Biological processing of coal

The irregular chemical structure of coal makes it an improbable substrate for bioconversion. Nevertheless, work completed in the last 20 years makes likely the development of practicable biological processes for beneficiation of low-rank coal and the conversion of low-rank coal to specific low-molecular-mass organic molecules and novel fluid fuels.     

>Decolourisation and depolymerisation of solubilised low-rank coal by the white-rot basidiomycete Phanerochaete chrysosporium

Peroxidases secreted by the white-rot basidiomycete Phanerochaete chrysosporium can oxidise a wide range of recalcitrant compounds including lignin and aromatic xenobiotics. Since low-rank coals such as brown coal and lignite retain structural features of the parent lignin, we investigated the possibility that P. chrysosporium is capable of acting on a brown coal, with the production of useful low-molecular-mass compounds. In nitrogen-limiting liquid medium containing 0.03% solubilised Morwell brown coal, P. chrysosporium was found to convert about 85% of the coal after 16 days incubation to a form not recoverable by alkali-washing and acid-precipitation. The modal molecular mass of the residual coal macromolecules was reduced from the initial 65kDa to 32 kDa. Extensive bleaching of the coal coincided with the presence of extracellular lignin peroxidase (LiP) and manganese-dependent peroxidase (MnP), although both LiP and MnP activity were lower in cultures containing coal. These reductions are accounted for by interference with the enzyme assays by solubilised coal and by binding of MnP to precipitated coal. LiP was about eight times more sensitive than MnP to inhibition by solubilised coal. In nitrogen-sufficient medium containing solubilised coal, neither coal modification nor LiP activity were observed, suggesting that LiP is an essential component of the bleaching process.     

Molecular dynamics simulations of nonionic surfactant adsorbed on subbituminous coal model surface based on XPS analysis

A molecular dynamics (MD) simulation was conducted to study the adsorption behaviour of a nonionic surfactant adsorbed on low-rank coal. Owing to the complicated chemical component and structure of the coal surface, a modified graphite surface with hydroxyl, carboxyl, and carbonyl groups was utilised for a representation of the subbituminous coal surface model. The compositions of the hydroxyl, carbonyl, and carboxyl groups on the coal surface were found to be at a proportion of 25:3:5 according to the X-ray photoelectron spectroscopy (XPS) results. The interaction of nonylphenol ethoxylate with 8 ethylene oxide groups (NPEO-8) using this coal model in an aqueous phase was then simulated. It was revealed that the nonionic surfactant molecules were adsorbed at the interface between water and coal. This agminated structure of the surfactant molecules on the surface of the coal demonstrated an attachment of the surfactant ethoxylate groups to these solid surfaces and an extension of the remaining hydrophobic portions into the solution. Therefore, a coal surface with greater hydrophobicity was created. The dynamic properties of the water molecules characterised through self-diffusion coefficients indicate greater water mobility resulting from the existence of NPEO-8.     

Extracellular oxidases and the transformation of solubilised low-rank coal by wood-rot fungi

The involvement of extracellular oxidases in biotransformation of low-rank coal was assessed by correlating the ability of nine white-rot and brown-rot fungi to alter macromolecular material in alkali-solubilised brown coal with the spectrum of oxidases they produce when grown on low-nitrogen medium. The coal fraction used was that soluble at 3.0?pH?6.0 (SWC6 coal). In 15-ml cultures, Gloeophyllum trabeum, Lentinus lepideus and Trametes versicolor produced little or no lignin peroxidase, manganese (Mn) peroxidase or laccase activity and caused no change to SWC6 coal. Ganoderma applanatum and Pycnoporus cinnabarinus also produced no detectable lignin or Mn peroxidases or laccase yet increased the absorbance at 400?nm of SWC6 coal. G. applanatum, which produced veratryl alcohol oxidase, also increased the modal apparent molecular mass. SWC6 coal exposed to Merulius tremellosus and Perenniporia tephropora, which secreted Mn peroxidases and laccase and Phanerochaete chrysosporium, which produced Mn and lignin peroxidases was polymerised but had unchanged or decreased absorbance. In the case of both P. chrysosporium and M. tremellosus, polymerisation of SWC6 coal was most extensive, leading to the formation of a complex insoluble in 100?mM NaOH. Rigidoporus ulmarius, which produced only laccase, both polymerised and reduced the A ₄₀₀ of SWC6 coal. P. chrysosporium, M. tremellosus and P. tephropora grown in 10-ml cultures produced a spectrum of oxidases similar to that in 15-ml cultures but, in each case, caused more extensive loss of A ₄₀₀, and P. chrysosporium depolymerised SWC6 coal. It is concluded that the extracellular oxidases of white-rot fungi can transform low-rank coal macromolecules and that increased oxygen availability in the shallower 10-ml cultures favours catabolism over polymerisation.     

