E M Ishiyama

A V Heins

W R Paterson

L Spinelli

D I Wilson

Scheduling cleaning in a crude oil preheat train subject to fouling: incorporating desalter control

 [1]. The methodology is illustrated using a case study based on an industrial network subject to fouling, where the fouling rates of heat exchangers were extracted through a data reconciliation exercise. The case study scenarios suggest that our simulation-based tool should be effective in controlling desalter inlet temperature within a fouling management strategy.

Introduction

Oil refineries resemble mini-economies in that they have to achieve effective use of resources, minimize energy consumption, and reduce emissions, in order to stay competitive in the current marketplace. Refineries are also major parts of a national economy: in the UK alone, there are nine major refineries, processing over 1.8 million barrels of crude oil per day [START_REF] Watson | Meeting our energy needs: The future of UK oil refining[END_REF], and consuming energy at the rate of gigawatts (~ 7.9 GW [START_REF] Marsh-Patrick | EU emissions trading scheme phase II: review of new entrants' benchmarks -refineries[END_REF]). Amongst the processes on a refinery, distillation has been identified as the main energy consumer, because the crude oil feed must be heated and partly vapourised as it passes from ambient temperature to around 380 ºC. A large fraction of the heat required for distillation (c. 60-70%) is recovered from the product and pump-around streams of the distillation unit, using heat exchangers (HEXs). These HEXs are usually connected together in a network called the preheat train (PHT). Many crudes give rise to fouling, which not only reduces the capacity for heat transfer, but also changes the surface roughness and the cross sectional area available for flow, causing problems in pumping a given flow rate through the exchanger. Consequences of fouling include (i) higher heating costs (with associated increases in greenhouse gas emissions); (ii) reduction in throughput; (iii) increase in capital expenditure for over-designed units; (iv) additional cleaning and maintenance; and, in the worst case, (v) plant shut-down.

HEX fouling is a widespread economic problem, accounting for 0.25% of gross national product (GNP) in the highly industrialized countries [START_REF] Esdu | Heat exchanger fouling in the pre-heat train of a crude oil distillation unit[END_REF]. Attention has therefore been given to fouling mitigation methodologies, such as the use of anti-foulant chemicals and tube inserts, the revamping of networks and the manipulation of operating parameters.

The complexity of the fouling problem and the variability of feedstocks has rendered it apparently impossible to completely eliminate fouling in PHTs. Cleaning of fouled HEXs is still, therefore, considered to be a desirable responsive action. This raises the question of which units should be cleaned and when. This example of a scheduling problem has spawned a variety of numerical approaches and has attracted the attention of the numerical optimization community [START_REF] Georgiadis | Optimal energy and cleaning management in heat exchanger networks under fouling[END_REF][START_REF] Georgiadis | Optimal cleaning policies in heat exchanger networks under rapid fouling[END_REF][START_REF] Wilson | Mitigating of fouling in refinery pre-heat trains by optimal management of cleaning and antifoulant treatment[END_REF][START_REF] Smaïli | Mitigation of fouling in refinery heat exchanger networks by optimal management of cleaning[END_REF][START_REF] Lavaja | On a new MILP model for the planning of heat exchanger network cleaning[END_REF][START_REF] Lavaja | On a new MILP model for the planning of heat exchanger network cleaning[END_REF][START_REF] Lavaja | On a new MILP model for the planning of heat exchanger network cleaning. Part III: Multiperiod cleaning under uncertainty with financial risk management[END_REF][START_REF] Markovski | Optimal cleaning schedule for heat exchangers in a heat exchanger network[END_REF][START_REF] Sanaya | Simulation of heat exchanger network (HEN) and planning the optimum cleaning schedule[END_REF]. The problem is combinatorial and includes non-linear models. This has prompted the development of several MINLP/MILP (Mixed Integer Non-Linear Programming/ Mixed Integer Linear

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-3 -Programming) methodologies which consider all possible actions over a given time period in a 'total horizon' approach. Rodriguez and Smith [START_REF] Rodriguez | Optimization of operating conditions for mitigating fouling in heat exchanger networks[END_REF] reported alternative, simulated annealing methods.

A simpler approach, based on the 'greedy algorithm', was proposed by Smaïli et al. [START_REF] Smaïli | Mitigation of fouling in refinery heat exchanger networks by optimal management of cleaning[END_REF] as this yields satisfactory scheduling solutions and permits the use of simulations incorporating operational behaviour that would cause stability problems in a 'total horizon' approach. Heuristic methods simply evaluate some of the (exceedingly many) combinations, guided by an ad hoc search, and, while still useful, cannot guarantee finding a global optimum (if one exists). Ishiyama et al. [START_REF] Ishiyama | Platform for techno-economic analysis of fouling mitigation options in refinery preheat trains[END_REF] have recently reported a modified 'greedy algorithm' that exploits a 'merit list' approach to reduce computational time. They included model-based representations of the dynamics of fouling, in addition to the thermal and hydraulic performance of the network.

A typical refinery, however, includes units such as a desalter, flash tower and furnace which are required to operate within a constraint set of operating parameters. Given that complete mitigation of fouling is rarely achieved, PHT operation tends to deviate from design targets. The operating band of these units can be critical; the importance of good control was discussed by Polley et al. [START_REF] Polley | Simultaneous consideration of flow and thermal effects of fouling in crude oil preheat trains[END_REF]. In this paper we explore how scheduling and temperature control can be combined in a PHT simulation to aid the management of fouling, for the particular case of a desalter.

Desalter

Fouling occurs throughout the PHT and different mechanisms are known to cause deposition in each section. Deposition of salts and wax, and corrosion, have been reported for HEXs upstream of the desalter, while chemical reaction fouling and corrosion fouling are dominant downstream of the desalter [START_REF] Crittenden | Fouling of crude oil preheat exchangers[END_REF]. Desalting, as the name implies, is intended to remove inorganic materials from the oil. Desalter malfunction hinders crude oil processing in several ways: (i) formation of inorganic/organic acids downstream of the desalter, causing corrosion, (ii) deposition of salts as mineral scale in

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HEXs, (iii) deactivation of catalysts and (iv) two-phase flow downstream of the desalter arising from water vapourisation.

Desalting consists of two processes; (a) formation of an emulsion by mixing oil with water and transfer of the ionic material to the aqueous phase, and (b) separation of water droplets from the oil, accelerated by electrostatic precipitation. Desalters commonly employ two electrodes which generate an electric field across the emulsion, promoting coalescence of water droplets [START_REF] Heimbaugh | Dehydration: the key to desalting crudes[END_REF].

