%), cucumber (1.7%), and summer squash (1.9%) generated fewer fines, leading to higher pelletization performance.

The feeding rate varied among biomass types even though a constant frequency of 20 Hz was used for both hopper and feeder, reflecting differences in flowability characteristics of biomasses. Sawdust (19.7 g/s), tomato (18.1 g/s), cucumber (16.9 g/s), and summer squash (16.4 g/s) exhibited the highest feed rates, indicating superior material flowability and ease of movement into the pellet mill. These biomasses likely had optimal particle size distribution and lower inter-particle friction, facilitating smoother feeding. This observation is consistent with the findings of a previous study by Gholami B. et al. (2024), as reported in their paper [49]. In contrast, corn (13.1 g/s) and eggplant (13.7 g/s) displayed lower feed rates, suggesting increased friction and lower bulk density that reduced their ability to flow consistently into the mill.

Pelletization performance and the net specific pelletization energy consumption (NSPEC) values revealed significant differences in pelletization efficiency. Summer squash (96.0%) and corn (95.5%) exhibited the highest pelletization performance, with minimal material loss. Soybean (90.4%) and tomato (90.0%) had the lowest pelletization performance, indicating greater material loss. The NSPEC values showed that soybean (49.6 kWh/t) and sawdust (47.3 kWh/t) required the highest energy consumption, likely attributed to their greater resistance to densification due to their larger particle sizes. In contrast, summer squash (18.7 kWh/t) and cucumber (21.8 kWh/t) exhibited the lowest NSPEC values, confirming their superior plasticity and compaction efficiency. The results of the NSPEC are investigated more in the following sections to identify the key parameters influencing its performance.

The high pelletization energy requirement in sawdust and soybean can be attributed to their larger particle sizes. The results of the particle size analysis by Gholami B. et al. (2024) support this observation [49]. This is mainly because larger particles resist compression due to irregular packing, leading to higher friction in the die and requiring greater force to deform and bond during pelletization [50,51]. This increases mechanical resistance and energy consumption during the pelletization process. Corn, tomato, and eggplant fall within an intermediate energy range, reflecting a balance between fiber rigidity, particle size, and chemical composition. In contrast, cucumber (21.8 kWh/t) and summer squash (18.7 kWh/t) require the lowest pelletization energy, suggesting that smaller particle sizes contribute to easier compression and densification. The possible justification is that the smaller particles fill voids efficiently and reduce resistance to compression, while their increased surface area enhances bonding and improves interlocking, leading to higher pellet durability [52,53].

Another possible factor contributing to the variation in net specific energy required for pelletization is the mechanical properties of the biomass materials. The mechanical characteristics and microstructure of tomato stems [54], corn stalks [55], and cucumber stems [56,57] have been extensively studied in recent years. Findings indicate that corn stalks exhibit higher shear strength, tensile strength, and elasticity modulus than tomato stems, while tomato stems possess superior mechanical properties compared to cucumber stems [54,55,56,57]. Additionally, recent research by Gholami B. et al. (2024) reported that sawdust, corn, and soybean required higher grinding energy compared to summer squash and cucumber [49]. This suggests that sawdust, soybean, and corn are stiffer and more elastic, whereas summer squash and cucumber, which require less grinding energy, are typically more plastic and softer.

The elastic modulus, which represents a material’s stiffness or resistance to deformation, also plays a crucial role in pelletization efficiency. The elasticity in biomass is attributed to the existence of the lignin component [15]. A higher modulus signifies greater rigidity, requiring higher compression force to achieve compaction. Materials with a high elastic modulus are more resistant to deformation, demanding greater pressure for densification [58]. Additionally, they store more elastic energy, which may result in a spring-back effect, causing pellets to expand slightly after exiting the die [58]. Furthermore, these materials are more challenging to densify, as their structure requires higher energy input to break fibers and facilitate particle rearrangement during pelletization.

4.3. Pellet Quality Analysis: Bulk Density and Bulk Durability

The bulk density (BD_e) and bulk durability (BD_u) of biomass pellets are critical indicators of the pellet quality affecting mechanical integrity, transport efficiency, and storage stability [59]. The results of the pellet bulk density and bulk durability analysis are shown in

Table 3. Pellet bulk density, bulk durability, and the correlation of physio-chemical biomass properties with pellet BD_e, BD_u, and specific pelletization energy.

