Applied Energy 89 (2012) 127–132

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Applied Energy

journal homepage: www.elsevier .com/locate /apenergy

Identifying and quantifying energy savings on fired plant using low costmodelling techniques

Robert Tucker a,⇑, John Ward b

a Zerontec Energy Consultancy, Stratford-upon-Avon CV37 6PQ, United Kingdomb Faculty of Advanced Technology, University of Glamorgan, Pontypridd CF37 1DL, United Kingdom

a r t i c l e i n f o

Article history:Received 21 July 2010Received in revised form 17 September2010Accepted 27 January 2011Available online 22 February 2011

Keywords:Heat transferModellingRadiationCombustionFurnaces

0306-2619/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.apenergy.2011.01.061

⇑ Corresponding author. Tel./fax: +44 (0)1789 2986E-mail address: robertjtucker@zerontec.com (R. Tu

a b s t r a c t

Combustion in fired heaters, boilers and furnaces often accounts for the major energy consumption onindustrial processes. Small improvements in efficiency can result in large reductions in energy consump-tion, CO2 emissions, and operating costs. This paper will describe some useful low cost modelling tech-niques based on the zone method to help identify energy saving opportunities on high temperature fuel-fired process plant.

The zone method has for many decades, been successfully applied to small batch furnaces through tolarge steel-reheating furnaces, glass tanks, boilers and fired heaters on petrochemical plant. Zone modelscan simulate both steady-state furnace operation and more complex transient operation typical of a pro-duction environment. These models can be used to predict thermal efficiency and performance, and moreimportantly, to assist in identifying and predicting energy saving opportunities from such measures as:

� Improving air/fuel ratio and temperature controls.� Improved insulation.� Use of oxygen or oxygen enrichment.� Air preheating via flue gas heat recovery.� Modification to furnace geometry and hearth loading.

There is also increasing interest in the application of refractory coatings for increasing surface radia-tion in fired plant. All of the techniques can yield savings ranging from a few percent upwards and candeliver rapid financial payback, but their evaluation often requires robust and reliable models in orderto increase confidence in making financial investment decisions. This paper gives examples of how lowcost modelling techniques can be applied to improve confidence in implementing energy efficiencyimprovements whilst safeguarding manufacturing output and quality.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Modelling is applied in many fields ranging from climate andweather forecasting to predicting the performance of the stockmarket. In the field of energy conservation on industrial processes,modelling can provide reliable assessments of the losses and thepotential for improved efficiency and performance. Unfortunately,the benefits afforded by modelling are often ignored on the basisthat models are too expensive or complex to apply, and that it isoften simpler and quicker to rely on engineering experience andsimple ‘‘rules-of-thumb’’. For example, application of large compu-tational fluid dynamic programmes can involve prohibitively largeexpense in expertise and computing resources. However, simplermathematical models can provide an alternative approach, which

ll rights reserved.

59.cker).

can be equally powerful when evaluating a number of alternativeoptions for improving plant performance. Rhine and Tucker [1],Mullinger and Jenkins [2] and Baukal and Schwartz [3] all describetechniques for mathematical modelling of industrial combustionplant. Many of the techniques described are based on the zonemethod first originally developed by Hottel and Sarofim [4].

The energy efficiency of high temperature plant can have a ma-jor impact on the unit cost of production. Often the fuel burnt in afurnace or fired heater accounts for the major source of energyconsumption within a manufacturing process. Measures such asflue stack heat recovery and improvements to the insulation andcontrols, have to be carefully applied to ensure that product qualityand productivity are not adversely affected. The potential risk toproduction is often used as a reason for not investing in improvedenergy saving measures. This paper gives examples of howlow cost modelling techniques can be applied to improve confi-dence in implementing energy efficiency improvements, whilst

128 R. Tucker, J. Ward / Applied Energy 89 (2012) 127–132

safeguarding manufacturing output and quality. A general descrip-tion of the zone modelling technique will first be described. Thiswill then be followed by description of its application to examplesof industrial furnaces and fired heaters.

