Optimizing Collection and Pretreatment Methods for Cost-Effective and Low-CO 2 Emission Biomass Supply Chains

doi:10.21203/rs.3.rs-5446256/v1

Download PDF

Research Article

Optimizing Collection and Pretreatment Methods for Cost-Effective and Low-CO 2 Emission Biomass Supply Chains

https://doi.org/10.21203/rs.3.rs-5446256/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 20 Jun, 2025

Read the published version in BioEnergy Research →

You are reading this latest preprint version

The high cost of biomass feedstock hinders the growth of the cellulosic ethanol industry, especially in regions like China where biomass is extensively dispersed. To tackle this issue, we devised a biomass supply chain model encompassing collection, pretreatment, storage, and transportation phases. We examined the effects of various collection and pretreatment method combinations on supply-chain costs and CO₂ emissions. The model's validity was confirmed using Heilongjiang and Zhejiang as representatives of northern and southern regions. Mechanized collection with shredding-baling proved to be the most economical, costing 226.6 y/t in Heilongjiang and 217.7 y/t in Zhejiang within a 60 km collection radius. For CO₂ emissions, mechanized collection with kneading emerged as the optimal choice. With governmental subsidies, mechanized collection with kneading became the preferred option, considering both cost and CO₂ emissions. By incorporating agricultural cooperatives, costs were further reduced by up to 70 y/t in Heilongjiang and 65 y/t in Zhejiang. This model facilitates the cost-effective collection of straw for ethanol production in biomass-scattered China. Biomass decentralization combining collection and pretreatment methods is key to achieving site-specific biomass supply. Furthermore, the model can be adapted for acquiring biomass feedstock in other sectors and offers insights for biomass procurement in diverse regions.

Cellulosic ethanol

Straw

Supply chain

Costs

CO2 emissions

As global fossil energy consumption rises, worsening climate change, biofuels have become an essential alternative to fossil fuels in the future[1, 2]. Ethanol has been used as a partial substitute for gasoline in several regions to alleviate energy shortages[3, 4]. Utilizing lignocellulose (agricultural and forestry residues, energy grasses) as raw material not only avoids the food competition problem in the first-generation biorefineries but also brings a certain positive impact on CO₂ emission reduction[5, 6]. Over the past decade, extensive research has been conducted on developing technologies for producing cellulosic ethanol from lignocellulosic materials, notably focusing on the dilute acid pretreatment process for cellulosic ethanol production developed by the National Renewable Energy Laboratory (NREL)[7–9]. To meet the demand for second-generation cellulosic ethanol feedstock, large-scale farms, exemplified by the United States, have shifted to cultivating energy crops to ease energy demand pressures[10–12]. However, in China, where there is high food demand and limited cultivation of energy crops, the primary feedstock for second-generation cellulosic ethanol remains agricultural waste. Moreover, China's intricate topography results in decentralized distribution of agricultural waste, leading to challenges in its collection, storage, and transportation[13–16]. Consequently, bioethanol plants in China are predominantly small-scale, relying on nearby agricultural residues as feedstock.

Internationally, appropriate biomass supply chain models have been developed by several studies for different countries and regions focusing on collection, transportation, storage, and pretreatment[17, 18]. Teresa et al. created a biomass supply chain model for northern Sweden to assess the costs and CO₂ emissions of harvesting forest residues and transporting them to fuel plants, which is a common model. In a different study[19]. R. L. Machado et al. designed a bioethanol supply chain model for sugarcane cultivation, ethanol processing, storage, transportation, and distribution to meet market demands. Their approach optimized cultivation areas and sizes to reduce transportation distances and enhance cost efficiency. However, this model is not suitable for China due to its reliance on decentralized straw as a biomass feedstock, unlike Brazil's centralized sugarcane cultivation[20]. Kapil M. Gumte et al. developed a comprehensive biomass supply chain model for India, covering all supply chain stages, with a focus on analyzing the cost implications of rail and road transportation modes. However, rail transportation of biomass remains challenging to implement in China at present[21]. Previous studies in China have mainly focused on straw acquisition in specific small areas, lacking suitable supply chain models for high-demand bioethanol feedstocks[22–24]. The decentralized and regionally varying distribution of agricultural residues in China underscores the need for a supply chain model with broad applicability. Enhancing vehicle transportation capacity is a key strategy in addressing long-distance transportation challenges arising from dispersed distribution[25, 26]. In rural China, to increase the transportation efficiency, farmers typically utilize baling equipment to compress the straw density[27, 28]. Particularly, for feed mills requiring larger quantities of straw, the straw is compacted in rural areas into high-density large square bales before transport.[29, 30]. Indeed, preprocessing biomass into high-density forms before transportation is an effective method to reduce transportation costs[31, 32]. In addition, the Chinese government has introduced a series of policy measures in the face of the CO₂ emissions caused by straw burning, and local governments have also established targets for local straw utilization[33–35], particularly with some regions requiring that the comprehensive utilization rate of straw in the local area should reach more than 90%, and corresponding subsidy policies have been established to increase the motivation of farmers and enterprises to utilize straw.

