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

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.

Ionic liquid pretreatment of stinging nettle stems and giant miscanthus for bioethanol production

“…Three of them were commercially available (Iolitech GmbH, Germany) imidazolium ILs: 1-ethyl-3-methylimidazolium acetate ([emim][OAc]), 1-butyl-3-methylimidazolium acetate ([bmim][OAc]) and 1-ethyl-3-methylimidazolium diethyl phosphate ([emim][DEP]). The remaining two, belonging to the group of ammonium ILs, namely butyl(cyclohexyl)dimethylammonium acetate (([CHDMA-C4][OAc]) and (cyclohexyl)hexyldimethylammonium acetate ([CHDMA-C6][OAc]), were synthesized according to already established protocols 41 , 59 .

Figure 7 Ionic liquids chosen for pretreatment of stinging nettle stems and giant miscanthus.

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“…The alcoholic fermentation was carried out accordingly to our previous reports 59 – 61 . Hydrolysate solutions, previously filtered to separate the lignocellulose residue, were subjected to alcoholic fermentation.…”

“…Glucose and ethanol were separated on an Aminex HPX-87P (Bio-Rad, USA) at 30 °C using a 5 mM H 2 SO 4 as the mobile phase at a flow rate of 0.6 cm 3 min −1 and then detected with a refractive index detector (Model L-7490, Merck Hitachi, Germany). All the analytical methods have been described in detail before in our previous works 59 – 61 .…”

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

“…ANNs have been used, among others, for the prediction of long-term climatic data [41], in the construction industry [42], and in medicine to identify neoplastic lesions based on mammogram images [43]. In addition, ANNs have been used in the power industry for the estimation of demand for electricity [44] and predicting the power of photovoltaic panels [45], as well as water quality forecasting in agricultural drainage river basins [46] or predicting bioethanol production process from lignocellulosic biomass [47].…”

Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids

“…Artificial intelligence methods for understanding complex and multidimensional relationships in biomass pretreatment processes using ILs have more advantages than regression and traditional modeling, and machine learning (ML) is highly regarded among the artificial intelligence methods [10,22,23].…”