Document Type
Thesis
Date of Award
Spring 5-31-1999
Degree Name
Master of Science in Environmental Engineering - (M.S.)
Department
Civil and Environmental Engineering
First Advisor
Jay N. Meegoda
Second Advisor
Manish Chandra Bhattacharjee
Third Advisor
Taha F. Marhaba
Abstract
Sediments contaminated with heavy metals due to past disposal practices threatens the environment and requires remediation. This research is an attempt to develop a technology to decontaminate heavy metals in the dredged sediments with an integrated set of processes using ultrasound. Acoustic cavitation caused by ultrasound energy can be used to remove chromium from the sediments. Two coupled processes were used to treat both coarse (Process#1) and fine (Process#2) fractions of the sediments. Full factorial experimental designs were carried out to evaluate the treatment technique and to statistically model and optimize the processes. The model for Process#1 had four contributing factors, namely power, soil-water ratio, vacuum pressure and dwell time, while Process#2 had power, soil-water ratio and dwell time as contributing factors. Removal efficiency was the dependent variable in both cases. The statistical analysis for Process#1 confirms that the chosen main factors significantly influence the removal efficiency and that a full quadratic model was adequate. The optimum removal obtained by the analysis was 97% with the factor levels at 1027.5 W power, 1:13 soil-water ratio, 18 psi vacuum pressure and 12 minutes of dwell time. The statistical analysis for the silt fraction in fines too, showed that a full quadratic model was adequate and the optimum removal of 99.4% can be obtained at factor levels at 1620 W power, 1:40 as soil to water ratio and 37 minutes of dwell time. The research showed that the proposed treatment technique is effective and economical for sediments with lower clay contents.
Recommended Citation
Perera, Ruvini, "Analysis and modeling of ultrasound to decontaminate heavy metals in dredged sediments" (1999). Theses. 872.
https://digitalcommons.njit.edu/theses/872
