Prediction of higher heating values of biomass from proximate and ultimate analyses

Two new empirical correlations based on proximate and ultimate analyses of biomass used for prediction of higher heating value (HHV) are presented in this paper. The correlations have been developed via stepwise linear regression method by using data of biomass samples (from the open literature) of varied origin and obtained from different geographical locations. The correlations have been validated via incorporation of additional biomass data. The correlation based on ultimate analysis (HHV = 0.2949C + 0.8250H) has a mean absolute error (MAE) lower than 5% and marginal mean bias error (MBE) at just 0.57% which indicate that it has good HHV predictive capability. The other correlation which is based on proximate analysis (HHV = 0.1905VM + 0.2521FC) is a useful companion correlation with low absolute MBE (0.67%). The HHV prediction accuracies of 12 other correlations introduced by other researchers are also compared in this study.     

On-line determination of the calorific value of solid fuels

In thermal processes with highly inhomogeneous fuels it is desirable to know real time fuel characteristics. In the case of municipal solid waste combustion (MSWC) it was up till now not possible to determine the calorific value of the waste on-line with a high accuracy. In this paper, a new method is presented where the calorific value is determined by means of an observer. A model based upon the mass balance is used together with concentration measurements in the flue gas to calculate on-line the calorific value of the waste. The background of this observer based sensor is discussed in detail, including a sensitivity analysis. Results from tests in different full-scale MSWC plants are presented as well as a comparison with other known off-line methods. It will be shown that the sensor works well and is more accurate than the present off-line methods. Furthermore, some applications of the calorific value sensor will be shortly discussed.     

An algorithm for determining the kinetics of devolatilisation of complex solid fuels from thermogravimetric experiments

An algorithm is introduced for determining the kinetics of devolatilisation of complex fuels, e.g. coal or biomass, when heated in an inert atmosphere. The algorithm uses information from thermogravimetric experiments, at several different, but constant, rates of heating, to identify and characterise the underlying distribution of reactions governing devolatilisation. The algorithm also provides an approximate way of inverting the distributed activation energy model (DAEM), commonly used to model the pyrolysis of coal. Such techniques provide the activation energy, E, and pre-exponential factor, A, for each parallel step participating in the thermal decomposition of a solid fuel. In addition, the amount of material associated with each pair of A and E can be derived.The method is tested on (i) imaginary data from simulated TGA experiments with one or more first-order reactions and (ii) real data from thermogravimetric experiments on the pyrolysis of sewage sludge. In every case the algorithm gave excellent predictions for heating rates other than those at which the pair of E and A were derived. Thus, the algorithm is a useful method of analysing and summarising measurements to provide kinetic data and facilitate predictions.     

A unified correlation for estimating HHV of solid,liquid and gaseous fuels

A unified correlation for computation of higher heating value (HHV) from elemental analysis of fuels is proposed in this paper. This correlation has been derived using 225 data points and validated for additional 50 data points. The entire spectrum of fuels ranging from gaseous, liquid, coals, biomass material, char to residue-derived fuels has been considered in derivation of present correlation. The validity of this correlation has been established for fuels having wide range of elemental composition, i.e. C — 0.00–92.25%, H — 0.43–25.15%, O — 0.00–50.00%, N — 0.00–5.60%, S — 0.00–94.08% and Ash — 0.00–71.4%. The correlation offers an average absolute error of 1.45% and bias error as 0.00% and thereby establishes its versatility. Complete details of few salient data points, the methodology used for derivation of the correlation and the base assumptions made for derivation are the important constituents of this work. A summary of published correlations along with their basis also forms an important component of present work.     