Separation is governed by the droplet settling velocity, given by Pruneda et al. [START_REF] Pruneda | Optimum temperature in the electrostatic desalting of Maya crude oil[END_REF] as

( ) oil oil w 2 s 9 2 µ ρ ρ - = gr u (1)
Here u s is the settling velocity, g is the gravitational acceleration, r the droplet radius, µ oil the viscosity of oil, ρ oil the density of crude oil, and ρ w the density of water/brine.

Equation [START_REF] Ishiyama | Platform for techno-economic analysis of fouling mitigation options in refinery preheat trains[END_REF] shows that desalter operation is dependent on the operating temperature via the viscosity and densities. A higher temperature promotes settling via the reduced viscosity but it also increases the electrical conductivity of the mixture. A high conductivity will result in larger voltage gradients between the electrodes, increasing the electricity cost, and in the worst case causing short-circuiting and desalter breakdown. A low temperature decreases the settling velocity and can reduce the unit throughput or cause carryover of brine into the crude. Both the above effects are undesirable, so upper and lower temperature bounds are often specified for desalter operations.

The mixing level of crude and water in the desalter, types and amount of demulsifiers, and water inlet temperature could also affect the operation of the desalter. In this paper we choose to concentrate on the control of desalter inlet temperature alone.

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Network simulation

Preheat trains are complex. Fouling rates are often non-linear in temperature and velocity dependency, so it is rarely possible to derive analytical solutions describing network performance. The network simulator based on MATLAB™ and Excel™ described by Ishiyama et al. [START_REF] Ishiyama | Platform for techno-economic analysis of fouling mitigation options in refinery preheat trains[END_REF] was extended here for evaluation of scheduling and control actions.

Heat transfer

The majority of the HEXs used in PHTs are shell-and-tube devices. The performance of individual HEXs were evaluated using lumped parameter models, as in most network simulation studies, in effect assuming uniform thermal properties, known values of heat transfer coefficients, and the existence of single phase flow. The NTU-effectiveness (ε) method is used to calculate the duty and outlet temperatures for each HEX using standard equations [START_REF] Hewitt | Process Heat Transfer[END_REF]. This method lends itself to simulating the thermal performance of the PHT network, as the inlet and outlet temperatures from each HEX appear in simultaneous linear equations which can be written in matrix form and solved rapidly [START_REF] Smaïli | Mitigation of fouling in refinery heat exchanger networks by optimal management of cleaning[END_REF].

Fouling

In this paper, fouling is quantified in terms of the fouling resistance, R f . Fouling is assumed to occur only on the tube-side, partly because reliable models for shell-side fouling of non-crude streams are not currently available. However, shell-side fouling effects could be readily incorporated in the simulation. Determining the contribution from shell-side fouling when only heat transfer data are available for reconciliation is infeasible as the problem is underspecified.

In the case study in section 4, fouling models for individual exchangers were constructed from refinery heat transfer data for each unit. For many of the exchangers located downstream of the desalter, where chemical reaction fouling is expected to dominate, the observed fouling rates were consistent with 'fouling threshold' models such as that presented by Panchal et al. [START_REF] Panchal | Threshold conditions for crude oil fouling[END_REF], viz

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT -6 - ( ) { } w film 33 . 0 66 . 0 exp , 0 max τ b RT E Pr aRe R f - - = - - & (2)
where f R & , is the fouling rate; a and b are dimensional constants which establish the timescale of the process; τ w is the wall shear stress on the inner tube/foulant surface, T film the tube-side film temperature, and E and R are the activation energy and the gas constant, respectively. The film temperature is estimated as the arithmetic average of the bulk and surface temperatures at the heat exchanger outlet. For some of the exchangers in the case study, however, the simpler constant (linear) fouling rate model proved suitable.

The Reynolds number, Re, and τ w is calculated via

( ) υ δ 2 i m - = d u Re (3a)         = 2 2 f m w u C ρ τ (3b)
u m being the axial mean velocity in the heat exchanger tube with the new reduced crosssectional area; d i is the tube internal diameter; δ is the deposit thickness; υ is the kinematic viscosity; C f is the Fanning friction factor. δ was estimated using the thin-slab approximation (δ = R f λ f ) assuming a constant value for the thermal conductivity, of 0.2 W m -1 K -1 [START_REF] Ishiyama | Platform for techno-economic analysis of fouling mitigation options in refinery preheat trains[END_REF]. All the thermo-physical properties of the crude oil associated with a particular HEX are calculated at the arithmetic mean temperature of the crude oil at the HEX inlet and outlet. Likewise the Prandtl number, Pr, for the crude is defined via Pr = C p µ oil /λ oil , with C p being the crude specific heat capacity, µ oil its dynamic viscosity and λ oil its thermal conductivity.

In Eq. ( 2), τ w is not likely to vary markedly across a HEX but T film is, and so the average fouling rate in the unit is evaluated using the exponential integral approach presented by Ishiyama et al. [START_REF] Ishiyama | Thermo-hydraulic channelling in parallel heat exchangers subject to fouling[END_REF].

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At any instant, the overall heat transfer coefficient, U, in a HEX is calculated using

o w cl i, o o i f i, cl i, 1 1 1 A R A R h A h A UA f + + + = (4)
where A i,cl is the internal (clean) surface area, A i,f is the internal surface area after fouling, A o is the external surface area of the tube, R f the (tube-side) fouling resistance and R w the tube-wall resistance; h i and h o are the internal and the external film heat transfer coefficients, respectively. When there is change in tube-side flow velocity, the effect of flow rate on the film heat transfer coefficient is included using standard correlations. For h i , the correlation developed by Gnielinski [START_REF] Gnielinski | New equations for heat and mass-transfer in turbulent pipe and channel flow[END_REF] is used with the tubeside Fanning friction factor, C f , evaluated for surface roughness, e, and flow velocity, using the explicit form of the Colebrook-White reported by Sousa et al. [START_REF] Sousa | An explicit solution to the Colebrook-White equation through simulated annealing[END_REF].

( ) ( )                 - - - - - = 87 . 0 i i 10 f 09 . 5 2 7 . 3 log 16 . 5 2 7 . 3 log 4 1 Re d e Re d e C δ δ (5)
A surface roughness value, e, of 43 µm was used [START_REF] Perry | Perry's Chemical Engineers' Handbook[END_REF].

It is assumed that there is no fouling on the shell-side. Initial values for the external heat transfer coefficient were calculated using standard methods (Bell-Delaware method).

