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Turning C1-gases to isobutanol towards great environmental and economic sustainability via innovative biological routes: two birds with one stone

Biotechnology for Biofuels and Bioproducts volume 15, Article number: 107 (2022) Cite this article

Abstract

Background

The dramatic increase in greenhouse gas (GHG) emissions, which causes serious global environmental issues and severe climate changes, has become a global problem of concern in recent decades. Currently, native and/or non-native C1-utilizing microbes have been modified to be able to effectively convert C1-gases (biogas, natural gas, and CO2) into isobutanol via biological routes. Even though the current experimental results are satisfactory in lab-scale research, the techno-economic feasibility of C1 gas-derived isobutanol production at the industrial scale still needs to be analyzed and evaluated, which will be essential for the future industrialization of C1-gas bioconversion. Therefore, techno-economic analyses were conducted in this study with comparisons of capital cost (CAPEX), operating cost (OPEX), and minimum isobutanol selling price (MISP) derived from biogas (scenario #1), natural gas (scenario #2), and CO2 (scenario #3) with systematic economic assessment.

Results

By calculating capital investments and necessary expenses, the highest CAPEX ($317 MM) and OPEX ($67 MM) were projected in scenario #1 and scenario #2, respectively. Because of the lower CAPEX and OPEX from scenario #3, the results revealed that bioconversion of CO2 into isobutanol temporally exhibited the best economic performance with an MISP of $1.38/kg isobutanol. Furthermore, a single sensitivity analysis with nine different parameters was carried out for the production of CO2-derived isobutanol. The annual plant capacity, gas utilization rate, and substrate cost are the three most important economic-driving forces on the MISP of CO2-derived isobutanol. Finally, a multiple-point sensitivity analysis considering all five parameters simultaneously was performed using ideal targets, which presented the lowest MISP of $0.99/kg in a long-term case study.

Conclusions

This study provides a comprehensive assessment of the bioconversion of C1-gases into isobutanol in terms of the bioprocess design, mass/energy calculation, capital investment, operating expense, sensitivity analysis, and minimum selling price. Compared with isobutanol derived from biogas and natural gas, the CO2-based isobutanol showed better economic feasibility. A market competitive isobutanol derived from CO2 is predicable with lower CO2 cost, better isobutanol titer, and higher annual capacity. This study will help researchers and decision-makers explore innovative and effective approaches to neutralizing GHGs and focus on key economic-driving forces to improve techno-economic performance.

Background

As a building block chemical, isobutanol has been widely applied in various fields, including food, solvents, extractants, rubber, fuel additions, and transportation fuels [1,2,3]. At present, isobutanol is generally produced via a chemical route under harsh conditions [4, 5]. Recently, the biological route has been widely considered as an environmentally friendly approach for isobutanol synthesis with sugar-based substrates, which may not be sustainable, as it inevitably compromises the future food supply [6, 7]. Therefore, it is urgently needed to explore alternative inedible and abundant carbon sources, such as greenhouse gas (GHG), that are consistent with the best interest of global sustainability [8]. CO2 and CH4 are two major GHGs causing the global warming effect, which is one of the greatest environmental problems in the world. These two C1-gases can be derived from fossil fuels [9] and anaerobic digestion (AD) [10, 11] in abundant amounts [12,13,14,15,16]. This in turn suggests that these gases have a large potential to be a promising carbon source for C1-utilizing microbes. It has been considered that biological valorization of C1-gases into isobutanol could be a promising route giving great environmental and economic sustainability as well as reduction of GHG emissions as two birds with one stone.

Currently, more efforts have been made to convert these abundant and low-cost C1-gaseous substrates into isobutanol by biological routes and systems engineering of C1-utilizing microbes [17], as shown in Fig. 1. More than 900 mg/L isobutanol has been demonstrated by using CO2 as the sole carbon source in the autotrophic cultivation of Synechocystis PCC 6803 [18]. In recent years, native and/or non-native C1-utilizing microbes have been modified or constructed by using genetic engineering tools, systematic manipulation, metabolic modeling, and carbon flux simulation to improve the C1-gas utilization rate [19, 20]. However, the challenges and opportunities for methane bioconversion into isobutanol by methanotrophs remain in both scientific and industrial applications. Although Precigen Inc. (formerly named Intrexon Corporation), a biosynthesis-based company, has claimed that CH4-derived isobutanol has been accomplished on a lab scale by engineered methanotrophic bacteria [21], no scientific results have been published thus far. Therefore, to verify the possibility of isobutanol biosynthesis from CH4, Methylomicrobium buryatense (an industrially proven methanotrophic bacteria) was genetically engineered to achieve direct conversion of CH4 into isobutanol in our laboratory. By heterologously expressing α-ketoisovalerate decarboxylase (Kivd) derived from Lactococcus lactis, isobutanol-producing M. buryatense was constructed, which accumulated approximately 35 mg/L in 5 days under unoptimized conditions in vials with a very limited gas transfer efficiency [22, 23]. It has been reported that a 1000-fold increase in isobutanol productivity can be achieved by using C1-gaseous substrates in airlift bioreactors [24,25,26]. In addition, the carbon conversion efficiency can also be significantly improved by genetically engineering key enzymes for isobutanol biosynthesis [18, 27]. Recently, a biotech pioneer company, LanzaTech, successfully scaled up a gas fermentation process to convert low-cost waste gas feedstocks into isopropanol using engineered autotrophic acetogen [28]. In that study, isopropanol productivity was enhanced from 0.6 g/L/h up to 3 g/L/h with optimizations in both strain construction and process development. Although the bioconversion of CO2 or CH4 into isobutanol has not been reported on a large scale, it is highly predictable that industrial applications of CO2/CH4-based isobutanol production will be achieved with the rapid development in synthetic biology technologies and processes.

