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The CV of a biomass is a crucial property needed to be considered for its conversion into fuels. The importance of oxygen:carbon (O:C) and hydrogen:carbon (H:C) ratios on the CV of solid fuels can be illustrated using a Van Krevelen diagram. High content of oxygen and H2 reduces the energy value of the fuel, since energy contained in carbon-oxygen (C-O) and carbon-hydrogen (C-H) bonds are lower than energy in carbon-carbon (C-C) bonds. Materials with low O:C and H:C ratios are usually preferred as fuels for gasification and combustion since they contain more energy. Composition of lignocellulose component of wood is seen to be closely related to coal since wood is the natural precursor for the formation of coal through diverse coal liquefaction processes [4]. The content of alkali metal such as Na, K, Mg, P, and Ca, in biomass is crucial for its thermo-chemical conversion into fuels. Reaction between alkali metals and silica present in ash produces sticky, mobile liquid phase
Types of biomass |
Biomass sources |
Oil palm (OP) |
Kernel shell (PKS), frond (OPF), trunk (OPT), mesocarp fiber (MCF), empty fruit bunch (EFB) |
Paddy (P) |
Rice husk (RH), paddy straw (PS) |
Sugar cane (SC) |
Bagasse (SCR) |
Coconut (C) |
Fiber (CF) |
Wood industries (W) |
Sawdust (SD), woodchips (WC) |
Rubber(R) |
Kernel (RSK) |
Table 17.5 Malaysian biomass samples |
which can lead to blockages of airways in furnace. Total silica content may increase significantly due to contamination with soil during harvesting.
In a work performed by Abdullah and Yusup [29], the characterization of eleven samples ofMalaysian biomass, listed inTable 17.5, has been performed andevaluated to determine their potential utilization as feedstock for biomass gasification into hydrogen.
In this work, the biomass is physically pre-treated via grinding and sieving. Analyses are carried out on sieved biomass to ensure homogeneous samples. Details on the types and working principle of the analysers used in characterizing the biomass are given in Table 17.6. Table 17.7 shows lists of characteristics of eleven types of biomass properties. The elemental analysis results of this biomass are given in Table 17.8. The composition of the hydrocarbon fuel is expressed in terms of its basic elements except for its moisture (M) and inorganic constituent. This is represented by the ultimate analysis. In comparison, the composition of biomass in terms of its volatile matter, FC, moisture, and ash content is represented by proximate analysis. A typical ultimate and proximate analysis is presented by Eqs. 17.4 and 17.5 [35];
Ultimate analysis: C + H + O + N + S + ASH + M = 100 % (17.4)
Proximate analysis: VM + FC + M + ASH = 100 % (17.5)
Table 17.6 Biomass characterization
Analysis Working principle
The C, H, O, N, and S are expressed as the weight percentages of carbon, hydrogen, oxygen, nitrogen, and sulphur, respectively in the fuel.
Biomass moisture content can either be expressed in dry or wet basis as presented in Eqs. 17.6 and 17.7 [35];
The dry-basis moisture is: Mdry = (Wwet — Wdry)/Wdry (17.6)
The wet-basis moisture is: Mwet = (Wwet — Wdry)/Wwet (17.7)
Table 17.8 Characteristics of fuels-ultimate analysis |
|||||
Biomass |
Ultimate analysis (daf, wt%) |
||||
C |
H |
N |
S |
O (by diff) |
|
EFB |
40.73 |
5.75 |
1.40 |
0.22 |
92.63 |
MCF |
40.97 |
5.96 |
0.77 |
0.51 |
92.76 |
PKS |
49.65 |
6.13 |
0.41 |
0.48 |
92.98 |
OPF |
42.10 |
5.46 |
0.70 |
0.13 |
93.71 |
RSK |
44.01 |
6.11 |
0.58 |
.03 |
93.28 |
SD |
43.68 |
6.65 |
0.23 |
0.04 |
93.08 |
SCR |
42.93 |
5.82 |
0.68 |
0.06 |
93.44 |
RH |
38.74 |
5.83 |
0.55 |
0.06 |
93.44 |
PS |
33.48 |
6.01 |
1.46 |
0.15 |
92.38 |
CF |
45.51 |
6.02 |
0.78 |
0.09 |
93.11 |
CS |
43.00 |
6.30 |
0.75 |
0.05 |
The wet basis (Mwet) and dry basis (Mdry) are related as in Eq. 17.8 [35];
Mdry = Mwet/1 — Mwet (17.8)
The FC content is calculated based on Eq. 17.9 [35];
FC = 1 — M — VM — ASH (17.9)
The suitability of biomass as a gasification feedstock is screened using an aggregate matrix as shown in Table 17.9 based on its CV, O:C and H:C ratios and moisture, ash, volatiles, and FC contents [29]. A weighting factor is assigned to each characteristic according its significance for gasification process. The biomass is ranked, based on the scoring performed using the weighting factor. It was found that palm kernel shell (PKS), after being subjected to physical pre-treatment (grinding and sieving) and thermal pre-treatment (oven-drying), is the most preferred biomass among the eleven biomass as the gasification feedstock as shown in Fig. 17.7.
