advanced process integration for low grade heat...
TRANSCRIPT
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Advanced Process Integration for Low Grade
Heat Recovery
Ankur Kapil, Igor Bulatov, Robin Smith, Jin-Kuk Kim
Centre for Process Integration School of Chemical Engineering and Analytical Science
The University of Manchester Manchester, M13 9PL, UK
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Abstract
A large amount of low-grade heat in the temperature range of 30 oC and 250 oC
are readily available in process industries, and wide range of technologies can be
employed to recover and utilize low-grade heat. However, engineering and
practical limitations associated with the integration of these technologies with the
site has not been fully addressed so far in academic and industrial communities.
Also, the integration of non-conventional sources of energy with the total site can
be a cost-effective and promising option for retrofit, however, carrying out its
design and techno-economic analysis is not straightforward, due to variable
energy demands. One of the key performance indicators for the evaluation and
screening of the performance of various energy saving technologies within the
total site is the potential of cogeneration for the site. A new method has been
developed to estimate cogeneration potential by a combination of bottom-up and
top-down procedures. In this work, the optimization of steam levels of site utility
systems, based on a new cogeneration targeting model, has been carried out
and the case study illustrates the benefits of optimising steam levels for reducing
the overall energy consumption of the site.
There are wide range of low-grade recovery technologies and design options for
the recovery of low grade heat, including heat pump, organic Rankine cycle,
energy recovery from exhaust gas, absorption refrigeration and boiler feed water
heating. Simulation models have been developed for techno-economic analysis
of the design options for each technology and to evaluate the performance of
each with respect to quantity and quality of low grade heat produced on the site.
Integration of heat upgrading technologies with the total site has been studied
and its benefits have been illustrated with a case study for the retrofit design.
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List of contents
1 Introduction ................................................................................................................. 4
2 Cogeneration potential ................................................................................................ 5
3 Optimization of steam levels .................................................................................... 12
Case Study Cogeneration potential ............................................................................... 15
4 Technology for utilization of low Grade heat ........................................................... 22
4.1 Vapour compression heat pump........................................................................ 22
4.2 Absorption Systems .......................................................................................... 23
4.3 Boiler feed water (BFW) heating ...................................................................... 27
4.4 Organic Rankine Cycle (ORC) ......................................................................... 27
4.5 Thermo-compressor .......................................................................................... 28
4.6 Drying ............................................................................................................... 29
5 Algorithm .................................................................................................................. 29
6 Case study ................................................................................................................. 31
7 Conclusions & future work ....................................................................................... 44
References ......................................................................................................................... 45
8 Appendix A ............................................................................................................... 47
8.1 Optimization framework ................................................................................... 47
8.1.1 Objective function ..................................................................................... 47
Optimization constraints ........................................................................................... 49
8.1.2 Electric balances ....................................................................................... 49
8.1.3 Mass balances ........................................................................................... 49
8.1.4 Heat balance .............................................................................................. 51
8.2 Equipments ....................................................................................................... 52
8.2.1 Multi-fuel boilers ...................................................................................... 52
8.2.2 Gas turbines (GT) ..................................................................................... 53
8.2.3 Heat recovery steam generators (HRSG) .................................................. 54
8.2.4 Electric motors (EM) ................................................................................ 55
8.2.5 Steam turbines (ST) .................................................................................. 55
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1 Introduction The typical sources of low grade heat are listed in Table 1. The opportunity
includes the waste heat recovery from liquids and gases, CHP (combined heat
and power), drying, steam generation and distribution and waste heat utilization.
The industrial application of low grade heat recovery is relevant to process
industries, including chemical, petroleum, pulp and power, food and drink,
manufacturing, iron and steel, and cement industries.
Table 1: Sources of low grade heat[1]
Opportunity Areas Industry
Waste heat recovery from gases and
liquids
chemicals, petroleum, forest products
Combined heat and power systems chemicals, food, metals, machinery,
forest products
Heat recovery from drying processes chemicals, forest products, food
processing
Steam (improved generation,
distribution and recovery)
all manufacturing
Energy system integration chemicals, petroleum, forest products,
iron and steel, food, aluminium
Improved process heating/heat transfer
systems (improved heat exchangers, new
materials, improved heat transport)
petroleum, chemicals
Waste heat recovery from gases in metals
and non-metallic minerals manufacture
iron and steel, cement
To avoid unnecessary capital expenditure for oversized equipment and to
enhance controllability of the energy systems, dynamic feature of the energy
supply and demand along with integration with energy recovery technology must
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be incorporated into the energy study in a systematic and holistic manner. The
implementation of these integrated energy saving projects within or beyond the
plant may not be favoured, due to practical constraints, for example,
considerable civil and piping works required, legislative limitations, different
energy utilisation patterns between sources and sinks, etc. Therefore, it is vital to
quantify the economic benefits of employing low grade energy recovery and its
impacts on the industrial site.
2 Cogeneration potential The extent of heat recovery and cogeneration potential is closely related to the
configuration of site energy distribution systems in an industrial site, in which
multiple levels of steam pressure are introduced, for example, VHP (very high
pressure), HP (high pressure), MP (medium pressure) and LP (low pressure).
Steam levels and its corresponding pressure is an important design variable as
they can be adjusted to either minimize the fuel requirement or maximise profits
by exploiting site-wide trade-off of heat recovery and power generation.
Optimization of levels of steam mains is based on the manipulation of targeting
models for the cogeneration potential for the site utility systems.
The performance of the system can be either optimized to obtain the best design,
or to obtain the optimum operating conditions for an existing design, considering
the part load performance of the equipment based on the optimum number of
steam levels and their pressure. The simulation and optimization of the utility
systems require an accurate and yet simpler model for each element of the
system. Accurate estimation of the cogeneration potential is vital for the total site
analysis as it aids in the evaluation of performance and profitability of the energy
systems. The overall cost-effectiveness of power and heat from the site is heavily
influenced by the optimum management and distribution of steam between
various steam levels. Furthermore, optimum import and export targets for
electricity can be obtained from steam levels, load and price of fuel and
electricity. Also, energy efficiency for the utilisation of low grade heat will be
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strongly influenced by operating and design conditions of existing energy
systems. Therefore, the accurate estimation of cogeneration potential is essential
for performing a meaningful economic evaluation of the design options
considered for heat upgrading and/or waste heat recovery.
A number of methods are available in the literature for estimating the
cogeneration potential of utility systems. The ideal shaftpower is calculated as
the exergy change of the steam passing the turbine[2]. The exergetic efficiency is
considered to be independent of the load and inlet-outlet conditions, and is
assumed to be a constant value. The steam conditions are approximated by the
saturated conditions, but the superheat in the inlet and outlet steam conditions
are neglected[3]. There is a difference of up to 30% in cogeneration potential in
comparison with simulations based on THM (turbine hardware model) developed
by Mavromatis and Kokossis[4].
Salisbury[5] observed that the specific enthalpy of steam (i.e. enthalpy per unit
mass flow) is approximately constant for all exhaust pressure values[6]. There is
a linear correlation between specific power w (power per unit mass flowrate of
steam) produced in the turbine and the outlet saturation temperatures. The
specific power corresponds to the area of the rectangle on a graphical
representation of the inlet and outlet saturation temperatures of the turbine with
respect to the heat loads of steam. This methodology is based on the following
assumptions: specific load (q) of steam is constant with variation in exhaust
pressure and specific power is linearly proportional to the difference of inlet and
outlet saturation temperatures.
Mavromatis and Kokossis[4] proposed a new shaftpower targeting tool called the
turbine hardware model (THM) based on the principle of Willans line. Willans line
approximates a linear relationship between steam flowrate and the power output.
THM has limitations as Varbanov addressed[7]: the effect of back pressure is not
taken into account, and modelling assumptions for part-load performance are too
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simplistic, such that the model assumes a linear relationship over the entire
range of operation.
Sorin and Hammache[8] introduced a different targeting method based on
thermodynamic insights and Rankine cycle. The ideal shaftpower is a function of
outlet heat loads and the difference in Carnot factor between the heat source and
heat sink. The deviation of the actual expansion from the ideal expansion is
defined in terms of isentropic efficiency.
New Method
Cogeneration targeting in utility systems is used to determine fuel consumptions,
shaftpower production and cooling requirements before the actual design of the
utility systems[8]. The previous methods available in literature have the following
drawbacks. TH model does not consider the contribution of superheat in the inlet
and the outlet stream in the power generation. THM parameters are based on
regression parameters derived from a small sample of steam turbines, and
consequently are not applicable for all the possible sizes of turbines.
In order to overcome shortcomings of previous methods, new method for
cogeneration targeting has been proposed in this work, and isentropic efficiency
is used in the new targeting method.
