Keywords
Reductive dechlorination, 1, 2-dichloroethane, multi-objective optimization, model complexity, Michaelis-Menten kinetics
Start Date
15-9-2020 12:00 PM
End Date
15-9-2020 12:20 PM
Abstract
1,2-Dichloroethane (1,2-DCA) is a major man-made chlorinated organic compound. It can often be found in plumes at sites contaminated with a mix of chlorinated ethenes that warrant some form of remedial treatment or at least monitored natural attenuation. A major attenuation pathway of 1,2-DCA is through reductive dechlorination by organohalide-respiring bacteria (OHRB), which is influenced by other chlorinated contaminants such as chloroethenes and chloropropanes, often co-existing with 1,2-DCA. To assess the effect of chloroethenes and 1,2-dichloropropane (1,2-DCP) on 1,2-DCA dechlorination by a microbial consortium containing ORHB, we simulated the (sequential) reductive dechlorination in a series of enriched batch cultures using a Michaelis-Menten kinetics approach with competitive inhibition. Each culture received three spikes of respective chlorinated compounds. To account for the high correlation of the Michaelis-Menten parameters describing dechlorination, we performed model calibration with AMALGAM, a multi-objective, multi-method evolutionary optimization technique. Concentrations of the individual dechlorination products served as objectives. For each culture, AMALGAM found an ensemble of optimal solutions that obey the Pareto-principle. The number of Pareto-optimal solutions is influenced by model complexity and the size of the parameter space and thus varies for different cultures. Therefore, we report for each batch culture a range based on the 50 best parameter combinations. These were ranked by their Euclidean distance to the zero-objective point of our n-dimensional space (n is number of culture-specific objectives). Model calibration was performed on data from the third spike, for which stable dechlorination patterns were encountered. Cultures with the simplest dechlorination sequences (i.e., vinyl chloride (VC) to ethene or 1,2-DCA to ethene) were simulated first. Subsequently model complexity was gradually increased by including additional dechlorination reactions (i.e., cis-dichloroethene to VC to ethene, etc.). As such, we could utilize prior information on some parameters to be optimized in more complex simulations and decrease the parameter space, from which optimal solutions were selected. Our results demonstrate that chloroethenes might have to be removed before effective reductive dechlorination of 1,2-DCA can be achieved. As such, our findings are relevant to contaminated site managers in their understanding of the site-specific attenuation processes.
Modeling reductive dechlorination of 1,2-dichloroethane in the presence of chloroethenes and 1,2-dichloropropane as co-contaminants – Using prior information and multi-objective optimization
1,2-Dichloroethane (1,2-DCA) is a major man-made chlorinated organic compound. It can often be found in plumes at sites contaminated with a mix of chlorinated ethenes that warrant some form of remedial treatment or at least monitored natural attenuation. A major attenuation pathway of 1,2-DCA is through reductive dechlorination by organohalide-respiring bacteria (OHRB), which is influenced by other chlorinated contaminants such as chloroethenes and chloropropanes, often co-existing with 1,2-DCA. To assess the effect of chloroethenes and 1,2-dichloropropane (1,2-DCP) on 1,2-DCA dechlorination by a microbial consortium containing ORHB, we simulated the (sequential) reductive dechlorination in a series of enriched batch cultures using a Michaelis-Menten kinetics approach with competitive inhibition. Each culture received three spikes of respective chlorinated compounds. To account for the high correlation of the Michaelis-Menten parameters describing dechlorination, we performed model calibration with AMALGAM, a multi-objective, multi-method evolutionary optimization technique. Concentrations of the individual dechlorination products served as objectives. For each culture, AMALGAM found an ensemble of optimal solutions that obey the Pareto-principle. The number of Pareto-optimal solutions is influenced by model complexity and the size of the parameter space and thus varies for different cultures. Therefore, we report for each batch culture a range based on the 50 best parameter combinations. These were ranked by their Euclidean distance to the zero-objective point of our n-dimensional space (n is number of culture-specific objectives). Model calibration was performed on data from the third spike, for which stable dechlorination patterns were encountered. Cultures with the simplest dechlorination sequences (i.e., vinyl chloride (VC) to ethene or 1,2-DCA to ethene) were simulated first. Subsequently model complexity was gradually increased by including additional dechlorination reactions (i.e., cis-dichloroethene to VC to ethene, etc.). As such, we could utilize prior information on some parameters to be optimized in more complex simulations and decrease the parameter space, from which optimal solutions were selected. Our results demonstrate that chloroethenes might have to be removed before effective reductive dechlorination of 1,2-DCA can be achieved. As such, our findings are relevant to contaminated site managers in their understanding of the site-specific attenuation processes.
Stream and Session
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