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SPS Optimisation Phd Steering Group

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SPS Optimisation Phd Steering Group SPS Optimisation PhD Steering Group 12th January 2018 Introduction • Thames Water Consumes Renewables play an important role in the 1,200GWh P/A • Their Renewables contribute to an water industries move towards a reduced offset of 156GWh Carbon Footprint. • TOTEX Model focusing more on Whole-life costs However, should the concept of generating ‘more’ to meet demands be revised? Should there be a shift in directive? How can we control our existing asset base more efficiently? This PhD Research is focusing on optimising the automation and control surrounding sewage pumping stations with a view to reducing the energy required to drive the assets. 2 Recap from Last SGM December 2016 • Discussion on Literature Review • High Level Review on ANN’s and Genetic Algorithms • UKWIR Report on 5x ANN Projects to Predict CSO Spills – reduced requirement on complex models • Research at Sheffield showed good results with Genetic Algorithms but was limited to one site and one week of data • Changes to the Live System Highly Unlikely without Proof of Concept • ICMLive Infoworks Model to be Utilised 3 Recap – Bordon from K-Means Algorithm Bordon Catchment Identified • Use of K-Means Algorithm Identified Bordon Catchment • This Catchment had most ‘Optimised Multismart’s’ at time of analysis • Potential for maximum control change with minimum investment in hardware. 4 Initial Model Review Consultation Session and 1-day Application Training with Pascal Lang from the Modelling Team enabled the research to continue with trialling Bordon • Opportunity to trial multiple profiles/start stop conditions on the model • Ability to forecast spills in a safe environment • Start / Stop Level Issues • Presented in meters above datum. • Not validated against the real system, lots of assumption. Decision made to Survey the Catchment 5 Site Surveys 33 Sites Visited Varying levels of FOG deposits in the catchment Tunbridge Lane SPS 6 Site Surveys Ordnance Survey Team • Select Surveys brought in to validate 33 SPS cover levels • All measurements then converted to OD and updated in the model. • The model was now reflective of the actual catchment • There were some major discrepancies between the previous settings and the new. 7 Head/Discharge Curves • Pump Tag Data Captured during Site The Hydraulic Model has the ability to Survey program the individual pumps in the • Manufacturers Literature used to extract catchment with head/discharge head discharge curves for each pump type. parameters. • Each pump updated in model with accurate head discharge curves according to the current Bordon catchment • Model now ready for test runs. 8 Rainfall July 2017 • Particularly wet month chosen to create design rainfall object • Inputted into model @ 1min Sample rate over 1month • Huge Data Blocks now emerging sparking potential strategy to move towards cloud based modelling? HEATHROW HURN AVERAGE yyyy mm tmax tmin af rain sun yyyy mm tmax tmin af rain sun yyyy mm tmax tmin af rain degC degC days mm hours degC degC days mm hours degC degC days mm 2016 1 9.5 3 5 74.8 54.8# 2016 1 10 2.1 10 190.8 66.7# 2016 1 9.75 2.55 7.5 132.8 2016 2 9.4 2.9 5 43.8 86.8# 2016 2 9.6 2.7 10 63.2 64.6# 2016 2 9.5 2.8 7.5 53.5 2016 3 10.7 3.2 3 72.8 123.3# 2016 3 10.9 1.4 11 105.6 132.4# 2016 3 10.8 2.3 7 89.2 2016 4 13.5 4.9 0 47.2 170.0# 2016 4 12.9 3.2 6 56.8 166.2# 2016 4 13.2 4.05 3 52 2016 5 19 9.7 0 60.4 192.7# 2016 5 18 7.5 2 58.8 202.9# 2016 5 18.5 8.6 1 59.6 2016 6 20.7 12.7 0 93.4 101.7# 2016 6 19.6 11.4 0 85.8 116.0# 2016 6 20.15 12.05 0 89.6 2016 7 24 14.5 0 16 182.8# 2016 7 22.4 12.6 0 12 190.0# 2016 7 23.2 13.55 0 14 2016 8 24.7 14.6 0 21.6 201.4# 2016 8 23.1 12.3 0 41 230.2# 2016 8 23.9 13.45 0 31.3 2016 9 22.4 13.7 0 42.2 122.1# 2016 9 20.7 12 0 85.2 125.8# 2016 9 21.55 12.85 0 63.7 2016 10 15.9 8.7 0 