It is worth noting that the Chinese government, in response to current agricultural challenges, has introduced an agricultural cooperative model to address the current problems of fragmented rural crop cultivation and low utilization of agricultural equipment[36, 37]. In 2019, the Chinese government recognized 25 exemplary agricultural cooperative models, of which 9 were designated as regional benchmarks. The integration of large-scale agricultural residue supply chains into these cooperative models has the potential to improve cost efficiency through optimized resource utilization and reduced transportation costs.

In this study, we developed a straw supply chain model for bioethanol feedstock in China, addressing challenges associated with decentralized straw biomass collection. The model integrates collection, storage, pretreatment, and transportation stages. Two collection methods (manual and mechanical) and four pretreatment methods (baling, Kneading, Crushing-baling, and Pelletizing) are considered to optimize the supply chain cost in a typical Chinese region. The model's applicability is validated using crop residues from Heilongjiang Province and Zhejiang Province, representing northern and southern agricultural regions. Optimal supply chain scenarios are identified for these regions. Additionally, regional policy subsidies and agricultural cooperative models in China are integrated to reduce input costs in the studied regions.

Figure1 illustrates the methodological framework for this analysis, encompassing data analysis on agricultural residues suitable for cellulosic ethanol production in representative regions, the establishment of a supply chain model, and an assessment of suitability and feasibility in a typical agricultural region in China.

Figure 1 Supply chain model construction of cellulosic ethanol plant feedstock straw, economic and environmental feasibility analysis process

Estimating quantities of agricultural residues

We selected two large agricultural provinces, Heilongjiang and Zhejiang, as representatives of the northern and northern regions of China, respectively, for assessing the potential agricultural residues available for cellulosic ethanol production. Data on annual crop production for eight common crops (rice, corn, and wheat) and five indigenous crops (oilseed rape and tobacco) were extracted from the 2023 statistical yearbooks of the selected provinces. The total straw yield in each region was determined based on the straw-to-grain ratio specific to each crop. All straw generated was assumed to be utilized off-site, considering losses during the supply chain, with a straw availability coefficient set at 0.85. Detailed calculations are provided in Appendix S1.

Cellulosic ethanol feedstock supply chain model

We analyze the straw supply chain, which serves as the primary source of raw material for cellulosic ethanol production, as illustrated in Fig. 1. The key stages include biomass collection, biomass pre-treatment (comprising storage site pre-treatment and comminution pre-treatment before cellulosic ethanol production), storage and transportation. Figure 2 offers comprehensive details for each component.

Fig 2. The eight straw supply chain models established and the abbreviations therein

A straw collection model (S2.1) was devised to determine the collection radius of straw mass suitable for cellulosic ethanol production in two specific regions. Moreover, the collected straw must be transported to a storage point for further preservation. Hence, it is essential to calculate the collection radius for the storage point as well. The collection radius was computed for storage stations of 10,000 t and 50,000 t capacities to facilitate a comparative analysis aimed at identifying the optimal storage station size.

The biomass transportation model mainly consists of the quantification of fossil fuel consumption and the costs of the straw transportation process. In addition to the obvious relationship between fuel consumption and the distance of straw transportation, and vehicle transportation capacity, it is also affected by the way of straw pre-treatment. The cost required for vehicle operation, generally includes fuel costs, labor assistance costs, vehicle purchase costs, and repair and maintenance costs. In addition, in this study, since transportation involves large trucks that need to be involved in the loading and unloading process, the time loss and cost of the straw loading and unloading process were further quantified.

We quantified the expenses associated with straw storage at the site, encompassing the stacking costs post-baling at the storage location and the supplementary stacking expenses following the transportation of straw in bulk to the collection and storage site during the harvest season.

This study examines three primary pretreatment methods: Kneading, Crushing-baling, and Pelletizing. Biomass baling, produced using automatic pick-up balers, falls into four categories. Varied pretreatment methods can impact transportation, storage, and crushing procedures in cellulosic ethanol facilities. Our focus is on quantifying the costs and CO2 emissions associated with the pretreatment process.

