@article{scholars18182,
          volume = {338},
            note = {cited By 3},
             doi = {10.1016/j.chemosphere.2023.139526},
            year = {2023},
           title = {Kinetic model derived from machine learning for accurate prediction of microalgal hydrogen production via conversion from low thermally pre-treated palm kernel expeller waste},
         journal = {Chemosphere},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165983478&doi=10.1016\%2fj.chemosphere.2023.139526&partnerID=40&md5=2c0dc6ca4fc905ff744d7288d3294f38},
        keywords = {Fossil fuels; Hydrogen production; Kinetic parameters; Kinetic theory; Machine learning; Microorganisms; Waste treatment, Accurate prediction; Energy demands; Fossil fuel sources; Hydrogen production process; Kinetic models; Machine-learning; Micro-algae; Modeling; Net energy ratios; Palm kernel, Microalgae, palm kernel oil; biofuel; fossil fuel; hydrogen, experimental study; fossil fuel; hydrogen; machine learning; microalga; modeling; reaction kinetics, algal virus; Article; Chlorella vulgaris; fermentative hydrogen production; machine learning; microbial kinetics; time series analysis; waste; biomass; fermentation; microalga, Biofuels; Biomass; Fermentation; Fossil Fuels; Hydrogen; Microalgae},
        abstract = {The depletion of fossil fuel sources and increase in energy demands have increased the need for a sustainable alternative energy source. The ability to produce hydrogen from microalgae is generating a lot of attention in both academia and industry. Due to complex production procedures, the commercial production of microalgal biohydrogen is not yet practical. Developing the most optimum microalgal hydrogen production process is also very laborious and expensive as proven from the experimental measurement. Therefore, this research project intended to analyse the random time series dataset collected during microalgal hydrogen productions while using various low thermally pre-treated palm kernel expeller (PKE) waste via machine learning (ML) approach. The analysis of collected dataset allowed the derivation of an enhanced kinetic model based on the Gompertz model amidst the dark fermentative hydrogen production that integrated thermal pre-treatment duration as a function within the model. The optimum microalgal hydrogen production attained with the enhanced kinetic model was 387.1 mL/g microalgae after 6 days with 1 h thermally pre-treated PKE waste at 90 {\^A}oC. The enhanced model also had better accuracy (R2 = 0.9556) and net energy ratio (NER) value (0.71) than previous studies. Finally, the NER could be further improved to 0.91 when the microalgal culture was reused, heralding the potential application of ML in optimizing the microalgal hydrogen production process. {\^A}{\copyright} 2023 Elsevier Ltd},
          author = {Ahmad Sobri, M. Z. and Khoo, K. S. and Sahrin, N. T. and Ardo, F. M. and Ansar, S. and Hossain, M. S. and Kiatkittipong, W. and Lin, C. and Ng, H.-S. and Zaini, J. and Bilad, M. R. and Lam, M. K. and Lim, J. W.}
}