Fig. 1
figure 1

Process scenarios of isobutanol production by different microorganisms using various C1 gases. Scenario #1: isobutanol production from biogas; scenario #2: isobutanol production from natural gas; scenario #3: isobutanol production from CO2

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Although the current experimental results are satisfactory in lab-scale research, the economic feasibility and effective applications at the industrial scale still need to be fully analyzed and evaluated. Therefore, a techno-economic analysis (TEA) was projected in this paper, which is a framework for quantitatively evaluating the economic performance of a specific process design based on published bioconversion and process integration research from the aspects of cost, benefit, risk and uncertainty, aiming to evaluate its future commercial potential and guide research and investment in the most beneficial direction [29]. The goal of this TEA study is to evaluate the economic potential of the three designed scenarios of the bioconversion of biogas, natural gas, and CO2 to isobutanol with comparisons of capital cost, operating cost, and minimum isobutanol selling price (MISP). Finally, single- and multiple-point sensitivity analyses were also carried out to guide the engineering practice of isobutanol production with the most economic potential. This study will help researchers and decision-makers focus on key economic-driving forces to improve the techno-economic performance of the bioconversion of C1-gases for isobutanol production.

Process designs and assumptions for isobutanol production from C1 gaseous substrates

Scenarios

To evaluate the technical and economic performances of isobutanol production from biogas, natural gas, and CO2, the following three bioroutes were designed and designated scenario #1, scenario #2, and scenario #3, as shown in Fig. 1. For scenario #1, both CH4 and CO2 in biogas are converted into isobutanol directly in a two-stage cultivation by cyanobacteria and methanotrophs, respectively. Natural gas and CO2 are used as the sole carbon sources for isobutanol biosynthesis in scenario #2 and scenario #3, respectively. The simulation was accomplished by commercial process software (AspenTech®, Cambridge, MA, USA) to obtain rigorous material and energy balances for each unit operation.

Process design

As shown in Fig. 2, a simplified flow diagram is composed of five sections: gas supply, isobutanol production, isobutanol purification, wastewater treatment, and utilities. The mass and energy flow balance in Aspen simulation models was calculated based on a nth isobutanol plant with an annual production capacity of 50,000 tons. TEA was carried out in the sequence of schematic design, process simulation and economic evaluation. The parameters related to the TEA, including operating, financing, and cost investments, are all summarized in Table 1 [16]. These data will be manipulated on an Excel spreadsheet that incorporates well-established formulas to determine the total capital investment (TCI) and total operating cost (TOC) of designed processes [29]. A 10-year discounted cash flow rate of return analysis was used to estimate the MISP at a net present value of zero with 10% internal rate of return (IRR) “nth-plant” costs and financing.

Fig. 2
figure 2

Schematic process flow diagram of isobutanol production from C1 gases. A100: gas supply; A200: isobutanol production; A300: isobutanol purification; A400: wastewater treatment; A500: utilities; PBR: closed tubular photobioreactors just for culturing autotrophic microbes using CO2; BCB: bubble column bioreactors just for culturing heterotrophic microbes using CH4

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Table 1 Assumptions for nth-plant isobutanol production
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Gas supply (area 100)

In this area, remote C1-gaseous substrates, such as biogas, natural gas, or CO2, are compressed to the ideal pressure of 5 bar [30] to satisfy the actual fermentation conditions and avoid the pressure fluctuation of the reaction system, which is then sent to bioreactors for isobutanol production. Although the biogas composition varies with the feedstock during the AD process, the ratio of CH4 and CO2 in biogas used in scenario #1 was assumed to be 1:1. Natural gas containing 90% CH4 and 10% harmful impurities on methanotroph growth was used in scenario #2 [31]. The substrate used in scenario #3 was assumed to be 100% CO2. The air stream routed to a pressure swing adsorption (PSA) unit is enriched into 95% O2 for methanotrophic cultivation in scenario #1 and scenario #2. The design specification tool in Aspen was used to obtain required amounts for different gas feeds at a specific production scale for calculations in other processes in this study. To achieve an annual production (plant capacity) of 50,000 tons isobutanol, the required amounts of gaseous substrates (oxygen not included) were individually calculated as 388,740 m3/day, 278,250 m3/day, and 321,338 m3/day for scenario #1, scenario #2, and scenario #3 according to Eqs. 3 and 4 in Aspen, respectively.

Isobutanol production (area 200)

The isobutanol production process involves both biomass generation and isobutanol biosynthesis by either cyanobacteria cultured in a series of closed tubular photobioreactors (PBRs) with a 50 m3 working volume [32] or methanotrophs grown in several 1000 m3 bubble column bioreactors (BCBs) with an 80% working volume [16, 33]. The stoichiometry equations for biomass generation by cyanobacteria (Eq. 1) and methanotrophs (Eq. 2) applied in Aspen simulations have been proposed based upon published literature [34, 35], in which the empirical formula CH1.934O0.473N0.23 and C4H8O2N represent the biomass from cyanobacteria and methanotrophs, respectively. The stoichiometry equation for isobutanol biosynthesis in cyanobacteria and methanotrophs has been assumed according to the published report as Eq. 3 and Eq. 4, respectively [5, 18]. For scenario #1, the biogas is first sent into the PBRs, where CO2 can be immobilized by cyanobacteria via photosynthesis for isobutanol biosynthesis and O2 production. The off-gases from PBRs containing CH4, O2, and unused CO2 are transferred to BCBs for isobutanol production by aerobic-obligate methanotrophs.

