Biomass gasification in India — DNES activities

Gasification of biomass, which includes a series of interlinked thermochemical processes like pyrolysis and gasoline refining, offers a promising scope to meet energy needs both for stationary and mobile purposes. The present communication is aimed at discussing the gasification programme in India with particular reference to the following five main areas: R& D, pilot-testing and evaluation including prototype production, demonstrative application and evaluation of biomass gasification, training and manpower development, commercialisation and manufacture development. Emphasis is laid upon the need to integrate energy plantation on sub-standard soils with biomass gasification.     

Energy production from biomass (Part 3): Gasification technologies

The conversion of biomass by gasification into a fuel suitable for use in a gas engine increases greatly the potential usefulness of biomass as a renewable resource. Gasification is a robust proven technology that can be operated either as a simple, low technology system based on a fixed-bed gasifier, or as a more sophisticated system using fluidized-bed technology. The properties of the biomass feedstock and its preparation are key design parameters when selecting the gasifier system. Electricity generation using a gas engine operating on gas produced by the gasification of biomass is applicable equally to both the developed world (as a means of reducing greenhouse gas emissions by replacing fossil fuel) and to the developing world (by providing electricity in rural areas derived from traditional biomass).     

Advances in thermochemical conversion of woody biomass to energy,fuels and chemicals

Biomass has been recognised as a promising resource for future energy and fuels. The biomass, originated from plants, is renewable and application of its derived energy and fuels is close to carbon-neutral by considering that the growing plants absorb CO₂ for photosynthesis. However, the complex physical structure and chemical composition of the biomass significantly hinder its conversion to gaseous and liquid fuels.This paper reviews recent advances in biomass thermochemical conversion technologies for energy, liquid fuels and chemicals. Combustion process produces heat or heat and power from the biomass through oxidation reactions; however, this is a mature technology and has been successfully applied in industry. Therefore, this review will focus on the remaining three thermochemical processes, namely biomass pyrolysis, biomass thermal liquefaction and biomass gasification. For biomass pyrolysis, biomass pretreatment and application of catalysts can simplify the bio-oil composition and retain high yield. In biomass liquefaction, application of appropriate solvents and catalysts improves the liquid product quality and yield. Gaseous product from biomass gasification is relatively simple and can be further processed for useful products. Dual fluidised bed (DFB) gasification technology using steam as gasification agent provides an opportunity for achieving high hydrogen content and CO₂ capture with application of appropriate catalytic bed materials. In addition, multi-staged gasification technology, and integrated biomass pyrolysis and gasification as well as gasification for poly-generation have attracted increasing attention.     

Biomass as an energy source: thermodynamic constraints on the performance of the conversion process

In the present work an equilibrium model (gas-solid), based on the minimization of the Gibbs energy, has been used in order to estimate the theoretical yield and the equilibrium composition of the reaction products (syngas and char) of biomass thermochemical conversion processes (pyrolysis and gasification). The data obtained from this model have also been used to calculate the heating value of the fuel gas, in order to evaluate the overall energy efficiency of the thermal conversion stage. The proposed model has been applied both to partial oxidation and steam gasification processes with varying air to biomass (ER) and steam to carbon (SC) ratio values and using different feedstocks; the obtained results have been compared with experimental data and with other model predictions obtaining a satisfactory agreement.     

Steam–air fluidized bed gasification of distillers grains: Effects of steam to biomass ratio,equivalence ratio and gasification temperature

In this study, thermochemical biomass gasification was performed on a bench-scale fluidized-bed gasifier with steam and air as fluidizing and oxidizing agents. Distillers grains, a non-fermentable byproduct of ethanol production, were used as the biomass feedstock for the gasification. The goal was to investigate the effects of furnace temperature, steam to biomass ratio and equivalence ratio on gas composition, carbon conversion efficiency and energy conversion efficiency of the product gas. The experiments were conducted using a 3 × 3 × 3 full factorial design with temperatures of 650, 750 and 850 °C, steam to biomass ratios of 0, 7.30 and 14.29 and equivalence ratios of 0.07, 0.15 and 0.29. Gasification temperature was found to be the most influential factor. Increasing the temperature resulted in increases in hydrogen and methane contents, carbon conversion and energy efficiencies. Increasing equivalence ratio decreased the hydrogen content but increased carbon conversion and energy efficiencies. The steam to biomass ratio was optimal in the intermediate levels for maximal carbon conversion and energy efficiencies.     

