A Conceptual Multi-Objective Optimisation Framework to ... · 1 Zinedine Khatir1, Sammer...
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Zinedine Khatir1, Sammer Taherkhani2, Joe Paton2,
Harvey Thompson2, Nik Kapur2 and Vassili Toropov2,3
A Conceptual Multi-Objective Optimisation Framework to Design Energy Efficient
Commercial Bread-Baking Ovens
SusTEM Special Sessions
on
Thermal Energy Management
1 School of Computing, University of Leeds, LS2 9JT, UNITED KINGDOM
2 School of Mechanical Engineering, University of Leeds, LS2 9JT, UNITED KINGDOM 3 School of Civil Engineering, University of Leeds, LS2 9JT, UNITED KINGDOM
{men6jbp,z.khatir}@leeds.ac.uk / {jbpaton,zkhatir}@hotmail.com
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Overview
1. Introduction
2. Experimental Analysis
3. Computational Methods
4. Baking Model
5. Energy Savings
6. Summary
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Introduction
The Worldwide Bread Industry
• ~ 95 million tonnes per year (i.e. EU – 53 %)
• ~ 8% of bread production classified as „industrial‟
• Energy used to bake bread ~ 800 kJ/kg
• Energy demand of industrial baking ovens ~ 8000 GWh/year
(688,000 toe)
The Bread Production Process
MIX SHAPE PROVE BAKE COOL SLICE BAG
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Baking Ovens
Typically
30 m length, 5 m width, 2 m height
Source: Spooner Industries Ltd
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Direct-Fired Oven
High-Speed Convection Oven
• High-speed nozzle jets
• Heat Transfer mode - convection
Hot moving gas transfers heat directly to the products
Three-zone direct fired oven: a) 3D Overview of the oven; b) 2D Simplified schematic showing the
mechanism for distributing air through the nozzles for a single zone
Hot air streams
Nozzles
Burners
a)
Return air Gas burner Air recirculation fans
b)
Hot air from nozzles Direction of product
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Process Diagram Method for obtaining the optimum design
• Energy savings / Oven design / Computational analysis
Optimum Design
EXPERIMENTS
HEAT TRANSFER
COEFFICIENT
HIGH FIDELITY
CFD
OPTIMISATION
BAKING
TIME
MODEL
ENERGY
EFFICIENCY
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Process Diagram
Method for obtaining the optimum design
• Energy savings / Oven design / Computational analysis
Oven design
Baking model
Energy per unit
Experiments
Computational
model
hc variable
Optimum
Design
hc representative
Quality
assessment
Temperature
uniformity
Energy
(fans)
Bake
time
Energy
(gas)
High Fidelity CFD
Optimisation
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Experimental Heat Flux
Profiles Heat flux sensor
Measures total and convective heat flux of nozzle
configurations
Enables accurate values for heat transfer coefficient to be
used for computational modeling
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Experimental Validation
Martin correlations for array round orifices
3
2
Re6201
221
Pr 420f)(H/d.
f.fK(H/d,f)
Nu.
Martin H. “Heat and Mass Transfer between
Impinging Gas Jets and Solid Surfaces”,
Advances in Heat Transfer; 1977
Convective heat transfer coefficient
hc = f (d,H/d,unoz)
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Modelled equation Inlet Outlet Wall
Energy
T = 513 K
T = 513 K (Top)
T = 513 K
(Bottom) Convective B.C
hc = f(d,H/D,unoz) Momentum
Vin= unoz Gauge pressure
P = 0 Pa No-slip
Turbulence
Wall function
Length scale lscale = 5×10-4 lscale = 5×10-4
Intensity I = 2 % I = 2%
Geometry of the baking chamber: solution domain.
Geometry
Computational Fluid Dynamics
(CFD)
Computational Fluid Dynamics
(CFD)
Boundary conditions
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Computational Fluid Dynamics
(CFD)
Computational Fluid Dynamics
Generic Model & Design
Generic Model (GM)
3 design variables: DV1 = D, DV2 = H/D, DV3 = unoz
5 mm ≤ DV1 ≤ 20 mm
2 ≤ DV2 ≤10
8 m/s ≤ DV3 ≤ 40 m/s
Generic model with design variables.
