30.3 Generation of Lignocellulosic and Starchy Wastes

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and possess rich quantity of carbohydrates thus, showing the potential of being con-

verted into biofuel.

The lignocellulosic feedstock for biobutanol production is categorized into waste

biomass, virgin biomass and energy crops. All natural terrestrial plants like grasses,

bushes, and trees are virgin biomass. Waste biomass is generated as a low quality

by-product from various industrial and agricultural sectors which includes, rice

straw, corn straw, wheat straw, pineapple peel, palm kernel, etc. [20]. Energy crops

like Elephant grass (Pennisetum purpureum), switch grass (Panicum virgatum),

poplar tree (Populus), carrot grass (Parthenium hysterophorus) and sugarcane

(Saccharum officinarum) have high lignocellulosic content [21]. The major portion

of lignocellulosic resources available for biofuel production is generated from the

agricultural activities. Some of the lignocellulosic resources already investigated for

biofuel production are wheat straw, corn stalk, oil palm biomass, rice straw, and

sugarcane bagasse. The energy crops such as phragmites, switch grass, and king

grass have been also explored for biobutanol production [21].

Starch residues generated from agro-industrial activities shows a greater poten-

tial for being converted into biobutanol economically. It was estimated that

4 × 10⁷ tons/year of starch waste is generated worldwide from agricultural activities.

Starch waste biomass generated from agricultural activities provides a compelling

advantage for biobutanol production since this biomass is readily available,

inexpensive and can be easily hydrolyzed into fermentable sugars. Biobutanol

production from starchy resources is often cost-effective due to lower pretreatment

costs and having a renewable fuel from waste greatly lowers waste treatment and

disposal costs.

30.3.2

Composition of Lignocellulose and Starchy Residues

The structural composition of lignocellulose is a key factor in biochemical con-

version of biomass into biofuel and can have significant influence on biofuel

productivity and cost of production. The composition analysis of lignocellulosic

feedstock reported by several studies revealed that the ratios of various constituents

present in the lignocellulose vary depending upon the plant type, age of the plant,

growth stage, and geographical location. The variability in feedstock composition

affects the process economics and conversion yield of biobutanol production;

therefore a reliable and effective method of biomass analysis is essential.

The efficiency of biomass to biofuel conversion is decided by estimating the lignin

and carbohydrate content present in the lignocellulosic materials by sulfuric acid

hydrolysis method. A review by Sluiter et al. [22] reveals the history of compositional

analysis of biomass based on sulfuric acid approach. For large-scale application,

the standard wet method of chemical analysis of lignocellulosic feed stock is not

feasible as it suffers from the drawback such as labor intensive and time consum-

ing process. Hou et al. [23] proposed an integrated method to analyze the chemical

composition of feedstock by multivariate calibration model. This method combines

the traditional chemical analysis with spectrophotometer. The study suggested that

near infrared (NIR) spectrophotometer analysis is able to provide rapid quantitative