Article Cite This: Energy Fuels XXXX, XXX, XXX−XXX
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Mobile Autothermal Pyrolysis System for Local Biomass Conversion: Process Simulation and Techno-Economic Analysis Xing Chen, Huiyan Zhang, and Rui Xiao* Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy & Environment, Southeast University, No. 2 Sipailou,Nanjing 210096, China ABSTRACT: This paper presents a mobile autothermal pyrolysis system for locally converting biomass feedstock into bio-oil that can be transported easily. The system includes a compact internally interconnected fluidized bed (IIFB) reactor, a biomass pretreatment facility, and a product recovery unit. On the basis of modified chemical kinetic models, the pyrolysis process of common forestry and agricultural residues in this mobile system has been simulated. The pyrolytic product distribution from simulation has good agreements with experimental results. Then, the techno-economic performance of the mobile pyrolysis system in China is evaluated and compared with other liquid biofuel production facilities, i.e., fixed biomass pyrolysis plants and Fischer−Tropsch liquids production via biomass gasification (BG-FT). The results indicate that the biomass feedstock cost of mobile pyrolysis systems can be effectively reduced. Compared with the fixed biofuel production plant, the labor cost is higher for the mobile plant. Though the fixed system has a lower initial investment, the mobile system has a lower biofuel production cost. Overall, the mobile pyrolysis system has a better long-term economy than the fixed plant, due to the larger profit. The mobile system can be profitable in the sixth year, which is one year earlier than the fixed ones. performance, performs slightly better than a fixed plant under appropriate conditions. It can be concluded that mobile biomass pyrolysis method is effective in reducing the conventional feedstock transportation costs. Whereas, detailed technical specefications of mobile biomass pyrolysis facilities, including reactor structure, system configuration, and operating conditions, have not been discussed in existing studies. To this end, this paper aims to establish a mobile biomass pyrolysis system suitable for field environment. Unlike the conventional fixed biomass pyrolysis plants, the mobile pyrolysis facilities should be kept small-scaled as possible for easy movement. Additionally, the mobile biomass pyrolysis systems should also be operated in the absence of inert gases and external heat sources. In order to realize a viable mobile biomass pyrolysis system, we develop a biomass pyrolysis reactor, called internally interconnected fluidized beds (IIFB).18 In brief, the IIFB reactor has a pyrolysis bed and a combustion bed, for biomass pyrolysis and biochar combustion, respectively. Its configuration can be seen in Figure 1. The carrier gas in pyrolysis bed is recycling noncondensable gases (NCG), while the heat required is provided by biochar combustion in the combustion bed. The two beds are integrated in a single reactor and connected by a dipleg and a draft tube with slots. This IIFB reactor, together with a pretreatment unit, a condensation unit, and a recycle unit, constitute the novel mobile biomass pyrolysis system. For a large-scaled biomass conversion plant as mentioned above, feasibility analysis should be carried out prior to the practice of the project investment. Because of the very limited
1. INTRODUCTION Biomass energy is considered as a renewable alternative to fossil energy due to its widespread sources and environmetal friendliness. Conversion from biomass into renewable biofuel has been regarded as one of the most promising solutions to the environmental and geopolitical problems caused by fossil fuels.1 However, nowadays biomass energy is underused due to the exorbitant transportation costs of biomass feedstocks. Limited by the low spatial and energy densities, high transportation cost is raised in terms of biomass collection.2 At present, most biomass feedstock, including both forestry and agricultural residues, can only be burned on-site in fields or forest farms, without effective utilization.3 Therefore, it is of great significance in finding a financially viable method to remove and utilize biomass.4 One solution to reduce the transportation cost of biomass feedstock is to implement mobile biomass pyrolysis facilities.5 These mobile facilities can be moved from a depleted region and relocated to another region with abundant biomass resources, then convert biomass feedstock into transportable bio-oil locally. Obviously the mobile facilities reduce feedstock haul distance, thus the cost of biomass feedstock (mainly the feedstock transportation cost) can be effectively reduced. Given that in a largescale fixed biomass pyrolysis plant more than 50% of the operating cost is for biomass feedstock,6 the application of mobile facilities can improve the biomass conversion system economy effectively. A lot of research has been carried out in the field of biomass pyrolysis, including pyrolysis mechanism,7−9 reactor design,10,11 and techno-economic analysis.6,12−15 However, most studies pay attention to conventional fixed biomass pyrolysis facilities, while few have been considered for mobile biomass pyrolysis system. To date, the research on mobile pyrolysis systems mainly focus on the configuration and performance analysis of the mobile biomass conversion supply chain.5,16,17 These studies show that the mobile plant, in terms of economic and environmental © XXXX American Chemical Society
Special Issue: 6th Sino-Australian Symposium on Advanced Coal and Biomass Utilisation Technologies Received: October 15, 2017 Revised: February 8, 2018 Published: February 8, 2018 A
DOI: 10.1021/acs.energyfuels.7b03172 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 1. Configuration of the mobile autothermal pyrolysis system.
