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Modeling of Käppala Waste Water Treatment Plant - Evaluation of the Influence of Storm water to the Treatment Process

Bashide, Maberana Moses LU (2015) VVAM01 20151
Chemical Engineering
Abstract
The Käppala WWTP data is modelled by using WEST software to evaluate how the storm water affects the treatment plant process and to use the flow data from Ryaverket WWTP to the Käppala WWTP model in order to model. The flow estimation method which gives best results for the flow data over a long period on the low flow occurrence is 95 percentile flow estimation method. This method was used to determine dry weather flow of 1.4 m3/s and 2.8 m3/s for flow data from Käppala WWTP and assumed flow data from Ryaverket WWTP used in Käppala model respectively. Flow data normalization by using PE is 2.3 and dry weather flow is 2. The Ryaverket WWTP inflow data were reduced by dividing with 2.3 and 2 which were used in order to get the same flow... (More)
The Käppala WWTP data is modelled by using WEST software to evaluate how the storm water affects the treatment plant process and to use the flow data from Ryaverket WWTP to the Käppala WWTP model in order to model. The flow estimation method which gives best results for the flow data over a long period on the low flow occurrence is 95 percentile flow estimation method. This method was used to determine dry weather flow of 1.4 m3/s and 2.8 m3/s for flow data from Käppala WWTP and assumed flow data from Ryaverket WWTP used in Käppala model respectively. Flow data normalization by using PE is 2.3 and dry weather flow is 2. The Ryaverket WWTP inflow data were reduced by dividing with 2.3 and 2 which were used in order to get the same flow situations as Kappala WWTP inflow data. The influent concentration data were different from the plants but the general results shows that regardless of the difference of inflow concentrations, the increase of inflow from 21,794.7 to 22,244.3 m3/d reduces the primary clarifier removal efficiency by 7.7% for incoming TSS and 19.3% for incoming BOD. Further increase of inflow from 21,794.3 to 25,580.9 m3/d reduces the removal efficiency by 10% for incoming TSS and 21.7% for incoming BOD.

The Käppala WWTP model effluent quality standards from secondary clarifier shows that annual average BOD is 7.5 mg/l, TN is 6.2 mg/l, NH4-N is 0.2 mg/l. When PE normalized flow data from Ryaverket WWTP used in the model, effluent quality standards shows that annual average BOD is 5 mg/l, TN is 5.2 mg/l, NH4-N is 0.2 mg/l.

The results illustrated that the Käppala plant data obtained is good enough to build up model. The flow normalization by using PE produced better effluent quality compared with flow normalization by using dry weather flow. The primary clarifier works well in removing SS and BOD for both flow data from Käppala and Ryaverket WWTPs. The aerobic sludge age for data from Käppala WWTP model is higher than the existing plant by 1.6 days. The increase of flow directly affects TN, BOD and SS removal efficiency but seems to have little effect on NH4-N removal efficiency. (Less)
Popular Abstract
When it rains, surface or subsurface water runoff occurs. Where does the water go? Is all rain water infiltrated into the ground or transported to the water bodies? If not, where does the rest of the infiltrated water go? Will it harm the environment? Rain water infiltrates the sewerage system and becomes part and parcel of wastewater which then requires treatment.
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author
Bashide, Maberana Moses LU
supervisor
organization
course
VVAM01 20151
year
type
H2 - Master's Degree (Two Years)
subject
keywords
wastewater treatment, storm water, Käppala, modelling, WEST, water engineering, environmental engineering, vattenförsörjningsteknik, avloppsteknik
report number
18
language
English
additional info
M.Sc Water Resources Engineering
id
8046524
date added to LUP
2015-10-07 13:17:44
date last changed
2015-10-07 13:17:44
@misc{8046524,
  abstract     = {The Käppala WWTP data is modelled by using WEST software to evaluate how the storm water affects the treatment plant process and to use the flow data from Ryaverket WWTP to the Käppala WWTP model in order to model. The flow estimation method which gives best results for the flow data over a long period on the low flow occurrence is 95 percentile flow estimation method. This method was used to determine dry weather flow of 1.4 m3/s and 2.8 m3/s for flow data from Käppala WWTP and assumed flow data from Ryaverket WWTP used in Käppala model respectively. Flow data normalization by using PE is 2.3 and dry weather flow is 2. The Ryaverket WWTP inflow data were reduced by dividing with 2.3 and 2 which were used in order to get the same flow situations as Kappala WWTP inflow data. The influent concentration data were different from the plants but the general results shows that regardless of the difference of inflow concentrations, the increase of inflow from 21,794.7 to 22,244.3 m3/d reduces the primary clarifier removal efficiency by 7.7% for incoming TSS and 19.3% for incoming BOD. Further increase of inflow from 21,794.3 to 25,580.9 m3/d reduces the removal efficiency by 10% for incoming TSS and 21.7% for incoming BOD. 

The Käppala WWTP model effluent quality standards from secondary clarifier shows that annual average BOD is 7.5 mg/l, TN is 6.2 mg/l, NH4-N is 0.2 mg/l. When PE normalized flow data from Ryaverket WWTP used in the model, effluent quality standards shows that annual average BOD is 5 mg/l, TN is 5.2 mg/l, NH4-N is 0.2 mg/l. 

The results illustrated that the Käppala plant data obtained is good enough to build up model. The flow normalization by using PE produced better effluent quality compared with flow normalization by using dry weather flow. The primary clarifier works well in removing SS and BOD for both flow data from Käppala and Ryaverket WWTPs. The aerobic sludge age for data from Käppala WWTP model is higher than the existing plant by 1.6 days. The increase of flow directly affects TN, BOD and SS removal efficiency but seems to have little effect on NH4-N removal efficiency.},
  author       = {Bashide, Maberana Moses},
  keyword      = {wastewater treatment,storm water,Käppala,modelling,WEST,water engineering,environmental engineering,vattenförsörjningsteknik,avloppsteknik},
  language     = {eng},
  note         = {Student Paper},
  title        = {Modeling of Käppala Waste Water Treatment Plant - Evaluation of the Influence of Storm water to the Treatment Process},
  year         = {2015},
}