生物质发电气化过程的机理建模分析

生物质气化发电的关键技术是生物质气化技术,目前国内外对生物质气化发电技术的研究,还缺乏通用的气化模型和方法来模拟气化过程的特性,不能准确地确定生物质燃气的组分和热值等参数,难以提供气化发电系统的可靠数据.最常用的气化过程建模方法是建立机理模型,文章在重点分析了气化过程机理的基础上,把气化模型划分为平衡模型和动态模型两大类,并比较了各模型的优缺点.     

生物质气化发电过程建模与优化

目前国内外在生物质气化发电技术方面,由于缺乏对生物质气化过程特性的建模和参数优化问题的研究,使得生物质燃气中焦油量和污染物的含量过高,燃气品质难以保证,进而对燃气轮机发电机组产生不利的影响,降低了气化燃气的利用价值。因此,对于生物质发电气化过程的建模和重要参数优化控制的研究和探讨具有重要的意义。     

生物质气化—燃料电池发电系统性能分析

建立了基于热力学平衡的生物质气化模型,利用平衡模型分析了气化过程的特性,研究了气化过程的反应规律及各种因素对气化性能指标的影响,详细分析了当量比及物料湿度对气体产物成分及气化产物热值的影响.同时,建立了以生物质气为燃料的固体氧化物燃料电池的数学模型,该模型考虑了燃料电池的能斯特电动势及各种极化损失.利用建立的模型分析了操作参数以及物料湿度和生物质种类对生物质气化—燃料电池发电系统性能的影响.结果表明,生物质气化—燃料电池发电系统的发电效率可达30％,热电联产效率最高可达95％以上.     

生物质热解气化气中焦油生成机理及其脱除研究

生物质气化气中焦油含量高成为制约生物质气化技术商业化发展的决定性因素之一。在对生物质热解气化过程中焦油的生成及其影响因素进行分析的基础上,采取优化炉内结构与炉外气体湿式净化相结合的方法来脱除气体中的焦油,研究开发出气化剂由侧向送入的气化反应炉,以及相应的集喷淋、水浴、水膜、冲激于一体的湿式净化装置。该生物质气化机组所得到的可燃气具有燃气热值高、焦油含量低、操作简单、安全可靠的特点。气化效率可达到 78％,燃气低位热值为 5.4 MJ/m3(玉米秸 ),焦油含量 48 mg/m3, O2含量为 0.7％,主要技术指标均低于有关行业标准。     

煤与生物质流化床共气化的CFD数值模拟研究综述

气化技术作为固体燃料(如煤和生物质等)清洁利用的重要方式,越来越广泛地被应用于生产合成气的工程实践中。针对煤与生物质在单独气化时存在转换效率低、气体产物热值低以及焦油含量高等问题,提出了共气化技术以改善气化工艺。文中主要介绍了基于计算流体力学(CFD)的煤与生物质共气化仿真模拟的研究,论述了两种固体燃料在单独气化和共气化时的反应机理,并详细介绍了冷态和热态流化床共气化CFD模拟所用到的模型。     

下吸式生物质气化炉气化性能研究

生物质固定床气化技术具有运行稳定、可提供清洁能源等优点,但也存在气化效率差,燃气热值低的问题.以采用炉膛集中供风技术和还原区热量包裹技术的下吸式气化炉为研究对象,研究护膛温度、空气当量比(ER)对燃气成分、燃气热值、气化效率等气化性能的影响,并与以往研究结果进行对比分析.实验表明,该气化炉能保证在较低ER内(0.1～0...     

氮气流量对稻草热解半焦性能的影响

慢速热解方法作为生物质气流床气化的前处理工艺,可以解决生物质在气流床气化过程中能量密度低、物料输送难度大及焦油含量高等问题,也可以提高气化合成气的热值.在热解过程中改变氮气流量,考察反应过程的固体产率、能量产率、半焦热值及半焦中碳、氧元素含量的变化结果.研究表明:当氮气流量为0.006m3/h时,固体产率和能量产率最高;当氮气流量为0.16 m3/h时,半焦热值和半焦中碳元素含量的增加量最大,但从整个生物质气流床气化工艺考虑,氮气流量应采用0.006 m3/h.     

