Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae

Abstract

• Introduced an ANFIS model to optimize sulfur concentration, run time, and wet biomass for improved biohydrogen yield. • Achieved a 6.87 % increase in hydrogen yield using ANFIS and red-tailed hawk optimization (RTH). • RTH outperformed PSO, SMA, SCA, HHO, and FBI in identifying optimal process parameters. • ANFIS reduced RMSE by 93.4 % compared to traditional ANOVA, demonstrating improved predictive accuracy. • Conducted extensive statistical comparisons across multiple optimization algorithms to ensure robustness and reliability. Enhancing biohydrogen production from microalgae is crucial in addressing environmental and energy challenges. It provides a sustainable, clean energy source while reducing greenhouse gas emissions. Moreover, it advances microalgae-based biotechnology, enabling innovative biofuel production and ecological revitalization. The main target of this study is to develop a robust ANFIS model to simulate the biohydrogen production process from microalgae within photobioreactors. The study focuses on enhancing hydrogen yield by optimizing three critical process parameters: sulfur concentration (%), run time (hours), and wet biomass concentration (g/L). Initially, an adaptive neuro-fuzzy inference system (ANFIS) model for biohydrogen production process is constructed based on empirical data. Subsequently, the red-tailed hawk algorithm (RTH) is used to determine the optimal values for the process parameters, corresponding to maximum hydrogen yield. The performance of ANFIS model in predicting hydrogen yield is assessed using root mean square error (RMSE) and coefficient-of-determination (R 2 ) values. The obtained RMSE values for training and testing data are 2.8477 × 10 −05 and 1.2807, respectively, while the corresponding R 2 values are 1.0 and 0.9911 for training and testing. The introduction of fuzzy logic into the model significantly improves its predictive accuracy, as evidenced by the drop in RMSE from 10.79 with ANOVA to 0.7159 with ANFIS, representing a substantial 93.4 % decrease. The remarkable precision of the ANFIS model, indicated by its low RMSE and high R 2 values, underscores the success of the modeling stage. The combination between ANFIS with the RTH technique yields impressive results, leading to a hydrogen yield enhancement of 6.87 % and 26.65 % when compared to both measured data and ANOVA.