טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
M.Sc Thesis
M.Sc StudentSivan Klas
SubjectDevelopment of a Cost Effective and Simple Process for
Nitrate Removel from Intensive Aquaculture Systems
by Denitrification Using Intrinsic
Carbon Source
DepartmentDepartment of Agricultural Engineering
Supervisors Full Professor Lahav Ori
Mr. Mozes Noam


Abstract

Aquaculture production has been expanding globally at a rate of over 10% a year since 1984, and production is expected to double by the year 2020. However, aquaculture in the form of marine fish cages has the potential to adversely affect the environment, with nutrient (N, P) release being the major cause for concern. The most promising alternative to fish cages is termed RAS (Recirculating Aquaculture Systems), where high densities of fish are grown in relatively small inland tanks with minimal water utilization, which enables efficient nutrients and organic matter removal. Nitrate (NO3-) is a nutrient that is generated during ammonium oxidation in RAS. In principal, nitrate is not toxic to fish. However, if released in large amounts into a water body, eutrophication may be induced.


The work is aimed at finding an economic solution to the removal of nitrate in RAS before its discharge to the environment. The approach investigated was to utilize the organic solids generated in RAS as the carbon and electron-donating source for the denitrification process, in which the nitrate is reduced to innocuous nitrogen gas (N2). The intrinsic solids were analyzed to establish a generic chemical formula. This formula was then used to produce a stoichiometric model to describe the process reaction. Integration of the conventional activated sludge process concepts allowed the theoretical prediction of various parameters of interest as a function of the mean solids retention time (SRT). The predictions were tested by the continuous operation of a lab-scale denitrification reactor. By and large, the empirical results agreed well with model predictions.