Transition of Biofuels from the First to the Fourth Generation

Srinivas, Gujjala Lohit Kumar; Singh, D. N.; Kumar, Sunil

doi:10.1201/9781003197737-2

Cited by 4 publications

(1 citation statement)

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“…Consequently, researchers are concentrating their efforts on the discovery of alternative energy sources and the use of these sources in order to reduce the adverse effects of their use [1]. The majority of the recent research that has been conducted on renewable energy sources has focused on a variety of waste products, including food waste, animal manure, organic waste, and municipal solid waste [2], [3]. Through the process of transforming these waste products into fuel, it is possible to lessen the many adverse impacts that these waste materials have on the environment and on living species, including people [4].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Potential of Biogas Yield from Anaerobic Co-digestion of Organic Waste with Ensemble Machine learning

Le,

Dong

2024

COMIEN

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The biogas has the potential to serve as a substitute as fossil derived fuel. The present study investigates anaerobic co-digestion of organic waste for predictive modelling. Several co-digestion studies were performed with different pH, solid concentration, temperature, and co-digestion ratios. A water displacement apparatus was used to test biogas yield, and data was collected meticulously. Linear regression (LR) and Random Forest (RF) based models were built with Python-based tools and tested using statistical measures for the prediction of biogas yield. The LR showed a strong linear association, with R and R2 values of 0.9892 and 0.9785, respectively. However, RF surpassed LR, with higher R and R2 values of 0.9919 and 0.9826, respectively. Furthermore, RF had lower MSE and MAE values, indicating higher prediction accuracy and precision. RF consistently scored well in tests, demonstrating its ability to capture complicated relationships while minimizing prediction mistakes. Taylor's diagrams further demonstrated RF's excellent performance during both the training and testing periods. Overall, RF emerges as the optimum model for reliably estimating biogas output in anaerobic co-digestion systems, with important implications for waste-to-energy processes.

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