TH model for targeting does not include the superheat conditions at each level
which results in significant error for estimating cogeneration potential. THM
model uses an iterative procedure based on specific heat loads to calculate the
mass flowrate for the turbines. The calculation of flowrates in Sorin’s
methodology is based on the flow of energy. Power produced by the system is
estimated with the isentropic efficiency, available heat for power generation and
inlet and outlet temperatures of Rankine cycle. However, there is no justification
for the assumption that thermodynamic behaviour of all the steam turbines to be
used acts as that of the Rankine cycle.
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The new algorithm calculates the minimum required flowrate from steam
generation unit (e.g. boiler) and the levels of superheat at each steam main
based on the heat loads specified by site profiles of heat sources and sinks.
The algorithm for the new procedure is given in Figure 1. The superheat
temperature calculation at each steam level is made, starting with a certain
superheat temperature of the steam from the boiler. The procedure is based on
the assumption that steam supplied to the site utility systems from a boiler is at
the superheated conditions required as VHP steam level. Figure 2 shows the
temperature entropy diagram for the process. The initial conditions of
superheated steam at higher pressure and temperature level are represented by
point 1. The steam at lower pressure level for an isentropic expansion is shown
as Point 2’ on the curve. Isentrotpic expansion with an efficiency of x% is used to
determine the enthalpy at point 2. It is assumed during targeting stage that all the
steam turbines are operating at their full load. The cogeneration potential of the
system is dependent on the expansion efficiency of x. This parameter is
dependent on the capacity of the turbine and detailed calculation is given below.
Steam properties are calculated for the given entropy and pressure at the lower
steam level. If the degree of superheat in the resulting LP steam main is less
than required, then operating conditions of VHP is updated and then re-iterates
the procedure above until the acceptable superheated conditions for LP steam
main is met.
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Figure 1: Algorithm for new method based on isentropic expansion
Given steam levels, inlet superheat of VHP steam, process load, BFW, Condensate temperature
Isentropic efficiency
Calculate superheat temperatures at subsequent lower steam level using isentropic efficiencies (Equation 2)
Starting from the lowest level, calculate the mass flow rates using Equation 1.
Add flow rates to determine the overall flow rates through each level (bottom up)
LP superheat temperature > LP saturation temperature + ∆T*
YES
STOP
Increase Boiler VHP superheat
NO
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Figure 2: Temperature Entropy diagram for change in level
In the bottom up procedure, the temperature of the lowest steam level pressure
is first used to calculate the steam mass flowrate for the expansion of steam
between the lowest steam level and the higher pressure next to the lowest one.
This procedure is sequentially repeated until the interval for highest steam
pressure level. Flowrates at the higher levels are determined from the flowrate in
the lower levels. The flowrate of steam for each expansion interval is a function
of the heat load at that level and the enthalpy change to the condensate
temperature at the given level. Superheated steam is condensed and supplied to
downstream processes at condensate temperature of the steam.
H
Qm
∆=
&
& Eq 1
Where,
m& = mass flow rate
Q& = heat load for a given level
H∆ = Enthalpy change from superheat conditions at the given level to
condensate conditions at that pressure
T
S
1
2’
2
P1
P2 Real
Isentropic
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Isentropic efficiency calculation
It is designer’s discretion to use the most appropriate value of isentropic
efficiency for the developed cogeneration targeting method presented in this
paper. On the other hand, information of isentropic efficiency available in the
literature can be also used. Mavromatis and Kokossis[4] developed a
thermodynamic model to estimate the isentropic efficiency of single and multiple
extraction turbines. Varbanov et al.[9] presented equations to determine the
parameters in terms of saturation temperature. Medina-Flores and Picón-
Núñez[10] modified the correlations of Varbanov et al.[9] to obtain the regression
parameters as a function of inlet pressure. The regression parameters obtained
by Varbanov et al.[9] from the turbine data of Peterson and Mann[11] are shown
in Table 2.
max,
max
is
isW
W=η
−=
B
AWW is max,
max
satTbbA ∆⋅+= 10
satTbbB ∆⋅+= 32
Eq 2
Where,
isη = isentropic efficiency
isH∆ = isentropic enthalpy change
3210 ,,,,, bbbbBA = Regression coefficients
satT∆ = Inlet pressure of the steam
Table 2: Regression coefficients for single extraction turbines[12]
Single extraction back pressure turbines
Wmax< 2 MW Wmax >2 MW
0b (MW) 0 0
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The results are investigated with STAR®, which is Process Integration software
for the design of utility systems for a single process or a group of processes
involving power (electricity) and heat (steam) generation, and associated heat
exchanging and distributing units. The design procedure of utility systems in
STAR® requires information about steam flowrates, heat supply and loads, VHP
(very high pressure) steam specification (e.g. VHP steam generation capacity
and temperature at the outlet of the boiler). At the initial targeting stage, some of
these design parameters are not known. The parameters, such as flowrate from
the boiler, steam level conditions, have to be specified for the detailed design in
STAR®. The information required for the calculation of cogeneration potential
from the utility systems is current flowrate of steam generated, maximum and
minimum flow rates of equipment, thermodynamic model and efficiency of steam
turbines, steam demand and surplus for each steam main, superheat condition of
steam generated from the boiler, etc. STAR® has two models isentropic and THM
model for the calculation of power generation of steam turbine in the detailed
design, while it uses TH and THM model for cogeneration targeting.
3 Optimization of steam levels As explained before, the choice of steam level in the design of site utility systems
are critical to ensure cost-effective generation of heat and power, and its
distribution in the site. In a new design, pressures of steam level can be readily
optimized. However, for the retrofitting of existing systems, opportunities for the
change of steam level conditions are limited. The mechanical limitation for the
steam mains limits a significant increase in steam pressure. However, long term
investment with a proper optimization of the steam levels may be economically
1b (MWoC
-1) 0.00108 0.00423
2b 1.097 1.155
3b (oC-1) 0.00172 0.000538
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viable in spite of the fact that the short term investment can not be justified[13].
VHP steam generation in the boiler and hence the fuel costs in the utility boilers
can be decreased by increasing number of steam mains which increases the
heat recovery potential. Number of steam mains has a significant impact on the
cogeneration potential. Therefore, to minimise fuel cost with maintaining high
cogeneration potential, the design should be thoroughly investigated.
Optimization model
In this study, the optimisation framework for determining the cost-effective
conditions of steam mains for the site utility systems had been proposed with
incorporating new cogeneration targeting method proposed in the work. The
optimisation model is formulated in an NLP (non-linear programming) problem
and the details of models are as follows:
Objective Function
The objective function is to minimize the amount of hot utility to be supplied from
the steam generation unit (e.g. boiler). It should be noted that the method
presented in this paper is generic for taking different objective functions, for
example, overall fuel cost, operating profit, etc, as long as the relevant cost
parameters are available.
minimise VHPsourceheatVHPkshifted HH ,,sin −
VHPkshiftedH ,sin Enthalpy of shifted heat sink for VHP
VHPsourceheatH , Enthalpy of heat source for VHP
Optimization Variables
iP Pressure at ∈i Steam levels (VHP, HP, MP, LP)
Four steam mains are used in the current optimisation model, as this is most
common in the large-scale industrial plant, while different number of steam
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mains, for example, three levels (HP, MP and LP), can be considered based on
needs and operating characteristics on the plant.
Constraints
Total source and sink profiles are generated from stream data of the site. Design
procedure for manipulating stream data to generate the site profiles is not a part
of this study and those details can be found from Smith [13] and Klemes et al,
[14]. In order to maintain feasibility of heat recovery across steam mains,
constraint between sink and source site profiles is needed. First, the sink is
shifted until the enthalpy of heat source at either of steam levels is the same as
the enthalpy of heat source corresponding to the pinch point, and then enthalpy
difference at each steam levels is always greater than zero.
0,,sin ≥− isourceheatikshifted HH ∈i Steam levels (VHP, HP, MP, LP)
Mass balance
The mass flow rate of steam between steam levels is given:
j
j
kjjiH
Qmm
∆+= →→
&
&&
Where,
jim →& Mass flow rate of steam through turbine between i and j steam levels
kjm →& Mass flow rate of steam through turbine between j and k steam levels
jQ& Heat duty at j steam level
jH∆ Enthalpy extracted by process from superheated steam at j level to reach
condensate conditions
Power is calculated base on the new design algorithm as shown in Figure 1.
Figure 3 shows the model for the determination of optimal steam pressure levels
for a site utility system. The change in the steam pressure levels shifts the site
sink and surplus profiles along with heat demand and supply. Cogeneration
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potential for the site composite is calculated from the new algorithm. The process
is repeated until optimum pressure levels corresponding to minimum value of
objective function are found for the site.
Figure 3: Flowchart to determine optimum steam pressure level
Case Study Cogeneration potential
An illustrative case study is used to test the different methodologies. The four
steam levels considered in this example are very high pressure (VHP), high
pressure (HP), medium pressure (MP), low pressure (LP) at 120, 50, 14 and 3
bar(a) respectively. The heat demand at HP, MP and LP steam levels is 50, 40
and 85 MW respectively. The efficiency of the boiler is assumed to be 100% for
the simplicity, which can be updated, according to boiler data available, and it is
supplying steam at a temperature of 575oC. Water supplied to the boiler and the
condensate returns are both assumed to be at a temperature of 105oC.
In this work, cogeneration targeting methods have been applied to the case
studies with only back pressure turbines. However, it can be easily extended to
condensing turbines. One of the additional constraints on condensing turbine is a
maximum wetness permitted at the exhaust. Wetness factor in the condensing
turbine can be controlled by adjusting the superheat in the steam mains, as
similary treated in the consideration of degree of superheat in LP steam.
New steam level pressure
Calculate shifted sink and source profiles & heat surplus or
deficit at each steam level
Cogeneration potential calculation from new algorithm
Minimum Utility requirement Optimum
Pressure
NO YES
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Table 3: Problem Data Parameters
VHP HP MP LP
Pressure (bara) 120 50 14 3
Saturation
Temperature (°C) 324.7 264 195.1 133.6
Heat Demand
(MW) 0 50 40 85
The isentropic efficiency was calculated as given in Equation 2, while the
mechanical efficiency was assumed to be 100%.
TH Model: The shaftpower targets from TH method are shown in Table 4Error!
Reference source not found.. The overall shaftpower calculated from TH model
is 33.02 MW. The value of conversion factor (CF) is assumed to be 0.00135.
THM Model: The targets for the three sections VHP-HP, HP-MP and MP-LP for
THM model are 9.4, 4.7 and 0 MW respectively (Table 4Error! Reference source
not found.). The overall shaftpower target from THM model was 14.2 MW.
Sorin’s Methodology: The work in the bottom section is used to calculate the
heat load in subsequent top section as described in the methodology in the
previous section. Shaftpower targets for VHP-HP, HP-MP and MP-LP of 18.2,
14.46 and 8.77 MW are shown in Table 4Error! Reference source not found..
New Method
Error! Reference source not found. Table 4 shows the shaftpower targets for
VHP-HP, HP-MP and MP-LP sections of 14.99, 14.37 and 9.75 MW respectively.
The main difference between the new method and existing TH and THM model is
the calculation of superheat temperature for each steam main, as explained
previously. Superheat temperature of the outlet LP steam should be greater than
saturation temperature of LP steam to avoid condensation of vapour at the outlet
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of turbine and thereby reduced performance and efficiency. The amount of
superheat in VHP steam determines the superheat in LP steam. In the new
algorithm, the superheat in VHP steam from the boiler is a variable and is
adjusted by trial and error to ensure the superheat in LP steam.
Figure 4: Results of the new method
STAR® Simulation – Constant Isentropic Efficiency
Once the steam levels and the heat surplus and deficit are known, a detailed
design procedure is used for the optimal design of the utility systems or to find
out the optimum operating conditions for an existing design. However, as
discussed before, the detailed design requires some additional parameters such
as flowrates and superheat steam temperatures. These additional parameters
are specified by trial and error. STAR® was used to test the targeting potential
against the actual production from the steam turbine. The shaftpower was
calculated by the isentropic model with isentropic efficiency calculated as shown
in Equation 2. The utility systems consist of a boiler supplying VHP steam at
575oC. The steam is passed from the boiler to the higher pressure steam main to
lower pressure steam main, via a steam turbine. Any unused steam can be
passed through the vent. The process cooling and heating duty at each steam
VHP Supply
Qusage = 85 MW
Qusage = 40 MW
Qusage = 50 MW
Heat Demand (MW)
VHP
HP
MP
LP
Satu
rati
on
tem
per
atu
re (
C)
14.99 MW
14.37 MW
9.75 MW
248.29 t/h
185.89 t/h
130.7 t/h
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main level is specified as given in Figure 3Error! Reference source not found..
The overall turbine shaftpower is 39.12 MW.
Comparison of Cogeneration Targeting Results
Table 4Error! Reference source not found. shows a comparison of
cogeneration targeting results from Sorin’s methodology, new method, TH and
THM model in STAR®. A detailed design simulation in STAR® with the constant
isentropic method is used to compare the shaftpower targets from the different
methodologies. As shown in Table 4Error! Reference source not found. the
total power target of 41.43 MW from Sorin’s methodology is significantly different
from the detailed design procedure of 39.0 MW with an error of 6.2%. The
shaftpower target obtained from TH model of 33.02 MW is 15.3% different from
the shaftpower obtained from the detailed design procedure. Similarly, THM
model target is 63.85% different from the actual shaftpower from the detailed
design procedure. These discrepancies in the shaftpower targets are due to the
assumptions used in these models. The shaftpower target obtained from the new
method of 39.12 MW is only 0.31% different from the detailed design procedure
in STAR®.
Figure 5: STAR® simulation isentropic efficiency
19
Table 4: Comparison of cogeneration targeting results
Optimization of steam levels
Site data was taken from an example available in the literature [15]. Site sink and
source profile is shown in Figure 6Error! Reference source not found.. Four
steam mains are available at very high pressure (VHP), high pressure (HP), low
pressure (LP) and medium pressure (MP) respectively. Sink profile is shifted by
the minimum of the enthalpy difference between the source and sink, which
identifies site pinch point for the utility system.
0
50
100
150
200
250
300
-500 -400 -300 -200 -100 0 100 200 300 400
Enthalpy (MW)
Tem
pera
ture
(o
C)
Sink
Source
Shifted Sink Profile
Figure 6: Sink and source profiles for a given site
The site utility grand composite curve (SUGCC) plots the difference between the
hot and the cold composite curves as shown in Figure 7Error! Reference
source not found.. The heat generation and use at individual steam level is
Methodology Total
(MW)
VHP-HP
(MW)
HP-MP
(MW)
MP-LP
(MW)
Sorin’s methodology 41.43 18.2 14.46 8.77
New Method 39.12 14.99 14.37 9.75
TH Model in STAR® 33.02 14.35 11.62 7.06
THM Model in STAR® 14.1 9.4 4.7 0
STAR® Simulation – Constant
Isentropic Efficiency
39.0 14..85 14.78 9.37
20
shown in Figure 7Error! Reference source not found. Error! Reference
source not found. and Error! Reference source not found. plot the
cogeneration potential between different steam levels as expansion zones for
steam turbines. The power output for these zones for the optimized case, based
on the new algorithm, is found to be 7.69 MW.
0
50
100
150
200
250
300
350
400
0 20 40 60 80 100 120 140 160 180 200
Enthalpy (MW)
Tem
pera
ture
(o
C)
Figure 7: Site Utility Grand Composite Curve with the optimum steam levels
0
50
100
150
200
250
300
350
400
0 10 20 30 40 50 60 70 80
Enthalpy (MW)
Tem
pera
ture
(o
C)
Figure 8: Site Utility Grand Composite Curve with cogeneration areas
0
50
100
150
200
250
300
350
400
-500 -400 -300 -200 -100 0 100 200 300 400
Enthalpy (MW)
Tem
pera
ture
(o
C)
Sink
Source
Figure 9: Site profile targets for steam generation and steam usage
21
0
50
100
150
200
250
300
350
400
-450 -400 -350 -300 -250 -200 -150 -100 -50 0 50 100
Enthalpy (MW)
Tem
pera
ture
(o
C)
Figure 10: Site profile with cogeneration potential area
The objective function is the minimization of the utility cost. The hot utility is
supplied as VHP steam from the boiler. The optimization framework described in
previous section and the model calculations are performed in Microsoft Excel®.
The size of the model and the optimization problem is small and therefore solver
function in Microsoft Excel® can be effectively used for the minimization of the
utility cost. The number of steam levels has been assumed constant as four
corresponding to VHP, HP, MP and LP respectively. Steam pressures at each
level are the design variables. They affect both the level of heat recovery and the
cogeneration potential, via the steam turbine network[13].
Table 5Error! Reference source not found. shows the base case conditions for
the four steam levels. Optimum steam level pressure and temperature along
with heat load at each level is shown in Table 6Error! Reference source not
found.. The optimum pressure in the steam mains for the lowest utility cost are
180, 46.55, 12.26 and 2.25 bar in the VHP, HP, MP and LP steam loads
respectively. The minimum VHP steam generation required from the boiler is
70.22 MW, while the VHP steam flowrate requirement from the boiler is 88.16
t/hr. Steam generation required at VHP mains has been reduced from 105.20
MW to 70.22 MW for the optimized case. However, the cogeneration potential
reduced from 8.8 MW for base case to 7.67 MW for the optimized case.
Therefore, increasing the heat recovery reduces the steam generation from the
boiler as well as the cogeneration potential for this particular example. If power
22
generation in the site should be increased, then additional VHP steam is
generated to pass through steam mains.
Table 5: Base case steam levels[15]
Pressure (bar) Temperature (oC) Heat Load (MW) Saturation temperature (
oC)
180 625 105.20 357.14
50 458.74 137.01 264.09
10 322.1 125.29 180.04
2 143.63 81.98 120.36
Table 6: Optimized steam levels
Pressure (bar) Temperature (oC) Heat Load (MW) Saturation temperature (
oC)
180 625 70.22 357.14
46.65 449.21 113.45 259.79
12.26 308.08 107.57 189.09
2.25 214.48 55.34 124.10
This optimisation framework can be extended to accommodate other economic
scenarios (e.g. to minimise the fuel costs with maintaining the same cogeneration
potential) or practical constraints (e.g. the number of steam levels allowed).
4 Technology for utilization of low Grade heat Low grade heat source can be very useful to provide energy to the heat sink by
upgrading low-grade energy (e.g. low pressure steam). The upgrade of low grade
heat can be carried out by heat pump, absorption refrigeration, thermo
compressor, etc, by recovering and/or upgrading waste heat from various
sources (e.g. gas turbine exhaust) and utilising them with the wide range of
applications (e.g. drying and boiler feedwater heating).
4.1 Vapour compression heat pump
Heat pump transfers the low grade heat at the lower temperature to higher
temperature heat by the compressor. Heat pump has been used in petroleum
refining, and petrochemicals, wood products, pharmaceuticals, utility system etc.
[16]. Figure 11 shows a typical closed cycle heat pump. The heat from lower
temperature source is transferred to the working refrigerant in the evaporator.
Electric or mechanical energy is used in the compressor to increase the pressure
23
of the vapour from the evaporator. High grade heat at higher temperature is
released from the condenser. Pressure of the vapour is reduced by throttle valve
to lower its temperature and convert it to liquid to exchange heat with low grade
heat source. The main issue with the utilization of the heat pump is that it uses
expensive external energy to convert low grade heat into high grade heat. In
general, one unit of high grade electrical energy can produce 2-4 units of high
grade thermal energy.
Figure 11: Heat pump cycle [17]
Co
E
Q
QCOP =
Eq 3
Where,
COP = coefficient of performance
EQ = Heat received at low temperature by the evaporator
CoQ = Electric power supplied in the compressor
4.2 Absorption Systems
Low grade heat can be recovered by absorption with three different types of
equipments absorption refrigeration, absorption heat pump and absorption
Condenser
Compressor Prime
Mover
Evaporator
Heat from lower temperature source
Throttle
valve
Mechanical
work input
24
transformers respectively. Iyoki and Uemura [18] compared the performance of
absorption refrigeration, absorption heat pump, and absorption transformer for
water-lithium bromide zinc chloride calcium bromide system.
a) Absorption refrigeration – There has been extensive work in literature on
absorption refrigeration system, with both experimental [19] and simulation
studies [20, 21] to determine the performance of absorption refrigeration. A
schematic diagram of ammonia-water absorption refrigeration cycle is shown in
Figure 12. Ammonia vapour at high pressure transfers heat to neighbourhood in
the condenser. Liquid ammonia from the condenser is passed through an
expansion valve to reach the evaporator pressure. Heat is transferred from the
low temperature heat source to convert liquid ammonia to vapour state.
Ammonia vapour is absorbed by a weak solution of water and ammonia to form a
concentrated solution of ammonia-water at the bottom of absorber. This
concentrated solution is passed to the generator for the production of ammonia
vapour while the lean solution from the generator is passed back to the absorber
unit. Low grade heat is used in the generator for the production of ammonia
vapour. Lean ammonia solution from the generator exchanges heat with the high
concentration ammonia solution from the absorber.
Figure 12: Ammonia water absorption refrigeration cycle [19]
25
The coefficient of performance for an absorption refrigeration system is defined
as the ratio of heat removed from the evaporator to heat supplied in the
generator.
G
E
Q
QCOP =
Eq 4
Where,
COP = coefficient of performance
EQ = Heat received at low temperature by the evaporator
GQ = High temperature heat used in the generator
b) Absorption heat pump – A single stage absorption heat pump consists of a
generator, absorber, evaporator, condenser and heat exchanger. High grade
heat is supplied at higher temperature to the generator to separate the refrigerant
from the solution. Low grade waste heat is supplied to the evaporator, while
medium temperature heat is released from the condenser. Thermal energy at
higher temperature is used to convert low grade heat into high grade heat.
Coefficient of performance of an absorption heat pump is the ratio of heat
removed from the medium temperature heat removed form the absorber and
condenser to the high grade heat supplied in the generator.
G
CA
Q
QQCOP
+=
Eq 5
Where,
COP = coefficient of performance
AQ = Heat released by the absorber
CQ = Heat released by the condenser
GQ = High temperature heat used in the generator
c) Absorption heat transformer – The basic schematic diagram of absorption
heat transformer is shown in Figure 13. Absorption heat transformer consists of
26
the same units as absorption heat pump. However, the main difference is that
evaporator and absorber are maintained at a higher pressure, while in absorption
pump they are at a lower pressure. Low grade heat is used in the generator and
evaporator to produce heat at higher temperature in the absorber. The process
can be described briefly as follows: High pressure refrigerant vapour from an
evaporator is absorbed into the lean refrigerant absorbent solution in the
absorber. High pressure strong solution of refrigerant absorbent is passed via a
throttle valve to reduce the pressure. This solution exchanges heat with weak
solution from a generator, before it reaches the generator. Low temperature heat
in the generator is used to separate the refrigerant from the solution. Refrigerant
vapour from the generator is condensed in a condenser. The refrigerant is
subsequently pumped to higher pressure where it gains heat at low temperature
to convert into vapour.
Figure 13: Absorption heat transformer (Ammonia water)
The ratio of high temperature heat from the absorber to the low grade heat
supplied in the generator and evaporator is defined as the coefficient of
performance of absorption transformer.
EG
A
QCOP
+=
Eq 6
Heat Exchanger
Absorber Evaporator
Generator Condenser
27
Where,
COP = coefficient of performance
AQ = Heat released by the absorber
EQ = Heat consumed in the evaporator
GQ = High temperature heat used in the generator
4.3 Boiler feed water (BFW) heating
Low grade heat can be used to increase the temperature of make-up water to
reduce the fuel cost in the boiler. Additional heat exchanger capital cost is
required for exchange of heat between the boiler make up water and low grade
heat. The increase in temperature of make up water using low grade heat
decreases the fuel consumption in the boiler.
4.4 Organic Rankine Cycle (ORC)
A Rankine cycle for extracting electricity from waste heat sources is possible with
the use of organic fluids as working fluids. Efficiency of operation of Rankine
cycle depends on conditions of the cycle and working fluid. A typical organic
Rankine cycle consists of an evaporator, turbine, condenser and pump
respectively (Figure 14). Organic fluid such as benzene, toluene, p-xylene and
refrigerants R113 and R123 [22] have been used as working fluids in ORC.
Working fluid vaporises by exchanging heat with low grade heat in the
evaporator. Vapour is passed through turbine for generation of electricity. Vapour
is condensed in condenser at lower temperature and releases heat to the outside
atmosphere. Organic fluid is raised from lower pressure to high pressure in the
pump. The amount of energy consumed in pumping the fluid is considerably low.
28
Figure 14: Organic Rankine Cycle (ORC)
Efficiency of ORC is defined as the ratio of power generated by the turbine to the
low grade energy supplied in the evaporator.
E
turbORC
Q
P=η
Eq 7
Where,
ORCη = Efficiency of ORC
EQ = Heat received at low temperature by the evaporator
turbP = Electric power generated by the turbine
4.5 Thermo-compressor
Thermo-compressor uses high pressure steam to compress low or intermediate
pressure waste steam into medium pressure steam. Figure 15 shows a thermo-
compressor where high pressure steam enters as a high velocity fluid, which
entrains the low pressure steam by suction. The resulting mixture is compressed
and discharged as a medium pressure steam from the divergent section of the
thermo-compressor. The main advantage of thermo compressor is high reliability
and less compression power requirement.
Turbine
Pump
Condenser
29
Figure 15: Thermo compressor1
4.6 Drying
Biomass (wood, bagasse, grass, straw, agriculture residues, etc.) have
significant amount of moisture. This moisture reduces the theoretical flame
temperature as a part of heat of combustion is used in evaporation of moisture
from the biomass [23]. Calorific value and theoretical flame temperature from the
biomass fuels can be increased by drying. Effective use of industrial waste heat
in drying of biomass increases the overall efficiency of the process, leading to
significantly lesser amount of fossil fuel to be burned and hence much less green
house emissions.
5 Algorithm
Once the number of steam levels and their pressure has been determined by
optimization in total site profiles, the performance of the system can be either
optimized to obtain the best design, or to obtain the optimum operating
conditions for an existing design, considering the part load performance of the
equipment. The simulation and optimization of the utility systems require
accurate and yet simpler model for each element of the system. Varbanov [9]
and Aguillar [24] developed simple models for the equipments in the utility
systems. Models developed by Aguillar [24] have been adopted for the purpose
1 (http://www.em-ea.org/Guide%20Books/book-2/2.8%20Waste%20Heat%20Recovery.pdf)
30
of optimization which determines the optimum design (i.e. the configuration of
utility systems) or operating conditions in this work (Appendix A).
The algorithm for evaluation of integration of low grade heat upgrade
technologies with an existing site utility system is shown in Figure 16. The
characteristics of low grade energy such as available heat load at temperatures
for use in heat pump, ORC, and boiler feed water heating is obtained from total
site sink and source profiles. HYSYS simulation is used to obtain the
performance indicators such as COP, efficiency, purchase cost etc. for low grade
heat upgrade technology. Heat load is varied for the HYSYS simulation to
calculate the change in performance and purchase cost. This information is fed
to the optimization framework for calculating the overall annual cost with
integration of these design technologies. The optimization framework [24] is used
for minimization of overall annual cost or operating cost minimization for a
multiperiod operational, retrofit or grassroots design problem. Linear models
have been derived for all the energy equipments so that MILP optimizers can be
used for optimization to reduce the computational cost.
Figure 16: Algorithm for evaluation of low grade heat upgrade technology
31
6 Case study
The various design options for low grade heat upgrade are evaluated with the
help of a case study. The base case design is shown in Figure 17. The base
design consists of four boilers each with capacity of 40 kg/s. There are four back
pressure turbines for generation of electricity from VHP to HP and one back
pressure turbine between HP and LP steam levels. Two multistage turbines are
available for expansion of steam between HP-MP and MP-LP respectively. Four
mechanical pumps having a steam turbo driver and an electric motor supply the
feed water to the boiler.
Figure 17: Base case design [24]
Site data for heat load, electricity demands, pump electricity demand,
condensate return and cooling water is shown in Table 7. The site operating
seasons are divided into two major categories summer and winter, with 67% of
year as winter. The ambient temperature, relative humidity, electricity natural gas
and fuel oil price is shown in Table 8. The total number of working hours for the
32
site is assumed to be 8600 hrs per year. The latent heat values for fuel oil and
natural gas are 45 and 50.24 MJ/kg respectively.
Table 7: Total site data - Requirements for the utility system
Units Winter Summer
Electricity demand MW 62 68
VHP steam demand MW 116.36 110.82
HP steam demand MW 30.61 21.4
MP steam demand MW 16.67 9.34
LP steam demand MW 88.54 73.62
Total steam demand MW 252.17 215.17
Condensate return % 80 80
Power Pump 1 MW 5.2 5.0
Power Pump 2 MW 1.3 1.1
Power Pump 3 MW 2.2 2.0
Power Pump 4 MW 0.6 0.6
Process CW demand
MW 200 300
Table 8: Site conditions
Season Units Winter Summer
Fraction of the year % 67 33
Ambient temperature oC 10 25
Relative humidity % 60 60
Electricity prices Peak ($/kWh) 0.07 0.08
Off- Peak ($/kWh) 0.05 0.05
Peak hours /day Hrs 7 12
Fuel Oil price $/kg 0.19 0.19
Natural gas price $/kg 0.22 0.22
Raw water price $/ton 0.05 0.05
Grand composite curves (GCC) of the individual process are modified by
removing the pockets corresponding to additional heat recovery within the
process. These modified process GCC are then combined together to form the
total site sink and source profile (Figure 18(a)). Sink profile is shifted until the
source and shifted sink profile touch each other (Figure 18(b)) or the source and
the sink steam generation and consumption lines touch each other
corresponding to site pinch. Site utility grand composite curve (SUGCC)
represents the horizontal separation between the source and the sink. Steam
demand at VHP, HP, MP and LP levels are 110.8, 21.4, 9.3 and 73.6 MW
33
respectively. Power generation potential is represented as areas in SUGCC with
VHP-HP, HP-MP and MP-LP cogeneration potential of 79.8, 58.4 and 49.1 MW
respectively (Figure 18(c)).
-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pe
ratu
re (
oC
)
-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
(a) (b)
0 50 100 150 200 2500
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
(c)
Figure 18: Site composite curves; (a) Site source and sink composite curve (b) Site source
and shifted composite curve with the cogeneration potential area (c) Site utility grand
composite curve (SUGCC)
Integration of heat pump
HYSYS model heat pump
A model of heat pump has been simulated in HYSYS. It consists of four
equipments evaporator (E-102), compressor (K-100), condenser (E-100) and a
throttle valve (VLV-100). Refrigerant R112-a is used as a working fluid. Low
grade heat is supplied in the evaporator at the temp of 115oC. High grade electric
energy is used in the compressor to raise the pressure of the vapour. LP steam
34
is generated from the condenser at temperature of 150oC. Throttle valve is used
to reduce the pressure of the vapour liquid mixture from the condenser.
Figure 19: Vapour compression heat pump
Figure 20 shows the variation of COP for heat pump system with respect to
variation in the evaporator duty. COP varies within a small range from 3.24-3.31
and can be assumed to be constant for the refrigerant (R-112a) and the
corresponding heat pump cycle (Figure 19). COP of 3.3, means that 1 MW of
electric energy and 2.3 MW of low grade energy generate 3.3 MW of high grade
energy.
35
3.23
3.24
3.25
3.26
3.27
3.28
3.29
3.3
3.31
0 10000 20000 30000 40000 50000 60000 70000 80000 90000
Evaporator Duty
CO
P
Figure 20: COP with respect to evaporator duty
Purchase cost of heat pump
Purchase cost of heat pump is calculated as the sum of the cost of evaporator,
condenser, and compressor. Purchase cost of heat pump is approximated based
on a linear correlation between the cost and the evaporator duty.
BHAPC evapumpheat +∆×= Eq 8
Where,
pumpheatPC = Purchase cost heat pump
evaH∆ = Evaporator duty (MW)
A, B = Regression coefficients
A = 0.1 MM$/MW
B = 1.15 MM$
Purchase cost
y = 0.0001x + 1.1491
0
2
4
6
8
10
12
0 10000 20000 30000 40000 50000 60000 70000 80000 90000
Evaporator duty (kW)
Pu
rch
ase C
ost
(MM
$)
Figure 21: Linear correlation between purchase cost and evaporator duty
36
The total site source and sink profile before and after integration of the heat
pump is shown in Figure 22 and Table 9. LP steam demand changes from 73.62
to 18.67 MW in summer and from 88.54 to 33.59 MW in winter. Low grade heat
is extracted from the site source only till 115oC corresponding to temperature diff
of 10oC in the evaporator of the heat exchanger. COP of heat pump as
calculated from HYSYS simulations is 3.3. Therefore, the external electricity
consumption from the site increases as shown in Table 10 from 68.82 to 85.42
MW in summer and from 62.2 to 78.8 MW in winter.
Table 9: LP steam demand before and after integration of heat pump
Summer (MW) Winter (MW)
Before heat pump 73.62 88.54
After heat pump 18.67 33.59
Table 10: Electricity demands before and after integration of heat pump
Summer (MW) Winter (MW)
Before heat pump 68.82 62.2
After heat pump 85.42 78.8
-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
Figure 22: Site composite curve with heat pump integration
Annualized capital cost with operational optimization of the existing plant
Operational optimization of total site annual cost with the integration of heat
pump is shown in Table 10. External power cost increases from 22.67 MM$ to
37
35.03 MM$ after integration of heat pump, while fuel cost decreases from 93.08
to 82.09 MM$. Total annual cost increases to 118.67 MM$/yr from 116.32
MM$/yr after integration of heat pump. Therefore, with these costs of fuel and
electricity and the capital cost of heat pump it is not economic to set up a heat
pump.
Table 11: Annual costs before and after integration of heat pump
External Power (MM$) Fuel Cost (MM$)
Before heat pump 22.67 93.08
After heat pump 35.03 82.09
Integration of Organic Rankine Cycle (ORC)
HYSYS model ORC
HYSYS is used to calculate the efficiency and the purchase cost function for
ORC. ORC set up consists of an evaporator (E-100), turbine (K-100), condenser
(E-101) and a pump (P-100). Benzene is used as the organic working fluid. Low
grade heat at 110 oC is used to vaporize benzene at high pressures (1.145 bar).
Benzene vapour is used to drive a turbine along with reduction in pressure (14.5
kPa). Vapour stream from turbine at low pressure condensed in the condenser
(27oC). Pump is used to pump the low pressure organic liquid stream to high
pressure (1.145 bar) before being fed to the evaporator.
38
Figure 23: Organic Rankine Cycle (ORC)
Efficiency of ORC
Figure 24 shows the variation of efficiency of ORC with respect to evaporator
duty. The efficiency of ORC is approximately constant around 11% with the
variation in evaporator duty.
0
2
4
6
8
10
12
0 2 4 6 8 10 12
Evaporator duty (MW)
Eff
icie
ncy
Figure 24: ORC efficiency with evaporator duty
Purchase cost of ORC
Purchase cost of ORC is given as the total cost of equipments such as
condenser, evaporator and turbine. The cost of the evaporator and condenser is
39
obtained from the online database2, while turbine cost is obtained from Peters et
al. [25]. Purchase cost of ORC is approximated based on a linear correlation
between the cost and the evaporator duty.
BHAPC evaORC +∆×= Eq 9
Where,
ORCPC = Purchase cost ORC
evaH∆ = Evaporator duty (MW)
A, B = Regression coefficients
A = 0.01 MM$/MW
B = 25.1 MM$
y = 1E-05x + 0.2506
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 10000 20000 30000 40000 50000 60000 70000 80000
Evaporator Duty (kW)
Pu
rch
ase C
ost
(MM
$)
Figure 25: Linear correlation between purchase cost and evaporator duty
The total site source and sink profile after integration of heat pump is shown in
Figure 26. Low grade heat corresponding to 62.11 MW is saved corresponding to
a temperature of 105oC. Cold utility requirement is reduced by 62.11 MW. As
shown before with the efficiency of 11%, the amount of electrical energy is
reduced from 68.82 to 61.99 MW during summer and from 62.2 to 55.39 MW
during winter. Purchase cost of ORC corresponding to given evaporator duty is
17.13 MM$.
2 http://www.matche.com/EquipCost/Compressor.htm.
40
-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
Figure 26: Site composite curve with ORC integration
Table 12: Electricity demands before and after integration of ORC
Summer (MW) Winter (MW)
Before heat pump 68.82 62.2
After heat pump 61.99 55.39
Absorption refrigeration
HYSYS model of absorption refrigeration
Absorption refrigeration system (Figure 27) consists of absorber (T-101), pump
(P-100), heat exchanger (E-104), generator (T-103), evaporator (E-100), and
condenser (E-103). Heat is released at temperature of 32oC to the surrounding at
a pressure of 13 bar in the condenser (E-103). Ammonia vapours are passed
through a throttle valve (VLV-101) to reduce the pressure to 14.50 kPa before
they can absorb heat from the surroundings at low temperature (-5oC) as
refrigeration load in the evaporator (E-100). Ammonia vapour is absorbed with
the lean solution of ammonia in the absorber (T-101). Heat is released to the
surroundings from the absorber. Concentrated solution of ammonia water is
pumped from 14.59 kPa to 13 bar into the generator. Low grade heat is used in
the generator (T-100) to separate ammonia from the concentrated solution to
produce a lean solution of ammonia water. Heat is exchanged between outgoing
41
lean solution of ammonia water and incoming strong solution in exchanger T-
103.
Figure 27: HYSYS model of absorption Refrigeration
Coefficient of performance
Low grade heat is used to provide the heat for refrigeration load for the system.
The low grade heat supplied in the generator is 265.4 kW, while 67.86 kW of
heat is removed as refrigeration load from the evaporator, with a COP of 0.26.
42
-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
Figure 28: Low grade heat can be used for refrigeration load on site
Boiler feed water heating
Low grade heat is used to raise the temperature of make up water to deaerator
from 25oC to 101.3oC. This reduces the cost of fuel consumed in the boiler. The
benefits of BFW heating depends on condensate recycling process and
condensate management. BFW heating doesn’t change the hot utility
requirement from the base case. However, the cost of fuel required to supply the
hot utility required decreases from 93.08 to 80.57 MM$/yr due to decrease in the
heating required for boiler feed water. The overall energy cost decreases from
117.83 MM$/yr in the base case to 107.63 MM$/yr.
Absorption Refrigeration
43
-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
Figure 29: Temperature of make up water to deaerator is increased by low grade heat
Comparison of design options
Techno economic analysis
Table 13Error! Reference source not found. shows the comparison between
the various low grade heat upgrade options. Heat pump decreases the hot utility
requirement by reducing the low pressure steam demand for the system. Hot and
low utility cost in the system decreases from 93.08 to 82.09 MM$/yr and 0.98 to
0.90 MM$/yr respectively. However, heat pump increases the electricity import
cost for the site from 23.77 to 36.06 MM$/yr. The overall operating cost increases
from 117.83 to 119.06 MM$/yr with the introduction of heat pump. Therefore,
heat pump is not economic for the current case study with the given cost of
electricity and fuel. Integration of ORC decreases the cold utility requirement and
therefore reduces the total utility cost from 94.06 to 94.02 MM$/yr. Electricity
produced from ORC reduces the cost of electricity import from 23.77 to 20.21
MM$/yr. The total energy cost decreases from 117.82 MM$/yr in the base case
to 114.23 MM$/yr for integration with ORC. Absorption refrigeration reduces the
cold utility cost from 0.98 to 0.90 MM$/yr. However, the main advantage of
absorption refrigeration is reduction in electricity cost in vapour compression
Boiler feed water heating
44
refrigeration by 5.46 MM$/yr. BFW heating reduces the cost of hot utility
requirement from 93.08 to 80.57 MM$/yr. Total energy cost decreases from
117.83 to 106.63 MM$/yr. This corresponds to an annual savings of 9.51% in the
operating cost. BFW heating is the most economical options amongst the heat
upgrade technologies. However, benefits of BFW heating depends on the
condensate recycle policy and condensate management.
Table 13: Techno economic evaluation of low grade heat upgrade technologies Options Hot utility (MW) Cold utility (MW) Hot
utility cost (MM$/yr)
Cold Utility cost (MM$/yr)
Total utility cost (MM$/yr)
Electricity import (MM$/yr)
Total energy cost (MM$/yr)
Winter Summer Winter Summer
Base case 252.17 215.17 368 368 93.08 0.98 94.06 23.77 117.83 Heat Pump 197.23 160.23 344.11 344.11 82.09 0.90 82.99 36.07 119.06 ORC 252.17 215.17 344.11 344.11 93.08 0.94 94.02 20.21 114.23 Absorption refrigeration
252.17 215.17 344.11 344.11 93.08 0.90 93.98 23.77 117.75
BFW heating
252.17 215.17 368 368 80.57 0.98 81.55 25.08 106.63
7 Conclusions & future work
The selection of steam level conditions is important as this significantly affects
heat and power management for the industrial site. A new cogeneration targeting
model has been developed in this work, as existing models have been shown to
give misleading results, compared to detailed design procedure. This new model
is based on isentropic expansion and the results obtained from the new model
have been shown to agree well with the results from the detailed isentropic
design method simulated in STAR®. The new method has been incorporated in
the optimisation study which systematically determines of the levels of steam
mains at minimum utility requirement.
Multiple options such as heat pumping, CHP, integrated gas turbines, absorption
refrigeration, drying, etc, are available for upgrading low grade heat. Heat pump
can reduce the LP steam requirement and subsequently the fuel consumed in
the boiler. However, electrical consumption in the site increases with the
integration of heat pump. The overall operating cost increases with the heat
pump for the current case study for the current ratio of fuel to electricity price.
45
ORC decreases the annual operating cost for the total site by reducing the
electricity demand from the site. Absorption refrigeration only reduces the
demand of cold utility. However the major savings comes from reduction in the
electricity demand for an existing vapour compression refrigeration system on the
site. Heating of boiler feed water decreases the fuel consumption in boiler and
hence the overall operating cost of the site. In conclusion BFW heating the
optimum option for integration with the total site in this case study. However, the
best heat upgrade technology is dependent on the site fuel and electricity cost,
condensate management system, and characteristics of low grade heat (quality
and size). The developed methodology will be applied to further extended to
other case studies. Integration of renewable energy sources such as solar, wind,
geothermal etc to the total site will be considered in future work. The variation in
renewable energy sources will be incorporate to the framework. The transfer of
low grade heat across the fence for neighbourhood integration will be considered
in future work. The cost benefit of over the fence process integration needs to be
evaluated.
Acknowledgement
Financial support from Research Councils UK Energy Programme
(EP/G060045/1; Thermal Management of Industrial Processes) is gratefully
acknowledged.
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Refrigeration-Revue Internationale Du Froid. 1990;13(3):191-6.
[19] Manzela AA, Hanriot SM, Cabezas-Gómez L, Sodré JR. Using engine
exhaust gas as energy source for an absorption refrigeration system. Applied
Energy. 2010;87(4):1141-8.
[20] Dincer I, Dost S. Energy analysis of an ammonia-water absorption
refrigeration system. Energy Sources. 1996;18(6):727-33.
[21] Sozen A, Yucesu HS. Performance improvement of absorption heat
transformer. Renewable Energy. 2007;32(2):267-84.
[22] Hung TC. Waste heat recovery of organic Rankine cycle using dry fluids.
Energy Conversion and Management. 2001;42(5):539-53.
[23] Amos WA. Report on Biomass Drying Technology. 1998 [cited NREL/TP-
570-25885; Available from: http://www.nrel.gov/docs/fy99osti/25885.pdf
[24] Aguillar O. Design and optimisation of flexible utility systems [PhD Thesis].
Manchester: University of Manchester; 2005.
[25] Peters MS, Timmerhaus KD, West RE. Plant Design and Economics for
Chemical Engineers: McGraw-Hill 2003.
8 Appendix A
8.1 Optimization framework
8.1.1 Objective function
The present work used overall operating cost along with annual capital cost for
the new design heat upgrade technology as the minimization function.
( ) FixOpCstFEmmCstWatCstPowCstFuelCstOpCst opcst +⋅+++= Eq
10
48
Where,
opcstF Factor to increase operating cost by a percentage (fraction)
OpCst Overall annual operating plant cost (MM$/yr)
FuelCst Overall fuel cost for the site utility system (MM$/yr)
PowCst Overall electricity cost for the site utility system (MM$/yr)
WatCst Overall water cost for the site utility system (MM$/yr)
EmmCst Overall emission cost for the site utility system (MM$/yr)
FixOpCst Fixed charge for operating cost (MM$/yr)
Capital cost for any additional unit is defined as a function of the purchase cost
( nPurCst ) for each piece of equipment.
fix
nninstcepci CapPurCstFFCapCst +
⋅⋅= ∑
Eq 11
Where,
cepciF Chemical engineering plant cost index
instF Installation factor to consider other plant expenses
fixCap Fixed capital cost for the whole plant (MM$)
nPurCst Purchase cost for n equipment unit (MM$)
Total cost for the whole site is given by the following expression
annFCapCstOpCstTotCst ⋅+= Eq 12
Where,
TotCst Total annualized cost (MM$/yr)
Fann Annualisation factor
49
Optimization constraints
8.1.2 Electric balances
The cost of electricity consumed or produced on a site on the overall annual
operating cost is calculated by the electrical balance between site sources and
sinks.
impgenlossauxdem WeWeWeWeWeWe +=+++ exp Eq 13
demWe Total electricity demand of the process in each period (kWe)
expWe Electricity exported by the utility system in each period (kWe)
auxWe Electricity consumed by auxillary units including boiler fans, pumps,
cooling fans, motor drivers (kWe)
lossWe Distribution and control electricity loss (kWe)
genWe Electricity generation from the site utility system (kWe)
impWe Electricity imported by the site (kWe)
8.1.3 Mass balances
The mass balance at each node is
∑∑ = outin MM Eq 14
Mass flow of steam into the deaerator where water is scrubbed with LP steam
before it is delivered to the boiler as saturated water.
vnt
dea
bfwmkupcondretstm
dea MMMMMM +=+++ Eq 15
Mass balance for the steam header is based on the mass flow from producers
(boilers, HRSG), receive or deliver steam to and from steam turbines, let down
valves or process etc.
∑ ∑ ∑∑
∑∑∑∑∑∑
+++
=+++++
−−
−−
k k k
vntk
outletk
k
outSTk
consk
k
dshk
k
inletk
k
inSTk
k
genk
k
HRk
k
boik
MMMM
MMMMMM
Eq 16
50
Make up water is equal to the condensate lost by the process, along with the
losses in the utility plant including losses in boiler, HRSG, vent, gas turbine, and
process etc.
( ) vntloss
vntdea
ret
k
genk
consk
k
vntk
injGT
HRbldwn
boibldwn
mkup MMMMMMMMMM +++−++++= ∑∑ Eq 17
Where,
k Steam header index in the utility plant
inM Mass flow into a mixing node (kg/s)
outM Mass of steam out from a mixing node (kg/s)
stmdeaM Mass flow rate of steam into deaerator (kg/s)
retM Returning condensate from the process (kg/s)
condM Mass flow rate of the condensate (kg/s)
mkupM Water make up for the utility system (kg/s)
bfwM Mass flow of water to the boiler (kg/s)
vntdeaM Vented steam from the deaerator (kg/s)
boikM Steam delivered by boiler to header k (kg/s)
HRkM Steam delivered by HRSG to header k (kg/s)
genkM Steam generated by process and delivered to the header (kg/s)
inSTkM − Discharge from steam turbine into header k (kg/s)
inletkM − Letdown steam entering header k (kg/s)
dshkM De-superheating boiler feed water injected into header k (kg/s)
conskM Steam consumed by process at header k (kg/s)
outSTkM − Steam release by steam turbine to header k (kg/s)
outletkM − Steam leaving header k by letdown (kg/s)
vntkM Vented steam for header k (kg/s)
mkupM Water make up for utility system (kg/s)
boibldwnM Blowdown for all boiler in utility system (kg/s)
HRbldwnM Blowdown from all HRSG in utility system (kg/s)
51
injGTM Steam injected to all gas turbine in utility system (kg/s)
vntkM Steam vented from header k (kg/s)
8.1.4 Heat balance
Heat balance for two streams in adiabatic mixing is shown in Equation 18-19.
∑∑ = outin QQ Eq 18
( ) ( )∑∑ ⋅=⋅ outoutinin MhMh Eq 19
Enthapy balance for the deaerator is given by Equation 20. The enthalpy balance
for a steam header consists of heat from the generator (boiler and HRSG), both
production and consumption from steam turbine, let down, and process
(Equation 21-22).
vntdea
gdea
bfwdea
fdea
mkupmkupcondcondretretstmdea
stmdea MhMhMhMhMhMh ⋅+⋅=⋅+⋅+⋅+⋅ Eq
20
( ) ( ) ( ) ( )
( ) ( ) ( )
+++++⋅
=⋅+⋅+⋅
+⋅+⋅+⋅+⋅
∑ ∑ ∑ ∑ ∑ ∑
∑∑∑
∑∑∑∑
−
−−
−−
k k k k k k
vntk
dshk
outSTk
genk
bHRk
boik
hdrk
k
vntk
hdrk
k
dshk
bfwk
k
inletk
inletk
k
inSTk
inSTk
k
genk
genk
k
bhrk
hdrk
k
boik
hdrk
MMMMMMh
MhMhMh
MhMhMhMh
Eq 21
( ) ( )
+++⋅
=⋅+⋅++⋅
∑ ∑∑
∑∑∑
−−
−−−
k k
dshk
inletk
inSTk
k
genk
hdrk
dshk
bfwk
k
inletk
inletk
k
inSTk
k
genk
genk
MMMMh
MhMhQMh
Eq 22
Where,
k Steam header index
inQ Heat entering a mixing node (kW)
outQ Heat leaving a mixing node (kW)
inh Specific enthalpy of heat entering a mixing node (kJ/kg)
52
outh Specific enthalpy of heat leaving a mixing node (kJ/kg)
fdeah Enthalpy of saturated steam at deaerator pressure (kJ/kg)
gdeah Enthalpy of saturated vapour at deaerator pressure (kJ/kg)
bfw
kh Enthalpy of feed water needed to de-superheat steam (kJ/kg)
stmdeah Enthalpy of stripping steam to the deaerator (kJ/kg)
reth Enthalpy of returning condensate from the process (kJ/kg)
condh Enthalpy of condensing water entering the deaerator (kJ/kg)
mkuph Enthalpy of make up water (kJ/kg)
genkh Enthalpy of steam generated by the process (kJ/kg)
hdrkh Enthalpy in steam header k (kJ/kg)
inletkh − Enthalpy of let down steam header k (kJ/kg)
inSTkh − Enthalpy of discharge from steam turbines at header k (kJ/kg)
8.2 Equipments
8.2.1 Multi-fuel boilers
In a boiler the chemical energy of the fuel is extracted to heat the condensate
or feed water to generate steam at the required temperature. There are
numerous types of boilers and control schemes along with different unit size
and actual load. This results in different performance trends. Aguillar [24]
assumed a linear relationship between fuel consumption and steam
production as shown in Equation Error! Reference source not found..
boi
boi
boi
fboi
stm DB
QQ −
=
Eq 23
Where,
boifQ Net heat from the fuel consumed inside the boiler (kW)
boistmQ Actual heat added to the water/steam inside the boiler (kg/s)
boiB , boiD Regression parameters
53
With the assumptions that boiler blowdown is extracted at saturated
conditions and as a fixed fraction of boiler steam output, the heat supplied to
the water/steam cycle can be expressed as:
∆
∆+∆=
boi
T
boi
bid
boi
ecoboi
T
boi
stm
boi
stmh
FhhMQ
.1..
Eq 24
Where,
boistmM Actual steam output from the boiler (kg/s)
boiTh∆ Enthalpy difference between feedwater and outlet steam conditions
(kJ/kg)
boiecoh∆ Enthalpy difference across boiler economiser (kJ/kg)
boibldF Boiler blowdown fraction taking as reference the outlet steam
flowrate (kg blowdown/kgsteam)
Equation Error! Reference source not found. is obtained by rearranging
Equations Error! Reference source not found. and Error! Reference
source not found.. The coefficients for this boiler model are obtained by
regression from operating or design data.
∆
∆+∆=−
boi
T
boi
bid
boi
ecoboi
T
boi
stm
boi
boi
boi
f
h
FhhMD
B
Q .1..
Eq 25
Here, boiB & boiD are regression coefficients.
8.2.2 Gas turbines (GT)
Gas turbines convert the chemical energy of fuels into electrical energy via a
three step process
� Compression: The inlet pressure and temperature of the ambient air is
increased by the compressor.
� Combustion: Heat is added at high pressure by fuel ignition.
� Expansion: The hot combustion gases are expanded through the
turbine to drive the compressor and to provide power (electricity).
54
The relationship between power output from the gas turbine to the required
heat input is approximated by a straight line which is known as the Willans
line.
gtgtgtgt WQCW int−⋅= Eq 26
gtW Gas turbine power output (kW)
gtQ Gas turbine fuel input (kW)
gtC , gtWint Regression parameters
8.2.3 Heat recovery steam generators (HRSG)
HRSG utilize the waste heat from the gas turbine to produce steam which can
be further used to generate power or provide heating to consumers. HRSG
can be further classified into the following types:
a) Unfired units: Steam production is limited by the temperature and
available energy in the exhaust gases.
b) Supplementary fired units: The remaining oxygen in the exhaust gases is
used to burn fuel to boost steam generation.
c) Fully fired units: Additional quantity of air is supplied for further
consumption of fuel and hence increases production of steam.
Aguillar [24] derived a simple equation for the steam production from a gas
turbine based on the mass and heat balance for the unfired HRSG.
( )( )( )gtgt
D
gtgt
D
gthr
m
hr
satgt
D
gtgt
Dgtgt
Dhr
eva
hr
sh
hr
exh
hr
radhr QQQTTQ
QQkTexh
hh
CpFM −⋅−⋅⋅
∆−−
−−⋅
∆+∆
⋅= βα1
Eq
27
Where,
hrM Maximum HRSG steam production from GT exhausts (kg/s)
hrradF Radiation losses factor for the HRSG
55
hrexhCp Average specific heat for the exhaust gases (kJ/kg-°C)
hrshh∆ , hr
evah∆ Steam enthalpy difference across HRSG superheater, evaporator
(kJ/kg)
hrmT∆ Minimum temperature difference between gas and steam/water
profiles (°C)
gtDTexh Design temperature at the exhaust of the gas turbine (oC)
hrsatT Saturation temperature for the steam produced in the HRSG (°C)
gtk , gtα , gtβ Regression coefficients
gtDQ Design heat from the gas turbine
gtQ Actual heat from the gas turbine
8.2.4 Electric motors (EM)
Electric motors are devices that convert electricity into shaft power by
inducing electromagnetic forces in its rotational wounding (i.e. rotor). The
units are broadly classified into synchronous, direct current, three phase
induction and single phase [24].
Willans line describes the part load performance of the electric motors in
terms of regression parameters with the full load performance of the motor.
emem
D
emem
D BWAWe +⋅= Eq 28
emDWe Design electric consumption of the motor (kWe)
emDW Design motor power output (kW)
emA , emB Regression parameters
8.2.5 Steam turbines (ST)
Steam turbines convert energy from steam into electrical energy by expanding to
lower pressure. They can be classified as single or multiple extraction turbines
56
according to number of equipments attached to the shaft. The back pressure
steam turbine expands steam to a lower pressure, while steam is expanded to
liquid water in a condensing turbine.
Single stage steam turbine
Aguillar [24] developed linear models to describe the performance of steam
turbines. The design steam flow rate ( st
DM ) in steam turbine is a function of
isentropic enthalpy change ( st
ish∆ ) and the design capacity of the unit ( st
DW ).
( )st
D
stst
st
is
st
D WBAh
M ⋅+∆
=1
st
sat
st TaaA ∆+= 10 st
sat
st TaaB ∆+= 32
Eq 29
Here a0, a1, a2, a3 are regression coefficients,
st
satT∆ is the saturation temperature difference across the turbine.
The power of the unit ( stW ) is proportional to the steam mass flow rate ( stM ) and
the ordinate intercept of the Willans line ( stWint ).
stststst WMnW int−⋅= Eq 30
The actual shaft power from a single stage turbine is a function of maximum
output size ( st
DW ), actual steam flow ( stM ) and inlet and outlet conditions of the
steam in the turbine.
( )
st
ststst
Dstst
st
ststis
st
B
ALWLM
B
LhW 1
1+−⋅−
+∆=
stsat
st TaaA ∆+= 10 st
satst TaaB ∆+= 32
stsatLL
st TbaL ∆+=
Eq 31
Here a0, a1, a2, a3, aL, bL are regression coefficients,
57
stsatT∆ is the saturation temperature difference across the turbine.
Multi-stage steam turbine
Muti-stage turbine discharges steam at different outlet steam levels. A multi-
stage steam turbine can be decomposed into several single stage turbines
connected in series. Aguillar [24] developed linear models to evaluate the
performance of multi-stage turbines. Isentropic enthalpy difference across the
downstream stages is evaluated by assuming a typical isentropic efficiency
across all the stages. The enthalpy for a three stage turbine is calculated by the
following equations:
( )mst
sPmstin
mstis hhh 0,1010 −=∆ −−
( )mst
ismst
sPmstin
mstis hhhh '
321,2121 −−−− ∆≈−=∆
( )mst
ismst
sPmstin
mstis hhhh '
322,3232 −−−− ∆≈−=∆
( )mst
sPmstin
mstis hhh '1,2'1'
21 −=∆ −−
( )mst
sPmstin
mstis hhh '2,3'2'
32 −=∆ −−
mstis
mstis
mstin
mstin hhh 10
'0'1 −−∆⋅−= η
mstis
mstis
mstin
mstin hhh '
21'
'1'2 −−∆⋅−= η
Eq 32
Where,
0,1,2,3 Sub indexes for steam conditions at inlet, first, second and third
extractions of the turbine.
1’,2’,3’ Sub indexes indicating approximate steam conditions at first,
second and third outlet of the turbine.
mstiiish )1( +−−∆ Isentropic enthalpy difference for the i stage of steam turbine (ie.
between i and (i+1) respectively) (kJ/kg)
mstinih Enthalpy of steam entering stage i (kJ/kg)
mstinih ' Approximate enthalpy of steam entering each stage i
mstis'η Isentropic efficiency value to approximate steam conditions in
downstream stage
58
The shaft power equation for single stage steam turbine is extended to each of
the stages of the multi-stage turbine. The overall shaft power for a multi stage
steam turbine ( msttotW ) is calculated as:
( )
mst
mstmstmst
Dmstmst
mst
mststis
mst
B
ALWLM
B
LhW
1
11111
1
1101 1
1+−⋅−
+∆= −−
( )mst
mstmstmst
Dmstmst
mst
mststis
mst
B
ALWLM
B
LhW
2
22222
2
2212 1
1+−⋅−
+∆= −−
( )mst
mstmstmst
Dmstmst
mst
mststis
mst
B
ALWLM
B
LhW
3
33333
3
3323 1
1+−⋅−
+∆= −−
mstmstmstmsttot WWWW 321 ++=
Eq 33
Where,
mstiW Shaft work from i stage of the multi-stage steam turbine
mstiA , mst
iB Regression coefficients for i stage of multi-stage steam turbine