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Estimation of bio-oil yield for biomass using hybrid models

Huanqiang GuoState Key Lab of Aridland Crop Science, Gansu Key Lab of Crop Improvement and Germplasm Enhancement, Lanzhou, ChinaFarag M. A. AltalbawyDepartment of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi ArabiaHardik DoshiMarwadi University Research Center, Department of Computer Engineering, Faculty of Engineering & Technology, Marwadi University, Rajkot, Gujarat 360003, IndiaAnupam YadavDepartment of Computer engineering and Application, GLA University, Mathura 281406, IndiaB JayaprakashDepartment of Computer Science & IT, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, IndiaAbhayveer SinghCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab 140401, IndiaB. BharathiDepartment of Computer Science And Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, IndiaPrabhat Kumar SahuDepartment of Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, IndiaShaxnoza SaydaxmetovaDepartment of Chemistry and Its Teaching Methods, Tashkent State Pedagogical University, Tashkent, UzbekistanAhmad AlkhayyatDepartment of computers Techniques engineering, College of technical engineering, The Islamic University of Babylon, Babylon, IraqSamim SherzodFaculty of Engineering, Nangarhar University, Nangarhar, AfghanistanKhursheed MuzammilDepartment of Public Health, College of Applied Medical Sciences, Khamis Mushait Campus, King Khalid University, Abha 62561, Saudi Arabia

2025en

ABI

Annotatsiya

The bio-oil yield from biomass during pyrolysis, influenced by multiple chemical and process parameters, is particular for gaining sustainable production of energy, necessitating accurate predictive models. This study employs a Gradient Boosting Machine (GBM) framework, augmented by four sophisticated optimization algorithms: Batch Bayesian Optimization (BBO), Evolution Strategies (ES), Bayesian Probability Improvement (BPI), and Gaussian Processes Optimization (GPO). The model leverages a dataset comprising 400 experimental samples, with 90 % allocated for training and 10 % for testing, using input variables such as content of carbon, content of nitrogen, content of hydrogen, content of ash, content of oxygen, crystallinity index, BET surface area, catalyst-to-biomass ratio, residence time, temperature and to predict bio-oil yield. To mitigate overfitting, k-fold cross-validation is useful during training. The performance of every optimization algorithm is assessed through computational runtime and metrics such as R-squared (R²), mean squared error (MSE), and average absolute relative error (AARE%). Correlation analysis reveals varied relationships, with BET surface area (0.18) and oxygen content (0.14) showing positive associations with bio-oil yield, while temperature (-0.26) and ash content (-0.22) exhibit notable negative correlations. Among the optimization approaches, GBM-BBO achieves the highest accuracy, with an R² of 0.99 for training set and 0.94 for test set, surpassing other methods. Regarding computational efficiency, GPO is the fastest, requiring 171.9 s, whereas BBO is the slowest at 298.2 s. SHAP analysis identifies BET surface area, ash content, and temperature as powerful factors affecting bio-oil yield, underscoring the efficacy of data-driven methodologies in addressing intricate systems. These models offer reliable tools for estimating bio-oil yield, reducing reliance on expensive, time-consuming, and resource-intensive experimental processes.

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DOI: 10.1016/j.indcrop.2025.121711

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