Multi-objective Optimal Design of Groundwater Bioremediation Using Multi-objective and Ant Colony Algorithm

Document Type : Research Paper



In situ bioremediation is one of the most regular technologies to clean up petroleum contaminated aquifers. Control process of such a complicated system is difficult and needs more than one management target. This study develops multi objective simulation/optimization model that consider cost and time of remediation process, and concentration violation from standard value as model objectives. For this propose two multi objective ant colony optimization (ACO) models have been developed,  cost-time and cost-violations. The BIOPLUMEII model applies to simulate aquifer hydraulics and bioremediation. Injection rate of oxygen and nutrient, extraction rate in wells and well locations are decision variables. Simulated groundwater model is hypothetic and homogenous. For the case studies, the Pareto front is derived which enhances the decision maker to choose one which more suitable for him/her according to the priorities. The results of time-cost trade off curve showed  minimum possible time for remediation process. Also, It was found maximum time  for remediation before contamination plume reaches to downstream monitoring wells. The  results of  cost-violation trade off curve showed how to decrease cost of process with relaxation of standard concentration constraint. The following research shows the proposed multi objective models are useful for decision makers and also reveals the capability of ACO in multi objective optimization of groundwater bioremediation system design.


1-Flathman, P. E., Jerger, D. E., and Exner, J. H. (1993). Bioremediation field experience, Lewis, Boca Raton, Fla.
2-Hinchee, R. E., Alleman, B. C., Hoeppel, R. E., and Miller, R. N. (1994). Hydrocarbon bioremediation, Lewis, Boca Raton, Fla.
3-Cookson, J. T. (1995). Bioremediation engineering: Design and application, McGraw-Hill, N.Y.
4-Alexander, M. (1994). Biodegradation and bioremediation, Academic Press, N.Y.
5-Safavi, H.R., Sookhak Lari, K., and Taebi, A. (2006). “Simulation of ‘Pump-and-Treat’ and ‘Air Sparging’ for in situ remediation of contaminated groundwater.” Journal of Water and Wastewater, 51, 31-38 (In Persian).
6-Sookhak Lari, K., and Safavi, H.R. (2008). “A simulation-optimization model for air sparing and pump and treat groundwater remediation technologies. ”J.  of Environmental Informatics, 12(1),44-53.
7-Sookhak Lari, K., and Safavi, H.R. (2006). “Using regression model for optimal groundwater remediation and comparison with genetic algorithm.” Secound Iran Water Resource Management Conference, Isfahan
(In Persian)
8-Minsker, B. S., and Shoemaker, C. A. (1996). “Differentiating a finite element biodegradation simulation model for optimal control.” Water Resour. Res., 32(1), 187-192.
9-Minsker, B. S., and Shoemaker, C. A. (1998). “Dynamic optimal control of in situ bioremediation of groundwater.” J. Water Resour. Plan. Manage., 124(3), 149-161.
10-Yoon, J-H., and Shoemaker, C. A. (1999). “Comparison of optimization methods for groundwater bioremediation.” J. Water Resour. Plan. Manage., 125(1), 54-63.
11-Smally, J. B., and Minsker, B. S., (2000). “Risk-based insitu bioremediation design using noisy genetic algorithm.” Water Resource Research, 36(10), 3043-3052.
12-Prasad, R. K., and Mathur, S. (2006). “Potential ell locations in insitu bioremediation design using neural network embedded Monte Carlo approach.” Practice Periodical Hazardous, Toxic, and Radioactive Management, 12(4), 260-269.
13-Shieh, H. J., and Peralta, R. C., (2005), “Optimal insitu bioremediation design by hybrid genetic algorithm-simulated annealing.” J. of Water Resources Planning and Management, 131(1), 67-78.
14-Kollat, J. B., and Reed, P. M. (2006).  “Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design.” Advances in Water Resources, 29(6), 792-807.
15-Beckfort, O., Amy, B., Hilton, C., and Liu, X. (2004) “Development of an enhanced multi objective robust genetic algorithm for groundwater remediation design under uncertainty.” Proceeding of Water Resource systems, ISBN: 0‐7844‐0685‐5.
16-Erickson, M., Mayer, A., and Horn, J. (2002). “Multi-objective optimal design of groundwater remediation Systems: Application of the niched Pareto genetic algorithm (NPGA).” Advances in Water Resources, 25(1), 51-65
