Semi-empirical approach to modeling of soil flushing: Model development, application to soil polluted by zinc and copper

• Published in: The Science of the total environment 2008-03, Vol.392 (2), p.187-197
• Main Authors: Svab, M, Zilka, M, Mullerova, M, Koci, V, Muller, V
• Format: Article
• Language: English
• Subjects: ammonia; Ammonia - chemistry; Applied sciences; Copper; Copper - chemistry; Decontamination. Miscellaneous; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Exact sciences and technology; Flushing; heavy metals; mathematical models; Modeling; Models, Theoretical; polluted soils; Pollution; Pollution, environment geology; Remediation; simulation models; Soil; Soil and sediments pollution; Soil Pollutants - chemistry; soil pollution; soil properties; Waste Management - methods; Zinc; Zinc - chemistry
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2007.12.001

This paper describes a semi-empirical approach to modeling the soil flushing technology. A new mathematical model aimed at predicting the course of the continuous soil flushing process by use of the input data obtained from simple batch laboratory experiments is described in the theoretical part. An objective of the study is to apply this new model to soil polluted by zinc and copper (11 949 mg kg − 1 and 1895 mg kg − 1 , respectively) by flushing the soil with an ammonia nitrogen solution. A set of batch experiments provided both equilibrium and kinetic data characterizing the leaching ability of both metals. By use of the model, the optimal ammonia concentration in the flushing solution was estimated (0.6 mol L − 1 ). For this concentration, validity of the model results was verified by a column experiment. The removal efficiency obtained was 44% (zinc) and 54% (copper). The model correctly predicted the period of time needed for the removal of weakly bound metal fractions as well as the estimate of the overall removal efficiency of metals from the soil during the flushing process. It has also proven that it is possible to use the column experiment for model calibration through the modification of the input data. Agreement of the model and experimental results can be further improved this way.