基于多目标遗传算法的生物质气化过程参数优化

生物质气化过程是一个复杂的多目标非线性过程。通过对气化过程的机理分析,针对麦秸和玉米秸这2种软质秸秆类生物质原料特性,建立了气化过程的优化目标函数。在此基础上,采用多目标遗传算法对该目标函数进行优化设计计算。计算结果表明,该目标函数对生物质气化过程参数优化具有良好效果,也验证了该算法对于全局优化以及解决复杂非线性问题的有效性。     

户用型下吸式生物质气化炉性能研究

由于生物质能具有储量大、环境友好等特点,生物质气化技术尤其是户用型气化技术在我国农村应用值得研究。论文在建立下吸式户用型气化系统上,研究了不同生物质原料的气化性能,如对温度分布、气化效率、燃气热值、燃气产量等,并进行了焦油脱除效率的研究。结果表明,该炉型的气化效率可达70％,燃气热值达到6MJ／m3,燃气中焦油含量降低至20mg／m3。     

木屑旋风空气分级气化试验研究

基于生物质空气气化机理,结合气流床气化工艺的优点,参考旋风分离器的设计原理,提出了生物质旋风空气分级气化工艺.对不同的位置加入二次风和改变二次风率进行了试验研究.研究结果表明,分级气化能够改善燃气品质.在还原区加入二次风有利于提高燃气热值和气化效率,在氧化区加入二次风有利于减少燃气中焦油的含量.燃气热值、气体产率、气化效率和碳转化率随着二次风率的增加而增加.     

High quality product gas from biomass steam gasification combined with torrefaction and carbon dioxide capture processes

Gasification is currently recognized as a mature technology to convert biomass into useful and versatile product gas for further energy and fuel applications. However, there are some remaining problems relating to the process operation and process efficiency due to inherent properties of biomass feedstock such as high moisture content, low energy density and high oxygen content. Strategies to improve the efficiency of biomass gasification as well as the quality of product gas are thus required. For this purpose, a combined process of torrefaction, gasification, and carbon dioxide capture is developed and simulated in a commercial simulator to investigate the performance of a biomass gasification coupled with a pre-treatment and a post-treatment processes. The results show that the quality of product gas is enhanced when combining gasification with a torrefaction and a CO₂ capture processes. The heating value of the product gas and the cold gas efficiency are both increased with additional torrefaction. The CO₂ capture process using monoethanolamine offers a CO₂ removal efficiency of about 83% and consequently increase the product gas heating value up to 27%.     

Performance prediction of steam gasification of wood using an ASPEN PLUS thermodynamic equilibrium model

In this article, an equilibrium model based on Gibbs free energy minimisation is presented for steam gasification of biomass in process simulator ASPEN PLUS. Carbon is assumed as fully converted into product gases and no tar content is assumed to be present in gaseous product. The objective is to arrive at the optimum process conditions of gasification. An analysis on the sensitivity of producer gas composition, lower heating value, combustible gas yield, and first and second law efficiencies on gasification process variables including reactor temperature, pressure and steam to biomass mass ratio is also envisaged. Simulations are performed with wood as the biomass material, based on real gas behaviour for product gases and gasifying medium. The predicted results of the model are compared with another Gibbs free energy model formulated using simulated annealing minimisation algorithm. The present ASPEN PLUS model is validated with published experimental results on steam gasification on a fluidised bed gasifier.     