17-Park, C. H., and Aral, M. M. (2004). “Multi-objective optimization of pumping rates and well placement in coastal aquifers.” J. of Hydrology, 290(1-2), 80-99.
18-Baveye, P., and Valocchi, A. (1989). “An evaluation of mathematical models of the transport of biologically reacting solutes in saturated soils and aquifers.” Water Resour. Res., 25(6), 1413-1421.
19-Rittmann, B. E., McCarty, P. L., and Roberts, P. V. (1980). “Traceorganics biodegradation in aquifer recharge.” Groundwater, 18(3), 236-243.
20-Molz, F. J., Widdowson, M. A., and Benefield, L. D. (1986). “Simulation of microbial growth dynamics coupled to nutrient and oxygen transport in porous media.” Water Resour Res., 22(8), 1207-1216.
21-Borden, R. C., and Bedient, P. B. (1986). “Transport of dissolved hydrocarbons influenced by oxygen-limited biodegradation: 1. Theoretical development.” Water Resour. Res., 2(13), 1973-1982.
22-Rifai, H. S., and Bedient, P. B. (1990). “Comparison of biodegradation kinetics with an instantaneous reaction model for groundwater.” Water Resour. Res., 26(4), 637-645.
23-Burges, K. S., Rifai, H. S., and Bedient, P. B. (1993).“Flow and transport modeling of a heterogeneous field site contaminated with dense chlorinated solvent waste.” Proceedings of the Petroleum Hydrocarbons and Organic Chemicals in Groundwater: Prevention, Detection, and Restoration, American Petroleum Institute and NGWA, Houston, Texas, 693-707.
24-Wiedemeier, T. H., Wilson, J. T., Miller, R. N., and Campbell, D. H. (1994). “United air force guidelines for successfully supporting intrinsic remediation with an example from hill air force base.” Proceeding of the Petroleum Hydrocarbons and Organic Chemicals in Groundwater: Prevention, Detection, and Restoration, NGWA  and American Petroleum Institute, Houston, Texas, 317-334.
25-USEPA. (1998). BIOPLUME III natural attenuation decision support system—User’s manual version 1.0, EPA/600/R-98/010, Washington,D. C.
26-Konikow, L. F., and Bredehoeft, J. D. (1978). Computer model of two dimensional solute transport and dispersion in groundwater, Techniques of Water Resources Investigation of the USGS, U. S. Geological Survey, Washington, D. C.
27-Mariano, C. E., and Morales, E. (2002). A multiple objective ant-Q algorithm for the design of water distribution irrigation networks, Instituto Mexicano de Tecnologia del Agua, Mexico.
28-Iredi, S., D., and Middendrof, M. (2001). “Bi-criterion optimization with multi colony ant algorithms.” Proceeding of the First International Conference on Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science, Springer, Berlin.
29-Doerner, K., Gutjahr, W.J., Hartl, R.F., Strauss, C. and Stummer, C. (2004). ‘‘Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolio selection, Annals of Operations Research, to appear.
30-Baran, B., and Schaerer, M. (2003). ‘‘A multiobjective ant colony system for vehicle routing problem with time windows.” Twenty first IASTED International Conference on Applied Informatics, Insbruck, Austria,
31-Afshar, A., Sharifi, F., and Jalali, M. R. (2008). ‘‘Nondomidated ARCHRIVING multicoloni and ant algorithm for multi objective optimization; Application to Multi purpose reservoir operation.” J. of Engineering Optimization, 41 (4), 313-325.
32-Hosseinzadeh, H., Afshar, A., and Sharifi, F. (2010). “Multi objective ant colony algorithm  for waste load allocation.” Iran Water Resources Research, 17, (2), 1-13. (In persian).
33-Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002) “A fast and elitist multiobjective genetic algorithm: NSGA-II” IEEE Transactions on Evolutionary Computation, 6, 182-197.
34-Wang, X. L., and Mahfouf, M. (2004). ‘‘ACSAMO: An adaptive multiobjective optimization algorithm using the clonal selection principle.” The Lanzhou University of Technology, Lanzhou, 730050, China
35-Shieh, H. J. (1997). ‘‘Optimal system design of  insitu bioremediation using simulated annealing and parallel recombinative simulated annealing.” Ph. D. Thesis, Utah State University.
36-Jalali M. R., Afshar A., and Marino, M. A. (2006). “Reservoir operation by ant colony optimization algorithms.” Iranian Journal of Science and Technology, Transaction B, Engineering, 30. (B1), 107-117.