2. Background to the zone method

To improve thermal efficiency, a furnace should be designedto ensure maximum heat is transferred to the load surface andthat the heat losses through the walls and in the gases leavingthe process are minimised. Thermal radiation is usually thedominant mode of heat transfer in high temperature industrialcombustion equipment. Radiation is emitted from the hot com-bustion products on account of the CO2 and H2O in the combus-tion products, and this radiation is transmitted, reflected andabsorbed within the furnace chamber. The complex interchangeof radiation in a furnace can be modelled using the zonemethod.

In the zone method, the radiating enclosure is subdivided into anumber of isothermal surface and volume elements (zones). A totalenergy balance is formulated around each surface or volume zone(i) which includes terms for:

� radiation leaving i,� radiation arriving at i from all other zones j in the enclosure,� enthalpy flow in and out of volume zone i due to flow of the

combustion products,� heat release due to combustion in volume zone i,� convection to and from contiguous surface and volume zones.

The radiation interchange is written in terms of radiation ex-change factors. The net radiation flux between any two zones isproportional to these factors and to the absolute temperatures tothe fourth power. The factors are a function of the geometric orien-tation and radiative properties (emissivities) of the zones. The radi-ation from the hot combustion products is a function of theiremissivity. These gases are not grey and include spectrally absorb-ing (and emitting) bands interspersed with clear non-participatingbands or ‘windows’ in the spectrum. In the zone models describedin this paper, the gases are represented by the weighted-sum-of-grey-gases model first introduced by Hottel and Sarofim [4].

In its simplest form, a single gas zone can be used to representthe furnace chamber. This is often referred to as the single well-stir-red zone (WSZ) model. The radiation interchange factors between asingle gas zone and a load and a wall surface can be derived fromsimple algebraic expressions. This simple model, involves onlythree temperatures (combustion products, wall and load surface)and can provide a very powerful technique for evaluating energysavings.

If there is a significant temperature gradient through the fur-nace, such as within a continuous steel-reheating furnace, then along furnace (LF) model can be applied. This entails a series of linkedwell-stirred zones with for example a wall surface and a load sur-face surrounding each gas zone. A typical configuration of a longfurnace model is shown in Fig. 1.

The long furnace model simulates the temperature profile of theload as it moves through the furnace in counter-current flow to thehot combustion products. Such models are able to predict:

� The specific fuel consumption.� The flue gas temperature and losses.� The heat losses to the refractory walls and hearth.� The internal and surface load temperatures at discharge and

during the heating process.� The internal wall temperatures.

Both the WSZ and LF models can provide a complete heat bal-ance on a furnace, which is often the crucial first step in identifyingopportunities for fuel saving. In larger furnaces there may be con-siderable variations in temperature across the length, width andheight of the system and in these situations multi-zone two andthree-dimensional zone models may be appropriate as describedby Correia et al. [5].

Some simple examples of applications will now be describedstarting with the WSZ model, followed by the LF model appliedto both steady-state and transient furnace applications.

3. Application of the WSZ model

The single well-stirred gas zone model provides a valuable firststep to evaluate energy saving options on fuel-fired furnaces andheaters. A version has been developed in Visual Basic, which re-quires negligible computing resources. The model only requiresthe wall and load surface areas and the chamber volume to bespecified. The radiation interchange between the gases and loadplus any convective heat transfer is calculated, and the fuel inputrate is derived which satisfies the following energy balance equa-tion on the furnace:

Heat in fuelþHeat in air ¼ Heat to load

þHeat loss in flue gases

þHeat loss through walls

3.1. A small continuous furnace

Predictions for a natural gas furnace of hearth area 5 � 5 mand height 2 m were prepared. The base case assumed an excessair level of 25% with an exhaust gas temperature of 1250 �C, anda load surface temperature of 900 �C. The model predicts a ther-mal efficiency of 29.6%net. This may be representative of a stea-dy-state metal heat treatment or metal melting application. TheWSZ model was applied to evaluate a number of energy savingopportunities. The results are shown in Table 1.

The priority should be to reduce the excess air level, if this canbe done without detriment to the safe operation of the plant, emis-sions or to product quality. Controlling excess air may be achievedthough better burner air–fuel ratio control, or by preventing air in-gress into the furnace by pressure control and sealing of gaps anddoors. The fuel saving achieved, then reduces the cost of subse-quent measures. For example if recuperation is applied, the sizeand installed cost of the equipment is reduced because smallerheat exchangers can be used.