Coal methanogenesis: a review of the need of complex microbial consortia and culture conditions for the effective bioconversion of coal into methane

Microbial biodegradation of coal into low-molecular-weight compounds such as methane has been extensively researched in the last two decades because of the underlying environmental and industrial applications of this technique as compared to the chemical and physical methods of coal conversions. However, the irregular structure of coal and the need for complex microbial consortia under specific culture conditions do not make this biotransformation an ideal process for the development of anaerobic bioreactors. The most abundant species in a methanogenic culture are acetoclastic and hydrogenotrophic methanogens which utilize acetate and H₂+CO₂, respectively. Medium- to low-rank coals such as high-volatile bituminous, sub-bituminous and lignite are more promising in this bioconversion as compared to semi- and meta-anthracite coals. While covering the details of the ideal culture conditions, this review enlightens the need of research setups to explore the complex microbial consortia and culture conditions for maximum methane production through coal methanogenesis.     

Molecular dynamics simulations and contact angle of surfactant at the coal–water interface

Wettability of nonylphenol ethoxylate with four ethylene oxide groups (NP-4) on a subbituminous coal was carried out. As the concentration of NP-4 gradually increases, the contact angle firstly increases and then decreases with maximum contact angle at about critical micelle concentration (CMC) of NP-4. The monolayer adsorption behaviour of NP-4 on the model surface of Hatcher subbituminous coal was investigated by means of molecular dynamics simulations. The surfactant molecules could be detected at the water–coal interface. The water molecules are repelled and stronger hydrophobicity of the coal is obtained in the presence of NP-4, which are consistent with contact angle results at low concentration. The aggregated structure of the surfactant molecules on the coal surface in terms of head group and tail group density profiles along the perpendicular direction shows that the ethoxylate groups of the surfactant are attached at the solid surfaces. The negative interaction energy between NP-4 and the subbituminous coal surface calculated suggests that adsorption process is spontaneous. The self-diffusion coefficients results indicate that the presence of NP-4 causes higher water mobility meaning improving the hydrophobicity of low-rank coal, which is consistent with the experimental results of contact angle.     

Lignite Biosolubilization by Bacillus sp. RKB 2 and Characterization of its Products

Abstract

Nowadays, the advancements of coal microbiology and biotechnology have been highly emphasized, providing leading-edge approaches in sustainable development of agriculture and the protection of the environment. The biosolubilization of low-rank coals, such as lignite and leonardite is a promising technology for converting these sedimentary rocks into valuable products. In this study, the process involved in lignite biosolubilization by Bacillus sp. RKB 2 was investigated. The biotransformed lignite and the produced humic substances were determined in vitro in a liquid medium and on a solid matrix. The bacterial strain was isolated from untreated Kazakhstani lignite and was shown to be capable of effectively solubilizing and transforming lignite (5% w/v). Fourier Transform Infrared (FTIR) and UHPLC-QqQ-MS/MS analyses were performed to examine the solubilization products and lignite humic substances processed by bacteria. The absorption peaks of FTIR showed the diverse nature of the bacteria-induced humic substances, and the vast majority of intense peaks detected are mainly below an m/z of 1000?Da (liquid chromatography-mass spectrometry [LC-MS] (QqQ)). Data analysis concluded that our isolate could depolymerize lignite and form bio-humic substances. Due to its ability to solubilize lignite Bacillus sp. RKB 2 may be useful in the coal-bed for in situ bioutilization of low-rank coal.     