$${\text{CO}}_{{2}} { + 0}{\text{.622H}}_{{2}} {\text{O + 0}}{\text{.23NH}}_{{3}} \to {\text{CH}}_{{{1}{\text{.934}}}} {\text{O}}_{{{0}{\text{.473}}}} {\text{N}}_{{{0}{\text{.23}}}} { + 1}{\text{.0745O}}_{{2}}$$
(1)
$${\text{CH}}_{{4}} { + 1}{\text{.5O}}_{{2}} { + 0}{\text{.118NH}}_{{3}} \to {0}{\text{.118C}}_{{4}} {\text{H}}_{{8}} {\text{O}}_{{2}} {\text{N + 0}}{\text{.529CO}}_{{2}} { + 1}{\text{.71H}}_{{2}} {\text{O}}$$
(2)
$${\text{4CO}}_{{2}} {\text{ + 5H}}_{{2}} {\text{O}} \to {\text{C}}_{{4}} {\text{H}}_{{{10}}} {\text{O + 6O}}_{{2}}$$
(3)
$$6{\text{CH}}_{{4}} {\text{ + 6O}}_{{2}} \to {\text{C}}_{{4}} {\text{H}}_{{{10}}} {\text{O + 2CO}}_{{2}} {\text{ + 7H}}_{{2}} {\text{O}}$$
(4)

Since carbon and nitrogen sources account for most of the raw material cost [36], only gaseous substrate and ammonia were considered for the economic analysis in this study. The usage of these raw materials could be calculated based on the aforementioned equations by Aspen software for operation cost. The isobutanol productivity of 1 g/L/h is the minimum productivity required for industrial applications of biomanufacturing, and the highest isobutanol titer of 1 g/L reported in the literature [18] was assumed to be the baseline value of the Aspen simulation in all three scenarios. By using the productivity and stoichiometry equations (Eqs. 3 and 4) for isobutanol biosynthesis, the total fermentation volume of scenarios #1, #2, and #3 was calculated as 10,059 m3, 10,120 m3, and 10,305 m3, respectively, which requires 126 bioreactors including 121 PBRs and 5 BCBs for scenario #1, 13 BCBs for scenario #2, and 206 PBRs for scenario #3. The highest utilization rate of gaseous substrates was observed as high as 90% in both PBR [37, 38] and BCB [39], which were applied in our TEA. As an extracellular product, isobutanol production is not associated with cell growth, and 20% of gaseous substrate supply was simulated to be used for cell growth based on carbon-balance, and the rest for isobutanol biosynthesis [40,41,42].

Isobutanol purification (area 300)

The effluent from A200 is first pretreated with a three-step dewatering process to separate the liquid phase and cell mass. A dewatering system containing decanting, dissolved air flocculation (DAF) and centrifuge was applied in this area. After decanting, a DAF separator with flocculants is followed by a solid/liquid separation step using centrifugation [43, 44]. The isobutanol–water mixture obtained from the dewatering process will be further purified using two distillation columns and one decanter to harvest isobutanol from the liquid phase [45]. In detail, the first column concentrates the overhead product by removing water, high boilers (organic acids) and solids to the ratio of isobutanol and water for a liquid–liquid split. The isobutanol-rich stream is sent to the second column for high purity isobutanol as the final product. The overall separation efficiency of this process is up to 90%, which was set in the Aspen simulation.

Wastewater treatment (area 400)

As shown in Fig. 2, all effluents collected from A300 composed of wastewater, spent cell mass, and other liquid streams are directed to an on-site AD plant. The biogas generated from AD along with other gases collected from A200 is transferred to combustion facilities for on-site energy generation [43]. A large amount of high-nitrogen sludge extracted from AD was assumed to be recovered as a byproduct for fertilization [46].

Utilities (area 500)

Area 500 facilitates overall energy, water, and power integration, including a cooling water system, chilled water system, process water manifold, and power systems. In this area, the amount of make-up water required in the process could be determined as well as the total power requirements for the system and electricity purchased from the grid.

Process economics analysis

This study aims to assess and analyze the economic feasibility and competitiveness of various integrated processes of isobutanol production from C1-gaseous substrates to guide practical engineering activities. It is worth mentioning that all methods or data used in this TEA are obtained from published literature and official reports [16, 29, 47].

Capital expenses (CAPEX) and discounted cash flow method

The capital expenses were calculated by considering the total cost of equipment purchases and associated installation. The costs of units or equipment used in this study are obtained from previous studies, as listed in Table 2, and the new equipment costs for different sizes were calculated using the exponential scaling expression (New cost = Base cost × (New size/Base size)n, where n is the economy scaling factor and varies with the equipment) based on the equipment size for the original price quote [21]. The Chemical Engineering Plant Cost Index (CEPCI) [48] was used to recalculate equipment costs to 2020 dollar (2020$) [29]. The assumptions used for this TEA can be found in Table 1, which were proposed based on previous literature [49, 50].

