PMID- 33418349
OWN - NLM
STAT- MEDLINE
DCOM- 20210203
LR  - 20210203
IS  - 1873-2976 (Electronic)
IS  - 0960-8524 (Linking)
VI  - 323
DP  - 2021 Mar
TI  - Machine learning prediction of cellulose-rich materials from biomass pretreatment 
      with ionic liquid solvents.
PG  - 124642
LID - S0960-8524(20)31916-7 [pii]
LID - 10.1016/j.biortech.2020.124642 [doi]
AB  - Ionic liquid solvents (ILSs) have been effectively utilized in biomass 
      pretreatment to produce cellulose-rich materials (CRMs). Predicting CRM 
      properties and evaluating multi-dimensional relationships in this system are 
      necessary but complicated. In this work, machine learning algorithms were applied 
      to predict CRM properties in terms of cellulose enrichment factor (CEF) and solid 
      recovery (SR), using 23-feature datasets from biomass characteristics, operating 
      conditions, ILSs identities, and catalyst. Random forest algorithm was found to 
      have the highest prediction accuracy with RMSE and R(2) of 0.22 and 0.94 for CEF, 
      as well as 0.07 and 0.84 for SR, respectively. Highly influential features on 
      making predictions were mainly from biomass characteristics andILS 
      treatment'soperating conditions, totally contributed 80% on CEF and 60% on SR. 
      One- and two-way partial dependence plots were used to explain/interpret the 
      multi-dimensional relationships of the most important features. Our findings 
      could be applied in designing new ILSs and optimizing the process conditions.
CI  - Copyright (c) 2020 Elsevier Ltd. All rights reserved.
FAU - Phromphithak, Sanphawat
AU  - Phromphithak S
AD  - Graduate Program in Energy Engineering, Faculty of Engineering, Chiang Mai 
      University, Thailand.
FAU - Onsree, Thossaporn
AU  - Onsree T
AD  - Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai 
      University, Chiang Mai 50200, Thailand. Electronic address: th.onsree@gmail.com.
FAU - Tippayawong, Nakorn
AU  - Tippayawong N
AD  - Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai 
      University, Chiang Mai 50200, Thailand. Electronic address: 
      n.tippayawong@yahoo.com.
LA  - eng
PT  - Journal Article
DEP - 20210102
PL  - England
TA  - Bioresour Technol
JT  - Bioresource technology
JID - 9889523
RN  - 0 (Ionic Liquids)
RN  - 0 (Solvents)
RN  - 9004-34-6 (Cellulose)
RN  - 9005-53-2 (Lignin)
SB  - IM
MH  - Biomass
MH  - *Cellulose
MH  - Hydrolysis
MH  - *Ionic Liquids
MH  - Lignin
MH  - Machine Learning
MH  - Solvents
OTO - NOTNLM
OT  - AI
OT  - Biomass bioenergy
OT  - Chemical conversion
OT  - Deep eutectic solvents
OT  - Lignin extraction
EDAT- 2021/01/09 06:00
MHDA- 2021/02/04 06:00
CRDT- 2021/01/08 20:18
PHST- 2020/11/20 00:00 [received]
PHST- 2020/12/27 00:00 [revised]
PHST- 2020/12/28 00:00 [accepted]
PHST- 2021/01/09 06:00 [pubmed]
PHST- 2021/02/04 06:00 [medline]
PHST- 2021/01/08 20:18 [entrez]
AID - S0960-8524(20)31916-7 [pii]
AID - 10.1016/j.biortech.2020.124642 [doi]
PST - ppublish
SO  - Bioresour Technol. 2021 Mar;323:124642. doi: 10.1016/j.biortech.2020.124642. Epub 
      2021 Jan 2.