Degradation of diphenylether by Pseudomonas cepacia

The microbial degradation of hard coal implies the cleavage of diaryl ether linkages in the coal macromolecule. We investigated the biodegradation of diphenylether as a model compound representing this substructure of coal. A bacterial strain isolated from soil and identified as Pseudomonas cepacia, was able to grow with diphenylether as sole source of carbon. During microbial growth, three metabolites were detected in the culture supernatant by high pressure liquid chromatography. As product of ring hydroxylation and subsequent rearomatization, 2,3-dihydroxydiphenylether was identified by UV, mass and nuclear magnetic resonance spectrometry and gas chromatography analyses. The cleavage of the ether linkage led to the formation of phenol and 2-pyrone-6-carboxylic acid, the latter being not further degraded by Pseudomonas cepacia. The possible cleavage mechanism of the ether linkage is discussed.Non-standard abbreviations DPE diphenylether - PCA 2-pyrone-6-carboxylic acid - GC gas chromatography - MS mass spectrometry - HPLC high pressure liquid chromatography     

Computer molecular models of low-rank coal and char containing inorganic complexes

Molecular models of low-rank coal containing water, aqua-ionic species, and transition metal aqua-complexes, were optimised using semi-empirical (SE) quantum mechanics; the model was constructed with properties similar to brown coal; 10–20 wt% water was hydrogen bonded to coal oxygen groups, and the remainder was bulk water. Single point self-consistent field (1scf) computations of coal models provided octahedral mono-, and di-nuclear complexes of Cr, Fe, Co, and Ni, but SE computations often provided distorted structures. Models of char were developed by transforming the coal model containing multi-nuclear metal species into char according to pyrolysis chemistry; the composition of char models containing iron oxides was similar to char samples obtained over 250–800°C. Density functional theory (DFT) optimisation of char models with metal clusters provided low energy configurations of disordered structures with a shallow energy minimum. SE and DFT calculations of char models containing metal clusters were conducted for mechanisms for H₂ and CO formation from pyrolysis and iron-catalysed steam gasification; the active site for gasification was [Fe-C] and its accessibility to H₂O was related to the configuration of the char model. The major steps in iron-catalysed steam gasification were chemi-adsorption of water on [Fe-C], hydrogen abstraction, and oxygen transfer.     

Depolymerization of low-rank coal by extracellular fungal enzyme systems.

 A miniscale screening system was developed to detect depolymerizing activities of fungi toward low-rank coals. This system was suitable for the determination of changes in molecular masses as well as for the measurement of the enzymes responsible. A total of 486 fungal strains of different ecophysiological and taxonomic groups were tested for their ability to decolorize agar media containing coal-derived humic acids; 38 wood- and litter-decaying basidiomycetes caused a strong bleaching effect and 49 a weak effect. In contrast, micromycetes were proved to be unable to decolorize the coal substances. The wood-decaying fungus Nematoloma frowardii b19 most effectively bleached the medium. It could be demonstrated by gel-permeation chromatography that the strain really depolymerizes the high molecular-mass fractions of coal humic acids by forming fulvic-acid-like compounds. Extracellular enzyme activities of oxidases and peroxidases towards 2,2′-azinobis(3-ethylbenzthiazolinesulphonate) were extractable from the agar media. Received: 5 February 1996/Received revision: 15 April 1996/Accepted: 22 April 1996     

Influence of culture parameters on extracellular peroxidase activity and transformation of low-rank coal by Phanerochaete chrysosporium

The white-rot fungus Phanerochaete chrysosporium can degrade macromolecules in low-rank coal, offering the potential for converting coal to specific products. We investigated the influence of temperature, veratryl alcohol and oxygen on transformation of a solubilised fraction of Morwell brown coal (SWC6 coal) and on the activity of lignin peroxidase and manganese (Mn) peroxidase in N-limited cultures of P. chrysosporium. After 20 days, the mass and A ₄₀₀ of SWC6 coal recovered from cultures containing 0.03% SWC6 coal, incubated at 28 °C under hyperbaric oxygen, were reduced by over 95%. The modal apparent molecular mass of the residuum was reduced by 50%. Addition of 2 mM veratryl alcohol had little effect on the transformation of SWC6 coal. The extent of transformation was reduced in cultures incubated at 37 °C or under air. In cultures under air, coal molecules were transiently polymerised. Decolourisation of SWC6 coal reflects conversion to products that cannot be recovered from the medium, not the destruction of chromophores within recoverable material. The activity of lignin peroxidase, measured in cultures free of SWC6 coal to avoid interference with the assay, correlates directly with the degradation of SWC6 coal as measured by the decline in A ₄₀₀. The data suggest that lignin peroxidase is more important than Mn peroxidase in converting SWC6 coal to products that are assimilated by cells. Received: 16 July 1997 / Received revision: 14 November 1997 / Accepted: 18 November 1997     

