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Own Use Gas

Review of Pre-heater Reports & Sensitivity Analysis

Topics

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

Review of Pre-heater Reports

• Review Report available to DNs.

Review of Pre-heater Reports

Report Conclusion

MRS I2912 Efficiency Tests on Water Bath Heaters at Coleshill AGI

The indirect method gives the efficiency as found for H5 as 66.5% and for H6 as 58.0%.

MRS N392 Methods of Improving the Thermal Efficiency of water bath heaters

The test work has shown that the efficiency of the heater, as delivered, can be between 62% and 69% according to the settings of gas pressure and primary aeration.

MRS N403 The Thermal Efficiency of Water Bath Heaters at Alrewas A.G.I

The gross thermal efficiency of the heater, as delivered, lay between 53 and 66% depending on the thermal input and the primary aeration level set at the burner.

MRS N 458 Performance Testing of Water Bath Heaters

At 61%, the heater efficiency was well below the manufacturers stated 70% figure.

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.

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• Concerns raised that original OUG model is out of date and may not be valid for other years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%

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.

mTCp

P

QDSGm AIR

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.

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.

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.

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.

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.

Matrix of correlations

Quarter

Flow

vs

Outlet Temp

Flow

vs

Inlet Press

Flow

vs

Outlet Press

Outlet Temp

vs

Inlet Press

Outlet Temp

vs

Outlet Press

Outlet Press

vs

Outlet Temp

Q1 -0.198 -0.033 -0.734 -0.336 -0.102 -0.102

Q2 -0.316 -0.025 0.135 -0.086 -0.380 -0.380

Q3 0.291 -0.076 -0.231 -0.159 -0.221 -0.221

Q4 -0.329 -0.046 -0.753 -0.159 0.216 0.216

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.

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.

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

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

95% distribution points

Parameter Low High Range Scaled Rating

F1 -78.3 86.8 165.1 100.0 T1 -53.5 86.2 139.8 84.6 GT 64.4 -36.9 101.3 61.3 P1 -59.6 85.6 145.1 87.9 P5 24.7 -17.6 42.4 25.7 E --- --- --- ---

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

95% distribution points


F1 -78.3 86.8 165.1 100.0 T1 -53.5 86.2 139.8 84.6 GT 64.4 -36.9 101.3 61.3 P1 -59.6 85.6 145.1 87.9 P5 24.7 -17.6 42.4 25.7 E --- --- --- ---

Correlations off

Correlations on


F1 -74.1 38.0 112.1 100.0 T1 -18.9 1.1 20.0 17.8 GT 64.4 -36.9 101.3 90.4 P1 -44.3 41.9 86.2 76.9 P5 49.9 -47.8 97.8 87.2 E

10% absolute pointsCorrelations off

Correlations on


F1 -10.0 10.0 20.0 23.9 T1 -5.7 5.7 11.4 13.6 GT 26.3 -22.5 48.8 58.3 P1 -37.3 46.5 83.8 100.0 P5 15.0 -13.4 28.4 34.0 E 11.1 -9.1 20.2 24.1


F1 -6.9 6.2 13.0 1.1 T1 -0.6 0.2 0.8 0.1 GT 26.3 -22.5 48.8 4.1 P1 -24.4 23.7 48.1 4.0 P5 1130.7 -63.2 1193.9 100.0 E 11.1 -9.1 20.2 1.7

OUG Indicative Impact


Percent OUG Low

Percent OUG High

F1 -10.0 10.0 20.0 23.9 0.0064 0.0064 T1 -5.7 5.7 11.4 13.6 0.0061 0.0068 GT 26.3 -22.5 48.8 58.3 0.0050 0.0081 P1 -37.3 46.5 83.8 100.0 0.0040 0.0094 P5 15.0 -13.4 28.4 34.0 0.0056 0.0074 E 11.1 -9.1 20.2 24.1 0.0058 0.0071

10% distribution points Correlations off

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%.

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