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DONG Hao, SUN Lin, OUYANG Feng. PREDICTION OF PM2.5 CONCENTRATION BASED ON INFORMER[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 48-54,62. doi: 10.13205/j.hjgc.202206006
Citation: ZHU Wen-bin, GAO Ming, YIN Zi-he, WU Chuan-fu, WANG Qun-hui. RESEARCH PROGRESS ON CAPROIC ACID PRODUCTION FROM ORGANIC WASTE BY ANAEROBIC FERMENTATION[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(1): 128-134. doi: 10.13205/j.hjgc.202001020

RESEARCH PROGRESS ON CAPROIC ACID PRODUCTION FROM ORGANIC WASTE BY ANAEROBIC FERMENTATION

doi: 10.13205/j.hjgc.202001020
  • Received Date: 2019-05-28
  • High-value carboxylate synthesis during organic waste treatment via anaerobic fermentation technology has become more and more mature. Especially, as one of the final products, caproic acid gained much more attentions, because of its high added-value, easy separation and wide utilization. As the electron donors and acceptors were both required for caproic acid synthesis by microorganisms, in this paper, we firstly introduced the mechanism of caproic acid biosynthesis (the reverse β-oxidation pathway) with using ethanol and acetic acid as the electron donor and acceptor, respectively. Moreover, metabolic substrates and fermentation influencing factors (temperature, pH, HRT, competitive pathways, hydrogen partial pressure, substrates ratio and nitrogen sources) were summarized. Currently, caproic acid production by Clostridium kluyveri with using ethanol as the electron donor was quite mature. Further, we explored the metabolic mechanism of caproic acid synthesis with using lactic acid as electron donor, and developing the fermentation technology of caproic acid production from lactic acid were considered as a promising direction in the future.
  • AGLER M T, WRENN B A, ZINDER S H, et al. Waste to bioproduct conversion with undefined mixed cultures: the carboxylate platform[J]. Trends in Biotechnology, 2011, 29(2): 70-78.
    GROOTSCHOLTEN T I M, STEINBUSCH K J J, HAMELERS H V M, et al. Chain elongation of acetate and ethanol in an upflow anaerobic filter for high rate MCFA production[J]. Bioresource Technology, 2013, 135(2): 440-445.
    STEINBUSCH K J J, HAMELERS H V M, PLUGGE C M, et al. Biological formation of caproate and caprylate from acetate: Fuel and chemical production from low grade biomass[J]. Energy & Environmental Science, 2010, 4(1): 216-224.
    AGLER M T, SPIRITO C M, USACK J G, et al. Chain elongation with reactor microbiomes: upgrading dilute ethanol to medium-chain carboxylates[J]. Energy & Environmental Science, 2012, 5(8): 8189-8192.
    GROOTSCHOLTEN T I M, STRIK D P B T, STEINBUSCH K J J, et al. Two-stage medium chain fatty acid (MCFA) production from municipal solid waste and ethanol[J]. Applied Energy, 2014, 116(3): 223-229.
    YIN Y A, ZHANG Y F, KARAKASHEV D B, et al. Biological caproate production by Clostridium kluyveri from ethanol and acetate as carbon sources[J]. Bioresource Technology, 2017, 241: 638-644.
    KUZNETSOV Y I, IBATULLIN K A. On the inhibition of the carbon dioxide corrosion of steel by carboxylic acids[J]. Protection of Metals, 2002, 38(5): 439-444.
    ALY M, BAUMGARTEN E. Hydrogenation of hexanoic acid with different catalysts[J]. Applied Catalysis A General, 2001, 210(1): 1-12.
    RENZ M. Ketonization of carboxylic acids by decarboxylation: mechanism and scope[J]. Cheminform, 2005,6:979-988.
    KENEALY W R, CAO Y, WEIMER P J. Production of caproic acid by cocultures of ruminal cellulolytic bacteria and Clostridium kluyveri grown on cellulose and ethanol[J]. Applied Microbiology and Biotechnology, 1995, 44(3/4): 507-513.
    GREENSTEIN G R. The Merck Index: An Encyclopedia of Chemicals, Drugs, and Biologicals (14th edition)[M]. Philadelphia: Philadelphia University, 2001.
    VAN I F, DE BUCK J, BOYEN F, et al. Medium-chain fatty acids decrease colonization and invasion through hilA suppression shortly after infection of chickens with Salmonella enterica serovar Enteritidis[J]. Applied & Environmental Microbiology, 2004, 70(6): 3582-6588.
    ZENTEK J, BUCHHEIT-RENKO S, FERRARA F, et al. Nutritional and physiological role of medium-chain triglycerides and medium-chain fatty acids in piglets[J]. Animal Health Research Reviews, 2011, 12(1): 83-93.
    BUTKUS M A, Hughes K T, Bowman D D, et al. Inactivation of Ascaris suum by short-chain fatty acids[J]. Applied & Environmental Microbiology, 2011, 77(1): 363-366.
    WOOLFORD M K. Microbiological screening of the straight chain fatty acids (C1-C12) as potential silage additives[J]. Journal of the Science of Food & Agriculture, 2010, 26(2): 219-228.
    LEVY P F, SANDERSON J E, KISPERT R G, et al. Biorefining of biomass to liquid fuels and organic chemicals[J]. Enzyme and Microbial Technology, 1981, 3(3): 207-215.
