Asset Details
Do you wish to reserve the book?
Accurate modeling of biochar yield based on proximate analysis
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Accurate modeling of biochar yield based on proximate analysis
Do you wish to request the book?
Accurate modeling of biochar yield based on proximate analysis
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Accurate modeling of biochar yield based on proximate analysis
2025
Overview
Accurate prediction of biochar yield from biomass pyrolysis is essential for optimizing production in sustainable agriculture, yet remains technically challenging due to multiple interacting factors. This study developed a predictive framework using a curated dataset of 211 samples, each including 14 normalized input features (chemical, physical, operational) and one output variable (biochar yield, wt%). Machine learning modeling utilized Gradient Boosted Decision Trees (GBDT), with hyperparameters exhaustively tuned via Gaussian Processes Optimization (GPO), Evolutionary Strategies (ES), Bayesian Probability Improvement (BPI), and Batch Bayesian Optimization (BBO). Models were evaluated on a train-test split (90% training, 10% testing) and the best performance was achieved by the GBDT–BPI model: total R² = 0.982, mean squared error (MSE) = 1.65, average absolute relative error percentage (AARE%) = 1.35; on the test set, R² = 0.693, MSE = 15.2, AARE% = 9.54. Comparative analysis showed GBDT–BPI outperformed GBDT–GPO (total R² = 0.978; MSE = 2.01; AARE% = 1.72), GBDT–ES (total R² = 0.976; MSE = 2.13; AARE% = 3.81), and GBDT–BBO (total R² = 0.980; MSE = 1.81; AARE% = 2.58). Sensitivity study presented reside duration, temperature, and fixed carbon as the top parameters of yield. Time efficiency was comparable for all optimizers, with BBO taking the longest (313 s/500 iterations). Diagnostic leverage analysis demonstrated high data quality, with less than 1% flagged as influential outliers. This integrated approach delivered high-accuracy, interpretable prediction, and revealed critical parameters for process optimization in biomass pyrolysis workflows.
Publisher
SAGE Publications,Sage Publications Ltd,SAGE Publishing
Subject