Table 2 Major equipment investments for three scenarios with an annual plant capacity of 50,000 tons
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Operating expenses (OPEX)

The OPEX, including gaseous substrates, other raw materials, and fixed operating costs, are depicted in Table 3. In this study, all values derived from published TEA reports or literature have been converted into 2020$ based on the Industrial Inorganic Chemical Index from SRI Consulting for raw materials [51] and the labor indices from the US Department of Labor Bureau of Labor Statistics for employee salaries [52]. The labor burden was estimated to be 60% of the total wage, and 2% of the inside boundary limit (ISBL) capital expenses were designated for maintenance. In addition, local property tax and property insurance were estimated at 0.5% of fixed capital investment [53].

Table 3 Costs of raw materials used in the base case study
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Economic comparison of isobutanol production from C1 gaseous substrates

Comparison of CAPEX and OPEX

To explore the most economic potential scenario, a detailed comparison of capital investments was obtained by using chemical engineering cost estimation techniques, as shown in Fig. 3, in which the necessary capital cost refers to the capital costs of warehouse, site development, additional piping and land. A similar CAPEX was observed in scenario #1 ($317 MM) and scenario #2 ($315 MM), which are relatively higher than that in scenario #3 ($259 MM). This is attributed to the lower equipment investment of A100 and A200 in scenario #3. Due to the high cost of traditional BCBs used in methanotrophic cultivation in scenarios #1 and #2 (Table 2), a total CAPEX reduction of more than 17% can be achieved in scenario #3, profiting from the low cost of PBRs for cyanobacteria cultivation. It should also be noted that the major difference in CAPEX among the three scenarios is that scenario #3 does not require BCBs, while the investment in BCBs purchases in scenarios #1 and #2 is more than $36 MM and $17 MM, respectively. Table 4 showed that the OPEX of the three scenarios presents the major difference in the cost of carbon and nitrogen sources. Scenario #3 has the lowest total operating cost of $55.09 MM due to the relatively low cost of CO2. Interestingly, the C1-gasous substrates in all three scenarios only contribute approximately 30–40% of the total OPEX, which is significantly lower than the proportion (60–80%) of conventional raw materials (e.g., glucose) and may improve the economic viability and market potential using C1-gasous substrates as carbon sources for isobutanol production. It is worth noting that the differences in cost for the three different scenarios are also caused by the sourcing of gases, equipment manufacturing or plant location. Novel bioreactor designs for CH4 or CO2 bioconversion may reduce both CAPEX and OPEX.

Fig. 3
figure 3

Capital cost distributions of three scenarios with an annual plant capacity of 50,000 tons. Scenario #1: isobutanol production from biogas; scenario #2: isobutanol production from natural gas; scenario #3: isobutanol production from CO2. Necessary capital cost refers to the sum cost of warehouse, site development, additional piping and land

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Table 4 Comparisons of operating costs in three scenarios with an annual plant capacity of 50,000 tons
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Minimum isobutanol selling price (MISP) from different scenarios

Determining a minimum selling price based on investment expense refers to analyzing the cost of a business decision in terms of the real-time relevant expenses. The minimum pricing is the break-even point for that given sale. According to the global isobutanol production market, the nth plant with an annual capacity of 50,000 tons isobutanol is projected, and the resulting MISP of the three scenarios at a 10% IRR is $1.51/kg isobutanol, $1.81/kg isobutanol, and $1.38/kg isobutanol in scenarios #1, #2, and #3, respectively. Apparently, because of the lowest CAPEX and OPEX, the most inexpensive MISP of $1.38/kg isobutanol is obtained in scenario #3, which is 10% and 20% lower than the MISP from scenario #1 and scenario #2, respectively. Given the state of the art, CO2-derived isobutanol is projected to be the best scenario by assuming 90% utilization efficiency of CO2. Given the promising economic feasibility of scenario #3, sensitivity analyses of the bioconversion of CO2 to isobutanol are also carried out to investigate the impact of key variables on MISP and to consider the options for optimizing process economics.

Sensitivity analysis for isobutanol production from CO2

Single-point sensitivity analysis

Sensitivity analysis is an efficient approach to quantify the impact of key variables on the overall MISP. Therefore, a single-point sensitivity analysis is performed on the isobutanol production process using CO2 by adjusting only one single variable between its minimum and maximum value with all others kept constant. In this study, nine variables associated with CO2-derived isobutanol production were evaluated for their influence on MISP. The baseline for all variables was the same as the assumptions used in the case of scenario #3. For the sensitivity analysis, a "Calculator" module in Aspen was applied to manually adjust these variables, and then by running the simulations in Aspen, the required values such as reaction volume, medium dosage, etc., under different production requirements were projected by Aspen software for calculating MISP.

As shown in Fig. 4, the annual plant capacity, gas utilization rate, CO2 price, flocculant dosage, and isobutanol titer have the largest impact on the MISP with a nearly 10% variation, which are key cost drivers. The annual plant capacity, gas utilization rate, and isobutanol titer are negatively correlated with MISP, whereas CO2 price and flocculant dosage are positively correlated. The plant capacity is directly associated with the gas supply and flow rate, thus affecting CAPX, OPEX and MISP. It can also be observed that the MISP can be finally reduced from $1.38/kg to $1.14/kg when the plant capacity doubled from 50,000 to 100,000 ton/year, resulting in an increase in the flow rate from 321,338 to 726,030 m3/day simulated in Aspen.