Properties	Ground Foliage Samples							Correlations with
	Corn	Soybean	Tomato	Eggplant	Cucumber	Summer Squash	Sawdust	Pellet BDe (kg/m3)	Pellet BDu (%)	NSPEC (kWh/t)
Pellet BDe (kg/m3)	629.4 c	586.8 e	663.3 b	630.1 c	691.2 a	665.5 b	612.5 d	1	0.729	−0.913
Pellet BDu (%)	97.6 b	92.3 e	96.9 c	96.6 d	97.9 a	96.6 d	96.6 d	0.729	1	−0.663
NSPEC (kWh/t)	33.0 c	49.6 a	28.2 d	32.4 c	21.8 e	18.7 f	47.3 b	−0.913	−0.663	1
Extractive (%, db)	5.7 a	1.5 d	4.8 b	4.4 b	1.8 cd	1.7 cd	2.2 c	0.018	0.410	−0.111
Lignin (%, db)	18.5 b	15.4 d	16.2 c	12.4 e	9.4 f	5.6 g	29.2 a	−0.566	−0.065	0.754
GCV (MJ/kg, db)	17.2 bc	17.5 b	14.8 d	16.7 c	14.3 d	13.6 e	21.0 a	−0.758	−0.261	0.861
Ash (%)	5.5 f	6.8 e	18.3 c	14.5 d	25.3 b	33.6 a	0.3 g	0.795	0.304	−0.890
C/O ratio (-)	0.953 b	0.964 b	0.795 d	0.836 c	0.640 e	0.455 f	1.158 a	−0.750	−0.280	0.889
TD (g/cm3)	1.239 e	1.398 d	1.424 b	1.242 e	1.412 c	1.489 a	1.395 d	0.337	−0.178	−0.226
BD (kg/m3)	107.7 f	200.2 c	259.9 b	196.4 d	259.5 b	289.0 a	186.9 e	0.615	0.038	−0.558
D50 (mm)	0.71 b	0.67 c	0.61 d	0.69 b	0.65 c	0.59 e	0.85 a	−0.510	0.031	0.670
AR (-)	0.32 a	0.37 a	0.40 a	0.45 a	0.32 a	0.40 a	0.33 a	−0.024	−0.022	−0.051
Deq (mm)	1.9 ab	1.9 ab	1.3 b	2.2 a	1.9 ab	2.1 ab	2.6 a	−0.211	0.019	0.169

Note I: Values followed by different letters (a to g) within a row show significant differences for each element among various samples (p < 0.05) based on least-square mean comparisons using Tukey’s HSD test. Note II: Values in bold show significant correlation by Pearson method at p = 0.05.

. The results indicate that pellet bulk density, pellet bulk durability, and net specific pelletization energy consumption vary significantly among the tested biomass samples. Cucumber exhibited the highest bulk density (691.2 kg/m³) and durability (97.9%), making it the most compact and durable pellet material. This was closely followed by summer squash (665.5 kg/m³, 96.6%) and tomato (663.3 kg/m³, 96.9%). In contrast, soybean (586.8 kg/m³) and sawdust (612.5 kg/m³) had the lowest bulk density, suggesting a looser pellet structure. The highest durability was observed in cucumber (97.9%) and corn (97.6%), while soybean pellets had the lowest (92.3%), indicating its weaker cohesion and tendency to form less-durable pellets.

The values of bulk density of the pellets are comparable to those reported for rice straw (640 to 650 kg/m³) [60] and wheat straw (480 to 550 kg/m³) [61,62]. The bulk durability values of the pellets in this study are also consistent with those reported for wheat straw (90 to 97%) [61,62] and rice straw (92 to 95%) [63,64]. These findings indicate that vegetable crop residues, when properly processed under controlled pelletizing conditions, can achieve compaction and mechanical integrity comparable to, or even exceeding, that of traditional agricultural residues such as rice and wheat straw. This performance highlights their potential for efficient storage, transport, and integration into large-scale bioenergy applications.

