INCA models

The Nitrate Directive 96/676/EC, with a cluster of projects related to nutrient modelling (EUROHARP, INCA) and to agricultural best practices for minimising the release of nutrients from agriculture (AGRIBMPWATER). 

After fabrication, the density of membrane was measured by dimension calculation. The crystalline phases of cermet membrane were investigated by using X-ray diffraction (XRD model JEOL, JDX-3530). Scanning electron microscope (SEM JEOL, JSM-6301F) was used to investigate the microstructure of Ni/AbOj membrane. For analysis of nickel dispersion of Ni/AbOj membrane, energy dispersive X-ray spectroscope (EDS Oxford Inca 300 and 350) with X-ray dot mapping was used. In addition, the pore size and porosity were determined by mercury porosimetry. 

INCA INCA is the Incident Capture and Analysis database used by LU to capture incidents which occur on the UndergrouiKi infrastructure. Data has been entered into this database since 1992. Incident data is entered under a particular classification, of which there are 105 different categories. For the data items used in the model, the 14 year period between 1992 and 1996 has been used. The aim has been to use incident data from the Victoria Line only. However, in the case that there were few or no incidents of a particular type on the Victoria Line, then the search has been extended to then the Bakerloo-Central-Victoria system and, failing that, then the whole Underground system. Where this has been done, the results have been reviewed to ensure applicability. 

Asset Data There are a small number of asset data items which ate needed in the model. However, it should be realised that all of the incident data contained in both the INCA and CuPID database is from a system which has a particular set of physical and operational conditions. It is an assumption in the use of these databases that the conditions over the time period that the data was obtained are sufficiently similar to the current system to allow the use of the data. 

The risk dne to PTI on Train has reduced by 71% in the CSPVL model, as a result of an improved alignment with INCA data. As this model constituted 50% of the LU 2003 QRA total risk for these 10 models, this reduction is the main contributor to the decrease in the total. 

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