Experimental study on air-stream gasification of biomass micron fuel (BMF) in a cyclone gasifier

Based on biomass micron fuel (BMF) with particle size of less than 250 microm, a cyclone gasifier concept has been considered in our laboratory for biomass gasification. The concept combines and integrates partial oxidation, fast pyrolysis, gasification, and tar cracking, as well as a shift reaction, with the purpose of producing a high quality of gas. In this paper, experiments of BMF air-stream gasification were carried out by the gasifier, with energy for BMF gasification produced by partial combustion of BMF within the gasifier using a hypostoichiometric amount of air. The effects of ER (0.22-0.37) and S/B (0.15-0.59) and biomass particle size on the performances of BMF gasification and the gasification temperature were studied. Under the experimental conditions, the temperature, gas yields, LHV of the gas fuel, carbon conversion efficiency, stream decomposition and gasification efficiency varied in the range of 586-845 degrees C, 1.42-2.21 N m(3)/kg biomass, 3806-4921 kJ/m(3), 54.44%-85.45%, 37.98%-70.72%, and 36.35%-56.55%, respectively. The experimental results showed that the gasification performance was best with ER being 3.7 and S/B being 0.31 and smaller particle, as well as H(2)-content. And the BMF gasification by air and low temperature stream in the cyclone gasifier with the energy self-sufficiency is reliable.     

Robustness under Functional Constraint: The Genetic Network for Temporal Expression in Drosophila Neurogenesis

Precise temporal coordination of gene expression is crucial for many developmental processes. One central question in developmental biology is how such coordinated expression patterns are robustly controlled. During embryonic development of the Drosophila central nervous system, neural stem cells called neuroblasts express a group of genes in a definite order, which leads to the diversity of cell types. We produced all possible regulatory networks of these genes and examined their expression dynamics numerically. From the analysis, we identified requisite regulations and predicted an unknown factor to reproduce known expression profiles caused by loss-of-function or overexpression of the genes in vivo, as well as in the wild type. Following this, we evaluated the stability of the actual Drosophila network for sequential expression. This network shows the highest robustness against parameter variations and gene expression fluctuations among the possible networks that reproduce the expression profiles. We propose a regulatory module composed of three types of regulations that is responsible for precise sequential expression. This study suggests that the Drosophila network for sequential expression has evolved to generate the robust temporal expression for neuronal specification.     

Neural network prediction of biomass digestibility based on structural features

Plots of biomass digestibility are linear with the natural logarithm of enzyme loading; the slope and intercept characterize biomass reactivity. The feed-forward back-propagation neural networks were performed to predict biomass digestibility by simulating the 1-, 6-, and 72-h slopes and intercepts of glucan, xylan, and total sugar hydrolyses of 147 poplar wood model samples with a variety of lignin contents, acetyl contents, and crystallinity indices. Regression analysis of the neural network models indicates that they performed satisfactorily. Increasing the dimensionality of the neural network input matrix allowed investigation of the influence glucan and xylan enzymatic hydrolyses have on each other. Glucan hydrolysis affected the last stage of xylan digestion, and xylan hydrolysis had no influence on glucan digestibility. This study has demonstrated that neural networks have good potential for predicting biomass digestibility over a wide range of enzyme loadings, thus providing the potential to design cost-effective pretreatment and saccharification processes.     

Catalytic processes towards the production of biofuels in a palm oil and oil palm biomass-based biorefinery

In Malaysia, there has been interest in the utilization of palm oil and oil palm biomass for the production of environmental friendly biofuels. A biorefinery based on palm oil and oil palm biomass for the production of biofuels has been proposed. The catalytic technology plays major role in the different processing stages in a biorefinery for the production of liquid as well as gaseous biofuels. There are number of challenges to find suitable catalytic technology to be used in a typical biorefinery. These challenges include (1) economic barriers, (2) catalysts that facilitate highly selective conversion of substrate to desired products and (3) the issues related to design, operation and control of catalytic reactor. Therefore, the catalytic technology is one of the critical factors that control the successful operation of biorefinery. There are number of catalytic processes in a biorefinery which convert the renewable feedstocks into the desired biofuels. These include biodiesel production from palm oil, catalytic cracking of palm oil for the production of biofuels, the production of hydrogen as well as syngas from biomass gasification, Fischer-Tropsch synthesis (FTS) for the conversion of syngas into liquid fuels and upgrading of liquid/gas fuels obtained from liquefaction/pyrolysis of biomass. The selection of catalysts for these processes is essential in determining the product distribution (olefins, paraffins and oxygenated products). The integration of catalytic technology with compatible separation processes is a key challenge for biorefinery operation from the economic point of view. This paper focuses on different types of catalysts and their role in the catalytic processes for the production of biofuels in a typical palm oil and oil palm biomass-based biorefinery.     