The correlation analysis in Table 3 reveals that lignin content, carbon-to-oxygen (C/O) ratio, biomass bulk density (BD), and particle size (D50) influence the bulk density of the pellet to varying degrees. Interestingly, this observation is consistent with the findings of Gholami B. et al. (2025) on the pelletization of the same material using a manual pellet press mill [35]. The correlation between lignin ($r=-0.566$) and the C/O ratio ($=-0.750$) with pellet bulk density indicates that biomasses such as sawdust, corn, and soybean with a higher lignin content exhibit lower pellet bulk density. This may be due to the higher lignin content in these biomass materials, which can hinder their ability to compact efficiently. However, this observation contrasts with findings from previous studies, where several researchers have suggested that lignin functions as a natural binder, enhancing pellet quality [11,15]. Although lignin plays a crucial role in improving pellet strength, its influence on compaction efficiency is complex. The elastic properties of certain biomass types, which are influenced by their lignin composition and structural characteristics, may alter their densification behavior, ultimately affecting how efficiently they compact during the pelletization process [11]. The findings also demonstrate that a higher biomass bulk density (BD) enhances pellet bulk density ($r=0.615$), while particle size (D50) correlates with pellet density ($r=-0.510$), confirming their influence on densification. This observation aligns with previous studies, which have reported that smaller particle sizes reduce void spaces, improve packing efficiency, and enhance inter-particle bonding, leading to denser pellets [52,53].

Energy consumption for pelletization (NSPEC) varied widely across biomass types, with summer squash (18.7 kWh/t) and cucumber (21.8 kWh/t) being the most energy-efficient materials, while soybean (49.6 kWh/t) and sawdust (47.3 kWh/t) required the most energy for pelletization. Notably, cucumber and summer squash required the least energy, reflecting superior compaction behavior and plasticity, while soybean and sawdust exhibited the highest energy demand due to greater resistance to densification. When compared to values reported for traditional agricultural residues, such as rice husk and wheat straw, the NSPEC values in this study are considerably lower. The literature suggests that energy requirements for pelletizing wheat straw and switchgrass typically fall within the range of 55–65 kWh/t and 110–130 kWh/t, respectively, depending on pellet mill type and feedstock conditioning [61,65]. This discrepancy suggests that the greenhouse residues used in this study, under optimized moisture content and particle size, possess favorable mechanical properties that enable efficient pelletization with reduced energy input.

This lower energy requirement for cucumber and summer squash implies a potentially significant reduction in operational costs, particularly when scaled to industrial pellet production volumes. For context, wood pellet production costs (including raw material acquisition, drying, grinding, and pelletization) are estimated to be roughly USD 180 per ton, depending on feedstock prices and regional electricity rates [66]. Since greenhouse crop residues are often treated as waste streams, they could offer cost advantages if sourced locally or through agricultural partnerships. Thus, residues such as cucumber and summer squash foliage may present economically viable alternatives to traditional wood pellets.

The significant correlation between BD_e and NSPEC ($r=-0.913$) indicates that materials requiring lower energy for pelletization tend to form denser pellets. This relationship suggests that biomass with lower resistance to compression achieves higher compaction, resulting in increased pellet density. Conversely, materials that require higher energy input for pellet formation may exhibit lower bulk density due to greater resistance during the pelletization process. This is likely because denser materials compact more easily, requiring less mechanical effort [67,68]. Similarly, BD_u correlated with NSPEC ($r=-0.663$), suggesting that materials requiring lower energy for pelletization tend to produce more durable pellets. This implies that such materials undergo plastic deformation more efficiently, facilitating better compaction and particle bonding, which enhances pellet integrity and durability.

The factors influencing the bulk durability of the pellets appear to be less straightforward, as the correlations with biomass properties are relatively weak and inconsistent. While ash content ($r=0.304$) and extractives ($r=0.410$) show some degree of positive correlation with BD_u, lignin content ($r=-0.065$) and particle size ($r=0.031$) exhibit negligible relationships. These results suggest that intrinsic biomass properties alone may not be the primary determinants of pellet durability. The lack of strong correlations in this study implies that pellet durability is likely influenced more by process parameters, such as moisture content, die temperature, and feeding rate, as well as equipment performance during pelletization [24,69]. These factors could play a critical role in determining the mechanical integrity of pellets and should be investigated further to better understand their impact on durability.

Several biomass properties significantly influenced the net specific energy required for the pelletization. Lignin content ($r=0.754$) and C/O ratio ($r=0.889$) exhibited a strong positive correlation with NSPEC, indicating that materials with higher lignin levels or a higher carbon-to-oxygen ratio require more energy for pelletization. Sawdust, which had the highest lignin content (29.2%), also exhibited one of the highest NSPEC values (47.3 kWh/t), confirming that high lignin content increases resistance during compression [11]. Similarly, soybean with 15.4% lignin content and a 0.964 C/O ratio had the highest NSPEC among all samples (49.6 kWh/t).