Figure 2. Process flow diagrams of the mobile pyrolysis system in simulation.
appropriate for biomass pyrolysis using inert gas as carrier gas. Using NCG as carrier gas, instead of N2, can influence the amounts of oxygenated compounds in the liquid phase.24,25 On the other hand, this prediction model lacks the analysis of energy balance, which limits its application in the system process simulation. A high fidelity process is helpful to minimize entangling assumptions in process simulation. Nowadays there have been some studies on the techno-economic analysis of biomass pyrolysis system, while these studies report bio-oil costs ranging from $0.90 to $1.73 per liter.26−28 The difference between the reported bio-oil costs can be attributed to the difference in the pyrolysis plant capacity, the region conditions, and the assumptions adopted. This paper presents the simulation and techno-economic analysis of a novel mobile biomass pyrolysis system which is
amount of existing mobile pyrolysis facilities, inventory data are difficult to be obtained and the assessments of the systems have to be conducted based on process simulation. There are several studies on the process simulation of biomass pyrolysis, including Yang et al.,19 Carrasco et al.,20 and Kabir et al.21 They have developed detailed simulations of a biomass pyrolysis process. However, they use a limited number of model compounds and black-box approachs for modeling the pyrolysis reaction, thus resulting in a considerable simplification of the process. Peters22 establishs a novel kinetic reaction model of biomass pyrolysis for calculating pyrolytic products. His model,23 based on the atomic and biochemical composition of biomass feedstock, can accurately predict the pyrolytic products by using up to 33 model compounds to represent bio-oil. However, his reaction model is B
DOI: 10.1021/acs.energyfuels.7b03172 Energy Fuels XXXX, XXX, XXX−XXX
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materials (alumina hollow sphere), separate from pyrolysis vapors in the fountain region (PYR-CYC1), and then slip into the combustion bed through the dipleg. They are combusted in the combustion bed for providing the heat biomass pyrolysis needed. The generated flue gas (stream DRYGAS) goes into the dryer for waste heat recovery. Three reactors are applied to model the complex biomass pyrolysis reactions: one yield reactor (IIFB-DEC), one Rcstir reactor (IIFB-PY1) and one Ryield reactor (IIFB-PY2). As is shown before, the pyrolysis reactions can be divided into two stages. The IIFB-DEC reactor repesents the decomposion of biomass into three subcomponents (cellulose, lignin, and hemicellulose), which is actually a virtual process. In the IIFB-PY1 reactor, a kinetic reaction model is implemented to simulate the primary pyrolysis stage. On the basis of the proportion of the subcomponents and over 150 kinetic equations, the composition of the primary pyrolysis products can be calculated according to the given reactor temperature and residence time. Then the IIFB-PY2 reactor simulates the secondary pyrolysis stage. It is difficult to describe the secondary reactions of biomass pyrolysis by kinetic equations, thus the reactor adjusts the yields of secondary pyrolysis products according to Hoekstra’s work.34 The combustion bed is modeled by a yield reactor (IIFB-CO1) and a Gibbs reactor (IIFB-CO2). The biochar and other organic compounds are decomposed into their atomic composition in IIFB-CO1 and combusted in IIFB-CO2. The combustion temperature is 650 °C while the pyrolysis temperature is 550 °C. The bed material residence time in the pyrolysis bed is 2 s while the gas residence time is 0.5 s. Additionally, the heat required is calculated upon the given reactions, including the primary and secondary pyrolysis stages. 2.2.3. Condensation Unit and Recycle Unit. The novel mobile biomass pyrolysis system also includes a condensation unit and a recycle unit. The condensation unit consists of a condensing tower (QUENCH), a heat exchanger (HEATEX), and a bio-oil lift pump (OIL-PUMP). The pyrolysis gases, after purification in a purge (PYR-CYC2), are quenched by condensed bio-oil in the condensing tower. Then the obtained bio-oil is further cooled to 30 °C by cold water in the heat exchanger. The recycle unit is mainly made up of a precombustor (PRE-COM) and some air blowers and cyclones. A portion of NCG is recycled as fluidizing gas in the pyrolysis bed of IIFB reactor. NCG is a mixture of CO, CO2, CH4, H2 and other organics,35 while the composition is calculated based on the reaction model. 2.3. Techno-Economic Analysis. The investment and costs of biomass pyrolysis systems vary widely in different regions. In this discussion, the techno-economic analysis is based on the reality in Nanjing, China. The costs of equipment are estimated according to Ji’s work,12 on the basis of the equation shown below.
movable and able to convert biomass feedstock into transportable bio-oil locally. The mobile pyrolysis system, using a IIFB reactor, operates without external heat sources and inert carrier gases. The system model was established in Aspen Plus. The pyrolytic products were calculated from a detailed kinetic reaction model and show good agreement with experimental results in the literature. On the basis of the system model, the techno-economic performance of the mobile pyrolysis system can be evaluated and compared with other biomass processing plants, including conventional fixed biomass pyrolysis process and Fischer−Tropsch liquids production via biomass gasification (BG-FT). The comparison and analysis are carried out according to the reality in China. The results will be helpful for the development of mobile biomass pyrolysis systems, especially in China.