21.6 105.6# 2016 10 15.5 6.1 0 56.6 125.7# 2016 10 15.7 7.4 0 39.1 2016 11 10.5 3.8 3 86.4 77.4# 2016 11 11.1 2 9 104.4 97.1# 2016 11 10.8 2.9 6 95.4 2016 12 10.2 3.4 7 10.4 55.1# 2016 12 10.3 2.1 10 32.2 55.8# 2016 12 10.25 2.75 8.5 21.3 2017 1 7.6 0.7 15 60.2 64.5# Provisional 2017 1 8.1 -0.1 17 103.4 74.1# Provisional 2017 1 7.85 0.3 16 81.8 2017 2 10 4.4 1 38.2 47.8# Provisional 2017 2 9.9 3.7 7 47.4 56.0# Provisional 2017 2 9.95 4.05 4 42.8 2017 3 14.1 6.6 0 25.8 116.3# Provisional 2017 3 13.2 5.5 1 32.4 107.0# Provisional 2017 3 13.65 6.05 0.5 29.1 2017 4 15.8 5.9 0 4.6 186.2# Provisional 2017 4 15 3.2 5 4.8 193.8# Provisional 2017 4 15.4 4.55 2.5 4.7 2017 5 19.8 10.4 0 64.8 164.8# Provisional 2017 5 18.3 8.3 0 69.4 185.9# Provisional 2017 5 19.05 9.35 0 67.1 2017 6 24 13.9 0 46.4 204.3# Provisional 2017 6 21.9 11.4 0 52.6 207.3# Provisional 2017 6 22.95 12.65 0 49.5 2017 7 23.8 14.9 0 90 178.0# Provisional 2017 7 22.2 12.8 0 76.8 185.4# Provisional 2017 7 23 13.85 0 83.4 2017 8 22 13.5 0 58.6 162.9# Provisional 2017 8 21.4 12.7 0 66.1 217.6 Provisional 2017 8 21.7 13.1 0 62.35 2017 9 19.2 11 0 59 120.1# Provisional 2017 9 18.1 11.2 0 90.3 * 129.4* Provisional 2017 9 18.65 11.1 0 74.65 2017 10 17.1 10.3 0 11.4 91.2* Provisional 2017 10 16.5 10 0 43.8 * 92.6* Provisional 2017 10 16.8 10.15 0 27.6 2017 11 11.1 4.5 2 34.2 66.7# Provisional 2017 11 11.9 4.6 2 74.8 * 104.6* Provisional 2017 11 11.5 4.55 2 54.5 9 Energy - Model Product Limitations • Some issues with product support • Eventually discovered that the software does not calculate power • New approach required catchment power to be calculated via Downstream Flow and Head • As Pumps only run for short durations, the model had to be set at high frequency sampling rates. • Model files generated are up to 100GB per run • For 1 month, this equated to 483841 lines of data per pump in CSV format Power Calculations • Use model to calculate mechanical energy in hydrostatic load (fluid on open circuit) 푃푓푙 = 푄 . 휌. 퐻. (9.81) • Degraded energy expressed by the output of the pump (power at the shaft of the pump) • Q = Flow in m3/s. 푃푓푙 푃 = • p = density of the liquid in kg/m3. 푚푒푐 • H = Hydraulic load in meter of water. 푅푣. 푅푡 • 9.81 = Average Intensity of gravity. • Pmec = Mechanical power necessary to the pump. • Pfl = Power transmitted to the fluid. • Rv = Output of the ventilator. • Rt = Output of the transmission. 10 Model Validation Bordon July 17 KWHr Outliars Removed N-Power Site Name Meter Model Variance Average Variance Across Model % Apollo Drive (Bordon) SPS 1,690 341.9778 -79.7646 37.01951478 Arford SPS 1,016 14945.5 1371.448 Ash Grove (Liphook) SPS 54 5.013251 -90.7162 Bramshott Court (Passfield) SPS 402 12.46703 -96.8987 Alan Jeffery from Business Chalet Hill (Bordon) SPS 365 1306.149 257.8491 Chapel Gardens (Lindford) SPS 221 98.35477 -55.4956 Systems Management Churt SPS 5,254 5228.312 -0.48324 provided energy data taken Conford SPS 3,142 6003.778 91.0814 Cypress Road (Bordon) SPS 574 168.1145 -70.7118 from the N-Power meters. Griggs Green (Liphook) SPS 1,620 495.2463 -69.4292 Hamilton Close (Bordon) SPS 48 6.368246 -86.7328 Headley Mill (Bordon) SPS 707 2344.538 231.6178 This is what the business is Heatherlands (Headley Down) SPS 272 Hogmoor Road (Whitehill) SPS 1,169 1229.496 5.175056 billed against. It is the most Kingsley Common (Bordon) SPS 815 399.7461 -50.9514 accurate for consumed kWHr Lions Field (Oakhanger) SPS 88 46.92733 -46.6735 Malthouse Meadows (Liphook) SPS 229 45.26516 -80.2336 and was used to validate the Maple Leaf Drive (Bordon) SPS 48 Marsh Close (Bordon) SPS 118 48.80282 -58.6417 model runs Monument Chase (Whitehill) SPS 129 34.4899 -73.2636 Oakhanger SPS 388 610.4727 57.33832 Passfield (Bramshott) SPS 3,688 Some sites showing really Royal Drive (Bordon) SPS 273 Stoney Bottom (Grayshott) SPS 1,654 close comparisons but The Meadows (Churt) SPS 42 33.1553 -21.0588 others are way out.
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