Monte carlo simulation of bacterial disinfection: Nonlinear and time-explicit intensity of transition

• Published in: Biotechnology progress Vol. 26; no. 5; pp. 1486 - 1493
• Main Authors: Argoti, Andres, Fan, L. T., Chou, S. T.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.09.2010; Wiley
• Subjects: Bacteria; Biological and medical sciences; Biotechnology; Data processing; Disinfection; Disinfection - methods; Fundamental and applied biological sciences. Psychology; Mathematical models; Monte Carlo Method; Monte Carlo simulation; nonlinear stochastic modeling; simulation; Standard deviation; Stochasticity; Simulation; Bacteria; Disinfection
• ISSN: 8756-7938; 1520-6033; 1520-6033
• DOI: 10.1002/btpr.446

Bacteria being disinfected in fluid media are discrete entities and mesoscopic in size; moreover, they are incessantly as well as irregularly in motion and in collision among themselves or with the surrounding solid surfaces. As such, it is highly likely that some of the attributes of the bacterial population, for example, their number concentration, will fluctuate randomly. This is especially the case at the tail‐end of disinfection when the population of bacteria is sparse. It might be effectual, therefore, to explore the resultant random fluctuations via a stochastic paradigm. Proposed herein is a Markovian stochastic model for the rate of bacterial disinfection, whose intensity of transition takes into account the contact time of the bacteria with the disinfecting agent to eliminate any given percentage of the bacteria in terms of a nonlinear function of time. The model's master equation has been simulated by resorting to the Monte Carlo method to circumvent the undue complexities in solving it analytically or numerically via conventional numerical techniques. For illustration, the mean, the variance (standard deviation), and the coefficient of variation of the number concentration of bacteria during disinfection have been estimated through Monte Carlo simulation. The results of simulation compare favorably with the available experimental data as well as with those computed from the corresponding deterministic model. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010