生物质热裂解生产生物燃料的研究进展

生物质能源是一种绿色的可以替代化石能源的一种可再生的能源。尽管高温分解生物质处于发展阶段,但在目前水平,高温裂解因其可以在氧存在下热分解将生物材料直接转化为固态,液态和气态能源产品而受到广泛关注。本文介绍了生物质的热裂解,包括慢速热裂解、快速热裂解、闪解、催化热裂解等过程,重点讨论了在各种生物质材料的热裂解过程中各种操作参数如温度和生物粒子大小等对生物燃料收率的影响。     

An artificial neural network for biomass estimation from automatic pH control signal

This study developed an artificial neural network (ANN) to estimate the growth of microorganisms during a fermentation process. The ANN relies solely on the cumulative consumption of alkali and the buffer capacity, which were measured on-line from the on/off control signal and pH values through automatic pH control. The two input variables were monitored on-line from a series of different batch cultivations and used to train the ANN to estimate biomass. The ANN was refined by optimizing the network structure and by adopting various algorithms for its training. The software estimator successfully generated growth profiles that showed good agreement with the measured biomass of separate batch cultures carried out between at 25 and 35_C.     

生物质制氢技术研究进展

氢能以其清洁,来源广泛及用途广等优点成为最有希望的替代能源之一,用可再生能源制氢是氢能发展的必然趋势。由于生物质制氢具有一系列独特的优点,它已成为发展氢经济颇具前景的研究领域之一。生物质制氢技术可以分为两类,一类是以生物质为原料利用热物理化学方法制取氢气,如生物质气化制氢,超临界转化制氢,高温分解制氢等热化学发制氢,以及基于生物质的甲烷、甲醇、乙醇的化学重整转化制氢等;另一类是利用生物转化途径转换制氢,包括直接生物光解,间接生物光解,光发酵,光合异养细菌水气转移反应合成氢气,暗发酵和微生物燃料电池等技术。本文综述了目前主要的生物质制氢技术及其发展概况,并分析了各技术的发展趋势。     

Fluctuations and synchronizations of neural activities during sleep: Neural basis of possible sleep functions?

Physiological and psychological evidence have been accumulated concerning the function of sleep in development and learning/memory. Many conceptual ideas have been proposed to elucidate the mechanisms underlying them. Sleep consists of a wide variety of physiological processes. It has not yet been clarified which processes are involved in development and learning/memory processes. We have found that single neuronal activity exhibits a slowly fluctuating rate of discharge during rapid eye movement (REM) sleep and a random low discharge rate during non-rapid eye movement (NREM) sleep. It is suggested that a structural change of the neural network attractor underlies this neuronal dynamics-alternation by mathematical modeling. Functional interpretation of the neuronal dynamics-alternation was provided in combination with the phase locking of ponto-geniculo-occipital (PGO)/pontine (P) wave to the hippocampal theta wave, each of which is known to be involved in learning/memory processes. More directly, by the long-term sensory deprivation, the dynamics of neural activity during sleep was found to progressively change in a non-monotonic way. This finding reveals a possible interaction between sleep and reorganization of neural network in the matured brain. Here, in addition to the related findings, we described our idea about how sleep contributes to the learning/memory processes and reorganization of neural network of the matured brain through characteristic neural activities during sleep.

     

Biomass gasification research and field developments by the Prince of Songkla University,Thailand

Studies on the production and utilization of producer gas from charcoal, wood and several types of crop residues have been undertaken at the Department of Mechanical Engineering, Prince of Songkla University. Details of the gasification systems suitable for operation on various types of fuels together with their performance are presented. Based on the results from the tests carried out at the University and data from the field trials, the technical and economic feasibility of biomass gasification technology is discussed. In addition, some practical aspects and problems concerning the performance, operation and maintenance of the gasification systems operating on difficult fuels are also discussed.     