2. MATERIALS AND METHODS 2.1. Process Description. As is shown in Figure 1, the mobile biomass pyrolysis system consists of four parts, including the pretreatment unit, the IIFB reactor, the condensation unit, and the recycle unit. In the feed pretreament unit, the biomass feedstock is dried and ground to meet the feed requirement of 2 mm diameter and 7% moisture content. Next, the treated biomass is sent into the IIFB reactor, where the biomass is thermochemically converted into biochar, pyrolysis vapors, and NCG. The biochar is recycled on-site and combusted in the IIFB reactor for providing the heat required by biomass pyrolysis. Then the pyrolysis vapors and NCG pass into the condensation unit. The condensation unit consists of a condensing tower, a heat exchanger, and a bio-oil lift pump. The pyrolysis vapor is condensed into bio-oil in the condensing tower, while the NCG is fed into the recycle unit. In the recycle unit, the noncondensable gas, mixed with excess air, is combusted in a precombustor outside the IIFB reactor for easy control. The flue gas and excess air is introduced into the combustion bed of the IIFB reactor as fluidizing gas and combustion-supporting gas. Then the flue gas from the IIFB reactor passes into the pretreatment unit for drying the biomass feedstock. 2.2. Process Simulation. The simulation of this mobile biomass pyrolysis system is conducted in Aspen Plus with its thermodynamic and property data. The nonconventional compounds, such as biomass and pyrolytic intermediates, are defined by users. The property data and reaction mechanisms of these nonconventional compounds are estimated based on the given structures. In this simulation, the biomass feedstock is assumed to be composed of cellulose, hemicellulose, lignin, ash, and water. Extractives and other subcomponents can be ignored because they have little effect on biomass pyrolysis.29 Biomass pyrolysis reactions can be divided into two stages, i.e., the primary pyrolysis stage and the secondary pyrolysis stage.30 In the primary pyrolysis stage, three principle subcomponents of biomass (cellulose, hemicellulose, and lignin), are volatilized and decomposed into low-molecular compounds. Next, in the secondary pyrolysis stage, the low-molecular compounds turn into the final pyrolytic products via complex secondary reactions. Chemical kinetic models have been established by some researchers to describe the pyrolysis process.31−33 In this simulation the pyrolytic products are calculated according to the chemical kinetics models. The process flow diagrams of this mobile pyrolysis system is shown in Figure 2. 2.2.1. Pretreatment Unit. Common biomass feedstock (wheat straw, corncob, or sawdust) passes into a roll crusher (CRUSHER) and screen (SCREEN) successively for reducing particle size. Then the feedstock is dried in a rotary dryer (DRYER) by the hot exhaust gas stream (DRYGAS) from the combustion zone of the IIFB reactor. The treated biomass feedstock is then introduced into the pyrolysis bed by the carrier gas. 2.2.2. IIFB Reactor. In the mobile pyrolysis system, biomass feedstock is fed into the draft tube (IIFB-PYR) with recycling NCG (stream RECYCLE1), while hot bed materials (stream SOLID1) are introduced into the draft tube from the combustion bed (IIFB-COM) through the slots. The biomass pyrolysis reactions mainly occur in the draft tube. The solid byproduct biochar, accompanied by ash and bed
⎛ S ⎞n C1 = C0⎜ 1 ⎟ ⎝ S0 ⎠
(1)
where C1 is the cost of a new plant with the capacity of S1. C0 is the cost of the initial plant with S0 capacity. The scale factor n of biomass pyrolysis systems is assumed to be 0.8, according to Shemfe.13 The costs of BG-FT plants are estimated based on the literature.36 The mentioned BG-FT process is a low-temperature FT process with
Table 1. Composition Parameters of Three Common Biomass Feedstocks wheat straw corncob saw dust proximate analysis wt % (ad) volatile matter fixed carbon ash water ultimate analysis wt % (db) carbon hydrogen oxygen biochemical composition cellulose wt % (db) hemicellulose lignin C
71.6 17.2 3.3 7.9 45.2 5.9 47.9 39.9 28.2 16.7
70.8 18.2 2.4 8.6 43.6 5.8 48.7 41.8 30.8 12.4
77.3 14.3 0.9 7.6 48.4 5.5 45.8 38.3 25.8 24.4
DOI: 10.1021/acs.energyfuels.7b03172 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels a cobalt catalyst at 220 °C, which is reported as the “BCo process” in Rafati’s work.36 The transform equation is shown below.37
C local = RσCabroad
have been limited studies available in the literature on biomass pyrolysis under recycling the NCG atmosphere. Corncob is applied as a feedstock for model validation in this work. The simulation results of corncob pyrolysis are compared with the experimental results reported by Zhang.35 The composition parameters of corncob, such as the ultimate analysis, the proximate analysis, and the content of the subcomponents can be seen in Table 1. In the reaction model, hemicellulose and cellulose are, respectively, represented by their monomers xylan and C6H10O5. Lignin is represented by 7 different monomers with different proportion of C/H/O due to its more heterogeneous
(2)
where Clocal is the equipment cost of a liquid biofuel production plant in China. Cabroad is the cost of the same equipment reported in the foreign literature. R is the monetary exchange rate, and σ is area comparability factor, assumed to be 0.6.38
3. RESULTS AND DISCUSSION 3.1. Model Validation. The pyrolysis model established in this work is validated with existing experimental results. There
Table 2. Molecular Structure of the Monomers Representing Lignin
D
DOI: 10.1021/acs.energyfuels.7b03172 Energy Fuels XXXX, XXX, XXX−XXX
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respectively. The difference between the simulation and test results can be explained by the different atmospheres. Even so, the pyrolysis heat estimated is close to the measured value, which demonstrates the reliability. By adjusting the model for a better agreement with the experimental results, a modified biomass pyrolysis model can be established, in the view of pyrolytic product distribution and energy consumption. Furthermore, a detailed analysis of the fractional composition of the bio-oil from the experiments and the simulation has been compared. Zhang35 obtained the chemical composition of the bio-oil produced under NCG atmosphere using GC/MS analysis. A qualitative validation can be conducted with the application of the experimental results. Figure 5 shows the comparison between the bio-oil compositions obtained from experiments with that obtained from the simulation. The comparison is conducted under the same base case. As is shown in Figure 5, the percentage of the organic acids, esters, ethers, phenols, alcohols agrees well with the experimental results. The unclassifiable compounds detected in experiments, are classified as degraded lignin and represented by aromatic derivatives.33 The satisfactory agreement can be observed from the figure. 3.2. Case Study and Capacity Analysis. On the basis of the obtained pyrolysis model, the system model of the mobile autothermal pyrolysis plant can be established. The comparison of the techno-economic performance between the conventional fixed liquid biofuel production process and mobile pyrolysis system can be conducted. The evaluation must be carried out under similar operating conditions. We modeled the mobile autothermal pyrolysis system based on the actual conditions in Nanjing City, Jiangsu Province, China. The capacity of liquid biofuel production systems and economic analysis are both calculated based on local conditions. The annual output of available crop straws in Nanjing is about 1.5 million tons, mainly consisting of wheat straw, corncob, rice straw, and rape straw. The handling capacity of one fixed pyrolysis plant can be 4000 kg/h considering the capacity limit of circulating fluidized beds. Thus, about 45 set of fixed pyrolysis plants should be constructed to meet the straw treatment requirement in Nanjing. On the other hand, the capacity of the mobile system is designed as 100 kg/h for easy movement. The calculation formula of collection radius for a biomass conversion facility is shown below.