On high temperature plant, where flue losses are significant,oxygen enrichment or oxy-gas firing can achieve substantial sav-ings. The cost savings in reduced fuel consumption has to begreater than the cost of supplying the oxygen. Hence, it isimportant to minimise fuel consumption before applying oxygen.In this example, oxygen has been supplied at a rate that givesthe same flue gas oxygen concentration (1.1%). The level has tobe set to ensure safe operation of the plant. In the oxy/gas caseabove, a total of 0.049 kg/s of natural gas is saved by supplying0.177 kg/s oxygen. To break even, the cost/kg of oxygen must beless than 27% of the cost/kg of natural gas. The same break evencost ratio applies for the oxygen enriched combustion case.

In the case of reducing excess air or applying oxygen, the pre-dicted flue gas temperature is reduced because the radiant emis-sivity of the gases is improved (owing to the high concentrationsof CO2 and H2O).

Fig. 1. A long furnace model representation of a small-scale steel-reheating furnace.

R. Tucker, J. Ward / Applied Energy 89 (2012) 127–132 129

3.2. A fired heater

Fired heaters account for the major energy consumption andcarbon emissions on refineries and petrochemical plant. They usu-ally comprise a high temperature radiant box in which combustionoccurs. The hot gases radiate to a series of tubes containing high-pressure hydrocarbon fluids. On most large fired heaters, the hotflue gases leaving the radiant section are passed across a convec-tive tube bank to further heat the hydrocarbon fluids. On modernplant the heat in the flue gases may then be further recovered topreheat the combustion air or to generate steam.

Safety is critical in these applications because of the seriousconsequences in the event of a release of high-pressure feedstock.The temperature at the top of the radiant section is often referredto as the bridge wall temperature (BWT). During operation, thismust be maintained below a certain value to avoid overheatingand potential failure of the tubes.

A schematic of a typical fired heater with convective tube bankand air preheater is shown in Fig. 2.

Over the last three decades, there has been a considerable inter-est in the application of coatings on the refractory walls of fuel-fired furnaces for energy savings. It is claimed that these specialcoatings raise the emissivity of the surface and improve the radia-tive heat transfer and hence the thermal efficiency of the process.Products under various brand names are commercially available

Table 1Prediction of fuel saving opportunities on a small continuous furnace.

Flue gas O2 % dry O2 conc. v/v% Air preheat, T (�C) E

Base casea 4.6 21 15 1Reduced xsab 1.1 21 15 1Recuperatorb 1.1 21 383 1Regeneratorb 1.1 21 875 1Oxy-enriched 1.1 25 15 1Oxy/gas 1.1 95 15 1

a Excess air = 25%.b Excess air reduced to 5%.

and can be applied by trowel or spray gun to the surface of hardface refractory or ceramic fibre walls. However, energy savingsclaims resulting from these products are often difficult to verifybecause of the variable nature of plant operation. It has alsobeen difficult to forecast the savings on different applications be-cause most thermal design models for heat transfer in furnaces failto show any influence of wall emissivity. This is due to the limita-tions of most models rather than the capability of the coatingproducts.

Docherty and Tucker [6] and Elliston et al. [7] independentlyshowed that if the combustion products are assumed to be grey,then changing the wall emissivity in an enclosed furnace, has noeffect on overall predicted heat transfer. This is because in general,for an adiabatic wall, these simple models do not distinguish be-tween radiation that is absorbed and re-radiated, and radiationthat is reflected at the wall surface. By introducing a non-greygas to represent more realistically the combustion products inthe furnace, both authors identified improvements when emissiv-ity was raised. The radiation model used in the zone method uses anon-grey gas combustion model (the weighted-sum-of-grey-gasmodel) and is therefore able to predict the increase in radiant heattransfer with surface emissivity.

A WSZ model has been applied to simulate the heat transferenhancement in the radiant section of a typical fired heater. Theheater parameters are:

xhaust gas, T (�C) Fuel input kWnet Efficiency (%net) Fuel saving (%)

250 6049 29.6 _243 4578 39.1 24.3243 3427 52.3 43.3243 2512 71.4 58.5236 3729 48.1 38.4216 2224 80.7 63.2

Fig. 2. A schematic arrangement of a typical fired heater.