A correlation for calculating elemental composition from proximate analysis of biomass materials

Elemental composition of biomass is an important property, which defines the energy content and determines the clean and efficient use of the biomass materials. However, the ultimate analysis requires very expensive equipments and highly trained analysts. The proximate analysis on the other hand only requires standard laboratory equipments and can be run by any competent scientist or engineer. This work introduces a general correlation, based on proximate analysis of biomass materials, to calculate elemental composition, derived using 200 data points and validated further for additional 50 data points. The entire spectrum of solid lignocellulosic materials have been considered in the derivation of the present correlation, which is given as: C = 0.637FC + 0.455VM, H = 0.052FC + 0.062VM, O = 0.304FC + 0.476VM, where FC – 4.7–38.4% fixed carbon, VM – 57.2–90.6% volatile matter, C – 36.2–53.1% carbon, H – 4.36–8.3% hydrogen and O – 31.37–49.5% oxygen in wt% on a dry basis. The average absolute error of these correlations are 3.21%, 4.79%, 3.4% and bias error of 0.21%, −0.15% and 0.49% with respect to measured values C, H and O, respectively. The major advantage of these correlations is their capability to compute elemental components of biomass materials from the simple proximate analysis and thereby provides a useful tool for the modeling of combustion, gasification and pyrolysis processes.     

What correlation is appropriate to evaluate biodiesels and vegetable oils higher heating value (HHV)?

The heating value is one of the most important properties of animal fats, vegetable oils and biodiesels for their use as fuels in stead of petroleum.There are lots of formulae or correlations encountered in the literature to evaluate biomass fuels’ higher heating value (HHV). Lots of them are not specially established for vegetable oils, animal fats and their derivatives. In this paper, some correlations previously published and based on ultimate analysis or fatty acid composition are applied to some bio oils samples collected from the literature. The aim of this article is to investigate what of them can be used to estimate animal fats, biodiesels, vegetable oils and their derivatives HHV with a high accuracy. With an absolute average error inferior to 4%, the results show that the formulae of : Channiwala and Parikh, Boie and Vondracek, Boie, Vondracek, Fassinou et al. Milne, IGT, Demirbas and Dulong can be used at this purpose with a high accuracy by comparison to the bomb calorimetric method.     

油页岩与煤路线制油的技术经济分析和比较

液体燃料广泛应用于交通、物流和生活等行业,然而液体燃料的生产严重依赖石油。我国石油资源相对贫乏,石油对外依存度高达60%。为减少对石油的依赖,我国正积极开发石油替代资源,特别是油页岩和煤炭。但迄今少有文献报道对油页岩与煤路线生产液体燃料过程进行全面的技术经济分析和比较。本文通过对油页岩制油和煤制油分别进行建模和模拟,根据模拟从能效、投资和成本等方面对这两种路线进行分析和比较。结果表明油页岩制油的能效比煤制油低5个百分点,因为油页岩制油的原料利用率低,产品收率低。经济方面,油页岩制油的固定投资为63.34元/GJ,相比煤制油节省70%,因为油页岩制油流程短,设备结构简单。但油页岩制油的原料消耗大,生产1t液体燃料消耗24.5t油页岩,所以其成本相比煤制油仅节省6%。     

Prediction of macerals contents of Indian coals from proximate and ultimate analyses using artificial neural networks

Coal, a prime source of energy needs in-depth study of its various parameters, such as proximate analysis, ultimate analysis, and its biological constituents (macerals). These properties manage the rank and calorific value of various coal varieties. Determination of the macerals in coal requires sophisticated microscopic instrumentation and expertise, unlike the other two properties mentioned above. In the present paper, an attempt has been made to predict the concentration of macerals of Indian coals using artificial neural network (ANN) by incorporating the proximate and ultimate analysis of coal. To investigate the appropriateness of this approach, the predictions by ANN are also compared with conventional multi-variate regression analysis (MVRA). For the prediction of macerals concentration, data sets have been taken from different coalfields of India for training and testing of the network. Network is trained by 149 datasets with 700 epochs, and tested and validated by 18 datasets. It was found that coefficient of determination between measured and predicted macerals by ANN was quite higher as well as mean absolute percentage error was very marginal as compared to MVRA prediction.     