Work by Kazi et al. [22] had estimated the cost of ethanol production from ligncel — lulosic biomass at US $1.14/kg (US $0.90/L), and this was dependent on feedstock and enzyme costs (assumed enzyme price as US $0.23/kg of ethanol produced). Due to the poor returns for ethanol, alternate uses of sugars for bio-products offer higher value propositions and some are listed in Table 9.4. For example, succinic acid market price is at US $5.9-8.8/kg [23] and can be used in a variety of food products and as a building block in polymers. Another valued bioproduct, PHA, has a market price at US $4.4-6.1/kg [24]. PHA can be produced from low value sugars and organic acids [1, 4] derived from woody-biomass feedstocks, rather than current practices of using refined sugar as a carbon source, which offers significant financial advances in reducing PHA manufacturing costs by 50 % [1]. Future work will investigate the use of these hydrolysates for the manufacture of PHA using mixed microbial consortia [6].
A simple hot-water pretreatment on hybrid poplar was achieved and optimized by using a response surface methodology on a 23 central composite design. The optimized pretreatment condition (temperature 200 °C, time 18 min, solid loading 20 %) was used in further experiments. A subsequent cellulase/xylanase hydrolysis step resulted in yielding high-level of sugars. Thus, pretreatment was shown to be an important step for cost effective enzymatic hydrolysis of wood with low levels of inhibitory side-products. Future work will focus on enzyme loading, conversion to targeted bioproducts (PHA), energy balance, and cost.
Gasifier performance could be expressed in terms of gasification efficiency and product quality, such as heating value and the amount of desired product gas. Mathematical modeling and simulation of the gasification process provides qualitative information on the gasifier performance, such as the effect of feedstock, design parameters, and operating conditions of the process even though it is not very accurate. A mathematical model or simulation is, however, useless if it cannot reproduce experimental data with acceptable deviation [14]. Gasifier models could be classified into the following groups: thermodynamic equilibrium, kinetic, artificial neural network and computational fluid dynamics (CFD).
Alcoholic fermentation has been the main focus for these feedstocks with bioethanol as its main product. With the current problems surrounding worldwide food supply, it is not ethically and politically justifiable to use food as a fuel while certain countries suffer famine. However, food conservation and storage may sometimes be very difficult and some considerable amounts of corn and grains may become unfit for human consumption. Nevertheless, considering the high starch content and appreciable fermentation yields with this feedstock, it has been rarely studied in fields other than bioconversion.
11.2.1.4 Oilseeds and Plants
Contrary to corn and grains, oilseeds and their plants show very poor starch content. Many species, such as colza, are dedicated to the production of biodiesel. Although biodiesel production has been demonstrated as technically feasible at a large scale, it is not economically sustainable without government grants or incentives. Several studies on oilseeds and plants pyrolysis can be found in the scientific literature, which indicates a strong interest for this conversion technology. As an example, castor bean slow pyrolysis yields easily over 65 % oil with as low as 20 % solid residue [22]. Due to the high oil yield, there is interest in mixing these oils with diesel to produce blends for transportation fuels. However, for the same reasons that were brought in Sect. 11.2.1.5, these feedstocks should not be diverted from their primary function, namely food supply.