Assessment and analysis

Costs and CO₂ emissions of straw supply processes

During the straw collection process, we made four assumptions. Firstly, we assumed that the distance between each collection and storage point is negligible in a large number of points, although this was directly overlooked. Secondly, we assumed that the purchase price of straw is consistent between the southern and northern regions. Thirdly, we assumed that the distribution of straw is uniform in both regions and is not influenced by topographical factors. Lastly, we assumed the use of the same type of machinery for straw collection in both regions in the model, regardless of variations in regional subsidies for agricultural machinery. Subsequently, the costs of eight supply chain models, as depicted in Fig. 2, were quantified based on S2. These models were generated by varying the sizes of collection and storage stations in the two regions.

Based on the CO₂ model constructed by S3, we quantified the CO₂ emissions of eight supply chain modes at the scale of 10000 t and 50000 t collection and storage station in the two regions, which provides a reference for the appropriate scale of cellulosic ethanol plant construction in the two regions under the comprehensive consideration of cost and environmental impact.

Cost analysis of collection in subsidized areas

Subsidy policies have been developed in various provinces of China to stimulate farmers and enterprises to utilize straw, but there are significant differences in subsidy standards. For Heilongjiang and Zhejiang, the subsidy policies are quite different, as Heilongjiang mainly subsidizes counties with major arable land in the Southeast and Northwest regions, and the subsidies have no standards and are based on land area, whereas Zhejiang Province subsidizes the utilization of straw across the whole province but sets the subsidy standards, and the detailed information is shown in S1.2. Therefore, we chose the circle range in Fig. S1 as the subsidized area for the analysis of straw supply chain, where we assumed that the distribution of straw in the circular area is uniform. We calculate the costs of the eight supply chain modes considering the subsidies, and provide appropriate supply chain models for the two regions, as well as a reference for other regions.

Straw supply under the agricultural cooperative model

Based on the current state of agricultural development in China, the Chinese government has promoted the establishment of agricultural cooperatives between regional enterprises and rural villages to collectivize Chinese agriculture and thereby increase the income of corporate farmers. We assume that the agricultural cooperative model is used to establish bases in villages and collect pre-treated straw using farmers' farm vehicles and equipment for collecting straw for making feed, etc. The company then buys the pre-treated straw directly from the bases in the countryside at a higher price without having to buy its equipment or collect the straw from the fields, but this process requires the company's trucks to transport it deeper into the villages, which adds a certain distance. Therefore, in our calculations, the transportation cost of the tractor in the field is excluded, the pretreatment cost is converted to the farmer's selling price of straw, the truck transportation distance is directly recorded as the distance from the field to the cellulosic ethanol plant, and the collection area is still the subsidized area of straw.

Costs and CO₂ emissions of straw supply chain

Heilongjiang and Zhejiang were selected as representative regions of northern and southern China, with their straw distributions detailed in Table S1.1. Heilongjiang and Zhejiang have total straw productions of 9381 t and 719 t, respectively. Based on Eq S1, the straw density was determined as 50.57 t/km² for Heilongjiang and 18.02 t/km² for Zhejiang, which were used for subsequent cost and CO₂ emission calculations. Furthermore, since the cost for a 10,000 t storage station is lower compared to a 50,000 t station (as shown in the appendix), a 10,000 t storage station size was selected.

Cost analysis

The primary constraint affecting the viability of the straw supply chain model is cost. This section examines the costs of eight supply chain models for Heilongjiang and Zhejiang, as illustrated in Fig. 3. For quantities below 10,000 t, the processes involve baling and transporting the straw to cellulosic ethanol plants, leading to two initial cost curves representing manual and mechanical collection. It is noteworthy that at the initial stage of the curve, the supply chain costs for manual and mechanical collection in both regions demonstrate contrasting trends as the collection radius expands. This disparity arises because, at lower collection volumes, manual collection necessitates only the adjustment of labor force, while mechanical collection entails higher expenditures on equipment procurement, which are amortized over increasing collection volumes. Ultimately, mechanical collection proves to be more economical than manual collection, with the point of intersection indicating the level at which their costs are equivalent. The intersection radius is below 5 km, implying that manual collection is more suitable for small-scale straw processing and utilization.

Subsequently, starting from collection radius of 7 km in Heilongjiang and 11 km in Zhejiang, the costs of supply chain modes increase as the collection radius expands. The cost escalation for manual collection modes is notably higher compared to mechanical collection modes. This difference is mainly attributed to the low efficiency of manual collection, which stands at 3 t/day in this study, reflecting real-world conditions, and the high labor costs in China amounting to 200 yuan/day, as per research findings, and showing a continuous upward trend. Despite the potential for creating rural job opportunities, manual collection costs more than double that of mechanical collection, rendering it economically unviable. Among the eight supply chain modes evaluated, model VI emerges as the most cost-effective option for both Heilongjiang and Zhejiang regions. This model integrates mechanical collection (AC) with storage station crushing-baling (SBG), making it particularly suitable for cellulosic ethanol plants. Cellulosic ethanol production necessitates two pretreatment stages: biomass pulverization for particle size reduction, followed by cellulose extraction through either physicochemical or biological means. Models III and VI offer pre-crushed straw, enabling direct utilization for cellulose extraction post bale breaking, while other modes require additional crushing at the bioethanol facility, resulting in increased energy consumption and costs.However, as the collection radius increases, the cost of model VIII gradually becomes lower than that of model IV and approaches that of model VI, particularly in Zhejiang. This is mainly because the transportation cost of granulated pretreated straw is lower than that of other pretreatment types, and with increasing collection radius and transportation distance, the higher pretreatment and crushing costs are gradually offset by the lower transportation costs. At a 300 wt collection volume, model VIII becomes the lowest-cost option.