The impact of sparsity in low-rank recurrent neural networks

Neural population dynamics are often highly coordinated, allowing task-related computations to be understood as neural trajectories through low-dimensional subspaces. How the network connectivity and input structure give rise to such activity can be investigated with the aid of low-rank recurrent neural networks, a recently-developed class of computational models which offer a rich theoretical framework linking the underlying connectivity structure to emergent low-dimensional dynamics. This framework has so far relied on the assumption of all-to-all connectivity, yet cortical networks are known to be highly sparse. Here we investigate the dynamics of low-rank recurrent networks in which the connections are randomly sparsified, which makes the network connectivity formally full-rank. We first analyse the impact of sparsity on the eigenvalue spectrum of low-rank connectivity matrices, and use this to examine the implications for the dynamics. We find that in the presence of sparsity, the eigenspectra in the complex plane consist of a continuous bulk and isolated outliers, a form analogous to the eigenspectra of connectivity matrices composed of a low-rank and a full-rank random component. This analogy allows us to characterise distinct dynamical regimes of the sparsified low-rank network as a function of key network parameters. Altogether, we find that the low-dimensional dynamics induced by low-rank connectivity structure are preserved even at high levels of sparsity, and can therefore support rich and robust computations even in networks sparsified to a biologically-realistic extent.     

Degradation of lignite (low-rank coal) by ligninolytic basidiomycetes and their manganese peroxidase system

Ligninolytic basidiomycetes (wood and leaf-litter-decaying fungi) have the ability to degrade low-rank coal (lignite). Extracellular manganese peroxidase is the crucial enzyme in the depolymerization process of both coal-derived humic substances and native coal. The depolymerization of coal by Mn peroxidase is catalysed via chelated Mn(III) acting as a diffusible mediator with a high redox potential and can be enhanced in the presence of additional mediating agents (e.g. glutathione). The depolymerization process results in the formation of a complex mixture of lower-molecular-mass fulvic-acid-like compounds. Experiments using a synthetic ¹⁴C-labeled humic acid demonstrated that the Mn peroxidase-catalyzed depolymerization of humic substances was accompanied by a substantial release of carbon dioxide (17%–50% of the initially added radioactivity was released as ¹⁴CO₂). Mn peroxidase was found to be a highly stable enzyme that remained active for several weeks under reaction conditions in a liquid reaction mixture and even persisted in sterile and native soil from an opencast mining area for some days. Received: 31 July 1998 / Received revision: 29 September 1998 / Accepted: 2 October 1998     

Low-Rank and Eigenface Based Sparse Representation for Face Recognition

In this paper, based on low-rank representation and eigenface extraction, we present an improvement to the well known Sparse Representation based Classification (SRC). Firstly, the low-rank images of the face images of each individual in training subset are extracted by the Robust Principal Component Analysis (Robust PCA) to alleviate the influence of noises (e.g., illumination difference and occlusions). Secondly, Singular Value Decomposition (SVD) is applied to extract the eigenfaces from these low-rank and approximate images. Finally, we utilize these eigenfaces to construct a compact and discriminative dictionary for sparse representation. We evaluate our method on five popular databases. Experimental results demonstrate the effectiveness and robustness of our method.     

Determination of the kinetic parameters of the phenol-degrading thermophile Bacillus themoleovorans sp. A2

Phenolic compounds are pollutants in many wastewaters, e.g. from crude oil refineries, coal gasification plants or olive oil mills. Phenol removal is a key process for the biodegradation of pollutants at high temperatures because even low concentrations of phenol can inhibit microorganisms severely. Bacillus thermoleovorans sp. A2, a recently isolated thermophilic strain (temperature optimum 65 degrees C), was investigated for its capacity to degrade phenol. The experiments revealed that growth rates were about four times higher than those of mesophilic microorganisms such as Pseudomonas putida. Very high specific growth rates of 2.8 h(-1) were measured at phenol concentrations of 15 mg/l, while at phenol concentrations of 100-500 mg/l growth rates were still in the range of 1 h(-1). The growth kinetics of the thermophilic Bacillus thermoleovorans sp. A2 on phenol as sole carbon and energy source can be described using a three-parameter model developed in enzyme kinetics. The yield coefficient Yx/s of 0.8-1 g cell dry weight/g phenol was considerably higher than cell yields of mesophilic bacteria (Yx/s 0.40-0.52 g cell dry weight/g phenol). The highest growth rate was found at pH 6. Bacillus thermoleovorans sp. A2 was found to be insensitive to hydrodynamic shear stress in stirred bioreactor experiments (despite possible membrane damage caused by phenol) and flourished at an ionic strength of the medium of 0.25(-1) mol/l (equivalent to about 15-60 g NaCl/l). These exceptional properties make Bacillus thermoleovorans sp. A2 an excellent candidate for technical applications.     