The dynamics of the network are evaluated by piece-wise integration in time. At any instant, t, the fouling resistance in each HEX is evaluated, the coefficients in the NTU-ε expressions updated and the network temperature field updated. Application of the simple Euler method between time t-∆t and t:

t dt dR R R t t f t t t ∆ ∆ ∆ ⋅ + = - - f, f, (6)
can generate problems if the time period, ∆t, is too coarse: the fouling rate will often slow dramatically as deposit accumulates (for instance, as a result of changes in T film ) but this would not be captured and the effect of fouling would be over-estimated. Very

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short time-steps are computationally undesirable, but identifying the optimal value of ∆t is complicated by the fact that HEXs foul at different rates, and the rank order of rates may change over the time span of the problem. To determine ∆t to maintain accuracy whilst minimising effort, an adaptive step size algorithm described by Ishiyama et al. [START_REF] Ishiyama | Platform for techno-economic analysis of fouling mitigation options in refinery preheat trains[END_REF] was therefore implemented based on that described by Press et al. [START_REF] Press | Numerical Recipes: The Art of Scientific Computing[END_REF] for solving sets of ordinary differential equations, as follows.

Let R f (t+∆t) denote the fouling resistance in a HEX at time t+∆t, and R f,1 (t+∆t) and R f,2 (t+∆t) the estimates of R f (t+∆t) calculated using one step of length ∆t and two steps each of length ∆t/2, respectively. The error involved in using the single step is

( ) ( ) t t R t t R err ∆ ∆ + - + = f,2 f,1 (7)
Now if the calculated error, err, is greater than the acceptable error, err*, the current time step, ∆t current , is shortened. Likewise, if err < err*, the current time step is increased. Integration with a fourth-order Runge-Kutta method gives the relationship between the new and old ('current') time steps:

2 . 0 current new * err err t t ∆ ∆ = (8)
In this work equation ( 8) is used with an initial ∆t value of 1 day; err* = 1×10 -6 m 2 K W -1 . For a typical refinery HEX with U ~ 500 W m -2 K -1 , this corresponds to a fouling Biot number, Bi f ( = U cl R f ), of ~ 0.00005, which is considered to be a small change in fouling resistance. Here U cl is the overall heat transfer coefficient of the heat exchanger in the clean state (see equation 4).

Scheduling of cleaning actions

HEXs may be isolated from service for cleaning, incurring an initial penalty in terms of heat transfer and network operability, in return for a longer term gain in heat duty and reduction in pressure drop. Scheduling cleaning in a PHT commonly employs costbased objective functions extending over the operating time span [START_REF] Rodriguez | Optimization of operating conditions for mitigating fouling in heat exchanger networks[END_REF].

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Solution of the scheduling problem employed here is based on discretisation of the operating time span into N p regular periods of months, which are divided into subperiods for cleaning, of length ∆t cleaning (7 days) and operation, ∆t operation , as indicated in Figure 1. The optimisation approach uses a simple -and robust -'greedy' algorithm, (GrA), which considers the cleaning actions allowed in the current period (say, t j ) and the impact of this action over a 'sliding' horizon, ∆t w , consisting of N s periods into the future.

Evaluating the objective function requires simulating the network over several time periods; we employ a shortcut 'merit list' algorithm to identify favourable candidates to be compared in a full simulation. In this paper, for brevity, only scenarios based on constant throughput are considered. A description of variable throughput operation and its simulation is given in [START_REF] Ishiyama | Platform for techno-economic analysis of fouling mitigation options in refinery preheat trains[END_REF]. At the start of each time period, the performance of the network at its current, fouled, condition is evaluated. The improvement obtained from cleaning each HEX at that point is estimated, and the difference between the two is used to generate an estimated benefit:

( ) { } w de de cl i, HEX f i, cl i, j i . . t P C Q C E ∆ ε ε γ ⋅ ⋅ + - = (9)
Here, γ i | j is the estimated benefit from cleaning HEX i at period j, carrying the benefit forward over a time window of length ∆t w and ignoring losses incurred during cleaning, ε i,cl the effectiveness of HEX i when clean, ε i,f its current, (possibly) fouled effectiveness, and Q HEX,i,cl the maximum heat duty of HEX i in the clean state, C E the energy cost, and C de a desalter penalty cost in units of US$/(desalter penalty× day). P de is the desalter penalty, written here as [START_REF] Lavaja | On a new MILP model for the planning of heat exchanger network cleaning[END_REF] where T* is the target temperature, T the current temperature, T H the upper limit of the desalter operating range, T L is the lower limit, and n is a skewness index. A value of n = 2 is used here, but any positive even integer could be used. Equation [START_REF] Lavaja | On a new MILP model for the planning of heat exchanger network cleaning[END_REF] represents a

( ) ( ) n T T T T P         - - = L H * de

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-10 -simple penalty function; more complex expressions could be used. Pruneda et al. [START_REF] Pruneda | Optimum temperature in the electrostatic desalting of Maya crude oil[END_REF] described a detailed model-based penalty function requiring detailed knowledge of the crude oil properties. The advantages and constraints associated with the use of penalty and barrier functions have been discussed elsewhere for many years (e.g. Fiacco and McCornick [START_REF] Fiacco | Non Linear Programming: Sequential Unconstrained Minimization Techniques[END_REF]). Our P de function employed here is simpler and of a general form: the cost is framed as a deviation scaled by a penalty, the size of which is set by management considerations.

Detailed simulations are performed for the three highest ranked HEXs in the merit list, over the sliding time window. The GrA decision parameter, G i | j , is calculated for each of the selected units from [START_REF] Lavaja | On a new MILP model for the planning of heat exchanger network cleaning. Part III: Multiperiod cleaning under uncertainty with financial risk management[END_REF] where C c,i is the cleaning cost for HEX i, N s is the time horizon, Q ne (t) is the heat duty of the network at time t (where the network heat duty is calculated as the sum of the heat duties of the individual heat exchangers). Subscripts 'no cleaning' and 'clean i in period j' refers to no cleaning action and the cleaning of HEX i at period j, respectively. The sliding time horizon is truncated when it exceeds t F , i.e. when t j+Ns > t F , then t j+Ns = t F .

{ } { } i c, j j de de s j j cleaning no ne j period in i clean ne E j i ) ( ) ( C dt P C dt t Q t Q C G s N t t N t t - + - = ∫ ∫ + +
There are other methodologies for handling the approach to t F in this scheduling problem, which were discussed by Ishiyama et al. [START_REF] Ishiyama | Platform for techno-economic analysis of fouling mitigation options in refinery preheat trains[END_REF]. A benefit threshold is set, viz. G G ∆ > j i [START_REF] Markovski | Optimal cleaning schedule for heat exchangers in a heat exchanger network[END_REF] where ∆G is the 'greedy threshold' value, which in practice will be some multiple of the cost of cleaning the exchanger. The HEX with the highest G value satisfying Eq. ( 12) is selected for cleaning in period j, and the algorithm then moves on to period j+1.

