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Combined Biological and Chemical/Physicochemical Pretreatment Methods of Lignocellulosic Biomass for Bioethanol and Biomethane Energy Production—A Review

Lignocellulosic biomass is a low-cost and environmentally-friendly resource that can be used to produce biofuels such as bioethanol and biogas, which are the leading candidates for the partial substitution of fossil fuels. However, the main challenge of using lignocellulosic materials for biofuel production is the low accessibility to cellulose for hydrolysis of enzymes and microorganisms, which can be overcome by pretreatment. Biological and chemical pretreatments have their own disadvantages, which could be reduced by combining the two methods. In this article, we review biological–chemical combined pretreatment strategies for biogas and bioethanol production. The synergy of fungal/enzyme–NaOH pretreatment is the only biological–chemical combination studied for biogas production and has proven to be effective. The use of enzyme, which is relatively expensive, has the advantage of hydrolysis efficiency compared to fungi. Nonetheless, there is vast scope for research and development of other chemical–biological combinations for biogas production. With respect to ethanol production, fungal–organosolv combination is widely studied and can achieve a maximum of 82% theoretical yield. Order of pretreatment is also important, as fungi may reduce the accessibility of cellulose made available by prior chemical strategies and suppress lignin degradation. The biofuel yield of similarly pretreated biomass can vary depending on the downstream process. Therefore, new strategies, such as bioaugmentation and genetically engineered strains, could help to further intensify biofuel yields.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).15 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