In general, the cost trends of the straw supply chain models in both regions are generally similar, with costs below 400 yuan/t in Heilongjiang and below 450 yuan/t in Zhejiang. The difference in costs is primarily attributed to variations in straw distribution, as the lower straw density in Zhejiang leads to increased transportation distances. Specifically, within a 100 km collection radius, the lowest costs were 239.2 yuan/t in Heilongjiang and 253.1 yuan/t in Zhejiang, considering an 80 yuan/t straw purchase price. These findings highlight the effectiveness of the pretreatment and compression density strategies in reducing expenses related to long-distance transportation. Moreover, these pretreatment models offer a practical approach for regions to procure biomass raw materials according to local circumstances and availability, avoiding the high costs of indiscriminate biomass acquisition. However, there is still a problem to be noted here. Although mechanical collection is a more appropriate collection method, there are still some rural areas in China where manual collection is used, which will convert high collection cost into straw purchase price. Therefore, improving farmers 'mechanized production through machine purchase subsidies is a powerful means of straw utilization.

Fig 3. Unit cost versus collection radius for eight straw supply chain models (a: Heilongjiang; b: Zhejiang)

CO₂ emissions analysis

In this section, we analyze the CO₂ emissions of eight supply chain modes following S3, as illustrated in Fig. 4. Both regions exhibit a consistent upward trend with slight variations. Within a collection radius ranging from 20 km to 60 km, mode VII (pelleting) demonstrates the highest CO₂ emissions in both regions. This is primarily attributed to the substantial energy consumption during the pelleting process and the resultant CO₂ emissions. However, with an expanding collection radius, mode I (baling) gradually surpasses pelleting in terms of emissions. This shift occurs because the low density of baled straw diminishes truck loading capacity, necessitating more transportation trips and consequently leading to higher CO₂ emissions. Among the eight modes, mode IV stands out with the lowest emissions, contrasting mode VI, which boasts the lowest cost. This distinction arises from the ease of pulverization of kneaded pretreated straw in mode IV, eliminating the need for bale loading and unloading as required in mode VI. Consequently, mode IV exhibits reduced energy consumption and CO₂ emissions. Comparing the two regions, Zhejiang's straw supply chain notably emits a significantly higher amount of CO₂ compared to Heilongjiang. This disparity is primarily attributed to the more dispersed distribution of straw in Zhejiang. Despite employing similar pretreatment methods, the extended transportation distances in Zhejiang contribute to elevated CO₂ emissions, aligning with the findings of the cost analysis. This observation underscores the model's applicability across diverse regions in China.

Additionally, this study examines China's global leadership in the new energy industry, focusing on new energy vehicles and new energy agricultural tractors. Despite advancements in various models of new energy agricultural tractors, the adoption of electric trucks remains limited. In S5, we analyze the costs and CO₂ emissions of the supply chain using electric tractors for straw transportation. Our findings indicate that electric tractors lead to a reduction in supply chain costs. The advantages of electric equipment are more noticeable for smaller collection volumes. For instance, at a collection radius of 100 km, costs in Heilongjiang and Zhejiang decrease by 5.3 y/t and 7.9 y/t, respectively, while CO₂ emissions decrease by 1.03 kg CO₂/t and 1.77 kg CO₂/t. This trend is primarily due to tractors playing a more significant role in transportation at smaller collection radius. However, as the collection radius expands, the reliance on truck transportation increases, diminishing the cost-saving and CO₂ emission benefits of electric tractors. The main costs and CO₂ emissions are associated with the truck transportation stage. The potential replacement of traditional trucks with electric or hydrogen-powered vehicles in the future could substantially contribute to cost and emission reductions.