Fig. 4
figure 4

Single-point sensitivity analysis of the minimum selling price of isobutanol (MISP) from CO2

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Following the annual capacity, the gas utilization rate is the second most important cost-driving force impacting MISP, which will boost MISP up to 120% if the utilization rate is decreased by 25%. This finding might be attributed to the fact that the utilization rate strongly affects the isobutanol yield, which in turn leads to a significant impact on raw material expenses and bioreactor investment. It is well known that the cost of glucose used in a bioprocess may contribute up to 60% of the total raw material costs [54], even if shell corn is directly used as raw material, the cost will also account for more than 45% [16, 45], while the cost of CO2 in scenario #3 only accounts for 36% of the cost of raw materials (Table 4), which dramatically reduces the MISP. It can be expected that using wasted CO2 collected from the concrete industry or power plants could further decrease the production cost [55]. In addition to the aforementioned variables, other parameters selected in this study (such as equity, nitrogen price and IRR) show minimal contributions to MISP. Based on the findings from single-point sensitivity analysis, key cost drivers with optimal values were chosen as targeted goals in the multiple-point sensitivity analysis for a long-term case.

Multiple-point sensitivity analysis

To obtain a comprehensive understanding of the effects of key factors on the economic performance of CO2-derived isobutanol, an exhaustive sensitivity analysis was conducted by simultaneously adjusting five key variables for MISP calculation. As shown in Fig. 5, two situations, including the base case (baseline in the single-point sensitivity analysis) and long-term case, were illustrated and compared to project the cost potentials of the proposed technology pathway. Given the optimization of genetic engineering, enhancement of the bioconversion process, and improvement of CO2 capture efficiency [56], the isobutanol concentration, annual production, and CO2 price were rationally predicted. The MISP for the long-term case can be as low as 0.99 $/kg isobutanol by using ideal targets. This MISP is close to the current market price of $1.0/kg isobutanol [57], which in turn shows the both environmental and economic potential of CO2-based isobutanol considering the carbon tax policy and GHG emission reduction. Although currently demonstrated technology and recently published results of isobutanol biosynthesis from CO2 are still far from our ideal targets, it is believed that the advanced biotechnologies can fill in the gap by constructing genetically engineered microorganisms with higher carbon yield and faster growth rate, reaching theoretical conversion efficiency, and having minimum raw material cost.

Fig. 5
figure 5

Multiple-point sensitivity analysis of various prospective targets on the minimum selling price of isobutanol (MISP) from CO2

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Conclusions and prospective

In this study, TEA was applied to calculate the OPEX and CPAEX of the proposed biological routes to evaluate the economic feasibility and industrialization potential of the bioconversion of C1-gaseous substrates for isobutanol production. With the lowest OPEX and CAPEX, the CO2-derived isobutanol presents the lowest MISP of $1.38/kg isobutanol compared with that derived from biogas and natural gas. By employing single/multiple-point sensitivity analyses, the annual plant capacity, gas utilization rate, and CO2 price are determined to be key cost drivers. With the expected research targets, the promising MISP of $0.99/kg isobutanol can be achieved by reducing CO2 cost and enhancing the production performance of isobutanol production. Currently, biogas, natural gas, and CO2 are the main sources of GHG emissions, of which the amounts are projected to further increase in the coming decades. By adopting the proposed biological routes, the C1-gases, including biogas, natural gas, and CO2, can be used as an alternative inedible substrate for isobutanol production, which is consistent with the best interest of global environmental and economic sustainability. It is expected that this study may provide engineering practice guidance and cost optimization strategies for the future biological conversion of C1 greenhouse gases to platform chemicals.

Availability of data and materials

Not applicable.

Abbreviations

GHGs:

Greenhouse gases

TEA:

Techno-economic analysis

MISP:

Minimum isobutanol selling prices

R&D:

Research and development

CCE:

Carbon conversion efficiency

IRR:

Internal rate of return

CAPEX:

Capital expenses

OPEX:

Operating expenses

AGR:

Amine-based acid gas removal

ASU:

Air separation unit

CSTR:

Continuous stirred tank reactors

DAF:

Dissolved air flotation

AD:

Anaerobic digestion

ISBL:

Inside battery limit

FCI:

Fixed cost investment

SOT:

State of technology

LCA:

Life cycle assessment

TCI:

Total capital investment

TDC:

Total direct costs

TIC:

Total indirect costs

TOC:

Total operating cost

GHG:

Greenhouse gas

GWP:

Global warming potential

References

  1. Moncada J, Posada JA, Ramírez A. Comparative early stage assessment of multiproduct biorefinery systems: an application to the isobutanol platform. Biores Technol. 2017;241:44.

    Article  CAS  Google Scholar 

  2. Karabektas M, Hosoz M. Performance and emission characteristics of a diesel engine using isobutanol–diesel fuel blends. Renewable Energy. 2009;34:1554–9.

    Article  CAS  Google Scholar 

  3. Erickson B, Nelson JE, Winters P. Perspective on opportunities in industrial biotechnology in renewable chemicals. Biotechnol J. 2012;7:176–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gao X, Zhang T, Wu Y, Yang G, Tan M, Li X, Xie H, Pan J, Tan Y. Isobutanol synthesis from syngas on Zn-Cr based catalysts: New insights into the effect of morphology and facet of ZnO nanocrystal. Fuel. 2018;217:21–30.

    Article  CAS  Google Scholar 

  5. Haynes CA, Gonzalez R. Rethinking biological activation of methane and conversion to liquid fuels. Nat Chem Biol. 2014;10:331–9.

    Article  CAS  PubMed  Google Scholar 

  6. Noda S, Mori Y, Oyama S, Kondo A, Araki M, Shirai T. Reconstruction of metabolic pathway for isobutanol production in Escherichia coli. Microb Cell Fact. 2019;18:124.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wess J, Brinek M, Boles E. Improving isobutanol production with the yeast Saccharomyces cerevisiae by successively blocking competing metabolic pathways as well as ethanol and glycerol formation. Biotechnol Biofuels. 2019;12:173.