Performance correlations for biomass gasifiers using semi‐equilibrium non‐stoichiometric thermodynamic models

Biomass gasification has been a viable alternative for decentralized electricity generation in developing countries. The efficiency of the biomass gasification process for operation of the engine‐generator set is mapped in terms of quality and quantity of the producer gas. In this study, we have attempted to devise generalized correlations for four principal parameters that form the benchmark for the performance of the gasifier. These parameters are lower heating value and net yield (per unit biomass) of producer gas, and volume fractions of CO and CO₂ in the gas resulting from biomass gasification process. The correlations have been constituted using simulations of gasification of three common biomass feedstocks (viz. rice husk, saw dust and corn cobs) using semi‐equilibrium non‐stoichiometric thermodynamic model. The independent variables used in the simulations are air ratio, carbon conversion, gasification temperature and three elemental ratios in the gasification mixture, viz. H/C, O/H and O/C. As many as eight expressions of linear and non‐linear type have been evaluated to best fit the simulations data for each performance parameter. On the basis of statistical indicators, the compatibility of the correlations for best fit of the data has been assessed. Finally, the predictions of the correlation have been tested against experimental data on gasification of different biomass. The best correlation for each performance parameter was chosen on the basis of least average absolute error and highest (absolute) regression coefficient. It was found that the set of best correlations could predict the values of performance parameters within engineering accuracy of ± 10–20%. The correlations proposed in this work are independent of the type of biomass gasifier. These correlations can form a useful tool for design and optimization of fixed or fluidized bed gasifier for any biomass feedstock. Copyright © 2011 John Wiley & Sons, Ltd.     

Model validation of a CFB biomass gasification model

The developed 1-dimensional biomass gasification mathematical model [1] was validated using the experimental results obtained from a circulating fluidized bed biomass gasifier. The reactor was operated on rice husk at various equivalence ratios (ER), fluidization velocities and biomass feed rates. The model gave reasonable predictions of the axial bed temperature profile, syngas composition and lower heating value (LHV), gas production rate, gasification efficiency and overall carbon conversion. The model was also validated by comparing the simulation results with two other different size circulating fluidized beds biomass gasifiers (CFBBGs) using different biomass feedstock, and it was concluded that the developed model can be applied to other CFBBGs using various biomass fuels and having comparable reactor geometries.     

Developing gasification process of polyethylene waste by utilization of response surface methodology as a machine learning technique and multi-objective optimizer approach

This study set out to evaluate the performance of response surface methodology as a machine learning technique on gasification process of polyethylene waste. Different models were developed for predicting gas yield, cold gas efficiency, carbon dioxide emission and lower heating value of syngas in gasification of polyethylene waste using response surface methodology. The accuracy and validity of these models were checked in comparison with the results obtained from the validated model. Most studies in the field of response surface methodology have only focused on its application for multi-objective optimization and largely have ignored its utilization as a machine learning technique. Central composite design was utilized to develop a model between the variables and the responses. Pressure and temperature of the gasifier, moisture content of polyethylene and equivalence ratio were the variables and the responses were gas yield, cold gas efficiency, carbon dioxide emission and lower heating value of syngas. The findings revealed that root mean square errors of the models developed by response surface methodology were 0.235, 0.438, 0.294 and 1.999 indicating their high validity. Finally, multi-objective optimization of polyethylene waste gasification was carried out using response surface methodology resulting in gas yield of 96.29 g/mol, cold gas efficiency of 76.22%, carbon dioxide emission of 4.66 g/mol and lower heating value of 493.44 kJ/mol. The optimum responses were predicted by response surface methodology with errors smaller than 5%.     

Modeling and multi-objective optimization of variable air gasification performance parameters using Syzygium cumini biomass by integrating ASPEN Plus with Response surface methodology (RSM)

The present study developed a robust method for the modeling and optimization of variable air gasification parameters using the ASPEN Plus simulator and Response surface methodology (RSM). A comprehensive thermochemical equilibrium based model of downdraft gasifier was developed by minimizing Gibbs free energy. Model validation was done by comparing the simulated result with the experimental result of four different feedstocks from the literature and, a good agreement was attained. The Complete modeling of the air gasification process was segregated into four phases viz. biomass drying, biomass decomposition, biomass gasification, and producer gas filtration. Drying operation and yield distribution during pyrolysis were computed by incorporating FORTRAN sub-routine statement. Sensitivity analysis was performed to obtain syngas composition using Syzygium cumini biomass fuel and different gasification performances like gas yield (GY), cold gas efficiency (CGE), and higher heating value (HHV) using gasification temperature (600–900)⁰C and equivalence ratio (ER) (0.2–0.6). Furthermore, RSM has been employed for the multi-objective optimizations of the variable gasification parameter. Central composite design (CCD) is adopted. Two independent parameters viz. temperature and equivalence ratio have opted as decision parameters for estimating the optimum performance parameters i.e., hydrogen concentrations, CGE, and HHV. Regression models created from the ANOVA results are found to be highly accurate in predicting output response variables. The optimal values of H₂, CGE, and HHV are found to be 0.1 (mole frac), 25.23%, and 3.96 MJ/kg respectively corresponding to optimized temperature at 887.879 °C and equivalence ratio 0.32 using response optimizer. The composite desirability observed was 0.59.     