Table 2The predicted performance of a fired heater with a high emissivity wall coating.

e = 0.5 e = 0.93

Case Basecase

1 2 3

Const. Qprocess

Constant bridgewall T fixedconvectionsectioneffectiveness

Constantbridgewall T fixedconvectionheattransfer

Flue gas, T (�C) 163 161 163 258Bridge wall, T

(�C)1000 985 1000 1000

Fuel input kWnet 6907 6903 7713 7713Orad. (kW) 3187 3245 3574 3574Qconv. (kW) 3094 3038 3455 3097QwalL (kW) 132 132 133 133Qflue. (kW) 495 488 552 910Oprocess (kW) 6281 6283 7028 6671Production

increase %11.89% 6.21%

Efficiencyoverall %

90.94% 91.02% 91.12% 86.49%

130 R. Tucker, J. Ward / Applied Energy 89 (2012) 127–132

Fuel type

Natural gas Excess air 20% Thermal input 6907 kWnet

Tube area

110 m2

Exposed wall area

152 m2

Radiant section load

3187 kW Convection section load 3098 kW

Fig. 3. Predicted and measured energy consumption on a continuous steel-reheating furnace.

The flue gas temperature downstream of the convection sectionis 162 �C and flue losses represent just 7.1% of the net thermalinput.

The wall emissivity in the model was then changed from 0.5 to0.93 to simulate application of a high emissivity coating. The re-sults are shown in Table 2. In Case 1, the total process load is heldconstant, and the model predicts the fuel saving. A negligible sav-ing is predicted. This is because the heater is already running atover 90% net thermal efficiency, and so there is little additionalenergy available to extract from the combustion products. How-ever, the model is able to predict a potential increase in productiv-ity, which could have a significant impact on overall plantperformance. In Case 1 the model predicts a reduction in BWT of15 �C, which results from the improved radiative heat transfer.There is a small increase in heat transfer in the radiant section.The model can be applied to determine the increase in thermalduty that could be achieved if the BWT were restored to 1000 �C(Case 2). This assumes that the convection section is also enlargedto maintain the same heat recovery effectiveness. In this case theproduction can be increased by 12% without exceeding a BWT of1000 �C. In practice it may not be feasible to enlarge the convectionsection. In this case, the flue gas temperature downstream of theconvection bank will increase, resulting in a loss of efficiency.However, production is still predicted to increase by over 6%.

4. Application of the long furnace zone model

The zone method lends itself well to modelling continuoussteel-reheating furnaces. The simple zoning arrangement of a LFmodel represents well the longitudinal temperature profile createdby the directional flow of steel through the furnace. A transientversion of the LF model can be very useful since most furnacesoperate under non-steady-state conditions with variable through-put, changes in load size and scheduled and unscheduled breaks inproduction. In these models, which are described in detail by Rhineand Tucker [1], the transient conduction into both the steel billetsand the refractory hearth and walls are calculated.

The approach has been validated against operating data from asteel-reheating furnace. A comparison of predicted and measuredenergy consumption is presented in Fig. 3. The plant was moni-tored and simulated from a cold start, and included a scheduledbreak in production. As can be seen the predicted consumption clo-sely follows the actual measured values through the trial.

Two applications of these models for steel reheating will nowbe described.

Fig. 4. A schematic of the small forge furnace.

R. Tucker, J. Ward / Applied Energy 89 (2012) 127–132 131

4.1. A small forging furnace

Fig. 4 is an example of a small forging furnace used to reheat150 mm diameter steel bars to 1150 �C. There are many similarfurnaces used in industry for reheating steel and non-ferrous met-als for rolling, forging or heat treatment applications. This study isbased on a small furnace in Belarus. The original specification is de-tailed in Table 3. Since this furnace is used in a semi-continuousmanner, with repeated cold starts every day, a transient long fur-nace model was applied [1]. This model simulates the start-up ofthe furnace from cold. Under such conditions the heat stored inthe refractory walls can represent a significant energy loss.

The furnace was represented by an 11-zone LF model withzones of length 0.36 m. A time step of 5 s was applied to simulatethe operation from cold. After 150 min, production is simulatedwith billets leaving from the hot zone 1 and cold billets enteringzone 11. The thermal input is modulated to simulate temperaturecontrol to maintain furnace roof temperature in zone 1. To main-tain desired output, a roof set point temperature of 1220 �C wasrequired.