Pyrolytic characteristics of biomass acid hydrolysis residue rich in lignin

Pyrolytic characteristics of acid hydrolysis residue (AHR) of corncob and pinewood (CAHR, WAHR) were investigated using a thermo-gravimetric analyzer (TGA) and a self-designed pyrolysis apparatus. Gasification reactivity of CAHR char was then examined using TGA and X-ray diffractometer. Result of TGA showed that thermal degradation curves of AHR descended smoothly along with temperature increasing from 150 °C to 850 °C, while a “sharp mass loss stage” for original biomass feedstock (OBF) was observed. Char yield from AHR (42.64-30.35 wt.%) was found to be much greater than that from OBF (26.4-19.15 wt.%). In addition, gasification reactivity of CAHR char was lower than that of corncob char, and there was big difference in micro-crystallite structure. It was also found that CAHR char reactivity decreased with pyrolysis temperature, but increased with pyrolysis heating rate and gasification temperature at 850-950 °C. Furthermore, CAHR char reactivity performed better under steam atmosphere than under CO₂ atmosphere.     

Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques.     

An experimental study on biomass air-steam gasification in a fluidized bed

The characteristics of biomass air-steam gasification in a fluidized bed are studied in this paper. A series of experiments have been performed to investigate the effects of reactor temperature, steam to biomass ratio (S/B), equivalence ratio (ER) and biomass particle size on gas composition, gas yield, steam decomposition, low heating value (LHV) and carbon conversion efficiency. Over the ranges of the experimental conditions used, the fuel gas yield varied between 1.43 and 2.57 Nm3/kg biomass and the LHV of the fuel gas was between 6741 and 9143 kJ/Nm3. The results showed that higher temperature contributed to more hydrogen production, but too high a temperature lowered gas heating value. The LHV of fuel gas decreased with ER. Compared with biomass air gasification, the introduction of steam improved gas quality. However, excessive steam would lower gasification temperature and so degrade fuel gas quality. It was also shown that a smaller particle was more favorable for higher gas LHV and yield.     

基于位置特异性谱和输入加权神经网络的蛋白质亚细胞定位预测

蛋白质必须处于正确的亚细胞位置才能行使其功能。文章利用PSI-BLAST工具搜索蛋白质序列,提取位点特异性谱中的位点特异性得分矩阵作为蛋白质的一类特征,并计算4等分序列的氨基酸含量以及1～7阶二肽含量作为另外两类特征,由这三类特征一共得到蛋白质序列的12个特征向量。通过设计一个简单加权函数对各类特征向量加权处理,作为神经网络预测器的输入,并使用Levenberg-Marquardt算法代替传统的EBP算法来调整网络权值和阈值,大大提高了训练速度。对具有4类亚细胞位置和12类亚细胞位置的两种蛋白质数据集分别进行＂留一法＂测试和5倍交叉验证测试,总体预测精度分别达到88.4%和83.3%。其中,对4类亚细胞位置数据集的预测效果优于普通BP神经网络、隐马尔可夫模型、模糊K邻近等预测方法,对12类亚细胞位置数据集的预测效果优于支持向量机分类方法。最后还对三类特征采取不同加权比例对预测精度的影响进行了讨论,对选择的八种加权比例的预测结果表明,分别给予三类特征合适的权值系数可以进一步提高预测精度。     

Brain networks of explicit and implicit learning

Are explicit versus implicit learning mechanisms reflected in the brain as distinct neural structures, as previous research indicates, or are they distinguished by brain networks that involve overlapping systems with differential connectivity? In this functional MRI study we examined the neural correlates of explicit and implicit learning of artificial grammar sequences. Using effective connectivity analyses we found that brain networks of different connectivity underlie the two types of learning: while both processes involve activation in a set of cortical and subcortical structures, explicit learners engage a network that uses the insula as a key mediator whereas implicit learners evoke a direct frontal-striatal network. Individual differences in working memory also differentially impact the two types of sequence learning.     

Biomass–oxygen gasification in a high-temperature entrained-flow gasifier

The technology associated with indirect biomass liquefaction is currently arousing increased attention, as it could ensure a supply of transportation fuels and reduce the use of petroleum. The characteristics of biomass–oxygen gasification in a bench-scale laminar entrained-flow gasifier were studied in the paper. Experiments were carried out to investigate the influence of some key factors, including reaction temperature, residence time and oxygen/biomass ratio, on the gasification. The results indicated that higher temperature favored H₂ and CO production. Cold gas efficiency was improved by > 10% when the temperature was increased from 1000 to 1400 °C. The carbon conversion increased and the syngas quality was improved with increasing residence time. A shorter residence resulted in incomplete gasification. An optimal residence time of 1.6 s was identified in this study. The introduction of oxygen to the gasifier strengthened the gasification and improved the carbon conversion, but lowered the lower heating value and the H₂/CO ratio of the syngas. The optimal oxygen/biomass ratio in this study was 0.4. The results of this study will help to improve our understanding of syngas production by biomass high-temperature gasification.