煤工业分析方法的改进

通过试验和数理统计分析,对应用MAC-G全自动工业分析仪与国标法测定煤的工业分析进行对比．结果表明,两种方法无显著差异,而且差值的置信区间也很小。MAC—G分析仪在180min内可分析19个样品的分析基水分、灰分和挥发分,不仅提升了测试效率,也减少了人为因素的影响,大大提高了分析结果的可靠性。     

Ash fusibility and compositional data of solid recovered fuels

Several approaches are established to analyse the fouling and slagging propensities of coal ashes, but the same cannot be said of solid recovered fuel (SRF) ashes. This work has been conducted by using some fouling and slagging indicators, which are commonly applicable to coal ashes, on SRF ashes to ascertain their applicability.In this work, laboratory prepared ashes derived from municipal solid waste (MSW), sewage sludge, demolition wood, shredded rubber tyres, and plastic/paper fluff are analysed for their fusibility leading to fouling and slagging using three approaches; the ash fusibility temperatures (AFT), ternary phase diagrams, and fouling/slagging indices. The results from each approach are examined to determine the inclination of the ashes toward fouling and slagging. A subsequent inter-comparison of the methods was conducted to validate the methods which are in agreement and are applicable to SRF ashes. The study showed that ternary equilibrium phase diagram SiO₂-CaO-Al₂O₃, various fouling and slagging indices, and AFT can be used to complement each other to predict ash fusion properties, fouling and slagging propensities of SRF ashes.     

油页岩工业分析与元素分析各项指标间的相互关系

通过对松辽盆地北部油页岩进行铝甑分析,得出w(油)基本上都大于6%,具有较好的工业利用价值。油页岩的工业分析和元素分析实验结果表明,工业分析中灰分、挥发分、固定碳、发热量与元素分析中的w(碳)、w(氢)、w(氮)指标间存在一定的关系。这对油页岩开发利用和实验数据的验证具有重要的意义。     

The effect of air preheating on the combustion of solid fuels on a grate

Combustion of solid fuels on a grate is widely used. Mostly, the combustion behaviour is explained by the classical theory of Rogers. However, that theory cannot explain the combustion process when primary air preheating is applied. Solid fuel grate combustion is studied by experiments in a pot furnace. Experiments with and without primary air heating are described. These are compared with conclusions learnt from real plant experiments. It was found that the pot furnace experiments have a limited value in explaining the combustion behaviour of solid fuels on a grate. In order to be able to explain the results from practice an quantitative extension of Rogers' theory for the case with air preheating is presented.     

Proximate analysis based multiple regression models for higher heating value estimation of low rank coals

In this paper, multiple nonlinear regression models for estimation of higher heating value of coals are developed using proximate analysis data obtained generally from the low rank coal samples as-received basis. In this modeling study, three main model structures depended on the number of proximate analysis parameters, which are named the independent variables, such as moisture, ash, volatile matter and fixed carbon, are firstly categorized. Secondly, sub-model structures with different arrangements of the independent variables are considered. Each sub-model structure is analyzed with a number of model equations in order to find the best fitting model using multiple nonlinear regression method. Based on the results of nonlinear regression analysis, the best model for each sub-structure is determined. Among them, the models giving highest correlation for three main structures are selected. Although the selected all three models predicts HHV rather accurately, the model involving four independent variables provides the most accurate estimation of HHV. Additionally, when the chosen model with four independent variables and a literature model are tested with extra proximate analysis data, it is seen that that the developed model in this study can give more accurate prediction of HHV of coals. It can be concluded that the developed model is effective tool for HHV estimation of low rank coals.     

