Pinch analysis for bioethanol production process from lignocellulosic biomass

2011 ◽  
Vol 31 (16) ◽  
pp. 3332-3336 ◽  
Author(s):  
S. Fujimoto ◽  
T. Yanagida ◽  
M. Nakaiwa ◽  
H. Tatsumi ◽  
T. Minowa
Keyword(s):  
Production Process ◽  
Lignocellulosic Biomass ◽  
Pinch Analysis ◽  
Bioethanol Production

Optimization of Hydrothermal Pretreatment of Lignocellulosic Biomass in the Bioethanol Production Process

ChemSusChem ◽  
2012 ◽  
Vol 6 (1) ◽  
pp. 110-122 ◽  
Author(s):  
Christos K. Nitsos ◽  
Konstantinos A. Matis ◽  
Kostas S. Triantafyllidis
Keyword(s):  
Production Process ◽  
Lignocellulosic Biomass ◽  
Hydrothermal Pretreatment ◽  
Bioethanol Production

scholarly journals Use of Machine Learning Methods for Predicting Amount of Bioethanol Obtained from Lignocellulosic Biomass with the Use of Ionic Liquids for Pretreatment

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 243
Author(s):  
Małgorzata Smuga-Kogut ◽  
Tomasz Kogut ◽  
Roksana Markiewicz ◽  
Adam Słowik
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Ionic Liquids ◽  
Random Forest ◽  
Production Process ◽  
Lignocellulosic Biomass ◽  
Bioethanol Production ◽  
Artificial Neural

The study objective was to model and predict the bioethanol production process from lignocellulosic biomass based on an example of empirical study results. Two types of algorithms were used in machine learning: artificial neural network (ANN) and random forest algorithm (RF). Data for the model included results of studying bioethanol production with the use of ionic liquids (ILs) and different enzymatic preparations from the following biomass types: buckwheat straw and biomass from four wastelands, including a mixture of various plants: stems of giant miscanthus, common nettle, goldenrod, common broom, fireweed, and hay (a mix of grasses). The input variables consisted of different ionic liquids (imidazolium and ammonium), enzymatic preparations, enzyme doses, time and temperature of pretreatment, and type of yeast for alcoholic fermentation. The output value was the bioethanol concentration. The multilayer perceptron (MLP) was used in the artificial neural networks. Two model types were created; the training dataset comprised 120 vectors (14 elements for Model 1 and 11 elements for Model 2). Assessment of the optimum random forest was carried out using the same division of experimental points (two random datasets, containing 2/3 for training and 1/3 for testing) and the same criteria used for the artificial neural network models. Data for mugwort and hemp were used for validation. In both models, the coefficient of determination for neural networks was <0.9, while for RF it oscillated around 0.95. Considering the fairly large spread of the determination coefficient, two hybrid models were generated. The use of the hybrid approach in creating models describing the present bioethanol production process resulted in an increase in the fit of the model to R2 = 0.961. The hybrid model can be used for the initial classification of plants without the necessity to perform lengthy and expensive research related to IL-based pretreatment and further hydrolysis; only their lignocellulosic composition results are needed.

Supercritical CO2and ionic liquids for the pretreatment of lignocellulosic biomass in bioethanol production

2013 ◽  
Vol 34 (13-14) ◽  
pp. 1735-1749 ◽  
Author(s):  
Tingyue Gu ◽  
Michael A. Held ◽  
Ahmed Faik
Keyword(s):  
Ionic Liquids ◽  
Lignocellulosic Biomass ◽  
Bioethanol Production

Bioethanol production process rheology

2017 ◽  
Vol 106 ◽  
pp. 59-64 ◽  
Author(s):  
E. Oropeza-De la Rosa ◽  
L.G. López-Ávila ◽  
G. Luna-Solano ◽  
D. Cantú-Lozano
Keyword(s):  
Production Process ◽  
Bioethanol Production

Rice Husk: A Potent Lignocellulosic Biomass for Second Generation Bioethanol Production from Klebsiella oxytoca ATCC 13182

2022 ◽  
Author(s):  
Shubhra Tiwari ◽  
Esmil Beliya ◽  
Monika Vaswani ◽  
Khushbu Khawase ◽  
Dristi Verma ◽  
...  
Keyword(s):  
Lignocellulosic Biomass ◽  
Rice Husk ◽  
Second Generation ◽  
Klebsiella Oxytoca ◽  
Bioethanol Production ◽  
Second Generation Bioethanol

Industrial Technologies for Bioethanol Production from Lignocellulosic Biomass

2019 ◽  
pp. 61-72
Author(s):  
Amrita Saha ◽  
Soumyak Palei ◽  
Minhajul Abedin ◽  
Bhaswati Uzir
Keyword(s):  
Lignocellulosic Biomass ◽  
Bioethanol Production

Hydrolysis of Lignocellulosic Biomass for Bioethanol Production

Biofuels ◽  
2011 ◽  
pp. 229-250 ◽  
Author(s):  
Parameswaran Binod ◽  
K.U. Janu ◽  
Raveendran Sindhu ◽  
Ashok Pandey
Keyword(s):  
Lignocellulosic Biomass ◽  
Bioethanol Production ◽  
Hydrolysis Of

Development of co-immobilized tri-enzyme biocatalytic system for one-pot pretreatment of four different perennial lignocellulosic biomass and evaluation of their bioethanol production potential

2018 ◽  
Vol 269 ◽  
pp. 227-236 ◽  
Author(s):  
Muthuvelu Kirupa Sankar ◽  
Rajarathinam Ravikumar ◽  
Manickam Naresh Kumar ◽  
Uthandi Sivakumar
Keyword(s):  
Lignocellulosic Biomass ◽  
Bioethanol Production ◽  
Production Potential ◽  
One Pot
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