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Baking Model Cooking time model
Temperature within the bread of the baking process:
ρ𝑐𝑝 𝑇𝜕𝑇
𝜕𝑡= 𝛻. [𝑘 𝑇 𝛻𝑇]
used to model the cooking time σcooking depending on oven
conditions.
Bread is cooked when its core temperature reaches 94oC.
Oven conditions
• Initial conditions: T(x,y,z,t=0) = 39°C
• Boundary conditions:
−𝑘𝜕𝑇
𝜕𝑥 𝑥
= 𝒉𝒄 𝒅,𝑯
𝑫, 𝑼 [𝑇 𝑥 , 𝑡 − 𝑇∞]
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• Minimize the objective function: σT
d = 18.3 mm
H/d = 8
unoz = 30.4 m/s
• Convective heat transfer coefficient:
hc = f(d,H/d,unoz) = 86.05 W/(m2K)
• Minimize cooking time (σcooking)
dV
dVTT zone
2)(
Multi-Objective Optimisation
Problem
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Surrogate modelling approach used to describe global
performance responses (i.e. temperature uniformity, cooking
time) of the ovens as a function of the design variables.
Optimal Latin Hypercubes used for Design of Experiments
(DoEs).
Moving Least Squares method used for surrogate model
building.
Global optimization performed using a Genetic Algorithm.
Optimisation Strategy
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CFD Simulation and
Results
RANS Steady State Simulation – ANSYS Fluent V14
Complex thermal air-flow and temperature distribution
Z Khatir et al., Applied Thermal Engineering x2; 2012
Z Khatir et al., Applied Energy; 2012, 2013
Related Publications Velocity contour plot
Pathlines coloured by velocity magnitude
m/s
Temperature uniformity
Contour plots of temperature within the baking chamber.
K
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Cooking Time Model
Finite Element Solution – Comsol
t = 5 min
t = 18 min
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Oven performance at stages of the design process.
Response
from
σT
[K]
σcooking
[min]
Rel. % Error
[100│(σMLS-σVal.)/σMLS│]
Best design from DOE
Optimized design after GA
CFD validation from Optimum
CFD
MLS
CFD
11.24
[σcooking =22.23min]
10.90
10.88
21.45
[σT=32.28K]
21.98
21.99
0.10
0.21
Multi-Objective Optimisation
Results
Surface responses of σcooking (Left) and σT (Right) from surrogate models.
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Optimum Design
Pareto Curve
Appropriate
Compromise
Design
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Energy Savings
Potential Energy savings
• Optimum design would allow the bread to cook the bread in
σcooking 22 min with σT 10 oC
• This would lead to 7-10% reduction in baking time that results in
increased plant efficiency for values of σcooking and σT in the
region of 23.5 - 24.0 min and 15 - 35 oC respectively
Region
Annual
production
(,000’s tonnes)
Percentage of
production classified
as 'industrial'
GWh saving
Asia-Pacific 8514.4 57% 75.50-107.85
Europe 50235.0 41% 320.39-457.70
Americas 28286.8 Not known >50.39-72.00
WORLDWIDE 94604.6 38.2% >446.28-637.55
Total energy savings based on 800 kJ/kg
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Industrial Relevance
Collaboration with industry
Largest UK bakery chain
Internationally-leading oven manufacturers
International energy consultancy
Dissemination of research via formation of close
relationships and trialling technologies on a pilot-
scale
Other drying industries
Paper, thin films etc.
Other foods – e.g. Pasta, baked products
Cooling
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Thank You
Questions?
Acknowledgements:
Grant EP/G058504/1
{men6jbp,z.khatir}@leeds.ac.uk / {jbpaton,zkhatir}@hotmail.com
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Rela
tive c
arb
on
em
issio
ns
fro
m o
perati
on
Carbon Trust, 2010, http://www.carbontrust.co.uk
Breakdown of CO2 emissions from an industrial bakery
Energy Use in Bakery
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Direct or Indirect Fired?
Direct Fired:
Combustion gases in direct contact with
product
Convection
Indirect Fired:
Combustion gases is used to heat metal
elements
Radiation
Lower air temperature
Higher air velocity
Higher air temperature
Lower air velocity