structure. In the simulation, the percentage of the seven monomers are adjusted to make the elemental composition of the decomposition products similar to that of the actual biomass feedstock. The seven monomers are listed in Table 2. As mentioned above, in the simulation the decomposition products (xylan, C6H10O5, and monomers of lignin) are thermally degraded in IIFB-PY1 and IIFB-PY2. Figure 3 shows the
Figure 3. Comparison of the product distribution fom corncob pyrolysis under NCG atmosphere between simulation and experiments.
simulation and experimental results of corncob pyrolysis products under recycling NCG atmosphere. As is shown in Figure 3, the bio-oil yield, biochar yield and NCG yield of corncob pyrolysis is calculated as 55.6%, 25.3%, and 19.1%, respectively. While the corresponding product yields experimentally measured by Zhang35 are 53.5%, 29.1%, and 17.4%, respectively. The simulation results show good agreement with experimental results from the available literature. The calculated pyrolytic product distribution of wheat straw and saw dust are listed in Table 3. Table 3. Calculated Pyrolytic Product Distribution of Different Biomass Feedstocks Bio-oil Biochar NCGs
corncob
wheat straw
saw dust
55.6 25.3 19.1
56.1 25.2 19.7
59.7 24.5 16.8
R=
The pyrolysis heat of the biomass feedstock should be determined to establish the energy balance of the system model. The thermal load of pyrolysis reactions can be measured by differential scanning calorimetry (DSC) analysis. We applied DSC tests to measure the heat required in the pyrolysis of the biomass feedstocks under NCGs atmosphere. Limited by the security requirement of DSC analysis, CO2 is used as the atmosphere used in DSC analysis. The DSC results are shown in Figure 4. It can be concluded that the pyrolysis reaction heat of corncob, wheat straw, and saw dust under CO2 atmosphere is 641.5 kJ/kg, 256.5 kJ/kg, and 140.75 kJ/kg, respectively. The significant difference in the pyrolysis heat of the feedstock can be explained by the fact that corncob contains more cellulose while saw dust contains more lignin. It is known that pyrolysis of hemicellulose and lignin involves exothermic reactions while pyrolysis of cellulose are endothermic.39 The pyrolysis heat of corncob, wheat straw, and saw dust under recycling NCG atmosphere is 527.5 kJ/kg, 271.4 kJ/kg, and 180.3 kJ/kg,
Q k1k 2k 3πQ 0
(3)
where R is the collection radius, Q is the processing capacity. Q0 is the per unit area yield of straws in Nanjing, k1 is the percentage of agricultural acreage in total area, k2 is the collection coefficient of straws, and k3 is the utilization coefficient of straws. In this work, Q0 is 156 t/km2, and the value of k1, k2, k3 is assumed 37.5%, 0.8, and 0.9, respectively.45 Therefore, the feedstock collection radius of a 4000 kg/h fixed plant and a 100 kg/h mobile system is 15.6 km and 2.44 km, respectively. On the basis of the pyrolysis reaction model, the entire process model of the mobile pyrolysis system can be established. The pyrolytic product of corncob pyrolysis is 55.6 kg/h bio-oil, 19.1 kg/h NCGs, and 25.3 kg/h biochar. The mass flow rate of the recycling NCG is 113.6 kg/h. The generated 19.1 kg/h NCG and 60% of the biochar are burned for heat recovery. Thus, the output of the mobile system is 55.6 kg/h bio-oil and 10.1 kg/h biochar. The detailed flow diagram of this mobile system using corncob as feedstock is shown in Figure 6. E
DOI: 10.1021/acs.energyfuels.7b03172 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 4. DSC analysis results of three biomass feedstocks: (a) saw dust, (b) wheat straw, and (c) corncob.