On the other hand, the results showed a positive correlation between particle size (D50) and NSPEC ($r=0.670$), indicating that larger particles require more energy for pelletization. This is likely due to their lower packing efficiency and increased resistance during compression, necessitating additional energy for crushing and deforming the larger particles during densification process. Previous studies support this finding, which highlight that smaller particle sizes enhance pelletization efficiency by minimizing friction and mechanical resistance [52,53]. Optimizing particle size through pre-processing, such as grinding, can therefore be a key strategy to lower energy consumption in pellet production.

The results show that extractives, true density (TD), aspect ratio (AR), and equivalent diameter (D_eq) have minimal influence on pellet bulk density, durability, and pelletization energy consumption, as their correlations with these parameters are weak. These findings suggest that physical properties may not be key determinants in pellet quality while other factors, such as process conditions and material composition, play a more dominant role in influencing pellet characteristics, warranting further investigation into their effects on densification.

4.4. Pellet Moisture Uptake Analysis

The results of the moisture uptake of the pellets made from various biomasses are presented in (Figure 4). To obtain the optimal performance of the humidity chamber, the test was run at 90% relative humidity and 40 °C temperature according to its user’s manual. The moisture uptake of biomass pellets varied significantly among the samples, demonstrating the effect of physicochemical properties on pellet moisture absorbance rate. Cucumber and summer squash exhibited the highest moisture uptake, reaching moisture contents of approximately 38–41% (db) at equilibrium. Eggplant and tomato followed with moderate moisture absorption in the range of 28–33% (db), while corn and soybean showed slightly lower moisture uptake, stabilizing around 24–27% (db). In contrast, sawdust had the lowest moisture absorption, with equilibrium moisture content around 16–18% (db), indicating superior resistance to humid conditions.

The results indicate that ash content, lignin content, and the carbon-to-oxygen (C/O) ratio significantly influence moisture uptake in biomass pellets. Among these, ash content exhibited a strong positive correlation with moisture absorption, where biomass types with a higher ash content, such as summer squash (33.6%) and cucumber (25.3%), absorbed the most moisture. This behavior can be attributed to the hygroscopic nature of certain mineral compounds in ash, including potassium (K), sodium (Na), calcium (Ca), and magnesium (Mg) salts [70,71]. These minerals attract and retain water molecules, increasing the water absorption capacity of pellets. Additionally, some alkaline oxides (e.g., K₂O, Na₂O, and CaO) react with water to form hydroxides, further enhancing moisture retention [70]. This explains why biomass with high ash content exhibited elevated moisture uptake despite their high bulk density and durability.

Conversely, lignin content played a critical role in reducing moisture uptake due to its hydrophobic properties [72]. Sawdust, which contained the highest lignin content (29.2%), showed the lowest moisture absorption among all samples. Lignin is known to act as a natural moisture barrier, repelling water and limiting the interaction between biomass and atmospheric humidity [72,73]. The strong inverse correlation between lignin content and moisture uptake suggests that biomass with higher lignin concentrations, such as sawdust, maintains better moisture resistance, making it more suitable for long-term storage under humid conditions. This highlights the importance of lignin-rich biomass for moisture-stable pellet production.

The C/O ratio also influenced moisture uptake, with a strong negative correlation observed between them. Biomasses with higher C/O ratios, such as sawdust (1.158), absorbed less moisture compared to those with lower C/O ratios, such as summer squash (0.455). A higher C/O ratio indicates a lower oxygen content, reducing the number of hydrophilic functional groups available for water interaction [74]. This results in reduced affinity for moisture, further supporting the idea that biomasses with high C/O ratios exhibit better moisture resistance.

Interestingly, pellets with high durability and bulk density, such as summer squash, cucumber, eggplant, and tomato, still exhibited high moisture absorption. This suggests that, while density and durability improve pellet integrity, they do not necessarily translate to lower moisture uptake. Instead, the intrinsic chemical composition of biomass, particularly ash and lignin content, plays a more crucial role in determining moisture resistance. These findings underscore the importance of considering moisture uptake alongside durability and density when selecting biomass materials for pelletization, particularly for applications requiring long-term storage in humid environments.