Given the short field transportation distances and the widespread distribution of biomass in China, emissions from field transportation are relatively low, thus limiting the potential economic and environmental advantages of electric tractors. Consequently, the development and deployment of electric trucks will play a pivotal role in significantly reducing biomass supply chain costs and CO₂ emissions in the future. In summary, considering both economic and environmental factors, processing straw as a bioethanol feedstock by direct crushing and baling at pretreatment bases in Heilongjiang and Zhejiang regions emerges as the optimal approach. This study focuses on the secondary pretreatment stage of a cellulosic ethanol plant, necessitating adjustments solely to the parameters related to ethanol in secondary pretreatment when evaluating biomass utilization for feed and paper production, among other applications. In conclusion, the establishment of appropriate straw supply chain models in regions like China, characterized by low straw distribution density, a large population, and abundant labor resources, holds significant importance. The biomass supply chain model proposed in this study, which integrates manual and mechanical access and compares various pretreatment methods, aligns effectively with the prevailing conditions in China.compares different pretreatment methods is undoubtedly in line with the actual situation in China.

Straw supply chain costs with government subsidies

Given the environmental impact of straw burning, the Chinese government has implemented straw utilization policies, and provincial governments have introduced corresponding subsidy policies. This chapter examines the impact of these policies on supply chain costs in Heilongjiang and Zhejiang, as outlined in Section 2.3.2. The cost implications of subsidized straw supply chain models are presented in Fig. 5. In Heilongjiang, upon the application of an 80 y/t subsidy, all supply chain costs within a 100 km storage radius decrease to below 300 y/t. Particularly, following the subsidy, the cost disparity between model IV and model VI in Heilongjiang diminishes, especially at smaller collection radius. As the subsidy does not influence CO2 emissions, model IV is deemed more favorable for low collection volumes, considering both cost and emissions. In Zhejiang, the subsidy policy involves a threshold: subsidies commence when straw utilization surpasses 1000 t, covering 50% of the costs, and escalate when utilization exceeds 10,000 t, with a maximum limit of 50,000 yuan. For the cellulosic ethanol industry, the straw supply chain constitutes less than 10% of total costs, but the Zhejiang government requires that over half of the subsidy be allocated to the supply chain. Here, we calculate the subsidy at an average of 50% of costs. Notably, all supply chain expenses in Zhejiang are under 250 yuan/t, lower than those in Heilongjiang, underscoring the significance of the policy for the straw industry's advancement. Despite similarities in trends between Zhejiang and Heilongjiang, model IV emerges as the preferred option for small-scale collection in terms of balancing costs and CO₂ emissions.

With the aim of promoting straw utilization, most Chinese regions have introduced subsidy schemes, with the exception of Heilongjiang and Zhejiang. These subsidies play a pivotal role in facilitating straw processing and utilization. In the examined regions, the application of subsidies leads to the preference of model IV over model VI as the optimal supply chain framework for straw used in cellulosic ethanol production when the collection radius is below 50 km, taking into account both financial and environmental considerations. This further underscores the significance of our model, emphasizing the influence of biomass pretreatment methods on supply chain expenses.

Figure 5. Eight supply chain models under subsidies (a: Heilongjiang; b: Zhejiang)

Straw Supply Chain Costs in Agricultural Cooperatives

Ethanol plants typically do not receive subsidies for purchasing biomass pretreatment equipment like crushers. Conversely, farmers stand to benefit from substantial subsidies, often exceeding 30% of the equipment's cost, if they acquire such machinery. This would lead to a notable decrease in supply chain expenses. Moreover, many farmers already possess similar equipment, such as crushers, negating the necessity for redundant acquisitions and further reducing costs. The Chinese government advocates for the formation of agricultural cooperatives to improve resource utilization, boost farmers' incomes, and cut down corporate expenditures. In this study, we introduces an agricultural cooperative model that integrates a straw supply chain, detailed in Section 2.3.3. The findings for Heilongjiang and Zhejiang, illustrated in Fig. 6, exhibit significant cost savings. In Heilongjiang, the cost of each supply chain model within the cooperative framework drops below 250 y/t, with a maximum reduction of 70 y/t compared to conventional models. Similarly, in Zhejiang, the maximum cost reduction amounts to 65 y/t. These savings can be divided between enterprises and farmers, effectively raising farmers' incomes and stimulating straw supply. The cooperative model not only streamlines costs but also aligns with national objectives of sustainable resource utilization and rural economic advancement.

In China, farmers in nearly all regions own their own agricultural vehicles, and many also possess crop harvesting and handling equipment, as well as feed processing facilities such as guillotine choppers, shredders, and pelletizers. By strategically integrating these existing resources into the straw supply chain model, significant cost reductions can be achieved. As outlined in this study, farmers can lease their equipment to cooperatives or directly participate in straw collection and processing, creating additional income streams. This cooperative model not only aligns with national goals of sustainable agricultural development, rural economic growth, and efficient resource utilization but also serves as a valuable reference for other regions in China. By leveraging existing resources and fostering collaboration between farmers and enterprises, this model provides a scalable and economically viable solution for straw utilization.

