Comprehensive Review of Pre-heater Efficiency and Sensitivity Analysis Methodology

1. Own Use Gas Review of Pre-heater Reports & Sensitivity Analysis

2. Topics • Background • Review of Pre-heater Reports • Sensitivity Analysis Methodology Used • Results • Conclusions

3. Review of Pre-heater Reports • Review Report available to DNs.

4. Review of Pre-heater Reports

5. Efficiency • The research indicates that over a series of four tests of water bath pre-heaters, their efficiency varied between 69% and 53%. • The results were found following tests to find the optimum settings for the burners, for example, in terms of the settings of gas pressure and primary aeration.

6. Sensitivity Analysis Background • Concerns raised that original OUG model is out of date and may not be valid for other years • Objectives • To identify if the model was valid for other years by • Carrying out a sensitivity analysis of the key input variables to the OUG model • And to identify which variables have greatest impact on OUG%

7. Sensitivity Methodology Where: is the mass flow rate (kg/h), is the rise in temperature in the heater (deg C), is the specific heat capacity of the gas (kJ/kg/K), is the efficiency of the heater and is the energy used in the heater (in kJ/h). The mass flow rate is calculated using: where Q is the flow through the heater, also referred to as F1.

8. Known inputs to equation: • F1 – flow through the heater • T1 – outlet temperature • GT – ground temperature • The best estimate of inlet gas temperature. • P1 – inlet pressure • P5 – outlet pressure • Specific Gravity and Density of Air – constants. • Heater efficiency is unknown, but assumptions are made.

9. Sites used • High number of analyses to be carried out. • SW LDZ data used • Within the timescales of the project this data was most readily available • Sample of 13 sites taken: • Good correlation of results in sample. • Data cleaned to remove zeros and outliers.

10. Analysis of Data • Each site within the LDZ is potentially unique. • Some common themes present across all/majority sites: • Strong relationship between inlet pressure and inlet (post-heater) temperature. • In other cases the relationship varies considerably from site to site. • E.g. between flow and inlet pressure or outlet pressure • The analysis was carried out on a site-by-site basis.

11. Analysis of Data • For any given site the data is seasonal. • Particular differences present between winter and summer operation. • Pre-heating is only used during the winter months. • For this reason, the data was split by quarter: • Jan-Mar, Apr-Jun, Jul-Sep, Oct-Dec • Analysis carried out separately for each • Four sets of relationships. • Sufficient data to construct robust relationships.

12. Analysis of Data • Each of these inputs is potentially related to the others. • For each site and each quarter; • Full analysis of the relationship of each input variable with every other input variable. • Matrix of correlations. • Ground Temperature not included – limited dataset. • Ground temperature provided as monthly average, other data inputs are hourly.

13. Matrix of correlations

14. Range of Inputs • Calculate the likely range of each input in order to assess how much to vary it to reflect the limits of standard operation. • Calculated as: • Mean ± 1.96 x Standard Deviation • Represents the points between which 95% of the distribution lie.

15. Fixed Range • To illustrate the sensitivity of the equation each input has also been varied by a fixed amount • These were set at the mean plus and minus 10%. • Represents the reaction of the equation to a fixed shift in inputs.

16. Summary of analysis • Sensitivity analysis was carried out, • For each site and quarter • First calculating the energy consumption under mean conditions for that quarter. • Each variable adjusted to its full set of limits • Low and high 95% distribution points, • Low and high 10% absolute points • Record the change in energy consumption. • Calculations were carried out twice for each site/quarter, • With correlations taken into account since operationally a change in one variable is related to changes in others. • With correlations ignored. • This creates a set of four different conditions

17. Results Sets • Vary inputs to 95% distribution points with correlations off • Vary inputs to 95% distribution points with correlations on • Vary inputs to 10% absolute points with correlations off • Vary inputs to 10% absolute points with correlations on

18. 95% distribution points Correlations off • Low is the % reduction in energy consumption from the calculated average consumption • High is the % increase in energy consumption from the calculated average consumption • Range is the magnitude of the change in energy consumption • Scaled rating is the relative strength of each parameter compared to the one with the biggest impact

19. 95% distribution points Correlations off Correlations on

20. 10% absolute points Correlations off Correlations on

21. OUG Indicative Impact 10% distribution points Correlations off

22. OUG Indicative Impact 10% distribution points Correlations off

23. Conclusions • F1 does not impact OUG%. • T1 has minimal impact. • P1 and P5 are associated with the operation of the network. • These are unlikely to change significantly, e.g. an outlet pressure is unlikely to change unless a main is up-rated. • GT – any change would have an impact. • Given climate change, this is most likely to be upwards and therefore a reduction in OUG% is the most likely result. • E – Has a direct % impact on OUG%. Currently assumed at 50%, earlier reports set this between 53-69%.