Abstract
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.
Highlights
The production of bioethanol is a current topic raised by scientists, technologists, and representatives of fuel companies in the European Union who are working on satisfying the percentage share of this biocomponent in conventional fuels
Mugwort is an example of biomass obtained without the need for cultivation and fertilization, with an average cellulose content of 45%, hemicellulose 13.8%, and lignin 20.4%
Results of this study suggest that machine learning (ML) is a good tool to predict the final concentration of ethanol obtained in a multistage process of hydrolysis and fermentation of lignocellulosic biomass
Summary
The production of bioethanol is a current topic raised by scientists, technologists, and representatives of fuel companies in the European Union who are working on satisfying the percentage share of this biocomponent in conventional fuels. An attempt was made to create a model that would allow for the estimation or prediction of bioethanol concentration from various lignocellulosic raw materials Such a model would allow the classification of biomass for bioethanol production on the basis of its chemical composition and facilitate choice of a suitable ionic liquid and enzyme preparation used for pretreatment. Such an approach would save time and costs in laboratory research, which is a way to search for ideal production methods and an efficient source of lignocellulosic biomass
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
