Hybrid gradient boosting machine for precise prediction of biomass net output power in terms of proximate analysis
Abstract
The net output power of biomass, influenced by proximate analysis factors, is pivotal for enhancing efficiency in bioenergy applications, necessitating accurate predictive tools. This study employs a gradient boosting machine (GBM) model, refined through four advanced optimization methods: batch Bayesian optimization (BBO), evolution strategies, Bayesian probability improvement (BPI), and Gaussian process optimization (GPO). The model is constructed using a dataset comprising 980 experimental samples, with 90% allocated for training and 10% for testing, incorporating key input variables such as temperature, moisture content, fixed carbon, volatile matter, and air-to-fuel ratio to forecast biomass net output power. To prevent overfitting, k -fold cross-validation is applied during the training phase. The performance of each optimization method is assessed via computational runtime and metrics such as R 2 ), mean-squared error, and average absolute relative error. Correlation analysis reveals that temperature exhibits the strongest positive correlation with net output power (correlation coefficient: 0.62), followed by fixed carbon (0.14), while moisture content (−0.29), volatile matter (−0.01), and air-to-fuel ratio (−0.06) show negative correlations. Among the optimization techniques, GBM–BPI delivers the highest accuracy, achieving an R 2 of 0.998521 for the training set and 0.9947336 for the test set, outperforming other approaches. Regarding computational speed, GPO is the most efficient, requiring 212.54 s, whereas BBO is the slowest at 521.14 s. Sensitivity analysis elucidates the influence of each input variable on net output power, underscoring the strength of data-driven methods in addressing intricate systems. These models offer reliable tools for predicting biomass net output power, reducing reliance on expensive, time-consuming, and labor-intensive experimental processes.