Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations

Abstract

Biochar is found to possess a large number of applications in energy and environmental areas. However, biochar could be produced from a variety of sources, showing that biochar yield and proximate analysis outcomes could change over a wide range. Thus, developing a high-accuracy machine learning-based tool is very necessary to predict biochar characteristics. In this study, a hybrid technique was developed by blending modern machine learning (ML) algorithms with cooperative game theory-based Shapley Additive exPlanations (SHAP). SHAP analysis was employed to help improve interpretability while offering insights into the decision-making process. In the ML models, linear regression was employed as the baseline regression method, and more advanced methodologies like AdaBoost and boosted regression tree (BRT) were employed. The developed prediction models were evaluated on a battery of statistical metrics, and all ML models were observed as robust enough. Among all three models, the BRT-based model delivered the best prediction performance with R 2 in the range of 0.982 to 0.999 during the model training phase and 0.968 to 0.988 during the model test. The value of the mean squared error was also quite low (0.89 to 9.168) for BRT-based models. SHAP analysis quantified the value of each input element to the expected results and provided a more in-depth understanding of the underlying dynamics. The SHAP analysis helped to reveal that temperature was the main factor affecting the response predictions. The hybrid technique proposed here provides substantial insights into the biochar manufacturing process, allowing for improved control of biochar properties and increasing the use of this sustainable and flexible material in numerous applications.

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

“…It is noted that solar energy, wind power, hybrid energy, geothermal energy, hydrogen energy, bioenergy, biofuels, biomass, and ocean energy can all employ AI models Chen et al, 2021;W.-H. Chen et al, 2022bW.-H. Chen et al, , 2022aJha et al, 2017;Tabanjat et al, 2018;Tuan Hoang et al, 2021). Besides, Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Ensemble, Wavelet Neural Networks (WNNs), SHapley Additive exPlanations, and Decision Trees are some examples of AI algorithms (de Ville, 2013; Le et al, 2023;Li et al, 2023;V. G. Nguyen et al, 2023;Said et al, 2022;Sharma et al, 2022b;Veza et al, 2022a;Zhang et al, 2022).…”

Leveraging Artificial Intelligence to Enhance Port Operation Efficiency

“…This estimates the amount of variation in the dependent variable that is predicted from the independent variables. Its value ranges from zero to one, with a higher value indicating a better match between the model and the data [79][80][81][82]. R 2 may be stated mathematically as follows:…”

“…Coefficient of determination (r 2 ): This estimates the amount of variation in the dependent variable that is predicted from the independent variables. Its value ranges from zero to one, with a higher value indicating a better match between the model and the data [79][80][81][82]where the coefficient of determination (R2. R 2 may be stated mathematically as follows:…”

Machinability study on abrasive waterjet drilling of YSZ-coated inconel 718 superalloy: optimization and modelling by machine learning

“…To improve the process and attain the required performance, it is vital to understand how these factors interrelate and how they create the impact on the drilled hole quality characteristics, such as circularity, taper angle, and surface roughness [17][18][19]. Manufacturing experts and researchers have been giving a lot of attention lately to machine learning (ML) algorithms for modeling and process optimization [20][21][22]. With the procedure of machine learning (ML) algorithms, complex relationships between input factors and output responses can be efficiently seized, agreeing for exact prediction-making and the identification of primary processes [23][24][25].…”