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Applied Thermal Engineering xxx (2014) 1e11
Contents lists available at ScienceDirect
Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng
Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand Peng Yen Liew a, Sharifah Rafidah Wan Alwi a, *, Jirí Jaromír Klemes b, Petar Sabev Varbanov b, Zainuddin Abdul Manan a a
Process Systems Engineering Centre (PROSPECT), Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia } Centre for Process Integration and Intensification e CPI, Research Institute of Chemical and Process Engineering e MUKKI, Faculty of Information Technology, University of Pannonia, Egyetem u. 10, H-8200 Veszprém, Hungary b
Temp. (°C)
Temp. (°C)
g r a p h i c a l a b s t r a c t
Temp. (°C)
TSL 3 5h
Energy Storage
TSL 1 4h
TSL 2 8h
DH (MW)
Temp. (°C)
TSL 4 6h
DH (MW)
DH (MW)
DH (MW)
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 December 2013 Received in revised form 11 February 2014 Accepted 5 March 2014 Available online xxx
Fluctuating renewable energy supply presents a challenge for applying energy-saving methodologies such as Process Integration. Graphical targeting procedures based on the Time Slices (TSLs) have been proposed in previous works to handle the energy supply/demand variability in TSHI. The targeting procedures for TSHI with TSLs include the construction of Composite Curves, Grand Composite Curve and Total Site Profile for each time interval. Heat Integration analysis utilising a numerical algorithm typically offers higher precision and more rapid calculations as compared to the graphical approach. This paper introduces an algorithm to efficiently perform utility targeting for a large-scale TSHI system involving renewable energy and variable energy supply/demand to include TSL. The presented tool is an extension of the Total Site Problem Table Algorithm (TS-PTA), which has been previously used for processes with steady energy supply/demand. Due to its algorithmic nature, the technique presented enables the accurate and rapid determination of the stream origins, and can be embedded into larger algorithms. Optimised heat storage facilities are used to manage the variable energy supply and demand. The Total Site Heat Storage Cascade (TS-HSC) is the core of the algorithm. The new developed tool is incorporated with the heat losses for the thermo-chemical energy storage systems. The process start-up and continuous operations are considered in the novel methodology. The tool is featured to analyse the heat excess in specific TSLs that can be cascaded to the next TSL via energy storage system during start-up and operation. The proposed tool can be also used to estimate the required heat storage capacity. Ó 2014 Elsevier Ltd. All rights reserved.
Keywords: Total Site Heat Integration Thermo-chemical heat storage Heat loss Utility system Pinch Analysis Cascade analysis Variable supply and demand
* Corresponding author. E-mail addresses: [email protected], [email protected] (S.R. Wan Alwi). http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014 1359-4311/Ó 2014 Elsevier Ltd. All rights reserved.
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
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P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
1. Introduction Widespread utilisation of renewable energy sources can help to reduce the dependency on fossil fuels and mitigate the environmental emissions. However, renewable energy has been mostly utilised for power generation and for buildings. It has been targeted that 21% of the energy feedstock for the manufacturing industry by the year 2050 to comprise of renewable energy [1]. In order to meet this target, more research emphasis needs to be given to the use of renewable energy in the industrial sector. The Pinch Analysis is an established tool for Heat and Power Integration to reduce the energy usage in the industry [2]. By maximising the heat recovery within processes, Heat Integration minimises the energy requirement and reduces the wasted energy as well as environmental emissions [3]. The Heat Pinch Analysis tool has been extended to consider energy integration across several plants or processes using indirect heat transfer [4], termed as Total Site Heat Integration (TSHI). The methodology has been extended by Raissi [5] and developed further by Klemes et al. [6]. The established tools involved in the analysis include the Grand Composite Curves (GCC), the Total Site Profiles (TSP), the Site Composite Curves (SCC), and the Site Utility Grand Composite Curves (SUGCC). These tools have been developed to enable visualisation of the heat availability and consumptions on the TS. Varbanov et al. [7] proposed to replace the global minimum temperature difference (DTmin) with the minimum temperature difference for process to process (DTmin,pp) and for utility to process (DTmin,up) in Heat Integration analysis. Numerical algorithms have been introduced for TS analysis by Liew et al. [8]. The numerical tools, such as the Total Site Problem Table Algorithm (TS-PTA), the Multiple Utility Problem Table Algorithm (MU-PTA), and the Total Site Utility Distribution (TSUD) table, have been extended from the graphical approaches. Total Site Sensitivity Table (TSST) is proposed by Liew et al. [9] for assessing the utility requirement sensitivity towards the process operational changes. The algebraic/algorithmic approach vitally complements the graphical approaches as it can provide an efficient base algorithm for site-wide utility targeting, pinch point determination and even network design (covering single, multiple and total site process as well as utility systems). Kapil et al. [10] introduced a mathematical optimisation method for integrating the low grade heat to satisfy the district heating requirement. Hackl and Harvey [11] applied the TS analysis and exergy analysis on a sub-ambient refrigeration system. Hackl et al. [12] analysed the TS energy integration of a chemical cluster in Sweden. The cluster consists of five chemical companies producing a variety of products, including polyethylene, polyvinyl chloride, amines, ethylene, oxygen/nitrogen and plasticisers. The TS analysis result shows that up to 129 MW of energy can be potentially saved via TS centralised utility