Abstract
The main aim of this work is the determination of aromaticity in biochar from easier accessible parameters (e.g., elemental composition). To this end, two machine learning models, including adaptive neurofuzzy inference system (ANFIS) and least-squares support vector machine (LSSVM), were used to predict this constant form 98 dataset gathered from earlier reported sources. The outputs of the statistical parameters showed that the LSSVM model has the ability to estimate the target parameter with R-squared values of 0.986 and a mean relative error of 3.821 for the overall dataset. Also, by analyzing the sensitivity on the input parameters, it was shown that the carbon percentage has the greatest effect on the target values, and a high focus should be placed on this parameter. Finally, by comparing the methods proposed in this paper with other models published in previous studies, our model has shown higher accuracy in predicting the target parameter.
Highlights
By undergoing biomass thermochemical transformation throughout an oxygen-scarce condition, biochar is created [1, 2]
leastsquares support vector machine (LSSVM) and adaptive neurofuzzy inference system (ANFIS) models are used for the first time to predict the biochar aromaticity with acceptable accuracy
The computed constants are capable of covering empirical locations while maintaining satisfactory performance standards
Summary
By undergoing biomass thermochemical transformation throughout an oxygen-scarce condition, biochar is created [1, 2]. E atomic H/C proportion was used by Maroto-Valer and his colleagues to create a linear quantitative method for predicting bituminous char’s C aromaticity [26]. Compared to the Maroto-Valer approach, the new one possesses a higher predictive ability since it considers the structural data of the C- and H-atoms. When the H/C proportion of biochar is more than 1.0, the altered model’s extension capabilities are restricted To this end, novel predictive models for biochar aromaticity must be developed that have greater extension capabilities. Is study’s primary aims are as follows: (1) to create fundamental composition-based forecasting algorithms with improved generalization capabilities for biochar aromaticity and (2) to uncover novel connections and regulations among the fundamental combinations and biochar C aromaticity. LSSVM and ANFIS models are used for the first time to predict the biochar aromaticity with acceptable accuracy. In terms of capacity to convert language words into computational variables, each layer has a distinct function in this algorithm, which comprises five separate levels [42, 43]. roughout the paper, more information concerning these levels may be reported [44, 45]
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