All organics substances originating from living nowadays or recently lived organisms are consideredas a biomass. For example, coal and oil are not a biomass as they were formed from organisms living millions years ago. On the other hand, a municipal and industrial waste can be considered as a biomass because an essential part of their organic weight is such kinds of a biomass as wood, rubbers, food waste, and other materials of an organic nature. Clearly, a biomass is one of the most diverse (by quantity of representatives) class of fuels. Therefore, determination of the perspectives of their energy use demands examination of energy characteristics and chemical composition along with the rates of biomass formation and expenditures connected with its production. We will examine four common types of biomass: wood, energy crops, animal waste, and poultry waste.
Wood, perhaps, is one of the most common types of biomass. Approximately 40.5 % of annually extracted forest resources are used for obtaining the roundwood, ~6.3 % in paper manufacturing, ~45.1 % as a fuel, and about 8.1 % at charcoal production (total wood consumption ~2.48 Gt) [9]. The average density of wood with moisture of 20 % (~624 kg/m3 [10]) was used to estimate mass of wood by its volume. The wood (moisture ~20 %) consumption during pyrolysis aimed on charcoal production was estimated by proximate analysis results on total yield of ash and fixed carbon (~ 12.1 % [11]). Wood, compared to fossil fuels, basically does not contain sulfur and other ecologically unfriendly elements. However, its use in the wood industry is more economically sound than its energy application. Therefore, it is more expedient to process lumbering wastes, residues, and municipal waste mostly consisting of woody materials.
Table 14.2 represents some organosolv pretreatment processes, their effects on the substrate features and their susceptibilities to enzymatic hydrolysis. Although a precise comparison is difficult to make from the data given in this table due to differences in the natures of lignocellulose and the reaction conditions employed, a rough evaluation is possible. By varying the process effective factors, such as the type of solvent (ethanol, acetone, butanol, glycerol, or other high boiling solvents) and aqueous content (50-90 %, w/w), catalyst type (H2SO4, NaOH, or MgCl2) and concentration (0.5-1.5 %, w/w), reaction temperature (140-200 °C) and time (10-60 min), ratio of biomass to solvent (1:5-1:15, w/w), the organosolv pretreatment produces a range of substrates with varying enzymatic digestibility of 10-90 %. As shown in the table, the organosolv pretreatment could increase the hydrolysis yields to 1.5-10 times that of the untreated substrate. Most of the previous work defined the delignification, recovery of hemicellulose and cellulose as well as hydrolysis rate and yield of the pretreated materials as the indicators to evaluate the efficiency of the process. To most kinds of biomasses 10 % (w/w) biomass loading, using the aqueous ethanol or aqueous acetone (around 50%, w/w), with 0.5-1.0% H2SO4 as catalyst, under 160-180 °C for 30 min appear to be the most effective parameters for organosolv pretreatment efficiency.
Reaction conditions were optimized to achieve the highest yield of them. Nevertheless, these reaction factors should not only be optimized to maximize the enzymatic saccharification yield, but also to take into account the energy cost and reduction of the formation of fermentation inhibitors. Using less severe pretreatment conditions for better processing economy, as well as minimizing the generation of fermentation inhibitors (furfural and 5-HMF), have to be taken into consideration [22, 72].
SB/SL consists of crystalline cellulose nanoflbrils embedded in an amorphous matrix of cross-linked lignin and hemicelluloses that impairs enzyme and microbial accessibility [18]. Table 16.2 summarizes the detail cell wall composition of SB and SL.
In general, SB contains more holocellulose (hemicellulose + cellulose) (67.8 %) than SL (61.7 %). In contrast, lignin and ash content is less in SB (23.6 %, 1 %) than SL (36.1 %, 7.8 %) which limits the biochemical-based conversion applications of latter. Apart from carbohydrates and lignin, the cell wall ingredients such as silica, ash, and extractives along with natural moisture resides in both kind of biomass [8]. Factors such as high structural carbohydrates and less lignin in SB/SL, large availability, almost no food/feed value make it better feedstock for bio-based products than the contemporary agro residues (wheat straw, rice straw, corn stover, etc.) [2]. Holocellulose content in SB/SL (67.8 % and 61.7 %) is of high importance for their bioconversion into various products by microbial fermentation. This content (% dry weight) is fairly comparable with the other lignocellulosic materials such as wheat straw (56.1), corn stover (64.1), switch grass (61.8), and Spruce wood (71.9) [9, 10, 13].