    Article  PubMed  PubMed Central  Google Scholar 

  8. OECD. OECD-FAO agricultural outlook 2015–2024. Food and agriculture organization of the United Nations. 2015. p. 1–148.

  9. US EPA: Overview of greenhouse gases. https://www.epa.gov/ghgemissions/overview-greenhouse-gases. Accessed 16 MAY 2022.

  10. Angelidaki I, Treu L, Tsapekos P, Luo G, Campanaro S, Wenzel H, Kougias PG. Biogas upgrading and utilization: current status and perspectives. Biotechnol Adv. 2018;36:452–66.

    Article  CAS  PubMed  Google Scholar 

  11. Muñoz R, Meier L, Diaz I, Jeison D. A review on the state-of-the-art of physical/chemical and biological technologies for biogas upgrading. Rev Environ Sci Biotechnol. 2015;14:727–59.

    Article  Google Scholar 

  12. Lindkvist E. System studies of biogas production: comparisons and performance. Linköping University Electronic Press; 2020.

  13. Scarlat N, Dallemand J-F, Fahl F. Biogas: developments and perspectives in Europe. Renew Energy. 2018;129:457–72.

    Article  Google Scholar 

  14. Liu Z, Si B, Li J, He J, Xing XH. Bioprocess engineering for biohythane production from low-grade waste biomass: technical challenges towards scale up. Curr Opin Biotechnol. 2017;50:25–31.

    Article  PubMed  Google Scholar 

  15. Clomburg JM, Crumbley AM, Gonzalez R. Industrial biomanufacturing: the future of chemical production. Science. 2017;355:aag0804.

    Article  PubMed  Google Scholar 

  16. Fei Q, Liang B, Tao L, Tan EC, Gonzalez R, Henard C, Guarnieri M. Biological valorization of natural gas for the production of lactic acid: techno-economic analysis and life cycle assessment. Biochem Eng J. 2020;158:107500. https://doi.org/10.1016/j.bej.2020.107500

    Article  CAS  Google Scholar 

  17. Hu L, Wang J, Yuan B, Zhu F, Fei Q, Fu R. Production of biofuels and chemicals from C1 gases by microorganisms: status and prospects. Chin J Bioprocess Eng. 2017;15:17–25.

    Google Scholar 

  18. Miao R, Xie H, Lindblad P. Enhancement of photosynthetic isobutanol production in engineered cells of Synechocystis PCC 6803. Biotechnol Biofuels. 2018;11:267.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hu L, Guo S, Yan X, Zhang T, Xiang J, Fei Q. Exploration of an efficient electroporation system for heterologous gene expression in the genome of methanotroph. Front Microbiol. 2021;12:717033. https://doi.org/10.3389/fmicb.2021.717033

    Article  PubMed  PubMed Central  Google Scholar 

  20. Guo S, Jiao Z, Fei Q. Progress in construction and applications of methanotrophic cell factory for chemicals biosynthesis. Synth Biol J. 2021;2(6):1017–29.

    Google Scholar 

  21. Coleman WJ, Vidanes GM, Cottarel G, Muley S, Kamimura R, Javan AF, Sun J, Groban ES. Biological production of multi-carbon compounds from methane. Google Patents. 2017.

  22. Fei Q, Hu L. A recombinant strain and its application in bioconversion of methane to isobutanol. CN113151130A, 2021.

  23. Fei Q, Hu LZ. Genetically engineered bacterium and application thereof in preparation of isobutanol by biotransformation of methane. University Xa ed., vol. CN113151130A. China; 2021.

  24. Shen Y, Brown R, Wen Z. Syngas fermentation of Clostridium carboxidivoran P7 in a hollow fiber membrane biofilm reactor: Evaluating the mass transfer coefficient and ethanol production performance. Biochem Eng J. 2014;85:21–9.

    Article  CAS  Google Scholar 

  25. Trevethick SR, Bromley JC, Simpson SD, Khosla V. Fermentation of gaseous substrates. Google Patent: US2011244538, United States; 2012.

  26. Hu L, Guo S, Wang B, Fu R, Fan D, Jiang M, Fei Q, Gonzalez R. Bio-valorization of C1 gaseous substrates into bioalcohols: potentials and challenges in reducing carbon emissions. Biotechnol Adv. 2022;59:107954.https://doi.org/10.1016/j.biotechadv.2022.107954

    Article  CAS  PubMed  Google Scholar 

  27. Wang X, Wang X, Lu X, Ma C, Chen K, Ouyang P. Methanol fermentation increases the production of NAD(P)H-dependent chemicals in synthetic methylotrophic Escherichia coli. Biotechnol Biofuels. 2019;12:1–11.

    Article  Google Scholar 

  28. Liew FE, Nogle R, Abdalla T, Rasor BJ, Canter C, Jensen RO, Wang L, Strutz J, Chirania P, De Tissera S. Carbon-negative production of acetone and isopropanol by gas fermentation at industrial pilot scale. Nat Biotechnol. 2022;40:335–44.

    Article  CAS  PubMed  Google Scholar 

  29. Davis R, Bartling A, Tao L. Biochemical conversion of lignocellulosic biomass to hydrocarbon fuels and products state of technology and future research. Natl Renew Energy Lab. 2021. https://doi.org/10.2172/1784889.