Six simulations were carried out as follows:

(1) A base case simulation according to the current furnace.Unfortunately, there was no metered data so it was not pos-sible to establish the reliability of the predictions.

(2) Simulation with the walls and roof replaced with light-weight ceramic fibre.

(3) Repeat of Case 1 with an improved flue gas recuperator (heatrecovery effectiveness increased to 50%).

(4) Repeat of Case 2 with improved flue gas recuperator.(5) Base case but with a single recuperative burner installed at

hot end (replacing all existing burners). Flue gas pull backto recuperator = 60% and 40% of combustion products goonto the existing flue.

Table 3Specification of the small forging furnace.

Load details Size 15 mm dia � 280 mm lengthWeight 38.6 kgMaterial Carbon steelOutput temperature 1150 �CNo. of billets infurnace

22 (850 kg)

Throughput 1000 kg/h

Furnace construction Length 4.1 mWidth 0 93 mHearth Dense refractory brick

2600 kg/m3

Walls and roof Insulating firebrick 860 kg/m3

Burner Number 3Total max thermalinput

54 m3 (s)/h

Excess air 5%Air preheat 285 �C

Production schedule 2.5 h to start of forging16 h production

The predicted results are shown in Table 4. Tb is the steel dis-charge temperature and DT is the predicted temperature differencein the steel billet.

The overall Specific Fuel Consumption (SFC) is the fuel con-sumption over the entire simulation divided by the tonnes of steeldischarge. It includes the energy to start the furnace from cold. Thesteady-state SFC is based on the last hour of continuous operationand hence is lower.

As can be seen from these predictions, the largest single fuelsaving (�15%) is achieved by installing an improved flue stackrecuperator (Case 3). Replacing the walls with ceramic fibre givesa fuel saving of around 11%. Combining these options (Case 4) givesa saving of �23%. The preferred option financially is dependent onthe cost of installation. However, the model provides reliable pre-diction of the running cost savings, which can be used to derive thefinancial payback of each option.

The model has also shown that in this application, installing arecuperative burner, although giving a 12.5% fuel saving, may leadto excessive temperature differences in the billets, which may seri-ously reduce forging quality. This results from the change in heatflux profile along the hearth. Also, the furnace throughput is re-duced. This demonstrates how a model can help avoid bad invest-ment decisions.

4.2. Application of heat recovery on a steel-reheating furnace

The above application demonstrated how a recuperative burnercould lead to poor performance. There are many applicationswhere recuperative and regenerative burners can yield very signif-icant performance benefits. Using a steady-state LF model, a simplestudy was carried out to compare different options for flue gas heatrecovery on a continuous natural-gas fired steel-reheating furnace,against performance with no heat recovery. These options were:

� Use of a flue stack recuperator (40% effective).� Use of regenerative burners with an effectiveness of 70%.

The furnace is 18.6 m in length and heats steel bars of thickness88 mm to 1100 �C with a throughput of 70 Te/h. The furnace wasmodelled using a 10-zone LF model. Burners are distributed alongthe length of the furnace in order to ensure that the steel is ade-quately ‘soaked’ before discharge. The original firing profile asshown in the first row in Table 5 was applied in the model and thispredicted a steel temperature distribution through the furnace asshown in Fig. 5 resulting in a (top to bottom) temperature differ-ence in the steel at discharge of 56 �C. The model was then appliedto predict the performance benefits of the different heat recoveryoptions. In the regenerative burner case, 80% of the flue gases arepulled back into the burner to preheat the air. The remaining20% of products travel to the main flue stack. The firing profileand burner locations had to be adjusted as shown in Table 5 to

Table 4Predicted results for the forge furnace.

Case Condition OverallSFC (GJ/Tenet)

Saving(%)

Steady-state SFC

GJ/Tenet

Tb

(�C)DT(�C)

kg/h

eff%net

(%)

1 Base case 1.728 – 1.39 1154 70 982 53.752 CF walls 1.539 10.94 1.29 1151 71 983 57.893 Recup 50% 1.467 15.10 1.19 1156 71 982 62.714 CF

walls + recup50%

1.33 22.92 1.19 1154 73 9S2 62.71

5 Recuperativeburner

1.51 12.50 1.29 1151 150 836 57.89

Table 5The assumed firing profiles in a continuous steel-reheating furnace.