3.3. Economic Analysis. 3.3.1. Total Project Investment. The total project investment (TPI) of liquid biofuel production systems consists of the cost of equipment and installation, cost of infrastructure and land, contingency cost, working capital, and start-up cost.12 For a mobile pyrolysis system, the cost of infrastructure and land can be ignored. The cost of equipment for a 100 kg/h mobile pyrolysis system is 0.44 million Chinese yuan according
to eq 1, taking its transporter into account. The installation cost is estimated to be 0.11 million yuan. The total installed cost is the sum of the equipment cost and the installation cost, estimated to be 0.55 million yuan. The contingency cost is assumed to be 20% of the total installed cost, which is 0.11 million Chinese yuan. The working capital (WC) is assumed to be 15% of the total capital investment (TCI).12 Therefore, the total capital investment is 0.78 million Chinese yuan, while the working F
DOI: 10.1021/acs.energyfuels.7b03172 Energy Fuels XXXX, XXX, XXX−XXX
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The biomass feedstock cost usually occupies a significant proportion in the production costs of biofuels. For a large-scale conventional fixed pyrolysis plant, up to 50% of the bio-oil production cost is for feedstock.6 The feedstock cost can be divided into three parts, including the collection cost, the transportation cost, and the storage cost. In China, nowadays the government encourages farmers to collect straws from the field instead of on-site incineration. However, the farmers prefer to burn the agricultural residues because it is troublesome to collect and transport the crop straws. A tractor and a tying machine are needed to collect straws from the field. Thus, the collection cost includes the owning and operating cost of the machines, the cost of fuel and lubricating oil, the human cost, and the cost of the string. According to Wei’s work,45 biomass feedstock should be collected in the center of the harvest area first and then transported from the center to the facility. When the collection radius is less than 25 km, the transportation cost in the harvest area is 33 yuan/ton feedstock, while the transportation cost from the harvest center to the facility is 29 yuan/ ton feedstock. The loading, uploading and interim storage cost is 33 yuan/ton feedstock.45 The interim storage cost means the cost for the feedstock stored in the center of the harvest area. The storage cost contains about 10% of the total feedstock cost.41 The detailed feedstock cost in Nanjing has been investigated in Table 5. The feedstock cost for a fixed liquid biofuel production system is about 330.4 yuan/ton feedstock. The mobile pyrolysis system can be placed anywhere in the harvest area. Thus, the transportation cost and storage cost of feedstock can be ignored. The feedstock cost of a mobile pyrolysis system is estimated to be 35 yuan/ton feedstock, including the human cost, string cost, fuel cost, and feedstock loss. On the basis of the simulation, the 100 kg/h mobile pyrolysis system would consume about 200 kg/h water and 1 kWh of electricity per hour. The water and electricity cost required for processing biomass feedstock of the mobile system is assumed to be 7840 yuan/year. The maintenance cost is assumed to be 5% of the total installed cost for the mobile pyrolysis system, which is 11 000 yuan/year. The annual finance cost, including insurance expense and taxes, is assumed to be 5% of the TPI, which is 43 100 yuan for the mobile system. The mobile pyrolysis system needs about 2 employees, including a driver and a maintenance technician. The annual salaries can be estimated to be 0.12 million yuan. The annual depreciation cost is related to the fixed asset cost and the depreciation period. In this case, the depreciation period is set as 10 years, thus the annual depreciation cost is 55 000 yuan. The biofuel storage and transportation cost, unlike the feedstock storage and transportation cost, depends on the distance from pyrolysis plants to sales markets. The biofuel storage and transportation cost of fixed pyrolysis plants is assumed to be 200 yuan per ton of liquid fuel.12 For a mobile system, the biofuel is transported within the truck. Thus, the biofuel storage cost of the mobile system can be ignored. The biofuel transportation cost of the mobile system is equivalent to the fuel cost of the truck. The mobile system is assumed to be driven 5000 km per year, thus the biofuel transportation cost is about 15 000 yuan. Similarly, the operating costs of different liquid biofuel production systems were calculated. The bio-oil production costs of a large-scaled fixed pyrolysis system are estimated. The production costs of biobased FT liquid are calculated with Im-orb’s work for reference.40 The detailed cost analysis is listed in Table 6.
Figure 5. Composition of corncob bio-oil obtained from simulation (a) and from literature35 (b).
capital is 0.12 million Chinese yuan. The total project investment (TPI) can be divided into the total capital investment (accounting for 90% of the TPI) and the start-up cost (SC). Thus, the start-up is estimated as 0.08 million Chinese yuan, and the TPI is 0.86 million Chinese yuan. In the same way, the TPI of a 4000 kg/h fixed biomass pyrolysis plant can be estimated as 26.1 million Chinese yuan.12 The TPI of a 4000 kg/h Fischer−Tropsch liquids production via biomass gasification (BG-FT) plant is 155.8 million Chinese yuan, according to Rafati and Luan’s work.36,38 The breakdown of capital costs of the BG-FT equipment is calculated based on Rafati’s investigation. The composition of the installation costs and the indirect capital costs is calculated based on Im-orb’s research.40 Detailed TPI analysis is listed in Table 4. 3.3.2. Production Cost of Biofuels. The production cost of biofuels can be divided into the depreciation cost and operating cost. The operating costs consist of labor cost, biomass feedstock cost, water and electricity cost, biofuel storage and transportation cost, depreciation cost, maintenance cost, and finance cost. All the biofuel plants discussed in this paper are considered as totally own capital in the techno-economic performance evaluation, taking no account of the investment loan. G
DOI: 10.1021/acs.energyfuels.7b03172 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 6. Detailed flow diagram of the mobile biomass pyrolysis system using corcob as feedstock.
Table 4. Detailed TPI Data of Different Liquid Bio-Fuel Production Systems
equipment cost
installation cost contingency cost infrastructure cost land cost working capital total capital investment (TCI) startup cost total project investment (TPI)
4000 kg/h fixed pyrolysis system12
100 kg/h mobile pyrolysis system
investment (million yuan) pyrolysis combustion separation and recycle quenching pretreatment
1.6 1.2 1.3
0.18 (IIFB reactor) 0.04 0.08
0.9 1.6
supporting
0.06
1.4
truck total
0.08 0.44 0.11 0.11 / / 0.12 0.78
/ 8 2 2 5 3 3.53 23.53
0.08 0.86
2.61 26.14
3.3.3. Economic Analysis of Liquid Biofuel Production Systems. According to the investment and cost analysis data estimated above, the economic performance of the different liquid biofuel production systems can be evaluated. As is listed in Table 6, the bio-oil production cost of a 100 kg/h mobile pyrolysis system is 0.28 million yuan per year, while the cost of a 4000 kg/h fixed system is estimated as 21.82 million yuan. The annual production cost of FT liquid is 27.7 million yuan. The production cost distribution of the biomass conversion systems can be seen in Figure 7. It can be seen that the labor costs are much more important for the mobile plant, while feedstock costs are the most important cost parameter for the fixed plants, including both biomass pyrolysis plants and BG-FT plants.
