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Enhancing the dilute acid hydrolysis process using a machine learning approach: investigation of different biomass feedstocks influences glucose and ethanol yields

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Abstract

The study focuses on enhancing the efficiency of bioethanol production as a means to reduce reliance on crude oil and mitigate environmental pollution. A key aspect involves the optimization of bioethanol production through the application of an artificial intelligence approach. The Artificial Intelligence Decision-Making System (AIDMS) algorithm was developed using a machine learning algorithm, utilizing datasets derived from experimental results and published research. In the optimization process, a Pearson correlation coefficient matrix was established for 250 training datasets, revealing positive and negative correlation coefficient values. These values underscore the significance of each parameter in ethanol production. Various biomass feedstocks, including cotton stalk, wheat straw, olive tree, potato peel waste, rice straw, and sugarcane bagasse, were selected for validation of the AIDMS algorithm. The validation process compared experimental results with predictions made by the AIDMS, demonstrating a commendable 94% accuracy. The weighted rank order aggregate analysis revealed that cellulose (%), s-temp (°C), acid conc. (%), lignin (%), s-time (min), and hemicellulose (%) show the importance of parameters in obtaining glucose yield. Similarly for ethanol yield, cellulose (%), f-temp (°C), f-time (h), lignin (%), and hemicellulose (%) show the order of rank and its importance. The artificial intelligence-based optimization method is suitable for bioethanol production.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Sri Sivasubramanian Nadar College of Engineering, Chennai, 603110, Tamil Nadu, India

    Nithianantharaj Vinitha & Jaikumar Vasudevan

  2. Mohamed Sathak Engineering College, Keelakarai, 623806, Tamil Nadu, India

    K. P. Gopinath

  3. Centre for Waste Management-International Research Centre, Sathyabama Institute of Science and Technology, Chennai, 600119, Tamil Nadu, India

    J. Arun

  4. Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India

    S. Madhu & S. Naveen

Authors
  1. Nithianantharaj Vinitha
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  2. Jaikumar Vasudevan
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  3. K. P. Gopinath
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  4. J. Arun
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  5. S. Madhu
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  6. S. Naveen
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Contributions

Nithianantharaj Vinitha: design of experiments, experimentation, analysis of data; Jaikumar Vasudevan, K.P. Gopinath: writing of the manuscript and investigation; J. Arun: curated data from the study; S Naveen, S Madhu: validation of data, review of the manuscript.

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Correspondence to S. Madhu.

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Vinitha, N., Vasudevan, J., Gopinath, K.P. et al. Enhancing the dilute acid hydrolysis process using a machine learning approach: investigation of different biomass feedstocks influences glucose and ethanol yields. Biomass Conv. Bioref. (2024). https://doi.org/10.1007/s13399-024-05714-y

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