NO reduction capacity of four major solid fuels in reburning conditions - Experiments and modeling

The combustion of solid fuels in the rotary kiln and in the calciner of a cement plant generates fuel and thermal NO. This NO can be reduced inside the reducing zone of the calciner. This occurs in two different ways: homogeneous reduction by hydrocarbons and heterogeneous reduction by char. The purpose of this paper is to identify the relative contribution of volatile matters or char on the NO reduction process, which largely depends on the nature of the solid fuel used for reburning.Experiments were undertaken in an Entrained Flow Reactor (EFR), at three temperatures: 800, 900 and 1000 °C. Four major fuels used in the cement production process were studied: a lignite, a coal, an anthracite and a petcoke. Specific experiments were undertaken to determine (i) their devolatilisation kinetics and the gas species released. A wide range of species influencing the NO chemistry was carefully analyzed. Then, (ii) the char oxidation and (iii) the char NO reduction kinetics were characterized. Finally, (iv), the “global” NO reduction capability of each fuel was quantified through experiments during which all phenomena could occur together. This corresponds to the situation of an industrial reactor in reducing conditions. Anthracite and petcoke reduce only very small quantities of NO whereas lignite and coal reduce, respectively, 90% and 80% of the initially present 880 ppm of NO (at 1000 °C after 2 s).The four types of experiments described above were then modeled using a single particle thermo-chemical model that includes heterogeneous reactions and detailed chemistry in the gas phase. This model reveals that both NO reduction on char and NO reduction by volatiles mechanisms contribute significantly to the global NO reduction. After short residence times (several tenth of a second), gas phase reactions reduce NO efficiently; after long residence times (several seconds) the char reduces larger quantities of NO.     

A particle scale model for municipal solid waste and refuse-derived fuels pyrolysis

The solid phase decomposition during pyrolysis of municipal solid waste (MSW) and refuse-derived fuels (RDF) is modelled on particle scale accounting for heat and mass transfer. Waste pyrolysis is expressed as a linear combination of pyrolysis of its components. The novel characterization method used expresses waste composition in terms of three reference species. The selected species are a mixture of cellulose and hemicellulose, a mixture of polystyrene and polyethylene terephthalate, and a mixture of polyethylene and polypropylene. The pyrolysis kinetics models for these components are taken from the literature. The fractions of the components in the mixtures are optimized to fit the model to non-isothermal mass loss curves from selected experimental reports. The particle scale model has been evaluated against experimental transient temperature profiles at the centre of a large waste pellet during pyrolysis. The model is able to predict the main trend, but shows a more fluctuating temperature curve.     

Characterization of a lab-scale platinum filament pyrolyzer for studying the fast devolatilization of solid fuels

Platinum filament pyrolyzers achieve very high temperature and heating rate and can provide useful parameters for practical applications in combustion, pyrolysis and gasification processes. The critical use of an experimental instrument is necessary to provide reliable data. In this work, a commercial pyrolyzer (CDS Pyroprobe 2000) is characterized to obtain a correspondence between the nominal and the effective operating conditions. This is the basis for the modeling estimation of the effective thermal history of the sample during each experimental run. The experimental results obtained performing the devolatilization of coals, biomass and waste fuels using the pyrolyzer are compared with those obtained in a conventional thermogravimetric balance, to evaluate the effects of extremely different operating conditions. The amount of volatile released programming the most severe thermal conditions using the pyrolyzer (thus in conditions more similar to large-scale plants) differs significantly from that of thermogravimetric runs. Global kinetics are obtained fitting the experimental results and using the thermal history of the sample from the model results. They depend strongly on the conditions used for the devolatilization. Global kinetics obtained in the thermogravimetric balance runs (low heating rate) overestimate the rate of devolatilization in the pyrolyzer (high heating rate).     

