Microbial-Based Carbon Sequestration Technologies Enhanced by Computational Modeling

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@article{1717785,
      author = {Yusuf, Mercy Emike, Oyeleke, Olufemi Micheal},
      title = {Microbial-Based Carbon Sequestration Technologies Enhanced by Computational Modeling},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {2769-2776},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717785.pdf},
      abstract = {The increasing concentration of atmospheric carbon dioxide (CO₂) is a major contributor to global climate change, necessitating innovative and sustainable carbon sequestration strategies. Microbial-based carbon sequestration technologies have emerged as promising alternatives due to their ability to biologically capture and store CO₂ through natural metabolic processes. However, their large-scale implementation is constrained by limited understanding of optimal environmental conditions and system dynamics. This study integrates experimental microbiological analysis with computational modeling to enhance carbon sequestration efficiency. A predictive simulation model was developed using Python and MATLAB to analyze microbial growth kinetics and CO₂ fixation rates under varying environmental parameters such as temperature, pH, and nutrient concentration. Simulated results demonstrate that optimized conditions (temperature: 30°C, pH: 7.5, nutrient concentration: 1.2 g/L) significantly improve carbon fixation efficiency by up to 42% compared to non-optimized systems. The model further reveals strong correlations between microbial biomass growth and CO₂ uptake rates (R² = 0.91). The findings highlight the critical role of computational modeling in optimizing microbial carbon sequestration systems, providing a scalable and cost-effective solution for climate change mitigation.},
      keywords = {Carbon Sequestration, Computational Modeling, MATLAB, Climate Chang.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717785}
  }