Anaerobic digestion using lignocellulosic material as the substrate is a cost-effective strategy for biomethane production, which provides great potential to convert biomass into renewable energy. However, the recalcitrance of native lignocellulosic biomass makes it resistant to microbial hydrolysis, which reduces the bioconversion efficiency of organic matter into biogas. Therefore, it is necessary to critically investigate the correlation between lignocellulose characteristics and bioconversion efficiency. Accordingly, this review comprehensively summarizes the anaerobic digestion process and rate-limiting step, structural and compositional properties of lignocellulosic biomass, recalcitrance and inhibitors of lignocellulose and their major effects on anaerobic digestion for biomethane production. Moreover, various type of pretreatment strategies applied to lignocellulosic biomass was discussed in detail, which would contribution to cell wall degradation and improvement of biomethane yields. In the view of current knowledge, high energy input and cost requirements are the main limitations of these pretreatment methods. In addition to optimization of fermentation process, further studies should focus much more on key structural influence factors of biomass recalcitrance and anaerobic digestion efficiency, which will contribute to improvement of biomethane production from lignocellulose.

Rhamnolipids (RLs) are important bioproducts that are regarded as promising biosurfactant for applications in oil exploitation, cosmetics, and food industry. In this study, the newly isolated Pseudomonas aeruginosa KT1115 showed high production of di-RLs. The highest yield of RLs by P. aeruginosa KT1115, reaching 44.39 g/L after 8 days of fermentation in a 5 L bioreactor, was obtained from rapeseed oil-nitrate medium after process optimization. Furthermore, we established a new separation process that achieved up to 91.82% RLs recovery with a purity of 89% and further obtained mono/di-rhamnolipids. Finally, ESI-MS analysis showed that the RLs produced by strain KT1115 have a high proportion of di-RLs (mono-RLs: di-RLs = 11.47: 88.53), which have a lower critical micelle-forming concentration (8 mN/m) and better emulsification ability with kerosene (52.1% EI24) than mono-RLs (167 mN/m and 41.4% EI24, respectively). These results demonstrated that P. aeruginosa KT1115 is a potential industrial producer of di-RLs, which have improved applicability and offer significant commercial benefits.

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A sophorolipid (SL) is a kind of glycolipid surfactant that has been much studied due to its excellent surface activity and physiological properties. However, the mixed structure of SLs and expensive separation costs have limited their application in advanced industries. Developing a tailored strategy to produce targeted SLs, with specific physicochemical properties, for direct application in various industries like agriculture, cleaning, and medicine, has become one of the main objectives of SL biological production. In this review, different characteristics of various SLs with various structures are classified in detail. The classification corresponds to their specific applications in different industries. Microorganisms for the production of specific SLs are also summarized, with emphasis on the metabolism pathways and regulation mechanisms. Strategies applied for SL biological tailoring, including selection of fermenting substrates, fermentation process optimization, metabolic engineering and downstream separation, are comprehensively reviewed.

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Feature extraction technology is an important part of bearing diagnosis, especially for early degradation detection. However, the traditional feature extraction technology can not effectively remove noise or is not sensitive to periodic weak faults, which leads to be inclined to raise false alarms and prediction delay for early degradation detection. In order to solve these two issues, a new feature extraction technique is presented based on Envelope Harmonic-to-noise Ratio (EHNR) and Adaptive Variational Mode Decomposition (AVMD). First of all, the minimum average envelope entropy is used as the objective function to search the optimal parameters of the Variational Modal Decomposition (VMD) adaptively by the Grey Wolf Optimization (GWO) algorithm. The problem of under-decomposition or overdecomposition caused by improper parameter setting is avoided. Then, a new index called Effective Weighted Sparseness Kurtosis (EWSK) is proposed. This index can separate the effective modal components and noise modal components only by the positive and negative results, so as to achieve the purpose of removing noise interference and retaining a large amount of fault information. Finally, the EHNR of the reconstructed signal is calculated, and its sensitivity to periodic fault shock is utilized to detect the early degradation starting point of the rolling bearing. Experimental results show that the proposed method outperforms several state-of-the-art detection methods in terms of early degradation point detection, false alarm rate and computational complexity. The superior performances of the presented AVMD-EHNR method can provide the basis for early fault diagnosis and remaining useful life prediction of rolling bearings.indicator TERMS early degradation detection, rolling bearings, envelope harmonic-to-noise ratio (EHNR), adaptive variational mode decomposition (AVMD), effective weighted sparseness kurtosis (EWSK) index.

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