1

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

2

Overview

1. Introduction

2. Experimental Analysis

3. Computational Methods

4. Baking Model

5. Energy Savings

6. Summary

3

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

4

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

7

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

8

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

14

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

15

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

21

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