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-11 -One could select more than one exchanger for cleaning in a sub-period, either simultaneously or in sequence. This requires a straightforward adaptation of the algorithm [START_REF] Wilson | Mitigating of fouling in refinery pre-heat trains by optimal management of cleaning and antifoulant treatment[END_REF]. The total network fouling penalty function, Γ, is calculated after the final period using Eq. ( 13) for comparison of different scenarios, such as the benefit of performing cleaning compared to taking no cleaning action.

(

)

∑ ∫ = ⋅ +           ⋅ + - ⋅ = HEX 1 , , 0 cleaning j period in i lean ) ( ) ( all i i c i c F t de de no ne c ne E N C dt P C t Q t Q C Γ (13)
Here, N c,i is the number of cleaning actions performed for HEX 'i'.

Desalter inlet temperature control

One of the most common strategies for controlling the exit temperature from a HEX is to bypass part of the cold or hot stream. In many shell-and-tube units where the crude is on the tube-side, the shell-side stream is bypassed, because bypassing part of the crude flow would reduce the tube-side velocity, promoting a higher fouling rate (see Eq. ( 2)).

In the following discussion the unit used for manipulation of the desalter inlet temperature, T de , is termed the 'control HEX'.

When the control HEX is clean, T de is likely to be high and the hot stream will be split to achieve the target T de value. The bypass fraction, x, will decrease as the control HEX (and network) is subject to fouling. It is possible to control T de up to a point where the bypass is fully closed (i.e. x = 0). To simplify the calculation (primarily to reduce computational time), the relationship between the parameters is approximated as linear, viz.

T de = x d c ⋅ + (14)
At each time step, parameters c and d are obtained by simulation. The bold and dashed lines in Figure 2 show loci of Eq. ( 14) in clean and fouled states, respectively. Solving for parameters c and d to obtain Eq. ( 14) at each fouled state involves solving a simultaneous equation with two sets of values for T de and x. Values of T de and x at the

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current period and values when x is zero (simulated), are used for this purpose. After obtaining the linearized form, the split fraction required to obtain the desired desalter inlet temperature is obtained through extrapolation (see Figure 2).

Case study

Figure 3 shows a PHT network consisting of 18 HEXs, resembling an existing refinery in Argentina. The PHT includes a desalter and a flash tower. The HEX design parameters are listed in Table 1 and the thermo-physical properties of the crude summarized in Table 2. Heat exchangers sharing a common numeric value have the same fluid on the hot-side. e.g. the heavy gas oil (HVGO) goes through the four HEXs numbered 8A-D. The HEXs located just upstream of the desalter (i.e. 6A,B) are taken to be the control HEXs for the desalter, as discussed below.

This study involved three stages: (i) data reconciliation, (ii) extraction of fouling rates, and (iii) simulation of different management scenarios. The approach is a general one and could be applied to any operating PHT.

Data reconciliation

In this step, plant operational data are inspected to obtain reliable estimates of operating parameters and performance measures (such as the time profile of fouling resistance).

Data filtering involved two steps:

(a) Removal of data over periods of 'process upset', such as when a unit was taken offline for cleaning.

(b) Selection of reliable data, in this case based on a trusted heat balance, i.e. only data points where the heat duties of the hot and cold streams matched within a specified error limit were included.

The heat duties of the cold and hot streams of the heat exchanger are given by

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT -13 - ( ) 6 in c, out c, c p, c c 10 / T T C m Q - = (15) ( ) 6 in h, out h, h p, h h 10 / T T C m Q - = (16)
Here subscripts c, h and in, out refer to the cold and hot streams and inlet and outlet streams, respectively. Q is the heat duty, C p is the specific heat capacity, m is the mass flow rate and T is the temperature.

An example of the heat duty comparison for HEX 9D is plotted in Figure 4. Data points lying within the specified region between the two dashed lines were considered reliable, and taken forward for performance evaluation. Estimation of the uncertainty limits (reflected in the sample error bar in the Figure ) is discussed in the appendix. Some heat exchanger data sets yielded more reliable data than others, with the percentage of data acceptance ranging from 30-85%.

Extraction of fouling rates

Refinery operating data collected over a 9 month period were filtered and used to generate R f -t plots for each exchanger. Regression tools were used to fit each profile to one of the three fouling resistance trends summarised in Table 3.

The fouling resistance models in Table 3 represent commonly reported trends and are not employed here as fouling models per se: rather, they are constructions used to interpolate the data for use in fouling model comparisons. All are continuous and differentiable to yield estimates of the local fouling rate. For each heat exchanger regression analysis yielded the dimensional constants for the most satisfactory trend line and its fouling rate could then be estimated for those instants when reliable flow and temperature measurements were available.

Chemical reaction fouling is expected to be the dominant mechanism in heat exchangers located downstream of the desalter. The fouling rates obtained for these units are compared against the maximum film temperature in the unit and the average tube-side velocity in Figure 5. HEXs 7 and 9A-E show an increase in fouling rate with increasing 4. The fouling rate predicted by equation 2 is compared to the observed values in Figure 6. The plot suggests that the model describes fouling in HEXs 7 and 9 well, but its use for HEXs 8 A-D would be ill-advised: linear fouling rates were there fore used for the latter units in the scheduling simulations.

The extracted activation energy, 36.4 kJ mol -1 , is very similar to that reported by Crittenden et al. [START_REF] Crittenden | Fouling of crude oil preheat exchangers[END_REF] for 'light' crude oils (~33 kJ mol -1 ). [Yeap et al. [START_REF] Yeap | Mitigation of crude oil refinery heat exchanger fouling through retrofits based on thermo-hydraulic fouling models[END_REF] analysed a range of fouling data sets from refineries and pilot plant studies and reported activation energies ranging from 28-86 kJ mol -1 ]. The above value (36 kJ mol -1 ) presents a region of mixed chemical and physical fouling mechanisms, where the physical mechanism could be diffusion-controlled. The calculation of activation energy involves an estimation of the film and surface temperatures, which are calculated using the value of the overall and the tube-side film heat transfer coefficients. The uncertainties involved in determining U will affect the accuracy of the film and surface temperatures, and any parameters relying on these. Uncertainty in the surface temperature could also serve to reduce the activation energy.

Irregular behaviour is evident in HEXs 8B-D, the devices located immediately downstream of the desalter (Figure 3). This fouling behaviour could arise because of (a)

poor operation of the desalter, or (b) non-negligible shell-side fouling. The shell-side fluid for HEX 8B-D is heavy gas oil (HVGO). Li and Watkinson [START_REF] Li | Characteristic of Heavy vacuum gas oil fouling in the presence of dissolved oxygen[END_REF] observed fouling caused by HVGO undergoing heating, but this is unlikely to be the case here, since HVGO is being cooled. This feature was reported to the company, with the suggestion that these units be inspected carefully at the next shutdown.

