Optimal In Situ Bioremediation Design of Groundwater Contaminated with Dissolved Petroleum Hydrocarbons
Publication: Journal of Hazardous, Toxic, and Radioactive Waste
Volume 20, Issue 2
Abstract
Aquifers are one of the main water supply resources for drinking, agricultural, and industrial purposes. Therefore, it is crucial to study and evaluate different remediation methodologies that can be used to enhance the quality of groundwater. In situ bioremediation is one of the most efficient and cost-effective cleanup methodologies to treat groundwater pollution. This methodology relies on the microorganisms of a contaminated aquifer to treat polluted groundwater. This paper presents a multiobjective simulation-optimization (S-O) model to achieve the best in situ bioremediation system design for a groundwater with contaminated dissolved hydrocarbons. Minimizing the design and operational costs along with the sum of squared cleanup standard violations (SCSV) are the two main objectives of this study. The results of optimization are presented in the form of Pareto possibility frontiers. A model from the literature and a nondominated sorting genetic algorithm are used for groundwater simulation and optimization, respectively, in this work. Single-objective and multiobjective cleanup optimized designs were obtained. The results of the single-objective design confirmed the capability and accuracy of the developed S–O model. The multiobjective optimization results yielded a Pareto frontier that can be used by regulators to select the best design tradeoff between the cleanup standard requirements and the financial resources. For example, an optimal option that reduces total cost about 51.3% increases the SCSV about 1.9%. Moreover, the option that increases the SCSV by about 12.6% decreases the total cost equal to 76.2%. A sensitivity analysis was performed for some of the bioremediation parameters such as hydraulic conductivity, initial oxygen concentration, injected oxygen concentration, remediation time, and the ratio of oxygen to hydrocarbon consumed. The results show that the hydraulic conductivity and remediation time had the most impact on the effectiveness of the bioremediation operation.
Get full access to this article
View all available purchase options and get full access to this article.
References
Aly, A. H., and Peralta, R. C. (1999). “Comparison of a genetic algorithm and mathematical programming to the design of groundwater cleanup systems.” Water Resour. Res., 35(8), 2415–2425.
Ashofteh, P. S., Bozorg Haddad, O., and Loaiciga, H. A. (2015a). “Evaluation of climatic-change impacts on multiobjective reservoir operation with multiobjective genetic programming.” J. Water Resour. Plann. Manage., 04015030.
Ashofteh, P.-S., Bozorg Haddad, O., Akbari-Alashti, H., and Mariño, M. A. (2015b). “Determination of irrigation allocation policy under climate change by genetic programming.” J. Irrig. Drain. Eng., 04014059.
Ashofteh, P.-S., Bozorg Haddad, O., and Mariño, M. A. (2013a). “Scenario assessment of streamflow simulation and its transition probability in future periods under climate change.” Water Resour. Manage., 27(1), 255–274.
Ashofteh, P.-S., Bozorg Haddad, O., and Mariño, M. A. (2015c). “Risk analysis of water demand for agricultural crops under climate change.” J. Hydrol. Eng., 04014060.
Ashofteh, P.-S., Bozorg-Haddad, O., and Mariño, M. (2013b). “Climate change impact on reservoir performance indices in agricultural water supply.” J. Irrig. Drain. Eng., 85–97.
Bear, J. (1979). Hydraulics of groundwater, McGraw- Hill, New York, 567.
Borden, R. C., and Bedient, P. B. (1986). “Transport of dissolved hydrocarbons influenced by oxygen-limited biodegradation: 1. Theoretical development.” Water Resour. Res., 22(13), 1973–1982.
Bozorg Haddad, O., Ashofteh, P.-S., Ali-Hamzeh, M., and Mariño, M. A. (2015a). “Investigation of reservoir qualitative behavior resulting from biological pollutant sudden entry.” J. Irrig. Drain. Eng., 04015003.
Bozorg Haddad, O., Ashofteh, P.-S., and Mariño, M. A. (2015b). “Levee’s layout and design optimization in protection of flood areas.” J. Irrig. Drain. Eng., 04015004.
Bozorg Haddad, O., Ashofteh, P.-S., Rasoulzadeh-Gharibdousti, S., and Mariño, M. A. (2014). “Optimization model for design-operation of pumped-storage and hydropower systems.” J. Energy Eng., 04013016.
Cookson, J. T. (1995). Bioremediation engineering: Design and application, McGraw-Hill, New York, 524.
Deb, K., Partap, A., Agarwal, S., and Meyarivan, T. (2002). “Fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput., 6(2), 182–197.
Erickson, M., Mayer, A., and Horn, J. (2002). “Multi-objective optimal design of groundwater remediation systems: Application of the niched Pareto genetic algorithm (NPGA).” Adv. Water Resour., 25(1), 51–65.
