首页|Research from University of Johannesburg Broadens Understanding of Machine Learn ing (Developing a predictive machine learning model and a kinetic model for the bioremediation of terrestrial diesel spills)
Research from University of Johannesburg Broadens Understanding of Machine Learn ing (Developing a predictive machine learning model and a kinetic model for the bioremediation of terrestrial diesel spills)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news originating from Doornfontein, Sou th Africa, by NewsRx editors, the research stated, “Terrestrial diesel spills si gnificantly threaten the natural environment and human health, necessitating eff ective bioremediation strategies for diesel-contaminated soil. This study aims t o evaluate the impact of diesel spills on soil water retention capacity and the effectiveness of different bioremediation methods.” Financial supporters for this research include University of Johannesburg. Our news editors obtained a quote from the research from University of Johannesb urg: “Four tanks (A-D) were used to compare natural attenuation, bioaugmentation , biostimulation, and a combination of bioaugmentation and biostimulation in enh ancing diesel degradation. The findings demonstrated that soil water retention d ecreases with higher diesel concentrations and increases with more compost. Afte r 21 days, the Diesel Range Organics (DRO) removal efficiencies for Tanks A, B, C, and D were 15.70 %, 23.31 %, 29.65 %, and 49.78 %, respectively. The degradation kinetics primarily follo wed first-order reaction models, with combined bioaugmentation and biostimulatio n showing the fastest reaction rate. The projected timelines for complete biorem ediation were 44 days for the combined method, 88 days for biostimulation, 116 d ays for bioaugmentation, and 178 days for natural attenuation. Machine Learning models further supported these findings, with the bilateral Artificial Neural Ne twork outperforming the Linear Regression model (R2 of 0.9990 vs.”
University of JohannesburgDoornfonteinSouth AfricaAfricaCyborgsEmerging TechnologiesMachine Learning
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