Comparison with other studies

The fouling rates obtained from the data reconciliation study are now compared with data sets collected from other preheat train fouling studies. Joshi et al. [START_REF] Joshi | Relate crude oil fouling research to field fouling observations[END_REF] reported

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industrial fouling data collected by Shell Global Solutions from different refineries.

They found that (i) the average rate of fouling decreased with increasing tube-side wall shear and (ii) the observed fouling rates were independent of temperature. The trendline presented in their paper is plotted alongside other data sets available in our group in In Figure 7(a) (this study), the open symbols show the fouling rate to decrease with increasing shear stress, following the trend reported by Joshi et al. The magnitude of fouling rates is similar. The solid symbols show units where shell-side fouling is suspected and these clearly deviate from the trend. Inspection of the temperatures, however, (e.g. Figure 5) shows a strong trend of increasing fouling rate with increasing temperature. In contrast, Figure 7(b) shows that the fouling rate is strongly related to the temperature and not shear stress, apart from the outlier datum, T 2 . Figures 6(c) to (e) do not show clear relationships between fouling rate, temperature and shear stress; the data do not follow the observation by Joshi et al. [START_REF] Joshi | Relate crude oil fouling research to field fouling observations[END_REF] in a meaningful way. Figure 7(e) includes data from heat exchangers located upstream of the desalter, which exhibit high fouling rates over an order of magnitude greater than the trend line. These units, like the outliers in Figure 7(a), highlight the need to recognise when other fouling mechanisms may be operating, and the need to determine whether shell-side fouling is occurring. It is noted that differentiating tube-side from shell-side fouling is not feasible with heat balance data alone.

Likewise, the fouling rate data from refinery-based pilot plant reported by Knudsen et al. [START_REF] Knudsen | The determination of the threshold fouling curve for a crude oil[END_REF] (Figure 7 (f)) show a strong dependency temperature compared to wall shear stress. Figure 8 presents the data Figure 7 (a-f) together; the existence of large scatter of fouling rate with shear stress clearly indicates a need for further work on modelling chemical reaction fouling in refinery applications.

Simulation and discussion

III. Combined (cleaning and bypass control).

The scheduling simulations cover an operating period of 3 years, starting from the clean state; a HEX cleaning cost of 10,000 US$/unit; a greedy threshold value of 10,000 US$;

an energy cost, C E , of 500 US$ MW -1 day -1 and a desalter penalty cost, C de , of 500 US$ day -1 . A sliding time horizon of 1 year was used in the consequent simulations.

If the network is operated without any stream temperature control or cleaning (Case I), shows that it is unsuccessful after 13 months.

Figure 9 shows that under Case III it is possible to maintain the desalter inlet temperature within the desired band over almost the whole plant operating period (Figure 9 (b)), apart from its last 2 months, and the last two cleaning actions. Whether it is acceptable for the desalter inlet temperature to drop below the lower control limit during cleaning is a question for refinery management. In this simulation it is noticeable that the shell-side split fraction of the control HEX is varied three times during the 3 year period, in contrast to the constant split case in Figure 9 (c). Comparison of cleaning schedules for Case II and III shows that there has been a rearrangement of cleaning actions before and after the desalter (Figure 9 (d)). In contrast, the fixed split scenario (Case II) has identified one single HEX to be cleaned 3 times. Case I can be used to identify which HEXs have the greatest effect on the desalter inlet temperature. Hence the simulation is used to evaluate which HEX should be considered to be the 'control HEX'. The CIT profiles in Figure 9 (a) show that Case II concentrates on maintaining CIT high.

Table 5 summarizes the performance of the three cases. Apart from the furnace penalty being slightly higher than for Case II, the network performance in Case III is considered best in terms of reduced overall fouling penalty, fewer cleaning actions and the capability to maintain T de within the limits for most of the operating period. It should be

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT -18 -
noted that the results are subject to the relative weighting of the various components in the objective function, e.g. cleaning vs. energy costs. The effect of different cost structures was discussed in detail previously [START_REF] Ishiyama | Platform for techno-economic analysis of fouling mitigation options in refinery preheat trains[END_REF]. The hydraulic performance of the network was modelled in this case study but the resistance to flow associated with fouling did not give rise to changes in throughput.

Conclusions

A data reconciliation study was performed for an existing crude oil refinery PHT. An analysis of the fouling rates extracted for different regions in the PHT revealed that the Ebert-Panchal model could be used to represent fouling in most of the exchangers at the hot end of the PHT. An operating strategy exploiting both the scheduling of cleaning and the manipulation of a bypass was implemented in the software.

Scheduling of cleaning for the control HEX alone was demonstrated to be an ineffective method of controlling crucial stream temperature, namely that of the desalter inflow.

One major reason is its drop in temperature during a cleaning period. Manipulation of flow split (in this case, the hot stream flow split of the control HEX) adds an extra degree of freedom in temperature control, which yields an effective method to achieve desired operation. The work could be extended to incorporate temperature control of other crucial streams, such as pump-around streams and flash column feeds.

i p, i p, 2 i i i i         - - +         +         =         T T T T d C dC m dm Q dQ (A1)
The relative error terms dY i /Y i refer to (in order) heat duty, mass flow rate, heat capacity and temperature change. The mass flow can be presented as the product of the volumetric flow rate, V, and the crude oil density, ρ ,

i i         - - +         +         +         =         T T T T d C dC V dV d Q dQ ρ ρ (A2) yielding. ( ) ( ) 2 in i, out i, in i, out i, 2 i p, i p, 2 i i 2 i i
The individual uncertainties are given in Table A.

Consider a hot stream data point with mass flow rate, 43.8 kg s -1 ; specific heat capacity, 2550 J kg -1 K -1 ; temperature changing from 280 o C to 260 o C. Equation (A2) gives,

dQ h /Q h = ( ) ( ) ( ) ( ) 2 2 2 2 ) 260 280 /( 2 02 . 0 01 . 0 02 . 0 - + + + = 0.1044 => dQ h = 0.2 MJ M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT -20 - Nomenclature Roman A surface area, m 2 a constant (Eq. 2), m 2 K J -1 b constant (Eq. 2), m 2 K J -1 Pa -1 C c,i cleaning cost for HEX i, US$ unit -1
C f Fanning friction factor, - Bulk temperature, T in o C. desalter -----------------------------------------------------------8A Linear f 2 = 2.66× 10

C p crude specific heat capacity, J kg -1 K -1 C E energy cost, US$ MW -1 day -1
t f f 2 1 + 2 f Kern & Seaton ( ) [ ] t f f 4 exp 1 3 - - ( ) t f f f 4 exp 4 3 - Falling rate ( ) 6 ln 5 f t f - t f 5 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT
a = 926 m 2 K kW -1 h -1 γ = 4.3× 10 -8 m 2 K kW -1 h -1 Pa -1 E = 36.4 kJ mol -1

  decreasing velocity, as described by the Ebert-Panchal fouling model [Eq. (2)]. This equation was fitted to the data sets and the result is plotted as a plane in Figure5: good agreement is evident. The fouling model parameters obtained are presented in Table

Figure 7 .