investment (million yuan)
4000 kg/h BG-FT plant
air separation
12.1
pretreatment and gasification gas cleaning and sygas processing FT production and upgrading power and steam production total
30.7 19.6 13.9 7.4 83.7 16.7 11.1 18.8 8.9 11.1 154 5.6 155.8
According to our calculation, the cost of biomass feedstock occupies about 40% of the total cost in the BG-FT system. A similar conclusion has also been reached by Seiler in his research.42 Therefore, it can be concluded that the feedstock cost accounts for about 40% to ∼50% of the total biofuel production cost in the fixed biomass conversion process. The biofuel production cost can be effectively reduced by reducing feedstock cost. The amount of straws for processing in Nanjing is 1.5 million tons. Thus, about 45 sets of 4000 kg/h fixed biomass conversion plants or 1800 set of 100 kg/h mobile systems should be constructed to meet the requirement of straw treatment. The total TPI of the mobile pyrolysis plants and fixed pyrolysis plants is 1548 million and 1176.3 million, respectively. The H
DOI: 10.1021/acs.energyfuels.7b03172 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels Table 5. Detailed Biomass Feedstock Cost Investigations cost (yuan/ton feedstock)
items owning and operating cost of the machines cost of fuel and lubricating oil human cost cost of the string transportation cost in the harvest area transportation cost from the harvest center to the facility cost of loading, uploading and interim storage
collection cost
transportation cost
storage cost
141.43
46.79 6.67 7.48 33 29
33
33 total
notes tying machines accompanied by 75 horsepower tractors and forklift
assumed to be 10% of the total feedstock cost
330.4
Table 6. Biofuel Production Costs of Different Biomass Consversion Systems 100 kg/h mobile pyrolysis system
cost items (yuan/year) feedstock
28 000
water and electricity liquid fuel storage and transportation maintenance labor depreciation finance total
4000 kg/h fixed pyrolysis system12
cost items (yuan/year)
4000 kg/h fixed BG-FT plant40
feedstock
7840
10.57 million 1.58 million
maintenance
10.6 million 6.6 million
15 000
2.74 million
personnel
1.2 million
11000 0.12 million 55 000 43 100 0.28 million
0.8 million 3.02 million 1.8 million 1.31 million 21.82 million
capital charge
9.3 million 27.7 million
annual bio-oil production cost of these systems is 504 million and 981.9 million, respectively. According to the market quotation, the pyrolysis bio-oil and biochar can be, respectively, sold at 1250 yuan and 1200 yuan per ton. With 800 640 t of bio-oil and 364 320 t of biochar for sale each year, the total annual revenue of the fixed pyrolysis plants is 1438.0 million yuan. The bio-oil and biochar yield of mobile pyrolysis systems is 800 t, 640 t, and 145 440 t, respectively. The total annual revenue of the mobile systems is 1175.3 million yuan. For the BG-FT process, the production of FT biofuels would not be economically feasible when the oil price is $60/barrel.36 The net present value (NPV) method is used to evaluate the economic feasibility of the mobile and fixed biomass pyrolysis systems. The NPV is calculated according to the following equation: k
NPV =
∑ j=1
Cj (1 + i) j
− C0
Figure 7. Fuel production cost distribution of three liquid biofuel production systems: (a) mobile pyrolysis system, (b) fixed pyrolysis system, and (c) BG-FT plant.
(4)
where C0 is the initial cost, Cj is the after tax net cash flow (ATNC) in the jth year (j = 1, 2, ..., k), and i is the internal rate of return (IRR), assumed 10%. When the NPV is positive, the return of the project can be greater than the bank discount, thus the business can be considered as an economic success.
For the biofuel plant, there are five taxes should be paid for selling bio-oil and biochar, including sales tax, value-added tax, income tax, urban maintenance and construction tax, and I
DOI: 10.1021/acs.energyfuels.7b03172 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels Table 7. Taxes for Selling Bio-Oil and Bio-Chara symbols
tax rate
formula
sales tax value-added tax
tax items
Rst Rvat
income tax urban maintenance and construction tax educational surtax
Rit Rumct
rst: 0 rvat: 13% rit: 25% rumct: 1% rest: 3%
SR(rst) SR/[(1 + rvat){rvat − FC(rvat)}] IN(rit) (Rst+ Rvat+ Rit)rumct
a
Rest
(Rst + Rvat + Rit)rest
SR, sales revenue; FC, feedstock cost; IN, income.
educational surtax. As high-tech enterprises, biofuel production plants can be exempted from the sales tax in China. The tax rates and calculation methods of the taxes are listed in Table 7.43 ATNC is equal to the difference between the income and the total tax of the year. Particularly, the system usually cannot operate normally and need adapting in the first year. The system is generally assumed to work half a year in the first year. The revenue, feedstock cost, the cost of water and electricity, and the cost of liquid fuel storage and transportation is half in the first year.12 The total taxes, annual pure profit (after tax), and total net cash flow of the fixed and mobile systems are listed in Table 8. As is shown in Table 8, the total taxes of the mobile systems are higher than the fixed ones, due to the higher pure profit of the mobile systems. Even so, the annual pure profit (after tax) of the mobile system is still higher than that of the fixed system. As a result, the mobile system can get profitable in the sixth year, while the payback period of the fixed system is about 7 years. The conclusion is similar to that of the biofuel plant payback period in Thailand (7.5 years).44 The NPVs of the mobile and fixed biomass pyrolysis systems can be seen in Figure 8. As is shown in Figure 8, the biomass pyrolysis plants, both fixed and mobile systems, can be economically successful after several years. The difference is that the return of the mobile system can be greater than the bank discount in the eighth year, while the NPV of the fixed systems cannot be positive until the 10th year. It indicates that the mobile system can be more economically competitive than the fixed systems over time. Though the fixed biomass pyrolysis plant has smaller initial investment, the mobile system has lower operating cost and higher profit, resulting in better longterm economy. The payback period of the mobile system is shorter than the fixed ones. Improving the capacity of mobile system can contribute to boost its competitiveness.