    Article  Google Scholar 

  30. Van Hecke W, Bockrath R, De Wever H. Effects of moderately elevated pressure on gas fermentation processes. Biores Technol. 2019;293: 122129.

    Article  Google Scholar 

  31. Andrea DLT, Aisha M, Frances C, Laurens LML, Beck DAC, Pienkos PT, Lidstrom ME, Kalyuzhnaya MG. Genome-scale metabolic reconstructions and theoretical investigation of methane conversion in Methylomicrobium buryatense strain 5G(B1). Microb Cell Fact. 2015;14:188.

    Article  Google Scholar 

  32. Beal CM, Gerber LN, Sills DL, Huntley ME, Machesky SC, Walsh MJ, Tester JW, Archibald I, Granados J, Greene CH. Algal biofuel production for fuels and feed in a 100-ha facility: a comprehensive techno-economic analysis and life cycle assessment. Algal Res. 2015;10:266–79.

    Article  Google Scholar 

  33. Davis RE, Grundl NJ, Tao L, Biddy MJ, Tan EC, Beckham GT, Humbird D, Thompson D, Roni MS. Process design and economics for the conversion of lignocellulosic biomass to hydrocarbon fuels and coproducts biochemical design case update; biochemical deconstruction and conversion of biomass to fuels and products via integrated biorefinery pathways. Nat Renew Energy Lab. 2018;2018:1–147.

    Google Scholar 

  34. Wiesberg IL, Brigagão GV, Medeiros JLD. Carbon dioxide utilization in a microalga-based biorefinery: efficiency of carbon removal and economic performance under carbon taxation. J Environ Manage. 2017;203:988.

    Article  CAS  PubMed  Google Scholar 

  35. Jiang H, Chen Y, Jiang P, Zhang C, Smith TJ, Murrell JC, Xing XH. Methanotrophs: multifunctional bacteria with promising applications in environmental bioengineering. Biochem Eng J. 2010;49:277–88.

    Article  CAS  Google Scholar 

  36. Kim H, Lee S, Ahn Y, Lee J, Won W. Sustainable production of bioplastics from lignocellulosic biomass: technoeconomic analysis and life-cycle assessment. ACS Sustainable Chem Eng. 2020;8:12419–29.

    Article  CAS  Google Scholar 

  37. Schädler T, Cerbon DC, de Oliveira L, Garbe D, Brück T, Weuster-Botz D. Production of lipids with Microchloropsis salina in open thin-layer cascade photobioreactors. Biores Technol. 2019;289: 121682.

    Article  Google Scholar 

  38. Guo W, Cheng J, Liu S, Feng L, Su Y, Li Y. A novel porous nickel-foam filled CO2 absorptive photobioreactor system to promote CO2 conversion by microalgal biomass. Sci Total Environ. 2020;713: 136593.

    Article  CAS  PubMed  Google Scholar 

  39. Al Taweel A, Shah Q, Aufderheide B. Effect of mixing on microorganism growth in loop bioreactors. Inter J Chem Eng. 2012. https://doi.org/10.1155/2012/984827.

    Article  Google Scholar 

  40. Kalyuzhnaya M, Yang S, Rozova O, Smalley N, Clubb J, Lamb A, Gowda G, Raftery D, Fu Y, Bringel F. Highly efficient methane biocatalysis revealed in a methanotrophic bacterium. Nat Commun. 2013;4:1–7.

    Article  Google Scholar 

  41. de la Torre A, Metivier A, Chu F, Laurens LM, Beck DA, Pienkos PT, Lidstrom ME, Kalyuzhnaya MG. Genome-scale metabolic reconstructions and theoretical investigation of methane conversion in Methylomicrobium buryatense strain 5G (B1). Microb Cell Fact. 2015;14:1–15.

    Google Scholar 

  42. Matsen JB, Yang S, Stein LY, Beck DA, Kalyuzhanaya MG. Global molecular analyses of methane metabolism in methanotrophic alphaproteobacterium, Methylosinus trichosporium OB3b. Part I: transcriptomic study. Front Microbiol. 2013;4:40.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Davis R, Fishman D, Frank ED, Wigmosta MS, Aden A, Coleman AM, Pienkos PT, Skaggs RJ, Venteris ER, Wang MQ. Renewable diesel from algal lipids: an integrated baseline for cost, emissions, and resource potential from a harmonized model. Office of Scientific & Technical Information Technical Reports. 2012.

  44. Davis R, Kinchin C, Markham J, Tan E, Laurens L, Sexton D, Knorr D, Schoen P, Lukas J. Process design and economics for the conversion of algal biomass to biofuels algal biomass fractionation to lipid-and carbohydrate-derived fuel products. National Renewable Energy Lab. 2014. p. 1–110.

  45. Tao L, Tan ECD, Mccormick R, Zhang M, Aden A, He X, Zigler BT. Techno-economic analysis and life-cycle assessment of cellulosic isobutanol and comparison with cellulosic ethanol and n-butanol. Biofuels Bioprod Biorefin. 2014;8:30–48.

    Article  CAS  Google Scholar 

  46. Lundquist TJ, Woertz IC, Quinn NWT, Benemann JR. A realistic technology and engineering assessment of algae biofuel production. Energy Biosciences Institute. 2010. p. 1–178.

  47. Tao L, Markham JN, Haq Z, Biddy MJ. Techno-economic analysis for upgrading the biomass-derived ethanol-to-jet blendstocks. Green Chem. 2017;19:1082–101.

    Article  CAS  Google Scholar 

  48. Engineering C. The chemical engineering plant cost index. https://www.chemengonlinecom/pci-home. Accessed 20 June 2018.