 7 Figure 7.Figure 7(a) shows the results from this study, while Figure 7 (b)-(e) represent unpublished data sets obtained from 4 different UK oil refineries.

  obtained in the previous section were used in predictions of network performance. The network initially has a coil inlet temperature, CIT, of 275 o C, clean network heat duty of 67 MW, desalter inlet temperature of 130 o C and a desalter operating band of 128 -132 o C. Three scenarios are compared under this case study, namely: I. Base case -without cleaning or bypass control. II. Cleaning only (no bypass control).

Figure 9 (

 9 Figure9(a) shows that the coil inlet temperature, CIT, will drop by 18 K over 3 years, and that T de lies outside the desired operating band after 12 months of operation (Figure9(b)). Analysis of the fouling penalty cost in Table5(for Case I) indicates that the additional energy cost associated with fouling constitutes 85% of the total fouling cost; the remainder is due to the desalter penalty, based on the values of n and C de used.

C 1 e 1 f parameters in Table 3 g gravitational acceleration, m s - 2 G 1 m 1 ρ

 113211 de desalter penalty cost, US$ day -1 c constant (Eq. 14), K d constant (Eq. 14), K d i tube internal diameter, m E activation energy for fouling, J mol -surface roughness parameter, m err, err* calculated, acceptable error, m 2 K W -greedy decision parameter, US$ h film heat transfer coefficient, W m -2 K -mass flow rate, kg s -1 n sharpness index in Eq. (10), -N c number of cleaning actions,oil , ρ w density of crude oil, water, kg m -3 τ w wall shear stress on the inner tube/foulant surface, Pa

3 ρ

 3 = 931.65 -0.6597T Specific heat capacity, J kg -1 K -1 C p = 1959.66 + 3.1093T Thermal conductivity, W m -1 K -1λ oil = 0.1749 -0.0002T Dynamic viscosity, mPa s µ oil = 1498.7Τ-1.5611

Figure 1 :

 1 Figure 1: Time discretisation used in scheduling formulation.

Figure 2 :

 2 Figure 2:Linearized relationship between desalter inlet temperature and bypass fraction of control HEX. Fouling requires the bypass fraction to decrease in order to achieve the desired value.

Figure 3 :

 3 Figure 3: Case study network consisting of 18 HEXs. CIT is the coil inlet temperature. The HYGO hot stream is passed through HEXs 8A-8D on the shell-side, counter-current to the crude. HEXs 6A, B are used to control the desalter inlet temperature.

Figure 4 :

 4 Figure 4: Calculated duties of hot and cold streams in HEX 9D. Solid locus represents the line of equality and dashed line represents error range generated by Eqs. (17) and[START_REF] Pruneda | Optimum temperature in the electrostatic desalting of Maya crude oil[END_REF]. The error bar marked on datum indicates the estimated measurement uncertainty in data values.

Figure 5 :

 5 Figure 5: Comparison of fouling rates in exchangers located downstream of the desalter. The surface indicates the best fit given by the Ebert-Panchal model [Eq. (2)] for the data in HEXs 7, 9A-E.

Figure 6 :

 6 Figure 6: Actual fouling rate against fouling rate predicted by equation 2. The error bars indicate the minimum and maximum fouling rates predicted/observed based on the filtered operating data.

Figure 7 :

 7 Figure 7: Plot of average fouling rate against estimated tube-side shear stress for 5 independent refinery data (a) to (e), and two pilot plant data (f). The filled circles in (a) indicates exchangers with fouling suspected to be on the shell-side. The numbers on the data point indicate average wall temperature in o C.

Figure 8 :

 8 Figure 8: Plot of average fouling rate against estimated wall shear stress. Solid locus: correlation reported by Joshi et al. [29]. Solid circles -fouling rates obtained from data reconciliation, this work; open triangle -Wood River tube flow; open square -Exxon tube flow (both open data setsreported by Knudsen et al.[START_REF] Knudsen | The determination of the threshold fouling curve for a crude oil[END_REF]).

Figure 9 :

 9 Figure 9: Simulated network performance over a three year period, starting in the clean state: (a) CIT, (b) desalter inlet temperature, (c) bypass fraction and

Figure 2 :

 2 Figure 2:Linearized relationship between desalter inlet temperature and bypass fraction of control HEX. Fouling requires the bypass fraction to decrease in order to achieve the desired value.

Figure 3 :Figure 4 :Figure 5 :

 345 Figure 3: Case study network consisting of 18 HEXs. CIT is the coil inlet temperature. The HVGO hot stream is passed through HEXs 8A-8D on the shell-side, counter-current to the crude. HEXs 6A, B are used to control the desalter inlet temperature.

Figure 6 :Figure 7 :

 67 Figure 6: Actual fouling rate against fouling rate predicted by equation 2. The error bars indicate the minimum and maximum fouling rates predicted/observed based on the filtered operating data.

T 1 T 2 T 3 T 4 T 1 < T 2 < T 3 Figure 8 :

 12341238 Figure 8: Plot of average fouling rate against estimated wall shear stress. Solid locus: observation reported by Joshi et al. [29]. Solid circles -fouling rates obtained from data reconciliation, this work; open triangle -Wood River tube flow; open circle -Exxon tube flow (both open data sets reported by Knudsen et al. [30]). Grey symbols -data collected from UK refineries: square, triangle and diamond, reported by Yeap et al. [27]; circle, data not previously reported. The solid circles inside the dashed locus represent exchangers in this study where shell-side fouling is suspected.

Figure 9 :

 9 Figure 9: Simulated network performance over a three year period, starting in the clean state: (a) CIT, (b) desalter inlet temperature, (c) bypass fraction and (d) cleaning schedule. Dashed lines in (a) and (b) represent the base case (Case I, no cleaning or bypass control); in (b) the target operating region of the desalter is highlighted in grey (128 -132 o C). The HEXs are numbered sequentially in (d) based on Figure 3, such that HEX 18 in (d) corresponds to HEX 9A in Figure 3.

Table 1 :

 1 Summary of heat exchanger design and operation under clean conditions, crude oil flow rate of 109.8 kg s -1 .

	Heat exchanger number

Table 3 :

 3 Fouling resistance and fouling rate correlations. f i are dimensional constants and t is the time elapses since fouling started.