Accessing biomass feedstock poses a significant challenge for the ethanol industry, especially in regions such as China, where biomass distribution is decentralized. This study developed a comprehensive biomass supply chain model that includes collection, pretreatment, storage, and transportation stages. By taking into account two primary cost constraints - collection and pretreatment methods, the study assessed various combinations of two collecting methods and four pretreatment methods to facilitate cost-effective and low-emission access to biomass for cellulosic ethanol feedstock. The study identified the optimal supply chain model for Heilongjiang and Zhejiang, representing northern and southern China, respectively, focusing on cost minimization and CO₂ emissions reduction. In these regions, employing mechanical collection followed by baling after shredding demonstrated the lowest cost at 226.6 yuan per ton and 217.7 yuan per ton within a collection radius of 60 km. Furthermore, utilizing mechanical collection followed by kneading showed the lowest CO₂ emissions at 21.48 kgCO₂/t straw and 35.01 kgCO₂/t straw in Heilongjiang and Zhejiang, respectively. Heilongjiang achieved lower costs and emissions due to its high straw distribution density. In Heilongjiang, the current subsidy policy provides 80 y/t, while in Zhejiang, it covers half of that amount, favoring the mechanical collection - kneading combination for reduced emissions and CO₂ emissions. Taking into account the existing agricultural cooperative model in China, cost savings can reach up to 70 y/t and 65 y/t in Heilongjiang and Zhejiang, respectively. The biomass supply chain model developed in this study enables cost-effective access to straw, a cellulosic ethanol feedstock, from various regions through decentralized biomass distribution in China. Emphasizing the combination of collection and pretreatment methods as crucial research aspects allows for locally tailored biomass supply solutions across different regions. Nevertheless, selecting the suitable combination of collection and pretreatment methods is essential, considering the varying distribution of biomass across regions, and should be based on empirical data prior to implementation.

Funding

This work was partially funded by the National Key Research and Development Program of China (2022YFB4201901).

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request

Author Contributions

Changliu He: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Writing - original draft. Xi Zhao: Supervision, Writing - review & editing. Lei Zheng: Supervision, Writing - review & editing. Jiayu Xin: Supervision, Writing - review & editing. Huimin Yun: Conceptualization, Supervision, Writing - review & editing, Funding acquisition. Xu Zhang: Conceptualization, Supervision, Writing - review & editing.

Declarations

Conflict of Interest The authors declare no competing interests.

