Substrate Inhibition Kinetic Modelling of the Growth of Bacillus sp. Strain Neni-10 on Phenol

significant portion of the more than 1.5 million tons of sludge waste produced globally for industrial use consists of phenol and phenolic compounds, some of which are released into the environment without adequate safety assessment or control, leading to soil and water pollution. The potential use of phenol as a carbon source by many bacterial species can help mitigate phenol pollution through bioremediation of this hazardous material. This study employs several microbial growth kinetics models that govern the growth rate of a bacterium on phenol, with a focus on the comparative performance of the popular Haldane model alongside other models. The specific maximum growth rate (μₘ) was initially estimated using the no-lag modified logistics model. Among the kinetic models evaluated, the Aiba model exhibited the highest precision and accuracy, as demonstrated by statistical indices, including the lowest MPSD and AICc values, and bias and accuracy factors closest to 1. Despite variability in its parameter estimates, the Aiba model provided a meaningful kinetic description of phenol inhibition at high concentrations. Models such as Monod, Moser, Pamukoglu and Kargi, and Han-Levenspiel showed poor fitting. The maximum reduction rate, half-saturation constant for maximal reduction, and half-inhibition constant were the designated values of the Aiba constants, which were represented by (m, Ks, and Ki) as 1.30±1.40 hr-1 (S.E.), 524.64±753.63 mg/L (S.E.), and 609.78±196.34 mg/L (S.E.), respectively. The results of curve fitting interpolation should not be regarded as the true value. The actual max is defined as the point at which the slope's gradient reaches zero; in this case, it was determined to be 0.295 h-1 at a phenol concentration of 360 mg/L. This study highlights the advantages of employing substrate inhibition models, such as the Aiba and Haldane models, for accurately characterizing microbial growth in the presence of toxic xenobiotics, like phenol, especially for optimizing bioprocesses, such as wastewater treatment.