	Fouling resistance model	Fouling rate
	Linear

Table 4 :

 4 Summary of fouling rates in each heat exchanger

	HEX	Fouling behaviour	Fouling rate (m 2 K J -1 )
	1-5	Linear	f 2 = 5.00× 10 -12
	6AB	Linear	f 2 = 9.95× 10 -11

Table 5 :

 5 Summary of case study PHT performance under different operational strategies

	Case
	I	II	III

Table A :

 A Estimated uncertainty in plant data

	Variable	Estimated uncertainty	Source
	Density	2%	Refinery data
	Volumetric flowrate	1%	[31]
	Specific heat capacity	2%	Refinery data
	Temperature	± 2 K	[31]

Acknowledgements

Funding from EPSRC project EP/D50306X and financial support for EMI from the Cambridge Overseas Trust is gratefully acknowledged, as well as helpful discussions with Dr. Graham Polley and Simon Pugh of IHS-ESDU. Sincere thanks to REPSOL YPF, Argentina, for assistance in providing data and giving permission to publish the work.

Appendix

The error limit for each HEX was calculated from the individual uncertainties involved in flow, temperature measurements, and thermo-physical properties:

)


W M S R Weerasinghe

R K Stobart

S M Hounsham

Thermal Efficiency Improvement in High Output Diesel Engines A Comparison of a Rankine Cycle with Turbo-compounding

. The present paper presents a thermodynamic analysis of turbocompounding and heat recovery and utilisation through a fluid power cycle, a technique that is also applicable to medium and small scale engines. In a fluid power cycle, the working fluid is stored in a reservoir and expanded subsequently. The reservoir acts as an energy buffer that improves the overall efficiency, significantly. This paper highlights the relative advantage of exhaust heat secondary power cycles over turbocompounding with the aid of MATLAB based QSS Toolbox [2] simulation results. Steam has been selected as the working fluid in this work for its superior heat capacity over organic fluids and gases.

INTRODUCTION

Exhaust gas heat utilisation in internal combustion engines has attracted a major interest due to the substantial potential of the amount of heat that can be recovered [START_REF] Stobart | Reat recovery and bottoming cycles for si and ci engines -a perspective[END_REF]. Exhaust heat utilisation cycles for internal combustion engines can improve the fuel efficiency and reduce carbon emissions of conventional engines. These cycles can have different configurations and use a number of working fluids. Studies on turbines, vane rotors, and Wankel engines that use steam and organic compounds as working fluids are found in literature [START_REF] Bayley | The saturated liquid reservoir for energy storage in hybrid vehicles[END_REF]. The most promising and technically viable technologies are however, turbo compounding and exhaust heat secondary fluid power cycles. As a exhaust heat utilisation technique, turbo-compounding is already established in heavy duty engines [START_REF] Porter | Turbocompounding revisited[END_REF], where a reduction in exhaust gas temperature is the consequence of an additional stage of expansion through an exhaust gas turbine. Small scale fluid power systems running on a Rankine cycle, for example can implement continuous exhaust heat utilisation cycles for both heavy and light duty engines. This represents another means of extracting energy

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from the exhaust gas stream. For light duty applications, the fluid cycle can utilize stored thermal energy after prolonged high load conditions at a time when the traction engine may be switched off. In heavy duty engines, the cycle will simply provide a means of recovering exhaust energy and thereby boost the vehicle's fuel economy. With a heat storage such as a steam accumulator, the energy can be used to meet hotel loads in long haul trucks and in agricultural vehicles. A theoretical comparison of turbo-compounding and exhaust heat steam Rankine cycles is presented in this paper, supported by simulation results.

THEORETICAL ANALYSIS Heat Recovery and Expansion vs Turbo-compounding

The two waste heat recovery techniques, exhaust heat secondary fluid cycle and turbocompounding can be analyzed theoretically to compare the effectiveness of each method through a second law study. In turbo compounding, exhaust gases are directly expanded through a turbine forming a part of a Brayton cycle. Alternatively, if heat is recovered to another working fluid for example, into water or into an organic fluid, the resulting vapour can then be expanded through a fluid expander. The efficiency of the exhaust heat secondary fluid power cycle depends on the combined effectiveness of heat recovery (utilisation) method and the expansion cycle [START_REF] Arias | Theoretical analysis of waste heat recovery from an internal combustion engine in a hybrid vehicle[END_REF]. Our approach adopts an exergy based analysis of the two energy recovery methods. In the T-s diagram in figure 1, 1-2 represents the expansion of exhaust gases in the existing turbocharger. The exhaust gases at 2 can either be expanded to 3 or can be used to recover heat to use in another step. The exhaust gases follow the line 2-4 when the heat is recovered through a heat exchanger, and 4-5 if further expanded at lower temperature. A typical example of exhaust gases that come out of an internal combustion engine at 1.5 bar and 400 o C (i.e. at 2)is considered for

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Thermal Efficiency Improvement in High Output Diesel Engines 3 expansion and exhaust heat utilisation. The available energy through heat recovery can be calculated as follows; Assuming a heat transfer effectiveness of 0.8 through a counterflow heat exchanger illustrated in figure 2. Effectively, ǫ is defined as:

T(K) T T T out in min X=0 X=L 673 K in 128 K Figure 2.
A typical counterflow heat exchanger arrangement in exhaust gas heat recovery

ǫ = T out -T in T out -T in min , (1)
using the following statement of the 1 st law, and

T ds = dh -vdp. (2)
assuming that heat transfer at constant pressure through the heat exchanger, the change in entropy is given by

T ds = c p dT, ds = 1 T c p dT (3)
For the conditions given above, the change in entropy is

T 2 T 4 c p T dT = c p ln T 4 T 2 = 520J/kgK, (4)
in our case T 4 = 401 K and T 2 = 673 K. The available work per unit mass is given by ∆b = ∆h -T 0 ∆s.

The available energy through exhaust heat recovery is 120kJ/kg. Figure 2 shows the relevant temperatures in the heat transfer from exhaust gases to steam. Further, 44 kJ/kg are available for expansion after heat recovery in the exhaust of the heat exchanger. An isentropic efficiency of 0.8 reduces this value to 35 kJ/kg. These figures add together to give a value of 155 kJ/kg through expansion through a secondry fluid power Rankine cycle.

When direct expansion of gases in the turbine is considered from the initial conditions given earlier, the turbine exit temperature is given by

T 3 = T 2 p 3 p 2 γ-1 γ (6)
where, T 2 is the inlet temperature to the turbine, for an inlet temperature of 673 K and a pressure ratio of 1.5, the exit temperature is 599K. The energy developed therefore, is 75 kJ/kg. An isentropic efficiency of 0.8 reduces this value to 60 kJ/kg as the total energy recovered through turbo-compounding.

