Abstract: Storage tank farms are essential industrial facilities to accumulate oil, petrochemicals and gaseous products. Since tank farms contain huge mass of fuel and hazardous materials, they are always targets of serious accidents such as fire, explosion, spill and toxic release which may cause severe impacts on human health, environmental and properties.
Although having a safe layout is not able to prevent initiating accidents, however it effectively controls and reduces the adverse impact of such accidents.
The aim of this paper is to determine the optimal layout for a storage tank contains different type of hydrocarbon fuels. A quantitative risk assessment is carried out on a selected tank farm in Jaipur, India, with particular attention given to both the consequence modeling and the overall risk assessment using PHAST Software. Various designs of tank layouts are examined taking into consideration several issues of plant operations and maintenance. In all stages of the work, standard guidelines specified by the industry are considered and recommendations are substantiated with simulation results and risk quantification.
Abstract: In this study, a mathematical model was proposed and
the accuracy of this model was assessed to predict the growth of
Pseudomonas aeruginosa and rhamnolipid production under nitrogen
limiting (sodium nitrate) fed-batch fermentation. All of the
parameters used in this model were achieved individually without
using any data from the literature.
The overall growth kinetic of the strain was evaluated using a
dual-parallel substrate Monod equation which was described by
several batch experimental data. Fed-batch data under different
glycerol (as the sole carbon source, C/N=10) concentrations and feed
flow rates were used to describe the proposed fed-batch model and
other parameters. In order to verify the accuracy of the proposed
model several verification experiments were performed in a vast
range of initial glycerol concentrations. While the results showed an
acceptable prediction for rhamnolipid production (less than 10%
error), in case of biomass prediction the errors were less than 23%. It
was also found that the rhamnolipid production by P. aeruginosa was
more sensitive at low glycerol concentrations.
Based on the findings of this work, it was concluded that the
proposed model could effectively be employed for rhamnolipid
production by this strain under fed-batch fermentation on up to 80 g l-
1 glycerol.