Zone number (1 = discharge zone)

1 (%) 2 (%) 3 (%) 4 (%) 5 (%) 6 (%) 7 (%) 8 (%) 9 (%) 10 (%)

Base case 7 0 50 43 0 0 0 0 0 0End recuperator 7 0 50 43 0 0 0 0 0 0Regenerative burner 7 0 0 18 25 25 0 25 0 0

Fig. 5. The predicted steel temperature distribution through the furnace.

Table 6The predicted results for a continuous steel reheater.

Efficiency (%gross) Fuel carbon saving (%) DT (�C)

Base case 48.2 – 56End recuperator 62.8 23.2 59Regenerative

burners65.9 26.9 51

132 R. Tucker, J. Ward / Applied Energy 89 (2012) 127–132

ensure that billet temperature difference at discharge was not ad-versely affected. The predicted efficiencies and steel temperaturedifferences are given in Table 6.

This final choice depends on the installed cost of an end recu-perator compared to installing regenerative burners.

5. Conclusions

This paper has shown how low cost mathematical models basedon the zone method for computation of radiation exchange inenclosures can be applied to evaluate fuel saving opportunitieson a range of high temperature plant. In many applications thereare often several opportunities that could be applied. It is impor-tant to identify low cost, fast pay-back options that should be givenpriority. These can then reduce the cost of more expensive solu-tions. This is demonstrated on a simple furnace where reducingthe excess air is given priority. This then reduces the cost of moreexpensive measures such as flue gas heat recovery or application ofoxygen to the combustion process.

In certain situations, fuel saving measures may lead to unex-pected reduction in throughput or product quality. When heatingthick material such as steel billets or slabs, any impact on the heatflux profile and temperature distribution in the steel has to becarefully evaluated before investment is made. Steady-state and

transient long furnace zone models can be usefully applied to suchsituations.

The zone model has also been further developed recently tosimulate more accurately the spectral absorption and emissionbands due to CO2 and H2O in the combustion gases [8]. This wasthen applied to predict the effects of wall emissivity on furnaceefficiency. The fuel savings were increased compared to those pre-dicted using the simpler weighted-sum-of-grey-gases model.

The zone method can also be applied to more complex 2- and 3-dimensional problems. A 3-dimensional zone model has recentlybeen applied to simulate different low NOx firing strategies on aglass-melting furnace [9]. Data on the complex flow and combus-tion fields were provided by computational fluid dynamic simula-tions and by the use of physical models – in this case using theacid-alkali technique to simulate fuel/air mixing.

References

[1] Rhine JM, Tucker RJ. Modelling of gas-fired furnaces and boilers. McGraw-Hill;1990.

[2] Mullinger P, Jenkins B. Industrial and process furnaces. Elsevier; 2008.[3] Baukal CE, Schwartz R. The John Zink combustion handbook. CRC Press; 2001.[4] Hottel HC, Sarofim AF. Radiative transfer. McGraw Hill; 1967.[5] Correia SAC, Ward J, Sousa JLVA. Numerical prediction of the transient operation

of a gas-fired reheating furnace. In: Proc ASME heat transfer division, HTD, vol367; 2000. p. 87–94.

[6] Docherty P, Tucker RJ. The influence of wall emissivity on furnace performance.J Inst Energy 1985;59:35–7.

[7] Elliston DG, Gray WA, Hibberd DF, Ho T-Y, Williams A. The effects of surfaceemissivity on furnace performance. J Inst Energy 1987;60:155–67.

[8] Ward J, Tan CK, Tucker RJ. Development of a spectral radiation model to predictthe transient performance of a metal reheating furnace. Proc ASME Int MechEng Cong 2009.

[9] Fricker N et al. A multimode modelling approach for NOx reduction on gas-firedglass melters through flame/furnace matching. In: Proc 16th IFRF membersconference, – combustion and sustainability: new technologies, new fuels, newchallenges. Boston (USA); June 2009. p. 13.