A probabilistic cellular automaton for two dimensional contaminant transport simulation in ground water

In recent years evolutionary computing algorithms have been proposed to solve many engineering problems. Genetic algorithms, Neural Networks, and Cellular Automata are the branches of evolutionary computing techniques. In this study, it is proposed to simulate the contaminant transport in porous media using a Cellular Automaton. The physical processes and chemical reactions occurring in the ground water system are intricately connected at various scales of space, time, transport coefficients and molecular concentration. The validity of continuous approach for the simulation of chemical systems with low concentration of species and intracellular environments has become subtle. Due to the difference in scales of various processes that occur in the ground water system, the description of the system can be well defined in the intermediate scale called mesoscopic scale, which is in between microscopic and macroscopic description. Mesoscopic models provide the relationship between various parameters and their evolvement in time, thus establishing the contact between modeling at various scales at the interface. In this paper, a Probabilistic Cellular Automaton (PCA) model has been developed based on the transport and reaction probability values. The developed model was verified and validated for one, two dimensional transport systems and also for the simulation of BTEX transport in two dimensional system in ground water.