Freeze, R. A., and Cherry, R. B. (1979). Groundwater, Prentice Hall, Englewood Cliffs, NJ, 604.
Guan, J., and Aral, M. M. (1999). “Optimal remediation with well locations and pumping rates selected as continuous decision variables.” J. Hydrol., 221(1–2), 20–42.
Hilton, A. B. C., and Culver, T. B. (2005). “Groundwater remediation design under uncertainty using genetic algorithms.” J. Water Resour. Plann. Manage., 25–34.
Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence, University of Michigan Press, Ann Arbor, MI, 211.
Kuo, C. H., Michel, A. N., and Gray, W. G. (1992). “Design of optimal pump-and-treat strategies for contaminated groundwater remediation using the simulated annealing algorithm.” Adv. Water Resour., 15(2), 95–105.
Liu, Y., and Minsker, B. S. (2004). “Full multi-scale approach for optimal control of in situ bioremediation.” J. Water Resour. Plann. Manage., 26–32.
Mategaonkar, M., and Eldho, T. I. (2012). “Simulation-optimization model for in situ bioremediation of groundwater contamination using mesh-free PCM and PSO.” J. Hazard. Toxic Radioact. Waste, 207–218.
MATLAB 9.0 [Computer software]. MathWorks, Natick, MA.
Minsker, B. S., and Shoemaker, C. A. (1998a). “Computational issues for optimal in-situ bioremediation design.” J. Water Resour. Plann. Manage., 0039–0046.
Minsker, B. S., and Shoemaker, C. A. (1998b). “Dynamic optimal control of in-situ bioremediation of groundwater.” J. Water Resour. Plann. Manage., 149–161.
Mondal, A., Eldho, T. I., and Rao, V. V. S. G. (2010). “Multi-objective groundwater remediation system design using coupled finite-element model and nondominated sorting genetic algorithm II.” J. Hydrol. Eng., 350–359.
Mylopoulos, Y. A., Theodosiou, N., and Mylopoulos, N. A. (1999). “A stochastic optimization approach in the design of an aquifer remediation under hydrogeologic uncertainty.” Water Resour. Manage., 13(5), 335–351.
Papadopoulou, M. P., Karatzas, G. P., and Pinder, G. F. (2004). “Economic parameters’ effects in the optimal design of a groundwater remediation system.” Dev. Water Sci., 55(2), 1181–1191.
Prasad, R. K., and Mathur, S. (2008). “Potential well locations in in situ bioremediation design using neural network embedded Monte Carlo approach.” Pract. Period. Hazard. Toxic Radioact. Waste Manage., 260–269.
Ren, X., and Minsker, B. (2005). “Which groundwater remediation objective is better: A realistic one or a simple one?” J. Water Resour. Plann. Manage., 351–361.
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.
Shieh, H. J., and Peralta, R. C. (2005). “Optimal in situ bioremediation design by hybrid genetic algorithm-simulated annealing.” J. Water Resour. Plann. Manage., 67–78.
Shokri, A., Bozorg Haddad, O., and Mariño, M. A. (2013). “Algorithm for increasing the speed of evolutionary optimization and its accuracy in multi-objective problems.” Water Resour. Manage., 27(7), 2231–2249.
Singh, A., and Minsker, B. (2008). “Uncertainty-based multi-objective optimization of groundwater remediation design.” Water Resour. Res., 44(2), W02404.
Singh, T. S., and Chakrabarty, D. (2011). “Multi-objective optimization of pump-and-treat-based optimal multilayer aquifer remediation design with flexible remediation time.” J. Hydrol. Eng., 413–420.
Sinha, E., and Minsker, B. S. (2007). “Multi-scale island injection genetic algorithms for groundwater remediation.” Adv. Water Resour., 30(9), 1933–1942.
Sturman, P. J., Stewart, P. S., Cunningham, A. B., Bouwer, E. J., and Wolfarm, J. H. (1995). “Engineering scale-up of in situ bioremediation process: A review.” J. Contam. Hydrol., 19(3), 171–203.
Wetzelhuetter, C. (2013). Groundwater in the coastal zones of Asia-Pacific, Springer, Netherlands, 393.
Yan, S., and Minsker, B. S. (2011). “Applying dynamic surrogate models in noisy genetic algorithms to optimize groundwater remediation designs.” J. Water Resour. Plann. Manage., 284–292.
Yoon, J. H., and Shoemaker, C. A. (1999). “Comparison of optimization methods for ground-water bioremediation.” J. Water Resour. Plann. Manage., 0054–0063.
Information & Authors
Information
Published In
Journal of Hazardous, Toxic, and Radioactive Waste
Volume 20 • Issue 2 • April 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Oct 30, 2014
Accepted: Sep 11, 2015
Published online: Nov 19, 2015
Published in print: Apr 1, 2016
Discussion open until: Apr 19, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.