This document summarizes work on energy efficiency and integration in the petrochemical and refining industry. It discusses using data reconciliation to increase measurement quality and calculate unmeasured properties. It also covers optimizing steam network operations to meet demand while minimizing costs, and identifying opportunities for energy integration and industrial synergies through techniques like heat exchanger modifications. The overall aim is developing tools and methods for optimizing energy usage and investments across industrial sites.

1. SCCER EIP WP4
Synergies and optimal investments in large scale industrial
steam networks.
Stéphane Bungener
Energy efficiency and integration in the petrochemical and refining industry
in collaboration with INEOS Technologies
2012-2016
5th May 2015
IPESEIndustrial Process and
Energy Systems Engineering
1

2. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Plan
Data reconciliation
Steam network
optimisation
Energy integration
Industrial synergies
2

3. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Energy efficiency in industry
3

4. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Plan
Data reconciliation
Steam network
optimisation
Energy integration
Industrial synergies
4

5. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Data reconciliation
. Increase quality of measurements
. Calculate unmeasured properties
. Close mass and energy balances
5
Aim

6. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
2014-1-2 2014-2-2 2014-3-6 2014-4-6 2014-5-8 2014-6-8
Letdown1
0
10
20
Reconciledσ
Reconciledvalue
2014-1-2 2014-2-2 2014-3-6 2014-4-6 2014-5-8 2014-6-8
Letdown2[t/d]
0
10
20
30
Data processing methodology
6
[1] Modelling of a steam network using data reconciliation as a management tool, Energy Systems Conference, London, 2015
2014-1-2 2014-2-2 2014-3-6 2014-4-6 2014-5-8 2014-6-8
Efficiency[-]
0
0.5
1
Estimatedvalue
Reconciledσ
Reconciledvalue
Steam losses
Turbine efficiency follow-up
minX,Y
nmesX
i=1
(
yi y⇤
i
i
)2
s.t. MassBalance(X, Y ) = 0
EnergyBalance(X, Y ) = 0
Thermodynamic(X, Y ) = 0
ConstituveEquations(X, Y ) = 0
Performance(X, Y, ⇡) = 0
Inequalities(X, Y ) 0
min
Θ, xj,i j=1
nvar
∑
xi, j − xi, j
*
( )2
σi, j
2
i=1
nexp
∑
Soumis à h(xi, j,Θ) = 0 modèle

7. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Plan
Data reconciliation
Steam network
optimisation
Energy integration
Industrial synergies
7

8. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Energy integration
. Calculate
. energy bill
. minimum energy requirement
. Identify
. heat exchanger modifications
. infrastructure investments
8
Aim

9. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Energy integration
9
Method
the possibility of improving the system configuration. The ut
shown in Table 5; note that only the electricity used as hot uti
Heat load [kw]
Temperature[C]
vaporisation

10. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Energy integration
. Total site analysis
10
Method
Industrial industry pollution ,http://wall4all.me/walls/sports/industrial-industry-pollution-754852-1965x1080.jpg

11. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
. Total site analysis - energy bill
CC Loads [MW]
0 200 400 600 800 1000
Temperature[C]
-100
0
100
200
300
400
500
600
700
800
900
1000
Process hot streams
Utilities cold streams
Utilities hot streams
Process cold streams
GCC Loads [MW]
0 200 400 600
Utilities GCC
Process GCC
Utiltiy Cooling Demand: 633.7 MW
Utility Heating Demand: 123.8 MW
Minimum Energy Requirement (Hot): 0 MW
Minimum Energy Requirement (Cold): 508.6 MW
Energy integration
11
Method
[3] Multi-period analysis of heat integration measures in industrial clusters, S. Bungener, R. Hackl, G. Van Eetvelde, S. Harvey, F. Maréchal. Energy (submitted), 2015
124 MW
634 MW

12. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
. Total site analysis - optimised
CC Loads [MW]
0 200 400 600 800 1000
Temperature[C]
-100
0
100
200
300
400
500
600
700
800
900
1000
Process hot streams
Utilities cold streams
Utilities hot streams
Process cold streams
GCC Loads [MW]
0 200 400 600
Utilities GCC
Process GCC
Utility Heating Demand: 0 MW
Minimum Energy Requirement (Hot): 0 MW
Utiltiy Cooling Demand: 509.9 MW
Minimum Energy Requirement (Cold): 508.6 MW
Energy integration
12
Method
[3] Multi-period analysis of heat integration measures in industrial clusters, S. Bungener, R. Hackl, G. Van Eetvelde, S. Harvey, F. Maréchal. Energy (submitted), 2015
0 MW
510 MW

13. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Energy integration
. Multi-period
13
Method
100 200 300
UnitE[-]
0
0.5
1
100 200 300
UnitC2[-]
0
0.5
1
1.5
100 200 300
UnitC1[-]
0
0.5
1
1.5
Time [d]
100 200 300
UnitD[-]
0
0.5
1
Original profile
Multi-period profile
[3] Multi-period analysis of heat integration measures in industrial clusters, S. Bungener, R. Hackl, G. Van Eetvelde, S. Harvey, F. Maréchal. Energy (submitted), 2015

14. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Energy integration
. Multi-period
14
Method
Time [d]
50 100 150 200 250 300 350
Heatingrequirement[MW]
0
50
100
150
Time [d]
50 100 150 200 250 300 350
Coolingrequirement[MW]
300
400
500
600
700
MER
Energy Bill
[3] Multi-period analysis of heat integration measures in industrial clusters, S. Bungener, R. Hackl, G. Van Eetvelde, S. Harvey, F. Maréchal. Energy (submitted), 2015

15. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Plan
Data reconciliation
Steam network
optimisation
Energy integration
Industrial synergies
15

16. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Steam Network Optimisation Tool (SNOT)
. Optimise operations of steam network(s)
. meet demand
. minimise fuel consumption
. maximise turbine use
. overcome boiler failures
16
Operations optimisation - Aim

17. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Steam Network Optimisation Tool (SNOT)
17
Operations optimisation - Complexity
17

18. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Steam Network Optimisation Tool (SNOT)
18
Operations optimisation - Complexity
18
2000 4000 6000 8000
0
50
100
150
200
250
300
350
400
450
500
01.01.2014 −31.12.2014
SteamdemandSite1[t/h]
2000 4000 6000 8000
0
50
100
150
200
250
300
350
400
450
500
01.01.2014 −31.12.2014
SteamdemandSite2[t/h]
5 bar: 51/72
20 bar: 111/140
90 bar: 1/19
5 bar: 74/160
30 bar: 151/256
90 bar: 114/258
mean/max
Site 2Site 1

19. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Steam Network Optimisation Tool (SNOT)
. Optimise operations of a steam network
. meet demand
. minimise fuel consumption
. maximise turbine use
. overcome boiler failures
19
self regulating but not trivial!
Operations optimisation - Aim

20. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Steam Network Optimisation Tool (SNOT)
20
Operations optimisation - Method
. SNOT
. simple model definition
. multi-period by nature
. milp formulation
. Meet demand
. Minimise operational costs
. load shedding

21. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar
0
100
200
300
400
500
600
700
800
900
SteamProduction[t/h]
Max production: 766 t/h
(a)
S1 Units: 30/51
S1 Desuperheat: 7/23
S1 Boilers: 178/180
S2 Units: 126/203
S2 Desuperheat: 12/36
S2 Boilers : 307/320
100
200
300
400
ainingcapacity[t/h]
Minimum remaining: 0 t/h
S1 Boilers: 2/41
S2 Boilers : 13/120
mean/max
Steam Network Optimisation Tool (SNOT)
21
Operations optimisation - Example
load shedding
ax production: 766 t/h

22. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Steam Network Optimisation Tool (SNOT)
22
Infrastructure optimisation - Aim
. Identify infrastructure investments
. operational optimisation
. resilience
long term stability through
supply chain management
concepts
[4] Optimisation of unit investment and load shedding in a steam network facing undercapacity. S. Bungener, F. Maréchal, G. Van Eetvelde, B. Descales, ECOS, 2015.
[5] Resilient decision making in steam network investments. S. Bungener, F. Maréchal, G. Van Eetvelde, B. Descales, PRES, 2015.

23. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Plan
Data reconciliation
Steam network
optimisation
Energy integration
Industrial synergies
23

24. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Industrial synergies
24
. Resilient investments
. Optimal operational strategies
Aim

25. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Industrial synergies
25
Aim

26. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Industrial synergies
26
Aim

27. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Energy efficiency and integration in the
petrochemical and refining industry.
27
Thank you for your attention
questions please

28. SCCER EIP May 2015 - Stéphane Bungener
IPESEIndustrial Process and
Energy Systems Engineering
Data reconciliation
. Flowsheeting tool + Optimiser
. p: penalty to minimise
. X: measurement/estimation
. X’: reconciled value
. σ: certitude of measurement X
28
Method
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ethodology!
⟶ Construct physical model.
⟶ Associate weight/certitude (σ) to each measurement/estimation (X)
⟶ X’ is the reconciled value of X.
⟶ Objective function (1) is to create a physical solution, while minimising
asthe penalty (p) associated with reconciling X.
ase Study!
⟶ Study carried out on major refinery and its utility system.
⟶ Process and utility modelled via flow sheeting tool with data
ssreconciliation engine using PIDs and surveys.
⟶ 435 online measures and 78 estimations were modelled.
ble: Weights and modelling technique for each type of measurements
min p =
X (X X0
)2
(1)
[2] Heyen, Georges, Eric Maréchal, and Boris Kalitventzeff. "Sensitivity calculations and variance analysis in plant measurement reconciliation." Computers & chemical
engineering 20 (1996): S539-S544.
[2]