In order to degrade the holocellulosic fraction of plant cell wall, the current pretreatment methodologies are unattractive due to economic concerns. This is basically because of the special arrangement of cross-linked lignin with holocellulose network; biomass has evolved a superb mechanism to protect itself from microbial invasion. This mechanism of natural resistance in plant cell wall is called “biomass recalcitrance” [18]. Accessibility to the carbohydrate fraction of cell wall is a multi-scale phenomenon encompassing several orders of magnitude due to both macroscopic (compositional heterogeneity, mass transfer limitations) and microscopic barriers (holocellulose crystallinity, lignin-holocellulose linkage) [20].
Table 16.3 Summarization of different pretreatment technologies applied to sugarcane feedstock and their mechanistic impact on plant cell wall
|
Cell wall is a complex and highly arranged structure and thus resists the accessibility of cellulase enzymes. Pretreatment allows its breakdown and increase the amenability of enzymes for the sugars monomers recovery. Vascular bundles are consequently arranged in native SB/SL. Pretreatment disrupts the compactness of cell wall. Scanning electron and atomic force microscopic view of cell wall after dilute acid mediated pretreatment, clearly shows the disorganization of cell wall components which is pivotal for the improved cellulase action on carbohydrate polymer in order to yield simple sugars. From raw SB/SL (highly complex structure) to glucose or other sugars (monomers) production is mutli-sclae phenomenon spanning several orders of magnitude (10-9 meters) [18, 20].
Pretreatment methods can be divided into four different categories: Physical, physico-chemical, chemical, and biological processes. Each method has its own specificity toward the plant cell wall fractions. Table 16.3 summarizes the effect of different pretreatment technologies applied to the sugarcane feedstock for the recovery of sugars.
Resin beads containing exotic catalytic sites can also be used as heterogeneous catalysts for lowering the FFAs content in a pretreatment process for feedstocks with
high content of FFAs. Resin beads do not dissolve with the reagents as homogeneous catalysts do, making their separation a great deal easier [31]. Furthermore, the use of such resins does not require neutralizing acid, so no water or salts are generated at the end of the biodiesel production process, leading to cleaner biodiesel and glycerol byproduct. Resin beads can be used in both stirred tank (batch and continuous) reactors and in continuous packed column reactors. Currently, one of the most promising resin catalysts for esterification of FFAs is AMBERLYST BD20 from Rohm and Haas (part of the Dow group). Emulsions form upon transesterification of high-FFAs feedstocks whereas a clean separation occurs when the FFAs content in feedstocks is first esterified using AMBERLYSTTM BD20 technology [32, 33].
Bayer Technology Services has designed a complete biodiesel production system to get the best performance from this catalyst. The technology converts FFAs into their FAME’s. It is a versatile system that can be adopted to deal with any amount of FFAs up to 100 % by adding more reactors in series. Depending on the initial FFAs content allowable in the oil going downstream, a single reactor will deal with oils containing around 5 % FFAs, two reactors push this to around 70 % and three to 100 % conversion. Several resins are available commercially. Rohm and Haas has Amberlite 1R 120 or IRA 900 or Duolite C20 as strong-cation exchange resins; IRA 93SP or Duolite A 378 as weak-anion exchange resins; and the mixed bed polishing resins C20MB and A 101D as strong-cation and anion exchange resins [34].
In the gasification step that follows pyrolysis, several parallel reactions occur:
• Char gasification involves the reaction between char and steam, carbon dioxide, hydrogen, and oxygen (R1, R2, R3, andR4 shown in Table 10.1). These reactions
Table 10.2 Summary of the proposed kinetics for biomass pyrolysis
are endothermic, except for those involving O2 and H2 which are exothermic. The rate of these reactions depends on the reactivity of char and the gasifying medium: Oxygen is the most reactive species followed by steam and carbon dioxide. Char oxidation reactions are so fast that most of the oxygen is used in this specific reaction step. The relative reaction rates of the gasification reactions are estimated by Walker et al. [38]:
RC+O2 ^ RC+H2O ^ RC+CO2 ^ rc+h2
• Char is usually assumed to be pure carbon for simplification. In reality, it is composed of small amounts of hydrocarbon. Biomass char is generally more
porous and reactive compared to coal char, so its reaction should be considered different [39].