水泥窑用固体替代燃料对比分析

本文针对目前水泥行业水泥窑用固体替代燃料,对工业边角料、生物质园林垃圾、废旧轮胎从各工业分析指标,相关燃烧特性等方面与煤粉展开相关对比分析。     

Co-firing of coal and cattle feedlot biomass (FB) fuels. Part I. Feedlot biomass (cattle manure) fuel quality and characteristics

The use of cattle manure (referred to as feedlot biomass, FB) as a fuel source has the potential to solve both waste disposal problems and reduce fossil fuel based CO₂ emissions. Previous attempts to utilize animal waste as a sole fuel source have met with only limited success due to the higher ash, higher moisture, and inconsistent properties of FB. Thus, a co-firing technology is proposed where FB is ground, mixed with coal, and then fired in existing pulverized coal fired boiler burner facilities. A research program was undertaken in order to determine: (1) FB fuel characteristics, (2) combustion characteristics when fired along with coal in a small scale 30 kW_t (100,000 BTU/h) boiler burner facility, and (3) combustion and fouling characteristics when fired along with coal in a large pilot scale 150 kW_t (500,000 BTU/h DOE-NETL boiler burner facility). These results are reported in three parts. Part I will present a methodology of fuel collection, fuel characteristics of the FB, its relation to ration fed, and change in fuel characteristics due to composting. It was found that FB has approximately half the heating value of coal, twice the volatile matter of coal, four times the N content of coal on heat basis, and due to soil contamination during collection, the ash content is almost 9-10 times that of low ash (5%) coal. The addition of <5% crop residues had little apparent effect on heating value. The main value of composting for combustion fuel would be to improve physical properties and to provide homogeneity. The energy potential of FB diminished with composting time and storage; however, the DAF HHV is almost constant for ration, FB-raw, partially composted and finished composted. The fuel N per GJ is considerably high compared to coal, which may result in increased NO_x emissions. The N and S contents per GJ increase with composting of FB while the volatile ash oxide% decreases with composting. Based on heating values and alkaline oxides, partial composting seems preferable to a full composting cycle. Even though the percentage of alkaline oxides is reduced in the ash, the increased total ash percentage results in an increase of total alkaline oxides per unit mass of fuel. The adiabatic flame temperature for most of the biomass fuels can be empirically correlated with ash and moisture percentage.     

A thermochemical concept-based equation to estimate waste combustion enthalpy from elemental composition

Waste combustion is an interesting alternative for waste management and energy recovery. Knowledge of the waste higher heating value (HHV) is important for judging it’s worth as fuel. This work introduces a new equation, based on thermochemical concepts, to calculate HHV from elemental composition. This equation is expressed in terms of mass percentages on a dry basis of carbon (%C), hydrogen (%H), oxygen (%O), nitrogen (%N), and sulfur (%S); the HHV is computed in MJ/kg. The equation is as follows: HHV=(1−(%H₂O/100))(−0.3708(%C)−1.1124(%H)+0.1391(%O)−0.3178(%N)−0.1391(%S)). The thermochemical concept on which this expression is based involves a wide applicability. This equation neglects the inorganic carbon, hence it is not very adequate when there is a significant concentration of it. The predictions from this approach were contrasted against those proceeding from equations currently used in combustion technology, and also against bomb calorimeter data. The new equation is clearly competitive with respect to other formulations, and it can be very helpful for presenting a waste HHV value based on different derivation suppositions.     

On the high‐gasification rate of Brazilian manganese ore in chemical‐looping combustion (CLC) for solid fuels

The high rate of char gasification observed when using a Brazilian manganese ore as compared to ilmenite is investigated in a batch fluidized‐bed reactor. Experiments were carried out at 970°C using petroleum coke, coal and wood char as fuel with a 50% H₂O in N₂ as fluidizing gas. A manufactured manganese oxygen carrier was also used, however, which presented a slower char conversion rate than the manganese ore. It is concluded that decrease in H₂ inhibition and oxygen release are unlikely to be the main responsible mechanisms for the ore's unexpected gasification rate. The ore was also mixed in different ratios with ilmenite and it was observed that the presence of even small amounts of ore in the bed resulted in increased gasification rate. Thus, the high‐gasification rate for the manganese ore could be due to a contribution from the impurities in the ore by catalyzing the gasification reaction. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4346–4354, 2013