  49. Humbird D, Davis R, Tao L, Kinchin C, Hsu D, Aden A, Schoen P, Lukas J, Olthof B, Worley M. Process design and economics for biochemical conversion of lignocellulosic biomass to ethanol dilute-acid pretreatment and enzymatic hydrolysis of corn stover. National Renewable Energy Lab. 2011.

  50. Lemmens S. Cost engineering techniques and their applicability for cost estimation of organic rankine cycle systems. Energies. 2016;9:485.

    Article  Google Scholar 

  51. Consulting S. US producer price indexes—chemicals and allied products/industrial inorganic chemicals index. Chemical Economics Handbook. 2008.

  52. Statistics UDoLBoL. National employment, hours, and earnings catalog, industry: chemicals and allied products. 2009. 1980–2009.

  53. Max SP, Klaus DT, Ronald EW. Plant design and economics for chemical engineers. 4th ed. New York: McGraw-Hill Inc; 1991.

    Google Scholar 

  54. Choi J-i, Lee SY. Process analysis and economic evaluation for poly (3-hydroxybutyrate) production by fermentation. Bioprocess Eng. 1997;17:335–42.

    Article  CAS  Google Scholar 

  55. Zang G, Sun P, Yoo E, Elgowainy A, Bafana A, Lee U, Wang M, Supekar S. Synthetic methanol/Fischer–Tropsch fuel production capacity, cost, and carbon intensity utilizing CO2 from industrial and power plants in the United States. Environ Sci Technol. 2021;55:7595–604.

    Article  CAS  PubMed  Google Scholar 

  56. Liu J, Chen C, Zhang K, Zhang L. Applications of metal–organic framework composites in CO2 capture and conversion. Chin Chem Lett. 2021;32:649–59.

    Article  CAS  Google Scholar 

  57. Wu J, Li B, Wang W, Yang S, Liu P, Yang C, Ding Y. Green refining of waste lubricating oil: a china perspective. Trends Renew Energy. 2019;5:165–80.

    Article  Google Scholar 

  58. Davis R, Aden A, Pienkos PT. Techno-economic analysis of autotrophic microalgae for fuel production. Appl Energy. 2011;88:3524–31.

    Article  Google Scholar 

  59. Xiujin Li, Bin Z, Hairong Y, Yunzhi P, Ying M. China biogas industry-challenges and future development. Trans CSAE. 2011;27:352–5.

    Google Scholar 

  60. Jouny M, Luc W, Jiao F. General techno-economic analysis of CO2 electrolysis systems. Ind Eng Chem Res. 2018;57:2165–77.

    Article  CAS  Google Scholar 

  61. Ikonnikova S, Gülen G, Browning J, Tinker SW. Profitability of shale gas drilling: a case study of the Fayetteville shale play. Energy. 2015;81:382–93.

    Article  Google Scholar 

  62. Fasaei F, Bitter J, Slegers P, Van Boxtel A. Techno-economic evaluation of microalgae harvesting and dewatering systems. Algal Res. 2018;31:347–62.

    Article  Google Scholar 

  63. Byun J, Han J. Stochastic techno-economic analysis for an integrated strategy to coproduce jet fuel range alkenes and pentanediols. Inter Conf Appl Energy. 2019;4:1–4 https://doi.org/10.46855/energy-proceedings-2876.

    Article  Google Scholar 

  64. Hu M, Ye Z, Zhang H, Chen B, Pan Z, Wang J. Thermochemical conversion of sewage sludge for energy and resource recovery: technical challenges and prospects. Environ Pollut Bioavailab. 2021;33(1):145–63.

    Article  CAS  Google Scholar 

  65. Puig-Arnavat M, Søgaard M, Hjuler K, Ahrenfeldt J, Henriksen UB, Hendriksen PV. Integration of oxygen membranes for oxygen production in cement plants. Energy. 2015;91:852–65.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Key R&D Programs of China (2018YFA0901500) and the National Natural Science Foundation of China (22178281, 21878241).

Funding

This work was supported by The National Key R&D Programs of China (2018YFA0901500), The National Natural Science Foundation of China (22178281, 21878241), The Science Fund for Distinguished Young Scholars of Shaanxi Province (2022JC-09), and The Youth Innovation Team of Shaanxi Universities.

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  1. Bobo Liang and Rongzhan Fu have contributed equally to this work

Authors and Affiliations

  1. Shaanxi Key Laboratory of Energy Chemical Process Intensification, School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an, China

    Bobo Liang, Yingqun Ma, Lizhen Hu & Qiang Fei

  2. Shaanxi Key Laboratory of Degradable Biomedical Materials, School of Chemical Engineering, Northwest University, Xi’an, 710069, China

    Rongzhan Fu

  3. MOE Key Lab of Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China

    Xin-Hui Xing

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  1. Bobo Liang
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  4. Lizhen Hu
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Contributions

BL and RF designed the process and wrote the manuscript. YM and XHX revised the manuscript. LH provided simulation data, designed the process design and wrote the manuscript. All authors read and approved the final manuscript.

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Correspondence to Qiang Fei.

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Liang, B., Fu, R., Ma, Y. et al. Turning C1-gases to isobutanol towards great environmental and economic sustainability via innovative biological routes: two birds with one stone. Biotechnol Biofuels 15, 107 (2022). https://doi.org/10.1186/s13068-022-02202-1

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Keywords

Biotechnology for Biofuels and Bioproducts

ISSN: 2731-3654

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