• Water-gas (R2) reaction involves hydrogen, which affects char and steam reaction negatively as shown by Barrio et al. [40]. The continuous removal of hydrogen from the reactor is necessary in order to accelerate water-gas reactions.
• The gasification of char with carbon dioxide (known as Boudouard reaction—R1) is a relatively slow reaction. The rate of this reaction is negligible below 1,000k [41].
• Water-gas shift reaction (R8) is an important kinetic step in the gas phase. It controls the production and the ratio of hydrogen and carbon monoxide, which is critical for downstream processes. It is a slightly exothermic equilibrium reaction with negligible sensitivity to pressure. Above 1,000 °C, it reaches equilibrium fast but a heterogeneous catalyst is required to reach equilibrium at lower temperatures. Probstein and Hicks showed that at lower temperatures the reaction has a higher equilibrium constant, which means a higher hydrogen yield with low reaction rates [42]. Different catalysts have been tested and employed for water-gas shift reaction, like copper promoted catalysts for the temperature range of 300510 °C, and a copper-zinc-aluminum oxide catalyst for the 180-270 °C range in commercial applications [43].
• As mentioned before, one of the products of gasification is a condensable heavy hydrocarbon, known as tar. Produced tar from the pyrolysis reactions undergoes further cracking and polymerization reactions to produce lighter or heavier hydrocarbons. Several studies have been done on the secondary pyrolysis reactions, which involve the fate of tar and its cracking. Boronson et al. and Liden et al. have reported separately the kinetic parameters of tar cracking derived from wood [17,44]. Rath and Staudinger also studied the tar cracking kinetics of birch wood particles in a thermo-gravimetric analyser and a coupling of thermo gravimetric analysis (TGA) with a tubular reactor [45]. They showed that the extent of tar cracking is not only dependent on the conditions in the reactor (temperature and residence time) but also on the temperature at which the tar was formed [46]. Most of the kinetic models proposed for tar cracking are based on a single step, first — order reaction. Among different kinetics, the results of Boronson et al. show comparable rates and are the most used. The kinetics of tar cracking has also been studied in another approach. Due to the complexity of the tar, several researchers have studied its cracking and decomposition reactions using a model-biomass-tar compound, such as phenol, toluene, naphthalene, 1-methylnaphthalene, and so on. In most of the proposed kinetics, a first-order reaction for tar cracking was used.
In these models, a feedstock first reacts to yield volatiles and intermediate solid products (Fig. 11.2c). The following steps then depend on the feedstock and the
model. In most models, the volatiles undergo thermal cracking while the intermediate solid products further decompose into other final and/or intermediate products and so on. For highly heterogeneous materials, where the notion of ‘pseudo-component’ loses its physical meaning, this conceptualization appears more realistic. However, the main disadvantage of this type of model is the high number of parameters and the difficulty to segregate the different reactions experimentally to clearly estimate their kinetics parameters. Considering all the possibilities for the different reactions under various conditions, this family of models will lead to a myriad of different models.
Table 11.2 lists the kinetics parameters of several models available in the scientific literature along with their range of validity. Note that the parameters reported in Table 11.2 were all obtained from static thermogravimetric experiments. Furthermore, some of these models assumed an order of reaction, while pyrolysis reactions are in reality characterized by multiple elemental reactions with their specific reaction order. Moreover, it has been demonstrated that the product yield during biomass pyrolysis is strongly dependent on the temperature, while all the models listed in Table 11.2 assume that the product yields follow the non-isothermal or isothermal TGA temperature profile used to evaluate the kinetics. Therefore, none of these models can confidently reproduce the variability of products yields with respect to temperature. Also, there is significant uncertainty as to whether these kinetic expressions will be accurate when extrapolated to industrial pyrolysis conditions. To model industrial scale pyrolysis processes, it is critical to develop reliable and robust pyrolysis models based on experimental data obtained at representative conditions.