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<p>Accurately predicting microalgal biofuel yield is critical for optimizing commercial bio-oil production and establishing sustainable alternative energy systems. While existing machine learning approaches often rely on narrow, isolated experimental datasets or default algorithmic parameters, this study introduces a novel approach by systematically benchmarking advanced global hyperparameter optimization strategies against a large-scale, heterogeneous database to maximize predictive robustness and interpretability. This study aims to develop a highly robust hybrid machine learning framework to map complex environmental and cultivation parameters to volumetric biofuel yield (L/m<sup>3</sup>&#xa0;Culture/day) and to identify the most effective optimization strategy. A comprehensive dataset of 2950 empirical observations, aggregated and curated from diverse peer-reviewed literature sources, was utilized, encompassing inputs such as light intensity, nutrient concentrations, cultivation time, temperature, and pH. A Gradient Boosting Decision Tree (GBDT) algorithm was employed as the primary predictive engine, and its hyperparameters were rigorously tuned using four distinct global optimization algorithms: Gaussian Process Optimizer (GPO), Evolutionary Strategies (ES), Bayesian Probability Improvement (BPI), and Bayesian Batch Optimizer (BBO), evaluated via a fivefold cross-validation framework. Performance assessment revealed that while the GPO offered the fastest computational runtime, the ES optimization yielded the most accurate and generalizable predictive model. It achieved a superior testing coefficient of determination (<i>R</i><sup>2</sup>) of 0.956 and the lowest mean squared error (<i>MSE</i>) of 2.000; given typical microalgal yield ranges, this low error magnitude represents a highly acceptable deviation for preliminary industrial bioprocess screening and decision-making. SHapley Additive exPlanations (SHAP) analysis further elucidated that cultivation time and species type are the most dominant parameters dictating yield magnitude, while nutrient availability and light intensity exhibit direct positive correlations with lipid accumulation. The developed hybrid framework successfully captures the complex non-linear biochemical dynamics of microalgal cultivation, offering a highly practical computational tool for scaling and monitoring sustainable biological energy systems.</p> Graphical abstract <p></p>

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Predictive machine learning optimization of microalgal biofuel yield using environmental parameters

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • Basem Abu Zneid,
  • S. Renuka Jyothi,
  • Noor Mazin Basheer,
  • Jayshree Nellore,
  • Vipasha Sharma,
  • Siya Singla,
  • Sardor Sabirov,
  • Rasul Usmanov,
  • Ahmad Khalid

摘要

Accurately predicting microalgal biofuel yield is critical for optimizing commercial bio-oil production and establishing sustainable alternative energy systems. While existing machine learning approaches often rely on narrow, isolated experimental datasets or default algorithmic parameters, this study introduces a novel approach by systematically benchmarking advanced global hyperparameter optimization strategies against a large-scale, heterogeneous database to maximize predictive robustness and interpretability. This study aims to develop a highly robust hybrid machine learning framework to map complex environmental and cultivation parameters to volumetric biofuel yield (L/m3 Culture/day) and to identify the most effective optimization strategy. A comprehensive dataset of 2950 empirical observations, aggregated and curated from diverse peer-reviewed literature sources, was utilized, encompassing inputs such as light intensity, nutrient concentrations, cultivation time, temperature, and pH. A Gradient Boosting Decision Tree (GBDT) algorithm was employed as the primary predictive engine, and its hyperparameters were rigorously tuned using four distinct global optimization algorithms: Gaussian Process Optimizer (GPO), Evolutionary Strategies (ES), Bayesian Probability Improvement (BPI), and Bayesian Batch Optimizer (BBO), evaluated via a fivefold cross-validation framework. Performance assessment revealed that while the GPO offered the fastest computational runtime, the ES optimization yielded the most accurate and generalizable predictive model. It achieved a superior testing coefficient of determination (R2) of 0.956 and the lowest mean squared error (MSE) of 2.000; given typical microalgal yield ranges, this low error magnitude represents a highly acceptable deviation for preliminary industrial bioprocess screening and decision-making. SHapley Additive exPlanations (SHAP) analysis further elucidated that cultivation time and species type are the most dominant parameters dictating yield magnitude, while nutrient availability and light intensity exhibit direct positive correlations with lipid accumulation. The developed hybrid framework successfully captures the complex non-linear biochemical dynamics of microalgal cultivation, offering a highly practical computational tool for scaling and monitoring sustainable biological energy systems.

Graphical abstract