Modelling of different measures for improving removal in a stormwater pond

The effect of retrofitting an existing pond on removal efficiency and hydraulic performance was modelled using the commercial software Mike21 and compartmental modelling. The Mike21 model had previously been calibrated on the studied pond. Installation of baffles, the addition of culverts under a causeway and removal of an existing island were all studied as possible improvement measures in the pond. The subsequent effect on hydraulic performance and removal of. suspended solids was then evaluated. Copper, cadmium, BOD, nitrogen and phosphorus removal were,also investigated for that specific improvement measure showing the best results. Outcomes of this study reveal that all measures increase the removal efficiency of suspended solids. The hydraulic efficiency is improved for all cases, except for the case where the island is removed. Compartmental modelling was also used to evaluate hydraulic performance and facilitated a better understanding of the way each of the different measures affected the flow pattern and performance. It was concluded that the installation of baffles is the best of the studied measures resulting in a reduction in the annual load on the receiving lake by approximately 8,000 kg of suspended solids (25% reduction of the annual load), 2 kg of copper (10% reduction of the annual load) and 600 kg of BOD (10% reduction of the annual load).

Estimation of pharmacokinetic parameters from non-compartmental variables using Microsoft Excel((R))

This study was conducted to develop a method, termed 'back analysis (BA)', for converting non-compartmental variables to compartment model dependent pharmacokinetic parameters for both one- and two-compartment models. A Microsoft Excel((R)) spreadsheet was implemented with the use of Solver((R)) and visual basic functions. The performance of the BA method in estimating pharmacokinetic parameter values was evaluated by comparing the parameter values obtained to a standard modelling software program, NONMEM, using simulated data. The results show that the BA method was reasonably precise and provided low bias in estimating fixed and random effect parameters for both one- and two-compartment models. The pharmacokinetic parameters estimated from the BA method were similar to those of NONMEM estimation.