Figure 8. Net present values (NPV) in different year of the biomass pyrolysis systems.
biomass pyrolysis process and the integrated system operation can be simulated in Aspen Plus. The model results were validated with experimental data from the literature and tests. The techno-economic performance of this mobile pyrolysis system and conventional fixed systems were evaluated and compared. The pyrolysis model shows good agreement with existing experimental data on pyrolysis yields and product distribution. The highest organic liquid yields are obtained from woody feedstock. DSC analyses were conducted to measure the pyrolysis heat of the biomass feedstocks under CO2 atmosphere. The DSC analysis shows that the energy consumption of woody feedstock pyrolysis under CO2 atmosphere is lower than that of straws. The similar conclusion can be obtained from the simulation. The error of the bio-oil composition between the modeled and the measured data is less than 15%. Detailed investigation and analysis of feedstock cost in biofuel production demonstrates that mobile pyrolysis system effectively reduces the cost of raw material with shorter biomass haul distances. In the mobile pyrolysis system, the labor cost accounts for a majority of the total biofuel production cost. While the feedstock cost is the most important cost parameter for the fixed plants, the biofuel production cost of a mobile system is much lower than that of a conventional fixed plant. Detailed economic analysis, including considering the sales taxes, reveals that both the fixed plant and mobile system can recoup the cost after several years. The fixed plant has a lower initial investment due to its large scale. The mobile system has lower operating cost and higher profit. The payback period of the mobile system is about 6 years while that of the fixed
4. CONCLUSION A mobile fast pyrolysis system was established with a simplified structure in this paper. On the basis of a kinetic model, the
Table 8. Total Taxes, Annual Pure Profit (After Tax), and Total Net Cash Flow of the Fixed and Mobile Systems (Million Yuans) fixed pyrolysis systems 1 2 3 4 5 6 7 8 9 10
mobile pyrolysis sytems
total taxes
annual pure profit (after tax)
total net cash flow
total taxes
annual pure profit (after tax)
total net cash flow
57.77 251.09 251.09 251.09 251.09 251.09 251.09 251.09 251.09 251.09
46.96 270.23 270.23 270.23 270.23 270.23 270.23 270.23 270.23 270.23
−1158.05 −928.29 −698.53 −468.77 −239.01 −9.25 220.51 450.27 680.03 909.79
81,68 383.51 383.51 383.51 383.51 383.51 383.51 383.51 383.51 383.51
114.15 463.26 463.26 463.26 463.26 463.26 463.26 463.26 463.26 463.26
−1487.15 −1127.58 −768.01 −408.44 −48.88 310.68 670.25 1029.82 1389.38 1748.95
J
DOI: 10.1021/acs.energyfuels.7b03172 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels
(23) Peters, J. F.; Petrakopoulou, F.; Dufour, J. Fuel Process. Technol. 2014, 119, 245−255. (24) Sharma, A.; Shinde, Y.; Pareek, V.; Zhang, D. Bioresour. Technol. 2015, 198, 309−315. (25) Mullen, C. A.; Boateng, A. A.; Goldberg, N. M. Energy Fuels 2013, 27, 3867−3874. (26) Swanson, R. M.; Platon, A.; Satrio, J. A.; Brown, R. C. Fuel 2010, 89, S11−S19. (27) Meyer, P. A.; Snowden-Swan, L. J.; Rappe, K. G.; Jones, S. B.; Westover, T. L.; Cafferty, K. G. Energy Fuels 2016, 30, 9427−9439. (28) Brown, T. R. Bioresour. Technol. 2015, 178, 166−176. (29) Peters, J. F.; Iribarren, D.; Dufour, J. Fuel 2015, 139, 441−456. (30) Di Blasi, C. Prog. Energy Combust. Sci. 2008, 34, 47−90. (31) Faravelli, T.; Frassoldati, A.; Migliavacca, G.; Ranzi, E. Biomass Bioenergy 2010, 34, 290−301. (32) Dupont, C.; Chen, L.; Cances, J.; Commandre, J. M.; Cuoci, A.; Pierucci, S.; Ranzi, E. J. Anal. Appl. Pyrolysis 2009, 85, 260−267. (33) Calonaci, M.; Grana, R.; Hemings, E. B.; Bozzano, G.; Dente, M.; Ranzi, E. Energy Fuels 2010, 24, 5727−5734. (34) Hoekstra, E.; Westerhof, R. J. M.; Brilman, W.; Van Swaaij, W. P. M.; Kersten, S. R. A.; Hogendoorn, K. J. A.; Windt, M. AIChE J. 2012, 58, 2830−2842. (35) Zhang, H. Y.; Xiao, R.; Wang, D. H.; He, G. Y.; Shao, S. S.; Zhang, J. B.; Zhong, Z. P. Bioresour. Technol. 2011, 102, 4258−4264. (36) Rafati, M.; Wang, L. J.; Dayton, D. C.; Schimmel, K.; Kabadi, V.; Shahbazi, A. Energy Convers. Manage. 2017, 133, 153−166. (37) Zhang, B.; Zhong, Z.; Yu, D.; Huang, D. Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) 2016, 46, 1227−1233. (38) Luan, C.; You, C. Qinghua Daxue Xuebao/Journal of Tsinghua University 2011, 51, 681−686. (39) Yang, H. P.; Yan, R.; Chen, H. P.; Lee, D. H.; Zheng, C. G. Fuel 2007, 86, 1781−1788. (40) Im-orb, K.; Simasatitkul, L.; Arpornwichanop, A. Energy 2016, 94, 483−496. (41) Zhang, X. P.; Luo, K. Y.; Tan, Q. L. Energy Policy 2016, 97, 276−290. (42) Seiler, J. M.; Hohwiller, C.; Imbach, J.; Luciani, J. F. Energy 2010, 35, 3587−3592. (43) Zhao, L.; Chang, S.; Xu, J.; Zhang, X. Qinghua Daxue Xuebao/ Journal of Tsinghua University 2015, 610 (55), 1023−1035. (44) Jaroenkhasemmeesuk, C.; Tippayawong, N. Energy Procedia 2015, 79, 950−955. (45) Wei, Q. Y. Research on supply chain logistics cost of straw for biomass power generation. Ph.D.Thesis, China Agricultural University, 2014.
