| description abstract | Groundwater pollution is a major problem in many parts of the world. Many of the contaminated sites, especially in industrial areas, are affected by organic biodegradable contaminants such as trichloro-ethylene (TCE), tetrachloroethylene, or other organic contaminants. In situ bioremediation is especially suitable for such organic contaminants. In situ bioremediation of groundwater involves the injection of oxygen to enhance the biodegradation and accelerate the remediation of contaminated sites using microorganisms (yeast, fungi, or bacteria) to break down or degrade hazardous substances into less toxic or nontoxic substances. For the optimal design of an in situ bioremediation system, one should know the optimal injection rate and other physical processes taking place. Because of the nonlinear, nonanalytical nature of the groundwater pollution transport and remediation process, numerical methods are a must to solve the problem. Modeling bioremediation involves solving biodegradation equations, locating the oxygen injection wells, and fixing up the time of remediation The mesh-free (MFree) method is a numerical approach to solve these partial differential equations (PDE) in a simple manner. The method uses the number of scattered nodes in the domain and on the boundary. In this paper, for simulation of bioremediation, an MFree point collocation method (PCM)–based simulation model, PCM-BIO, is proposed. To determine the optimal injection rate, use of an optimization model along with a simulation model ensures that designs are optimal for the specified site conditions. To solve complex optimization problems, particle swarm optimization (PSO) is found to be very effective and simple. In the present study, a PSO-based model is proposed for optimization of in situ bioremediation. Furthermore, combining the PCM simulation model and PSO optimization model, a PCM-BIO-PSO model is developed to study the complex bioremediation process of groundwater contamination. The developed PCM-transport model is initially verified with the available analytical solution, and PCM-BIO is verified with the BIOPLUME II model. Moreover the PCM-BIO-PSO model results are compared with the results of a model based on the FEM–successive approximation linear quadratic regulator (FEM-SALQR) results. Both of the model comparisons showed good agreement with each other, proving the applicability of the present approach. | |