Rossi LM, Gallo JMR, Mattoso LHC, Buckeridge MS, Licence P, Allen DT (2021) Ethanol from Sugarcane and the Brazilian Biomass-Based Energy and Chemicals Sector. ACS Sustain Chem Eng 9(12):4293–4295. https://doi.org/10.1021/acssuschemeng.1c01678
Cherwoo L, Gupta I, Flora G, Verma R, Kapil M, Arya SK, Ravindran B, Khoo KS, Bhatia SK, Chang SW, Ngamcharussrivichai C, Ashokkumar V (2023) Biofuels an alternative to traditional fossil fuels: A comprehensive review. Sustain Energy Technol Assess 60:103503. https://doi.org/10.1016/j.seta.2023.103503
Guo M, Li C, Facciotto G, Bergante S, Bhatia R, Comolli R, Ferré C, Murphy R (2015) Bioethanol from poplar clone Imola: an environmentally viable alternative to fossil fuel? Biotechnol Biofuels 8(1):134. https://doi.org/10.1186/s13068-015-0318-8
Howard MS, Issayev G, Naser N, Sarathy SM, Farooq A, Dooley S (2022) Correction: Ethanolic gasoline, a lignocellulosic advanced biofuel. Sustainable Energy Fuels 6(12):3080–3083. https://doi.org/10.1039/D2SE90024F
Manochio C, Andrade BR, Rodriguez RP, Moraes BS (2017) Ethanol from biomass: A comparative overview. Renew Sustain Energy Rev 80:743–755. https://doi.org/10.1016/j.rser.2017.05.063
Periyasamy S, Beula Isabel J, Kavitha S, Karthik V, Mohamed BA, Gizaw DG, Sivashanmugam P, Aminabhavi TM (2023) Recent advances in consolidated bioprocessing for conversion of lignocellulosic biomass into bioethanol – A review. Chem Eng J 453:139783. https://doi.org/10.1016/j.cej.2022.139783
Yildirim O, Ozkaya B, Altinbas M, Demir A (2021) Statistical optimization of dilute acid pretreatment of lignocellulosic biomass by response surface methodology to obtain fermentable sugars for bioethanol production. Int J Energy Res 45(6):8882–8899. https://doi.org/10.1002/er.6423
Jung YH, Kim IJ, Kim HK, Kim KH (2013) Dilute acid pretreatment of lignocellulose for whole slurry ethanol fermentation. Bioresour Technol 132:109–114. https://doi.org/10.1016/j.biortech.2012.12.151
da Silva ARG, Torres Ortega CE, Rong B-G (2016) Techno-economic analysis of different pretreatment processes for lignocellulosic-based bioethanol production. Bioresour Technol 218:561–570. https://doi.org/10.1016/j.biortech.2016.07.007
Illukpitiya P, Reddy KC, Bansal A (2017) Modeling net energy balance of ethanol production from native warm season grasses. Energy Econ 64:346–352. https://doi.org/10.1016/j.eneco.2017.04.008
Lin T-S, Kheshgi HS, Song Y, Vörösmarty CJ, Jain AK (2023) Which crop has the highest bioethanol yield in the United States? Front Energy Res 11. https://doi.org/10.3389/fenrg.2023.1070186
Dien BS, Anderson WF, Cheng M-H, Knoll JE, Lamb M, O’Bryan PJ, Singh V, Sorensen RB, Strickland TC, Slininger PJ (2020) Field Productivities of Napier Grass for Production of Sugars and Ethanol. ACS Sustain Chem Eng 8(4):2052–2060. https://doi.org/10.1021/acssuschemeng.9b06637
Wang B, Shen X, Chen S, Bai Y, Yang G, Zhu J, Shu J, Xue Z (2018) Distribution characteristics, resource utilization and popularizing demonstration of crop straw in southwest China: A comprehensive evaluation. Ecol Ind 93:998–1004. https://doi.org/10.1016/j.ecolind.2018.05.081
Wang Ja, Liang S, Shi P (2022) Topography and Landforms. In: Wang Ja, Liang S, Shi P (eds) The Geography of Contemporary China. Springer International Publishing, Cham, pp 63–84. doi:https://doi.org/10.1007/978-3-031-04158-7_3
Jiang D, Zhuang D, Fu J, Huang Y, Wen K (2012) Bioenergy potential from crop residues in China: Availability and distribution. Renew Sustain Energy Rev 16(3):1377–1382. https://doi.org/10.1016/j.rser.2011.12.012
Qiao J, Yu D, Wang Q, Liu Y (2018) Diverse effects of crop distribution and climate change on crop production in the agro-pastoral transitional zone of China. Front Earth Sci 12(2):408–419. https://doi.org/10.1007/s11707-017-0665-9
Sharma B, Ingalls RG, Jones CL, Khanchi A (2013) Biomass supply chain design and analysis: Basis, overview, modeling, challenges, and future. Renew Sustain Energy Rev 24:608–627. https://doi.org/10.1016/j.rser.2013.03.049
Mafakheri F, Nasiri F (2014) Modeling of biomass-to-energy supply chain operations: Applications, challenges and research directions. Energy Policy 67:116–126. https://doi.org/10.1016/j.enpol.2013.11.071
de la Fuente T, González-García S, Athanassiadis D, Nordfjell T (2017) Fuel consumption and GHG emissions of forest biomass supply chains in Northern Sweden: a comparison analysis between integrated and conventional supply chains. Scand J For Res 32(7):568–581. https://doi.org/10.1080/02827581.2016.1259424
Machado RL, Abreu MR (2024) Multi-objective optimization of the first and second-generation ethanol supply chain in Brazil using the water-energy-food-land nexus approach. Renew Sustain Energy Rev 193:114299. https://doi.org/10.1016/j.rser.2024.114299
Gumte KM, Mitra K (2019) Bio-Supply Chain Network Design to tackle ethanol deficiency in India: A mathematical framework. J Clean Prod 234:208–224. https://doi.org/10.1016/j.jclepro.2019.06.160
Wang S, Yin C, Jiao J, Yang X, Shi B, Richel A (2022) StrawFeed model: An integrated model of straw feedstock supply chain for bioenergy in China. Resources, Conservation and Recycling 185:106439. https://doi.org/10.1016/j.resconrec.2022.106439
Wang Z, Wang Z, Tahir N, Wang H, Li J, Xu G (2020) Study of synergetic development in straw power supply chain: Straw price and government subsidy as incentive. Energy Policy 146:111788. https://doi.org/10.1016/j.enpol.2020.111788
Wu J, Zhang J, Yi W, Cai H, Su Z, Li Y (2021) Economic analysis of different straw supply modes in China. Energy 237:121594. https://doi.org/10.1016/j.energy.2021.121594
Huq F, Stafford TF, Khurrum S, Bhutta M, Kanungo S (2010) An examination of the differential effects of transportation in supply chain optimization modeling. J Manuf Technol Manage 21(2):269–286. https://doi.org/10.1108/17410381011014404
Chan FTS, Zhang T (2011) The impact of Collaborative Transportation Management on supply chain performance: A simulation approach. Expert Syst Appl 38(3):2319–2329. https://doi.org/10.1016/j.eswa.2010.08.020
Ren J, Yu P, Xu X (2019) Straw Utilization in China—Status and Recommendations. Sustainability 11(6). https://doi.org/10.3390/su11061762
Xinxin L, Zuliang S, Jiuchen W, Rongfeng J Review on the Crop Straw Utilization Technology of China. ate 2020. https://doi.org/10.11648/j.ajese.20200404.12
Wang Y-j, Bi Y-y, Gao C-y (2010) The Assessment and Utilization of Straw Resources in China. Agricultural Sci China 9(12):1807–1815. https://doi.org/10.1016/S1671-2927(09)60279-0
Hong J, Ren L, Hong J, Xu C (2016) Environmental impact assessment of corn straw utilization in China. J Clean Prod 112:1700–1708. https://doi.org/10.1016/j.jclepro.2015.02.081
Guo L, Wang D, Tabil LG, Wang G (2016) Compression and relaxation properties of selected biomass for briquetting. Biosyst Eng 148:101–110. https://doi.org/10.1016/j.biosystemseng.2016.05.009
Poddar S, Kamruzzaman M, Sujan SMA, Hossain M, Jamal MS, Gafur MA, Khanam M (2014) Effect of compression pressure on lignocellulosic biomass pellet to improve fuel properties: Higher heating value. Fuel 131:43–48. https://doi.org/10.1016/j.fuel.2014.04.061
Sun D, Ge Y, Zhou Y (2019) Punishing and rewarding: How do policy measures affect crop straw use by farmers? An empirical analysis of Jiangsu Province of China. Energy Policy 134:110882. https://doi.org/10.1016/j.enpol.2019.110882
Huang L, Zhu Y, Wang Q, Zhu A, Liu Z, Wang Y, Allen DT, Li L (2021) Assessment of the effects of straw burning bans in China: Emissions, air quality, and health impacts. Sci Total Environ 789:147935. https://doi.org/10.1016/j.scitotenv.2021.147935
Li H, Miao Z, Zhang B (2022) Review and prospect of comprehensive straw utilization and government policy in China. Chin J Popul Resour Environ 20(4):402–406. https://doi.org/10.1016/j.cjpre.2022.11.010
Zheng S, Wang Z, Awokuse TO (2012) Determinants of Producers' Participation in Agricultural Cooperatives: Evidence from Northern China. Appl Economic Perspect Policy 34(1):167–186. https://doi.org/10.1093/aepp/ppr044
Zhong Z, Jiang W, Li Y (2023) Bridging the gap between smallholders and modern agriculture: Full insight into China's agricultural cooperatives. J Rural Stud 101:103037. https://doi.org/10.1016/j.jrurstud.2023.103037