    WITHOLT B, KESSLER B. Perspectives of medium chain length poly (hydroxyalkanoates), a versatile set of bacterial bioplastics[J]. Current Opinion in Biotechnology, 1999, 10(3): 279-285.
    VASUDEVAN D, RICHTER H, ANGENENT L T. Upgrading dilute ethanol from syngas fermentation to n-caproate with reactor microbiomes[J]. Bioresource Technology, 2014, 151(1): 378-382.
    GE S J, USACK J G, SPIRITO C M, et al. Long-term n-caproic acid production from yeast-fermentation beer in an anaerobic bioreactor with continuous product extraction[J]. Environmental Science & Technology, 2015, 49(13): 8012-8021.
    ZHU X Y, TAO Y, LIANG C, et al. The synthesis of n-caproate from lactate: a new efficient process for medium-chain carboxylates production[J]. Scientific Reports, 2015, 5: 14360-14367.
    GROOTSCHOLTEN T I M, BORGO F, KINSKY D, et al. Promoting chain elongation in mixed culture acidification reactors by addition of ethanol[J]. Biomass & Bioenergy, 2013, 48(1): 10-16.
    AGLER M T, SPIRITO C M, USACK J G, et al. Development of a highly specific and productive process for n-caproic acid production: applying lessons from methanogenic microbiomes[J]. Water Science & Technology, 2014, 69(1): 62-68.
    BARKER H A, TAHA S M. Clostridium kluyverii, an organism concerned in the formation of caproic acid from ethyl alcohol[J]. Journal of Bacteriology, 1942, 43(3): 347-363.
    BARKER H A, KAMEN M D, BORNSTEIN B T. The synthesis of butyric and caproic acids from ethanol and acetic acid by Clostridium Kluyveri[J]. Proceedings of the National Academy of Sciences of the United States of America, 1945, 31(12):373-381.
    DING H B, TAN G A, WANG J Y. Caproate formation in mixed-culture fermentative hydrogen production[J]. Bioresourse Technology, 2010, 101(24): 9550-9559.
    SEEDORF H, FRICKE F W, VEITH B, et al. The genome of clostridium kluyveri, a strict anaerobe with unique metabolic features[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(6): 2128-2133.
    LONKAR S, FU Z, HOLTZAPPLE M. Optimum alcohol concentration for chain elongation in mixed-culture fermentation of cellulosic substrate[J]. Biotechnology & Bioengineering, 2016, 113(12): 2597-2604.
    STEINBUSCH K J J, ARVANITI E, HAMELERS H V M, et al. Selective inhibition of methanogenesis to enhance ethanol and n-butyrate production through acetate reduction in mixed culture fermentation[J]. Bioresource Technology, 2009, 100(13): 3261-3267.
    KUCEK L A, NGUYEN M, ANGENENT L T. Conversion of L-lactate into n-caproate by a continuously fed reactor microbiome[J]. Water Research, 2016, 93: 163-171.
    BORNSTEIN B T, BARKER H A. The energy metabolism of Clostridium kluyveri and the synthesis of fatty acids[J]. Journal of Biological Chemistry, 1948, 172(2): 659-669.
    GROOTSCHOLTEN T I M, STEINBUSCH K J J, Hamelers H V M, et al. Improving medium chain fatty acid productivity using chain elongation by reducing the hydraulic retention time in an upflow anaerobic filter[J]. Bioresourse Technology, 2013, 136(12): 735-738.
    BYOUNG S J, BYUNG-CHUN, YOUNGSOON U, et al. Production of hexanoic acid from D-galactitol by a newly isolated Clostridium sp. BS-1[J]. Applied Microbiology & Biotechnology, 2010, 88(5): 1161-1167.
    HINO T, MIYAZAKI K, KURODA S, et al. Role of extracellular acetate in the fermentation of glucose by a ruminal bacterium, Megasphaera elsdenii[J]. Journal of General and Applied Microbiology, 1991, 37(1): 121-129.
    ZHU X Y, ZHOU Y, WANG Y, et al. Production of high-concentration n-caproic acid from lactate through fermentation using a newly isolated Ruminococcaceae bacterium CPB6[J]. Biotechnology for Biofuels, 2017, 10(1): 102-113.
    KENEALY W R, WASELEFSKY D M. Studies on the substrate range of Clostridium kluyveri; the use of propanol and succinate[J]. Archives of Microbiology, 1985, 141(3): 187-194.
    GE S J, USACK J G, SPIRITO C M, et al. Long-term n-caproic acid production from yeast-fermentation beer in an anaerobic bioreactor with continuous product extraction[J]. Environmental Science & Technology, 2015, 49(13): 8012-8021.
    TOMLINSON N, BARKER H A. Carbon dioxide and acetate utilization by Clostridium kluyveri Ⅰ. Influence of nutritional conditions on utilization patterns[J]. Journal of Biological Chemistry, 1954, 209(2): 585-595.
    WERNER J J, KNIGHTS D, GARCIA M L, et al. Bacterial community structures are unique and resilient in full-scale bioenergy systems[J]. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(10): 4158-4163.
    HOLTZAPPLE M T, GRANDA C B. Carboxylate Platform: The mixalco process Part 1: comparison of three biomass conversion platforms[J]. Applied Biochemistry & Biotechnology, 2009, 156(1/3): 95-106.
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