Small‐scale production of synthetic natural gas by allothermal biomass gasification

The gasification of biomass can be coupled to a downstream methanation process that produces synthetic natural gas (SNG). This enables the distribution of bioenergy in the existing natural gas grid. A process model is developed for the small‐scale production of SNG with the use of the software package Aspen Plus (Aspen Technology, Inc., Burlington, MA, USA). The gasification is based on an indirect gasifier with a thermal input of 500 kW. The gasification system consists of a fluidized bed reformer and a fluidized bed combustor that are interconnected via heat pipes. The subsequent methanation is modeled by a fluidized bed reactor. Different stages of process integration between the endothermic gasification and exothermic combustion and methanation are considered. With increasing process integration, the conversion efficiency from biomass to SNG increases. A conversion efficiency from biomass to SNG of 73.9% on a lower heating value basis is feasible with the best integrated system. The SNG produced in the simulation meets the quality requirements for injection into the natural gas grid. Copyright © 2012 John Wiley & Sons, Ltd.     

Exergy analysis and multiobjective optimization of a biomass gasification based multigeneration system

Biomass gasification is a process of converting biomass to a combustible gas suitable for use in boilers, engines and turbines to produce combined cooling, heat and power. This paper presents a detailed model of a biomass gasification system and designs a multigeneration energy system which uses the biomass gasification process for generating combined cooling, heat and electricity. Energy and exergy analyses are first applied to evaluate the performance of the designed system. Next, minimizing total cost rate and maximizing exergy efficiency of the system are considered as two objective functions and a multiobjective optimization approach based on differential evolution algorithm and local unimodal sampling technique is developed to calculate the optimal values of the multigeneration system parameters. A parametric study is then carried out and Pareto front curve is used to determine the trend of objective functions and assess the performance of the system. Furthermore, a sensitivity analysis is employed to evaluate effects of design parameters on the objective functions. Simulation results are compared with two other multiobjective optimization algorithms and effectiveness of the proposed method is verified using various performance indicators.     

The effect of air preheating in a biomass CFB gasifier using ASPEN Plus simulation

In the context of climate change, efficiency and energy security, biomass gasification is likely to play an important role. Circulating fluidised bed (CFB) technology was selected for the current study. The objective of this research is to develop a computer model of a CFB biomass gasifier that can predict gasifier performance under various operating conditions. An original model was developed using ASPEN Plus. The model is based on Gibbs free energy minimisation. The restricted equilibrium method was used to calibrate it against experimental data. This was achieved by specifying the temperature approach for the gasification reactions. The model predicts syn-gas composition, conversion efficiency and heating values in good agreement with experimental data. Operating parameters were varied over a wide range. Parameters such as equivalence ratio (ER), temperature, air preheating, biomass moisture and steam injection were found to influence syn-gas composition, heating value, and conversion efficiency. The results indicate an ER and temperature range over which hydrogen (H₂) and carbon monoxide (CO) are maximised, which in turn ensures a high heating value and cold gas efficiency (CGE). Gas heating value was found to decrease with ER. Air preheating increases H₂ and CO production, which increases gas heating value and CGE. Air preheating is more effective at low ERs. A critical air temperature exists after which additional preheating has little influence. Steam has better reactivity than fuel bound moisture. Increasing moisture degrades performance therefore the input fuel should be pre-dried. Steam injection should be employed if a H₂ rich syn-gas is desired.