Low-Rank Regularization for Learning Gene Expression Programs

Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets.     

Biosynthesis of polyhydroxyalkanoates from low-rank coal liquefaction products by Pseudomonas oleovorans and Rhodococcus ruber

A screening identified several bacteria that were able to use chemically heterogeneous low-rank coal liquefaction products as complex carbon sources for growth. Pseudomonas oleovorans and Rhodococcus ruber accumulated polyhydroxyalkanoic acids (PHA) amounting to 2%–8% of the cell dry weight when the cells were cultivated on these liquefaction products in the absence of any other carbon source. R. ruber accumulated, in addition to PHA, small amounts of triacylglycerols. The accumulated PHA consisted of 3-hydroxyhexanoate, 3-hydroxydecanoate, and 3-hydroxydodecanoate (P. oleovorans) or 3-hydroxybutyric acid and 3-hydroxyvaleric acid (R. ruber). Low-rank coal liquefaction products obtained from Trichoderma atroviride were better substrates for P. oleovorans than chemically produced fulvic acids. Received: 13 May 1998 / Received revision: 11 August 1998 / Accepted: 12 August 1998     

Depolymerization of low-rank coal by extracellular fungal enzyme systems. III.In vitro depolymerization of coal humic acids by a crude preparation of manganese peroxidase from the white-rot fungus Nematoloma frowardii b19

The in vitro depolymerization of humic acids derived from German lignite (low-rank coal, brown coal) was studied using a manganese peroxidase preparation from the white-rot fungus Nematoloma frowardii b19. The H₂O₂ required was continuously generated by glucose oxidase. Mn peroxidase depolymerized high-molecular-mass humic acids by forming fulvic-acid-like compounds. The depolymerization process was accompanied by the decolorization of the dark-brown humic acid fraction soluble in alkaline solutions (decrease in absorbance at 450 nm) and by the yellowish coloring of the fraction of acid-soluble fulvic-acid-like compounds (increase in absorbance at 360 nm). The Mn peroxidase of N. frowardii b19 has been proved to be highly stable; even after an in vitro reaction time of 7 days in the presence of humic acids, less than 10% loss in total oxidizing activity was detectable. Received: 16 September 1996 / Received revision: 16 December 1996 / Accepted: 20 December 1996     

两株高效苯酚降解菌的筛选、鉴定及生物强化-DTRO组合工艺初步验证

【目的】从煤化工废水中分离、筛选苯酚高效降解微生物,初步考察微生物与DTRO技术联用,构建含酚废水生物强化处理工艺的可行性。【方法】采用苯酚浓度梯度培养基对苯酚降解微生物进行分离和筛选;根据菌体形态电子显微镜观察、菌株生理生化特性考察和16S r RNA基因系统发育树构建,对菌株进行初步生物学鉴定;将筛选出的高效苯酚降解菌制备成相应的菌剂与碟管式反渗透(DTRO)技术组合形成"生物强化-DTRO"工艺,并试用于含酚废水的处理。【结果】共获得7株纯化细菌,其中Phe-03和Phe-05为高效苯酚降解菌;该2株菌均可以苯酚为唯一碳源生长。经鉴定Phe-03为壤霉菌属(Agromyces)菌株;Phe-05为棒杆菌属(Corynebacterium)菌株。到目前为止,壤霉菌属(Agromyces)菌株降解苯酚尚未见报道。在初始苯酚浓度达到1 300 mg/L条件下,Phe-03和Phe-05菌株44 h内对苯酚降解率均达到70%以上;76 h后苯酚降解率均超过90%。组合形成的"生物强化-DTRO"工艺不仅可以有效去除废水中的酚类化合物,而且还能减少反渗透膜污染,以及增加膜的通透性。【结论】研究表明微生物技术可与DTRO技术联用,构建含酚废水生物强化处理工艺,可为含酚废水处理技术研究提供一种选择思路。     

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases.