optimizing a refinery using the pinch technology and the mind method

Pergamon Heat Recovery Systems & ClIP Vol. 14, No. 2, pp. 211-220, 1994

Copyright © 1994. Elsevier Science Ltd Printed in Great Britain. All rights reserved

08904332/94 $6.00 + .00

O P T I M I Z I N G A R E F I N E R Y U S I N G T H E P I N C H

T E C H N O L O G Y A N D T H E M I N D M E T H O D

KATARINA N I L S ~ N * a n d BENGT S U N D ~ "

*Link6ping Institute of Technology, Department of Mechanical Engineering, Energy Systems, S-581 83 Link6ping, Sweden and 1"Lund Institute of Technology, Division of Heat Transfer, Box 118, S-221 00

Lund, Sweden

(Received 21 February 1993)

Almtraet--The Pinch Technology and the MIND method are combined in the analysis of a Swedish refinery. The heat exchanger network of the crude distillation system is analysed using the Pinch Technology. The results show that the steam demand from the boiler units in the energy supply part of the system can be reduced by 20% in the optimized heat exchanger network and by 21°/, when a heat pump is added to the system. A multi-period cost optimization of the operating strategy is performed using the MIND method. The results from the Pinch analysis are then input to the MIND optimization. The system cost of the total energy system of the refinery is optimized with regard to flexibility in the process system as well as changes of energy costs and the operating conditions of the cogeneration unit. The combination of methods shows that significant capital savings can be achieved when the energy saving potential of the process system is integrated in the overall operating strategy of the energy system. It is, in this ease, possible to compare investments in energy saving measures to investments in increased steam production capacity.

1. I N T R O D U C T I O N

The possibilities of saving energy and costs in industrial applications are in many cases very good. A widely used method for industrial heat integration is the heat exchanger network analysis. The use of the pinch technology [1] brings a powerful tool to the method. It is possible to find the trade-off between energy and cost [2, 3] as well as the proper location of heat pumps in the heat exchanger network [4]. The recent development has shown that other aspects can be added to the method, e.g. the utility system can be included in the process integration [5], environmental consequences of process integration can be evaluated [6] and the three-way trade-off between energy, cost and flexibility in the process operation can be accomplished [7]. In the pinch analysis of a refinery, it is possible to concentrate on the preheating of crude, or to heat integrate all units or a combination of units, in order to include the whole process system of the refinery [8]. Parallel to the pinch technology, mathematical methods have been developed for the synthesis of heat exchanger networks. Mixed integer linear programming (MILP) can be used in the systematic synthesis of chemical processing systems [9]. The process system, the utility system and the heat exchanger network are in this reference added for the optimal synthesis of a total integrated system. The processing plant is optimized from a superstructure including all interesting equipment alternatives. A trans-shipment model is used for the representation of the heat recovery network, whereas the utility system is synthesized using a single-period and a multi-period model. A method for synthesizing the heat exchanger network in steps has also been developed [10]. This sequential decomposition of the problem includes a three-step analysis where a linear programming trans-shipment model is used in the first step to predict the minimum utility cost and to locate the pinch points. The pinch points divide the system into subnetworks. In the second step, the subnetworks are optimized separately using a MILP trans-shipment model in order to predict the fewest number of units and stream matches. In the third step, non-linear programming is used to synthesize the optimal heat exchanger network from a superstructure. The decomposition of the problem has since evolved to a simultaneous approach for optimization of the number of units, the heat exchanger area and the heat recovery in the system [ll-13]. Advanced methods for solving mixed integer non-linear programming (MINLP) problems make it feasible to optimize the heat exchanger network integrated in the process system. In this analysis, the heuristic rules

HRS 14/2--1 211

212 K. NILSSON and B. SUND~N

of the pinch technology are not used and very good results are still achieved for a static representation of the energy system. The fact that the boundary and the process conditions will vary in different time periods may in many cases affect the optimal operating strategy of the energy system. It is essential to include the aspect of time and to let the flexibility of the system give an input for new strategies where both energy and capital can be saved.

In this study, results from a pinch technology [2, 3] analysis are included in the MIND method [14], an optimization method for industrial energy systems using MILP. The combination procedure is suggested and explained in ref. [15]. The combination of methods includes the analysis of the heat exchanger network, the process system and the utility system. The pinch technology is used for the heat exchanger network analysis. The output from the pinch analysis provides input to the MIND method. Using the MIND method, structural analyses of thermal and mechanical processes can be performed as well as operating strategy analyses. A Fortran code is used for the generation of the industrial model which can easily be adjusted to each specific case. The optimization is performed with the ZOOM code for zero/one mixed integer linear problems [16]. The representation of the process system may refer to equipment units or to defined parts of the process system. Such parts may constitute process lines or groups of equipment units where the energy demand is known. Variations occurring in the energy system or its boundary conditions can be represented in a flexible time division. The optimization method of MILP provides an adequate tool for the process and utility system analysis.

The combination of methods makes it possible to benefit from the advantages that each method can provide. The pinch technology has shown very good possibilities of heat recovery in industrial applications. In the MIND method, the whole industrial energy system can be represented in one calculation including the flexibility of the processes and the variation of the boundary conditions with the accuracy that is desired from case to case. A process line with an integrated heat exchanger network is represented with a reduced energy demand. The reduction of the energy demand can be related to the production level. It is also possible to represent investments in energy recovery measures. In industrial analyses it is significant to include the heat recovery network, the process system and the utility system as well as the time variations occurring in the system and its boundary conditions. The interconnection of the utility system and the production system with respect to time-dependent aspects is disregarded if the whole system is not represented in a multi-period model. In the suggested procedure for the integration of the pinch technology in the MIND method the time aspects can be included.

2. DESCRIPTION OF THE OPTIMIZATION MODEL

In the analysis of the refinery, the two methods are used, each focusing on different parts of the energy system. The pinch technology is used for the analysis of the heat exchanger networks surrounding three crude distillation units at the refinery. The MIND method is used to analyse the energy system of the refinery, in order to find the optimal trends for the operating strategy of the cogeneration unit and the seasonal production planning. The possibilities to recover energy in the process system calculated with the pinch technology are integrated in the MIND method and conclusions can be drawn from the combination of methods. Thus, the analysis strategy is performed in three steps:

(1) Heat exchanger network analysis using the Pinch Technology. (2) Cost optimization of the operating strategy using the MIND method. (3) Integration of the heat exchanger network in the process system at optimal operating

conditions--a combination of the Pinch Technology and the MIND method.

In the Pinch Technology analysis the possible energy recovery is achieved as a trade-off between the optimal energy solution and the optimal cost solution, whereas, in this case, the MIND analysis is performed without investment costs. Investments are compared on the basis of the pay-off-time as is shown in some of the tables below.

The possibilities to recover heat in the flow network of three crude distillation units are calculated using the pinch technology. Two of the crude distillation units are analysed separately in ref. [2]. The heat exchanger networks of these units are so-called threshold problems, implying that below

Methods used to optimize a refinery

Table I. Percentage results from heat integration in the crude distillation system

Heat recovery Increased electricity Decreased recovery network Energy recovery demand from products

Without heat pump 20°/, 0% 40/, With heat pump 21% 2% 5%

213

a certain value of Atmin there is no need for external cooling. In these cases the threshold values of Atmin are 27 and 26°C, respectively. With Atmin = 20°C, the whole energy recovery system is found to be above the pinch temperature and no external cooling is permitted. In this kind of system the integration of a heat pump is not recommended. A total heat recovery network for the two distillation units can provide a saving of 19% of the steam demand.

The third crude distillation unit is analysed with Atmi~ = 30°C and the minimum energy demand for external heating and cooling is calculated. With account taken of practical limitations in the system a possible energy recovery is found at 23% of the unit steam demand from the steam boilers in the energy supply part of the system [3]. The calculations have been extended to also embrace a heat pump which renders a possible energy recovery of 27% based on the same terms as above.

When the heat recovery networks surrounding the three crude distillation units are added, the total energy demand can be reduced to 20% (19% in units I and II, 23% in unit III). The addition of a heat pump leads to an overall energy demand reduction of 21% (27% in unit III). The energy recovery is identified as a reduction of the total steam demand from the energy supply boiler units, while the electricity demand remains constant except for the case of heat pump integration. The total electricity demand is then increased by approximately 2%. In view of the total process system, the heat integration proposed in the crude distillation system will affect the heat recovered by cooling of the products. The heat recovery will be reduced by approximately 4% without a heat pump and by 5% with a heat pump in the system, see Table 1.

The MIND optimization is initially performed without the pinch technology input in order to find the optimal operating strategies without energy recovery measures [17]. In the optimization, the energy system of the refinery is described as a network of nodes and branches as is shown in Fig. 1. The material flow of the production is characterized by the crude input in node 27. It passes through the darkly shaded nodes 28, 29 and 30 representing different cases of process systems. Node 28 represents the process system of 1991. The electricity and the steam demand, as well as the recovered heat from cooling of the products, are expressed as functions of the material flow passing through the node. Non-linear relations can be represented in these functions. Node 29 represents the process system in the same way, but here the heat recovery networks of the crude distillation units are included. The steam demand is decreased (20%) and the heat recovery from products is decreased (4%) in comparison with node 28. Node 30 represents the process system including the heat recovery networks and a heat pump which renders a decreased steam demand (21%o), a decreased heat recovery from products (5%) and an increased electricity demand (2%).

The steam demand and the electricity demand which are not related to the processes, e.g. heating of tap water and premises, are represented with seasonal changes in node 25.

The refinery delivers heat to the municipal district heating system. This is represented in the optimization model as a heat flow which brings an income to the system, node 26. The flow is limited with upper and lower bounds representing the contracted flow limits for delivery to the district heating system, 100-150 MWh h-~ during winter, spring and autumn and 36-42 MWh h- during summer. If the total heat flow through this node exceeds the upper bound, there is an extra flow possibility for sea water cooling. If the lower bound is not reached by the heat recovered from the processes and the heat from the exhaust steam of the backpressure turbine, superheated water is produced in Boiler 3, node 13.

Steam which is produced and consumed within the processes is regarded as an internal flow in the process nodes and is not represented in the flow chart. The steam demand required externally is produced at high pressure in Boilers 1 and 2, represented as nodes 11 and 12, and delivered to a steam system with three pressure levels. The maximum boiler steam output is respectively, 50 MW and 60 MW. The steam flows are represented as energy flows in the MIND analysis. The two boilers deliver steam to the processes and to the heating of tap water and premises as well as to the backpressure turbine.

214 K. Ntt&qor~ and B. SUNDI~N

Stmu~ mpmlwated water

S33-39

F31

. . . . . Electricity -- -- Fuel

Bx'rERNAL ELECTRICITY

TAP WA'I ' I~ : El0 musses W

~\-,, '~,~11-14

$ 4 8

FASS-OUT 0-10

CRUDE OIL

I , M67

f I | | i ii M -70

I |

, i i

S53-58

BOILER3

"~- S59-61 PASS-OUT 10-20

DISTRICT HEATING

$65-66

Fig. 1. The node network of the refinery.

I I i i i w

! / M71-73 I I ii

J

I i PRODUCTS

The backpressure turbine is represented with the lightly shaded nodes 14-24. These nodes represent one baekpressure turbine with three operating alternatives. The whole output range is included in the optimization. The turbine can be operated with no pass-out steam (nodes 14, 17 and 20) or with two levels of pass-out steam, 0-10tonh -~ (nodes 15, 18, 21 and 23) or 10-20 ton h -~ (nodes 16, 19, 22 and 24). The maximum electricity output for the three alternatives are respectively, 10.35MW, 9.55MW and 8.85MW. A further description of the turbine representation including its non-linear characteristics is given in ref. [17]. Input steam is taken from the boiler nodes 11 and 12, and the electricity output is delivered to the electricity demand nodes. The exhaust steam is condensed and the heat is delivered to the district heating system, while the medium pressure pass-out steam is delivered to the processes.

Electricity is bought externally or produced in the backpressure turbine. The prices for electricity, fuel, crude oil and the income from selling heat to the district heating system are shown in Table 2.

The production level is usually kept approximately constant, but it may change within + 5-10% over the seasons depending on the customer demand for different products and the storage capacity for products and crude oil as well as maintenance stops. The operating strategy of the cogeneration unit is optimized at a constant production flow level of 425 ton h-~ of crude oil input and it is optimized together with the production flow level. At the optimization of the production flow level the material flow passing through the process nodes are restricted with an upper bound of + 5% and allowed to change only over the seasons.

Methods used to optimize a refinery

Table 2. The prices used in the optimization (SEK kWh -~)

215

Winter Spring/Auturan Summer

Electricity price Day 0.321 0.220 Night, weekend 0.239 0.191 Demand charge 235.75 SEK kW -I

Fuel price Boiler 1, 2 46.39 SEK MWh -I Boiler 3 189.00 SEK MWh -I

Crude oil 0.9 SEK kg - i (average for 199 I) District heating income 0.120 SEK kWh -~ (average for 1991)

0.156 0.144

The optimization is performed for the year 1991 with the use of two different time divisions. One represents the seasons in three time steps, winter, spring/autumn and summer. The other represents the seasons including the electricity price periods (day, night/weekend) in six time steps.

3. RESULTS

The result from the MIND optimization is achieved as the total system cost. In this specific case the system cost embraces the cost for electricity, fuel, raw materials and the income from selling heat to the district heating system. Since the income of the system is larger than the total energy cost, the cost for raw materials is included in order to give a positive result.

In order to evaluate the results they are compared to the system cost of a standard day-to-day flow situation. The flexibility of the process system will in reality cause temporary changes in the operating strategy. These kind of fluctuations are not included in the optimization. The standard operating strategy of the refinery, which is referred to in the following text, is a constant production flow of 425 ton h -1 of crude oil input and the electricity output of 9.9 MW from the backpressure turbine operating with no pass-out steam. The difference between the system cost of the standard operation and the system cost of other operation alternatives is the profit or the loss which could be made at a change of the operating strategy or at structural changes in the energy system. In this way different structures or flow situations can be compared on equal terms. In the MIND optimization it is, in this case, possible to optimize the production level over the seasons and to find the optimal operating strategy of the backpressure turbine. It is also possible to find the optimal operating strategy when energy recovery measures are introduced. In the MIND optimization, ref. [17], it was found that the optimal operating strategy of the backpressure turbine changed at an increased availability of steam in the system. It is then possible to compare investments in increased steam production capacity to energy saving measures, with account taken of the operating conditions.

3.1. Heat recovery network at standard operation

The introduction of a heat recovery network in the process system at standard operation affects the energy system as a reduced steam demand and a reduced heat recovery from cooling of the products. When a heat pump is added, the steam demand and the recovery of heat are further reduced while the electricity demand is increased. These changes will, of course, also affect the system cost. The difference in system cost between the optimized strategies and the standard operation without energy savings are shown in Table 3.

In these results the production level and the backpressure turbine are kept at standard operation, while the introduction of the heat recovery network and the heat pump will lead to a change of the fuel demand and of the external electricity demand as is shown in Table 4. The fuel demand in Boilers 1 and 2 will be reduced during all seasons while the demand for the expensive fuel in

Table 3. Results achieved at the integration of a heat recovery network and a heat pump at standard operation

Reduction of system Case cost (MSEK) Investment (MSEK) Pay-off-time (yr)

Heat recovery network 4.13 12.16 2.9 Heat recovery network 3.21 13.05 4.1

including heat pump

216 K. NmSSON and B. SUNDI~N

Table 4. Percentage change of the fuel demand and the demand for external electricity for the cases shown in Table 3 in comparison to

standard operation [%]

Case Winter Spring/Autumn Summer

Heat recovery network Boiler 1,2 - 5 - 8 --8 Boiler 3 - 13 +28 +0 External electricity - 24 _+ 0 +_ 0

Heat recovery network including heat pump

Boiler 1, 2 - 6 - 9 - 8 Boiler 3 - 9 +35 +0 External electricity - 17 + 9 + 9

Boiler 3 will be reduced during winter and increased during spring/autumn. At standard conditions a deficiency of steam is recognized during winter, meaning that the backpressure turbine cannot operate continuously at the electricity output of 9.9 MW. The lower output also implies that the delivery of heat from the backpressure turbine to the district heating system is reduced and has to be replaced by superheated water from Boiler 3. In these cases the winter deficiency of steam is removed by the process energy recovery and the output from Boiler 3 can be reduced during winter. The increased fuel demand in Boiler 3 during spring/autumn is caused by the reduced possibility to recover heat by cooling of the products at the introduction of energy recovery measures in the process system. The contracted lower level of heat demand in the district heating system is then covered by superheated water. Boiler 3 is ordinarily not used during summer and this is valid also in these results. The demand for external electricity is reduced during winter and unchanged during the rest of the year. The introduction of a heat pump increases the total electricity demand throughout the year.

3.2. Optimal operating strategy without a heat recovery network

In the MIND optimization, the optimal operating strategy without energy recovery measures can be found [17]. Two cases of operating conditions have been examined. One is the optimal operation of the backpressure turbine at constant production flow. During the winter period, the existing steam production capacity does not fully cover the process steam demand and the stated electricity output of the cogeneration unit at standard operation. The case of sufficient steam capacity during winter is also investigated. The steam capacity is then increased by the addition of a boiler of 8.5 MW.

In Table 5 the optimization alternatives are all compared with the case of standard operation at the existing steam production capacity. The introduction of optimal production planning over the seasons and/or optimal operation of the cogeneration unit will reduce the system cost by several million Swedish crowns. The investment in an additional boiler will, in combination with optimal operating conditions, lead to a short pay-off time.

An increase of the steam production capacity only affects the optimal operating strategy during winter as is seen in Table 6. The purchase of external electricity is reduced and the internal

Table 5. Results from optimization of operating conditions in comparison to standard operation at existing steam production capacity and at increased steam production capacity


Existing steam production capacity Standard operation 0 Constant production, 3.93

optimal cogeneration Optimal production, 7.09

optimal cogeneration Increased steam production capacity

Standard operation 3.14 Constant production, 8.73

optimal cogeneration Optimal production, 12.18

optimal cogeneration

m

m

6.8 0.6

6.8 2.2 6.8 0.8

M e t h o d s used to op t imize a ref inery 217

Table 6. Percentage change of the fuel demand and the external and internal electricity for the cases shown in Table 5 in comparison to standard operation [%]


Existing steam production capacity Constant production, optimal cogeneration

Boiler I, 2 _+0 + 3 - 5 2 Boiler 3 + 2 - 2 8 + 0 External electricity - 8 - 16 +352 Internal electricity + 3 + 5 - 100

Optimal production, optimal cogeneration Boiler I, 2 + 0 + 3 - 5 3 Boiler 3 - 18 - 6 2 + 0 External electricity - 3 - 13 +345 Internal electricity + 2 + 5 - 100

Increased steam production capacity Constant production, optimal cogeneration

Boiler 1, 2 + 8 + 3 - 5 2 Boiler 3 - 5 1 - 2 8 _ 0 External electricity - 36 - 16 + 352 Internal electricity + 15 + 5 - 100

Optimal production, optimal cogeneration Boiler 1, 2 + 8 + 3 - 5 3 Boiler 3 - 7 5 - 6 2 + 0 External electricity - 3 3 - 13 +345 Internal electricity + 15 + 5 - 100

electricity production is increased while a large reduction of the demand for expensive fuel in Boiler 3 is achieved.

The optimal operation of the backpressure turbine is maximum electricity output during winter and spring/autumn. During summer the external electricity price is low and the heat demand of the district heating system can be met by process heat, which makes the operation of the backpressure turbine unprofitable. At the existing steam production capacity medium pressure pass-out steam should be bled from the turbine during winter at an amount of approximately 10 ton h -I. At full steam production capacity no pass-out steam should be taken out.

The demand for external electricity is decreased during winter and spring/autumn and strongly increased during summer when it is cheap.

When the backpressure turbine is closed during summer the steam production in Boiler 1 and 2 is reduced by approximately 50%. The production of superheated water in Boiler 3 is only serving the heat demand of the district heating system. Changes in the operation of the backpressure turbine and the production flow level will affect the amount of heat delivered to the district heating system and thereby also the need for superheated water. The fuel demand in Boiler 3 is reduced in all cases except during summer and one winter ease when it is increased by 2%. This increase is introduced when pass-out steam is taken out from the backpressure turbine. The amount of exhaust steam from the turbine is thereby reduced and the heat demand of the district heating system is covered by an increase of superheated water production.

The optimal material flow of the production for both cases of Table 5 and 6 is maximum flow level (+ 5%) during winter and spring/autumn and a reduced flow level ( - 1 1 % ) during summer. An increase of the production flow level will slightly increase the fuel demand in Boilers 1 and 2 unless full output is not already reached. It will reduce the fuel demand in Boiler 3 due to increased energy recovery from cooling of the products while the need for external electricity will slightly increase.

3.3. Combination of optimal operating strategy and heat recovery network

The results from the Pinch Technology analysis are included in the MIND optimization as separate options in the node structure, see Fig. 1. In this way it is possible to integrate a heat recovery network and a heat pump in the process system in combination with optimal operating conditions. The operating conditions are examined as two eases. One of them is constant material flow of the production together with optimal operation of the cogeneration unit. The other is optimal material flow of the production over the seasons together with optimal operation of the cogeneration unit. These two cases are combined with a heat recovery network and a heat pump in order to find the optimal conditions and to find the results of different strategies. In this analysis


Table 7. Results from a combination of heat recovery network, heat pump and optimal operating conditions in comparison with the case of standard operation.


9.72 12.16 1.3 Constant production, optimal cogeneration, heat recovery network

Optimal production, optimal cogeneration, heat recovery network

Constant production, optimal cogeneration, heat recovery network, heat pump

Optimal production, optimal cogeneration, heat recovery network, heat pump

13.03 12.16 0.9

8.81 13.05 1.5

12.08 13.05 1.1

the existing steam production capacity is studied. The results are shown in Table 7 in comparison with the case of standard operation.

The integration of the heat recovery network reduces the process steam demand and thereby the steam deficiency during the winter period is eliminated. As a result the backpressure turbine can be operated at full electricity output during winter. The optimal operation of the backpressure turbine becomes similar to the case of increased steam production capacity in the system. It should operate with full electricity output during winter and spring/autumn and be shut down during summer, see Table 8. In this situation it is not optimal to take out medium pressure pass-out steam from the turbine.

As a result of the increased output level from the backpressure turbine during winter and spring/autumn the external electricity demand is reduced. During summer, when the turbine is closed, the external electricity demand is naturally increased.

The overall reduction of the process steam demand will reduce the fuel demand in Boilers 1 and 2 in all the cases shown in Table 8.

During winter the fuel demand in Boiler 3 is significantly reduced while during spring/autumn it is reduced at optimal production and increased at constant production. The reason for this

Table 8. Percentage change of the fuel demand and the external and internal electricity for the cases shown in Table 7 in comparison to

standard operation [%]


Heat recovery network

Constant production, optimal cogeneration

Boiler 1, 2 - 2 - 6 - 6 0 Boiler 3 - 3 2 +0 +0 External electricity - 36 - 16 + 352 Internal electricity + 15 + 5 - 100

Optimal production, optimal cogeneration

Boiler 1, 2 - 2 - 5 -61 Boiler 3 - 5 5 - 3 3 ±0 External electricity - 33 - 13 + 345 Internal electricity + 15 + 5 - 100

Heat recovery network with heat pump

Constant production, optimal cogeneration

Boiler 1, 2 - 3 - 6 - 60 Boiler 3 - 28 + 7 ± 0 External electricity - 29 - 8 + 360 Internal electricity + 15 + 5 - 100

Optimal production, optimal cogeneration

Boiler I, 2 - 2 - 5 -61 Boiler 3 - 50 - 26 ± 0 External electricity - 27 - 5 + 353 Internal electricity + 15 +5 - 100

Methods used to optimize a refinery 219

change is the increased production level at optimal conditions, which renders a larger heat recovery from cooling of the products for delivery to the district heating system. The demand for superheated water from Boiler 3 can then be reduced. This reduction is not as large as in the case of optimal conditions without a heat recovery network, shown in Table 6. When a heat recovery network is included in the system there is a lower possibility to recover heat from cooling of the products. During summer, Boiler 3 is not ordinarily in use which can also be seen in Table 8.

In Tables 3, 5 and 7 the reduction of the system cost is given in comparison with the system cost at standard operation conditions. In the cases where investments are concerned the investment cost and the pay-off-time are given in the tables. The pay-off-time is used as a way of comparing the different alternatives with respect to the available annual profit and the necessary investment cost in each case.

The results from the introduction of a heat recovery network and a heat pump at standard operation conditions are presented in Table 3. The pay-off-time for the investment in a heat recovery network is 2.9 yr. The addition of a heat pump prolongs the pay-off-time to 4.1 yr.

When the operating conditions are optimized at the existing steam production capacity and no investments are made an annual profit of 3.9-7.1 MSEK can be reached, see Table 5. If a steam boiler is added to the system, in order to increase the availability of steam during winter, the profit from optimal operating conditions is increased. The pay-off-time at standard operation with account to the investment in the boiler is 2.2 yr, while at optimal operation it can be reduced to 0.6-0.8 y depending on which measures are taken.

The combination of optimal operating conditions and a heat recovery network will give a pay-off-time of 0.9-1.3 yr without a heat pump and 1.1-1.5 yr with a heat pump, as is shown in Table 7.

4. DISCUSSION

Two analysis methods have been used in order to study the energy system of a Swedish refinery. The proper integration of a heat recovery network in the crude distillation part of the process system is accomplished with the use of the pinch technology. The addition of a heat pump is also investigated. An overall multi-period cost optimization of the operating strategy is performed using the MIND method. The results from the energy recovery analysis are included in the optimization of the operating strategy. This analysis structure allows for the combination of energy saving measures and operating conditions where the cost for electricity and fuel are included, as well as the flexibility of the production processes. In this case, an income from selling heat is available which will strongly influence the optimal operating strategy of a cogeneration unit in the system.

As is shown in the result tables, it is possible to compare the different optimization alternatives by the use of the pay-off-time. The results are presented as a difference of the system cost in comparison with standard operating conditions. At the standard operation, which is taken as an ordinary day-to-day situation, it is possible to integrate a heat recovery network with or without a heat pump or just to optimize the flow situation without investments or to combine these two alternatives.

During the winter period the steam production capacity cannot fully cover the steam demand of the processes and of the cogeneration turbine for the stated output level at standard operating conditions. The results show that it is profitable to increase the steam production capacity or to reduce the process energy demand by the introduction of a heat recovery network in the process system. An additional steam boiler will, at standard operation, have a pay-off-time of 2.2 yr, while a heat recovery network at standard operation will have a pay-off-time of 2.9 yr.

When optimal operating conditions are added to the cases of either an additional steam boiler or a heat recovery network the pay-off-times can be further reduced. The additional steam boiler in combination with optimal operating conditions has a pay-off-time of 0.6-0.8 yr. At the introduction of a heat recovery network with optimal operating conditions the pay-off-time becomes 0.9-1.3 yr while the addition of a heat pump to the heat recovery network increases the pay-off-time to 1.1-1.5 yr.

The results show that there is a small difference in the pay-off-time between an increased steam production capacity and the introduction of a heat recovery network at optimal operating


conditions. The two analysis methods in combination yield an insight in the flexibility of the total energy system which cannot be reached by separate use of the methods. By the combination of the two analysis methods it is possible to include aspects such as:

----energy recovery measures --new investments --flexibility in the process system --flexibility in the industrial utility system ----energy demand changes --price changes --non-linear constraints in energy demand and operating conditions.

R E F E R E N C E S

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optimizing a refinery using the pinch technology and the mind method

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