The above illustrates shows that exhaust gas secondary expansion yields considerably higher amounts of energy than turbo-compounding for the same inlet conditions and outlet pressure. It is assumed that the heat transfer rates of both the schemes are adequately fast and similar.

SIMULATIONS AND RESULTS

We proceed to make drive cycle simulations of the two engine configurations.

Simulations

The QSS (quasi static simulation)[2] toolbox was used to emulate an engine with turbocompounding and another with heat recovery and expansion through a steam expander. Both engine systems were driven through a US Federal Heavy Duty Transient Test Cycle. Simulations confirm the findings of the above calculations and show that considerable savings of fuel can be made using heat recovery and expansion method compared to turbo compounding. The following tables and graphs show the power developed by each exhaust heat utilisation system,the power developed by internal combustion engine, and in each case, the fuel savings. Figure 3 shows a schematic of the vehicle model in QSS. A backward facing one dimensional calculation procedure is used in QSS.

Power Output and Fuel Saving Comparisons

The power developed by each bottoming cycle is illustrated in table 1. At least two percentage points of more power can be developed with the exhaust heat utilisation secondary fluid power system compared to turbo-compounding.

Based on the values obtained through simulations, Exhaust heat utilisation steam hybrid can result in fuel savings of 20% or more compared to around 2.5% savings with turbo-compounding.

The question arises as to how a 7.8% increase in power leads to a 22%increase in fuel economy. This can be explained in terms of the heat storage ability of the exhaust heat secondary fluid power cycle. The 'response' to temeperature fluctuations in a turbo compounding cycle is inferior to a heat recovery and expansion cycle. When the engine is heavily loaded, the extra energy wasted is recovered and stored in the secondary fluid reservoir. The secondary fluid (in this case steam ) reservoir that is intricsic to the cycle acts as an energy buffer. All the energy that is recovered is not consumed immedieately, but is stored and expanded smothly on demand. This feature gives the ability to keep the Figure 3. A schematic of QSS toolbox vehicle model overall efficiency at a maximum. On the other hand, in turbo compound cycle, the enrgy recovered is limited by the top temperature and maximum flow rate through the turbine limiting the overall efficiency. This is a significant difference that makes heat recovery and expansion cycles favoured against turbocompound systems, and even against conventional electric hybrids. The main aim of the present study is to highlight this feature and the relative advantage of having an 'energy buffer' in the form of a steam storage. The added weight of a turbo compounding system of a Scania production engine that uses an electric turbocompound device is in the order of 80 kg (Scania press release) whereas a comparable Rankine cycle secondary fluid power system would weigh around 100 kg (see table 3). The weight of a mechanical turbocompound drive of a Caterpillar engine would also be in the same order.

While introducing the concept, it is important to discuss the negative effects of the technology. One major point is the use of a fluid power system that uses a secondary fluid. This means an added fluid power circuit to the vehicle system that adds another parameter to the complexity. As a general rule, dry technologies are preferred opposed to fluid power systems. However, air conditioning circuits have served extremely well over the past twenty to thirty years as an integral part of the engine. A secondary fluid power system, arguably would have the same degree of complexity as an air conditioning system. Another challenge is employing a relatively high pressure steam reservoir and a circuit. The technology is however, available. The expanders that are to be used are small scale steam expanders. This is relatively an underdeveloped area. University of Sussex has already carried out a few trials using small scale steam expanders and the results look favourable.

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CONCLUSIONS

This communication highlights the applicability of exhaust heat utilisation through secondary fluid power cycles to internal combustion engines and its potential gains. A theoretical comparison backed by simulations show that heat recovery and expansion using steam as the working fluid can offer substantial gains in fuel economy, potentially in the order of 20%, compared to much established turbo-compounding. This is a very attractive figure even compared to other developed hybrid techniques. Considering fuel economy as the primary gain, there are other gains in the form of improved power on demand and lowered carbon emissions.

The future potential of exhaust heat secondary fluid power cycles depend on the ease of adaptability and the savings obtained. The technique of using heat recovery and expansion is very promising [START_REF] Heywood | Automotive engines and fuels: A review of future options[END_REF]due to its relative simplicity and attractive weight. Modifications to conventional engines are possible with a modification to the exhaust system. Future engines may be designed with better packaging of the secondary fluid power system. The energy harnessed can be used to give auxiliary power systems or to drive the electrical appliances, potentially replacing the alternator. This brings about the complexities of engine management and exhaust temperature control. A conventional IC engine and a exhaust gas secondary fluid power cycle attachment will form a hybrid system that is analogous to an electric hybrid system. This approach will become more attractive due to its greener nature that favours it against electric hybrids that has an environmental issue with battery disposal in addition to the added weight. The added weight by a Rankine cycle hybrid system which is around 100 kg will be comparable to turbo-compounding attachment.

An exhaust heat secondary fluid power hybrid system will give the added advantage of being a heat storage system. The heat recovered can be used in the same way as in an electric hybrid to start from rest to save energy and to obtain high torque. Meeting hotel loads in heavy duty engines is another potential application wherereplacing electric and fuel cell auxiliary power units.

It is also noted that the performance figures depend on the drive cycle. In practical terms, this means that the performance changes according to terrain. Studies have been done on FTP US-06 and NEDC drive cycles [START_REF] Stobart | Energy recovery systems for engines[END_REF] and they have shown promising potential.

This study opens the avenues for research in several areas. Optimization of the heat recovery mechanism, the control of the engine parameters for best fuel economy and emissions, and other hybridization issues such as the physical configuration, response analysis with drive cycles and implementation issues. Research is already being done on optimized heat exchanger design [START_REF] Kew | Development of a highly compact steam generator[END_REF] and control of the hybrid system [START_REF] Stobart | Controllability of vapour based thermal recovery systems in vehicles[END_REF]. The selection of the working fluid of such a system is critical [START_REF] Devotta | Comparison of theoretical rankine power cycle performance data for 24 working fluids[END_REF], but current research primarily focuses on steam due to the high heat capacity of water compared to organic fluids. Engine thermal and fuel control are major areas that require an object oriented approach in optimizing.Other technical areas of exploration are control issues in the form of engine control, heat recovery control and hybrid control. Research is already under way in most of these areas. University of Sussex is engaged in the development of [START_REF] Stobart | Energy recovery systems for engines[END_REF] steam expanders, and control of an optimized steam hybrid system.

Figure 1 .

 1 Figure 1. T-s diagram for heat recovery and utilisation options

  in High Output Diesel Engines Table3Added weight comparison of turbo-compounding and heat recovery fluid power cycles Scania

Table 1

 1

	Power output comparison
	Simulation

This work was funded by the EPSRC grant number GR/T19810/01