A characteristic-based decision tree approach for sustainable energy applications of biomass residues from two major classes

Abstract

The characteristic potential of biomass residues from two major classes for sustainable energy applications has been critically evaluated. These include sheesham wood dust (SWD), sheesham bark (SB), eucalyptus chips (ECLP), gamhar wood chips (GWC) from wood and woody biomass (WWB) class; and coconut shell (CS), coconut husk (CH), rice straw (RS), rice husk (RH), wheat husk (WH), bagasse (BGS), tea waste (TW), corn cob (CC), pista shell (PS), akhrot shell (AS) and mustard stalk (MS) from herbaceous and agricultural biomass (HAB) class. Overall, biomass residues cover a wide range of acceptable moisture levels for various thermal conversion processes. Fixed carbon (FC), C, and H content of all the samples showed linearity with calorific value (CV), but H/C and O/C atomic ratios, besides linearity with cellulose and hemicellulose, have non-linearity with CV and lignin. Specifically, agricultural wastes (RS, RH, and WH) have relatively less moisture, volatile matter, FC, C, and CV; and higher ash, H/C, and O/C atomic ratios, N, and S content. WWBs have higher CV than other residues (particularly agricultural residues), owing to higher lignin content (with higher heat value) than hemicellulose and cellulose.Among various ash components, assigning a clear trend is difficult. BGS, PS, and AS indicate extremely-high slagging and high fouling tendencies, and the remaining 12 samples have low to medium concerns. Amongst hemicellulose (H), cellulose (C), lignin (L), and extractives (Ext), four structural types (CLHExt > CHLExt > HCLExt > LExtHC) exist. CLHExt follows in 3WWB and 2HAB; CHLExt in 8 HAB; HCLExt in 1 HAB; LExtHC in 1 WWB sample. Thermal analysis results show that HABs are mostly more reactive and have less activation energy than WWBs. The observed experimental findings and characteristic associations form a basis for placing these feedstocks for indicator purposes linked with further advance and sustainable feedstock processing needed for gasification, combustion, and steelmaking. A novel decision tree approach has been developed for propositions about characteristic parameters, which provides a systematic means of selecting the feedstocks for sustainable energy applications.