4.5. Mathematical Modelling of Pellet Bulk Density and Bulk Durability

Scaling up from single-pellet parameters to predict bulk pellet properties is essential for optimizing large-scale pellet production and storage. Pellet density and pellet durability serve as fundamental indicators of pellet quality properties, as they influence how pellets compact and withstand mechanical stress in bulk form [59]. To establish a predictive relationship, a multilinear regression model (Figure 5) was developed, incorporating single-pellet density and durability as independent variables to estimate bulk pellet density (BD_e) and bulk pellet durability (BD_u). The regression model enables the prediction of bulk pellet characteristics based on measurable single-pellet properties, allowing for process optimization without the need for large-scale experimental trials. This modeling was conducted under controlled conditions, specifically with a pellet moisture content of 8–10%. Any deviation from this moisture range could alter pellet properties, potentially affecting the accuracy and reliability of the model, and the model is shown as follows:

$${BD}_e=2408.3-1563.6\times {SPD}_e-20.7\times {SPD}_u+18.1\times {SPD}_e\times {SPD}_u$$

(7)

$${BD}_u=-477.8+392.9\times {SPD}_e+5.86\times {SPD}_u-4\times {SPD}_e\times {SPD}_u$$

(8)

where ${BD}_e$ is predicted bulk density (kg/m³) with ${\mathrm{R}}^2=0.936$, ${BD}_u$ is predicted bulk durability (%) with ${\mathrm{R}}^2=0.861$, ${SPD}_e$ is single pellet density in (g/mm³), and ${SPD}_u$ is single pellet durability in (%).

5. Conclusions

The findings of this study provide valuable insights into the pelletability of foliage from six greenhouse crops for bioenergy and biomaterial applications. Converting agricultural residues into pellets enhances energy density, transportability, and usability, offering a sustainable alternative to conventional biomass sources. This research bridges the gap between single-pellet analysis and batch-scale production, providing a comprehensive understanding of physicochemical properties, pellet quality, and energy consumption during pelletization. The results are particularly relevant for scaling up agricultural biomass utilization, reducing dependency on traditional wood-based feedstocks, and improving the efficiency of biomass-based renewable energy systems. These findings contribute to supply chain diversification, waste valorization, and environmental sustainability, making them highly applicable for biofuel production, bio-composite manufacturing, and agricultural residue management. Key findings are shown as follows:

• Successful batch-scale pellet production was achieved for six types of biomasses, demonstrating the viability of greenhouse crop residues as a sustainable feedstock for bioenergy applications.
• Biomass composition significantly impacted pellet density, durability, color properties, and moisture uptake. Higher lignin content contributed to improved pellet cohesion, while ash content influenced moisture absorption and combustion properties.
• Net specific pelletization energy consumption (NSPEC) varied across feedstocks, with cucumber (21.8 kWh/t) and summer squash (18.7 kWh/t) exhibiting the lowest energy requirements, while soybean (49.6 kWh/t) and sawdust (47.3 kWh/t) required the most energy due to higher resistance to densification.
• Cucumber and summer squash produced the most compact and durable pellets, with bulk density exceeding 665 kg/m³ and durability above 96%, making them ideal candidates for long-term storage and transportation.
• Sawdust exhibited the lowest moisture absorption (16–18% db) due to its high lignin content, while cucumber and summer squash had the highest moisture uptake (38–41% db), making them more susceptible to humidity related degradation.
• A predictive model was developed to relate single-pellet density and durability to bulk pellet properties, providing a scalable framework for optimizing industrial pellet production.

This study highlights the potential of agricultural and horticultural biomass residues as a viable feedstock for pelletized biofuels, contributing to the circular economy, renewable energy production, and sustainable resource management. Future research should explore long-term storage stability, combustion behavior, and industrial-scale process optimization, including moisture conte, die/roller gap, and feeding rate, to enhance the practical application of these biomass resources in global bioenergy markets. While the current study concentrated on the technical aspects of pelletization, subsequent investigations should broaden the scope to include economic and environmental assessments. In particular, conducting a comprehensive techno-economic analysis (TEA) and life cycle assessment (LCA) is essential to evaluate the scalability, commercial viability, and sustainability of utilizing greenhouse crop residues as bioenergy feedstocks. These future assessments should incorporate factors such as carbon footprint, energy input costs, and market competitiveness in comparison to conventional wood pellets, thereby providing a more holistic evaluation of their potential in renewable energy systems.