Download PDF

Journal Publication

published 20 Jun, 2025

Read the published version in BioEnergy Research →

Editorial decision: Accept as is
18 May, 2025
Reviewers agreed at journal
19 Apr, 2025
Reviewers invited by journal
19 Apr, 2025
Editor assigned by journal
31 Mar, 2025
First submitted to journal
21 Mar, 2025

You are reading this latest preprint version

Optimizing Collection and Pretreatment Methods for Cost-Effective and Low-CO 2 Emission Biomass Supply Chains

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials and Methods

Estimating quantities of agricultural residues

Cellulosic ethanol feedstock supply chain model

Assessment and analysis

Costs and CO₂ emissions of straw supply processes

Cost analysis of collection in subsidized areas

Straw supply under the agricultural cooperative model

Results

Costs and CO₂ emissions of straw supply chain

Cost analysis

CO₂ emissions analysis

Straw supply chain costs with government subsidies

Straw Supply Chain Costs in Agricultural Cooperatives

Conclusion

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1

Optimizing Collection and Pretreatment Methods for Cost-Effective and Low-CO 2 Emission Biomass Supply Chains

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials and Methods

Estimating quantities of agricultural residues

Cellulosic ethanol feedstock supply chain model

Assessment and analysis

Costs and CO2 emissions of straw supply processes

Cost analysis of collection in subsidized areas

Straw supply under the agricultural cooperative model

Results

Costs and CO2 emissions of straw supply chain

Cost analysis

CO2 emissions analysis

Straw supply chain costs with government subsidies

Straw Supply Chain Costs in Agricultural Cooperatives

Conclusion

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1

Costs and CO₂ emissions of straw supply processes

Costs and CO₂ emissions of straw supply chain

CO₂ emissions analysis