system is 7 years. NPV analysis indicates that the return of mobile systems can be greater than the bank discount in the eighth year, about 2 years earlier than the fixed ones.
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AUTHOR INFORMATION
Corresponding Author
*Phone: +86-25-83795726. E-mail:
[email protected]. ORCID
Huiyan Zhang: 0000-0002-4818-7156 Rui Xiao: 0000-0001-8080-8859 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The support from National Science Fund for Distinguished Young Scholars (Grant No. 51525601) and the National Natural Science Foundation of China (Grant No. 51561145010) for this study are gratefully acknowledged.
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REFERENCES
(1) Mohan, D.; Pittman, C. U.; Steele, P. H. Energy Fuels 2006, 20, 848−889. (2) Cambero, C.; Sowlati, T. Renewable Sustainable Energy Rev. 2014, 36, 62−73. (3) Guo, M. X.; Song, W. P.; Buhain, J. Renewable Sustainable Energy Rev. 2015, 42, 712−725. (4) Sorenson, C. B. A Comparative Financial Analysis of Fast Pyrolysis Plants in Southwest Oregon. M.A. Thesis, The University of Montana, Missoula, MT, 2010. (5) Mirkouei, A.; Mirzaie, P.; Haapala, K. R.; Sessions, J.; Murthy, G. S. J. Cleaner Prod. 2016, 113, 495−507. (6) Rogers, J. G.; Brammer, J. G. Biomass Bioenergy 2012, 36, 208− 217. (7) Wang, K. G.; Kim, K. H.; Brown, R. C. Green Chem. 2014, 16, 727−735. (8) Zhou, S.; Garcia-Perez, M.; Pecha, B.; McDonald, A. G.; Kersten, S. R. A.; Westerhof, R. J. M. Energy Fuels 2013, 27, 1428−1438. (9) Parshetti, G. K.; Hoekman, S. K.; Balasubramanian, R. Bioresour. Technol. 2013, 135, 683−689. (10) Garcia-Nunez, J. A.; Pelaez-Samaniego, M. R.; Garcia-Perez, M. E.; Fonts, I.; Abrego, J.; Westerhof, R. J. M.; Garcia-Perez, M. Energy Fuels 2017, 31, 5751−5775. (11) Zmiewski, A. M.; Hammer, N. L.; Garrido, R. A.; Misera, T. G.; Coe, C. G.; Satrio, J. A. Energy Fuels 2015, 29, 5857−5864. (12) Ji, L. Q.; Zhang, C.; Fang, J. Q. Renewable Sustainable Energy Rev. 2017, 70, 224−229. (13) Shemfe, M. B.; Gu, S.; Ranganathan, P. Fuel 2015, 143, 361− 372. (14) Do, T. X.; Lim, Y. I.; Yeo, H. Energy Convers. Manage. 2014, 80, 525−534. (15) Michailos, S.; Parker, D.; Webb, C. Chem. Eng. Res. Des. 2017, 118, 206−214. (16) Yazan, D. M.; van Duren, I.; Mes, M.; Kersten, S.; Clancy, J.; Zijm, H. Biomass Bioenergy 2016, 94, 173−186. (17) Brown, D.; Rowe, A.; Wild, P. Bioresour. Technol. 2013, 150, 367−376. (18) Zhang, H. Y.; Shao, S. S.; Xiao, R.; Pan, Q. W.; Chen, R.; Zhang, J. B. Energy Fuels 2011, 25, 4077−4084. (19) Yang, Y.; Brammer, J. G.; Wright, D. G.; Scott, J. A.; Serrano, C.; Bridgwater, A. V. Appl. Energy 2017, 191, 639−652. (20) Carrasco, J. L.; Gunukula, S.; Boateng, A. A.; Mullen, C. A.; DeSisto, W. J.; Wheeler, M. C. Fuel 2017, 193, 477−484. (21) Kabir, M. J.; Chowdhury, A. A.; Rasul, M. G. Energies 2015, 8, 7522−7541. (22) Peters, J. F.; Banks, S. W.; Bridgwater, A. V.; Dufour, J. Appl. Energy 2017, 188, 595−603. K
DOI: 10.1021/acs.energyfuels.7b03172 Energy Fuels XXXX, XXX, XXX−XXX