system. The TS methodology also has been implemented to heavy chemical complex [13], steel plant [14], and large dairy factory [15]. Varghese and Bandyopadhyay [16] proposed a systematic methodology for integrating the fired heater into the site utility system. A new model for estimating the cogeneration potential in a site utility system is published by Khoshgoftar Manesh et al. [17]. Chaturvedi and Bandyopadhyay [18] proposed a new targeting methodology using indirect heat transfer in TS integration for batch processes. Chew et al. [19] summarised the issues to be considered to ensure the practical industrial application of TS system. The TS concept was initially proposed as a systematic tool for energy conservation among industrial processes. The Locally Integrated Energy System (LIES) is a TS problem addressed by Perry et al. [20] where batch processes, renewable energy and urban energy consumptions are considered. Varbanov and Klemes [21]
proposed a TS heat cascade, which has shown the relation between process, steam system, renewable energy and heat storage. However, the intermittent renewable energy sources (e.g. solar for heat generation) typically varies with time and location [22]. Similar to the methodology for individual Batch Process Heat Integration, the Time Slice (TSL) methodology is introduced to handle the variable nature of the renewable energy supply and urban energy demands [21]. Fluctuations in energy supply/demand in a TS system are addressed by installing a Thermal Energy Storage (TES) facility and by implementing the TSL methodology in the system. The concept of TS energy consumption and generation can be understood from the perspective of a centralised utility system. Fig. 1 shows the TS Heat Cascade proposed by Varbanov and Klemes [21]. TSP targets multiple utility requirements, which are indicated by black arrows in Fig. 1 as steam generation and consumption. Heat storage facilities can be available for any type of utility, represented by horizontal arrows. The storage systems connect the utility from one TSL to another. The heat excess within a cycle of operation has to be discarded to prevent the waste of energy, which cannot be accumulated in the system. It is important to maintain the energy storage facility capacity at the optimal condition. Three types of TES facility among those available are: the sensible heat, latent heat, and chemical energy storage systems [23]. The energy capacity and storage volume depends on the material and temperature. Sensible heat storage is based on liquid media storage (e.g., water, oil-based fluids, and molten salts) or solid media storage (e.g., rocks and metal) [24]. Latent heat storage is more attractive compared to sensible heat storage because it requires less material weight and volume to store a certain amount of energy. Latent heat also requires large heat storage densities and capacities at constant temperatures. Numerous studies on latent heat storage materials, also known as phase change materials, have been reported. Phase change materials can be organic or inorganic depending on their temperature range, suitability for heating or cooling, thermophysical properties and long term stability [25]. The energy density provided by thermo-chemical storage systems is significantly higher than that of other storage technologies; as such, this system requires additional studies [26]. The high energy density of reversible chemical reactions/processes encounter the problem for other types of energy storage facility: a very large volume of media is required to store a certain amount of energy [27].
Fig. 1. Total Site Heat Cascade [21].
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
TES has been conceptually implemented in the Heat Integration of batch processes as a type of indirect heat recovery for heat transfer between TSLs [21]. Stoltze et al. [28] proposed an algorithm to formulate the number of storage tanks required to minimise the energy target. Jung et al. [29] found that direct heat transfer should be maximised before indirect heat transfer with a heat storage system is used and proposed a systematic reschedule methodology. Using a time decomposition strategy, Pourali et al. [30] analysed the combination of different TSLs to minimise the total cost for the Heat Integration of batch processes. Foo et al. [31] developed a methodology to estimate the minimum number of heat exchangers for Batch Heat Integration, considering cases with and without TES. Previous studies [21] on TS variable supply and demand targeting techniques still faced several limitations: i) The energy-targeting method using TSL only considers steady-state processes without start-up/shutdown and normal process operation. ii) Mostly TES system at one utility level in the TS system is considered. iii) The heat losses for storage facility are not incorporated in the methodology. iv) The possibility of using heat excess after charging sufficient energy into the TES facility at higher level of energy storage for lower level energy demand via letdown is not taken into account. This work overcomes these previous limitations. The TS-PTA algorithm [8] is used to improve the Total Site Heat Cascade [21]. The Total Site Heat Storage Cascade (TS-HSC) Table is a new tool introduced to represent the time dependent energy flows between processes, storage facility, and centralised utility system. The new tool is build based on the thermo-chemical energy storage system that can provide the highest efficiency of energy storage [32] with minimum losses compared to sensible and latent heat storage [23]. The tool is used to determine the external utilities and heat storage capacity required during start up and operation for batch TSHI. 2. Targeting methodology The proposed algorithmic targeting methodology for TS with variable energy supply and demand is described below: 2.1. Step 1: identification of TSLs In the first step, TSLs should be identified by observing the temporal variations of the process energy demands and the renewable sources. The energy demand at a processing site could be time-dependent when a batch process is integrated. These variations are more significant if service, leisure, and commercial buildings are integrated into the TS. A TSL is a time interval with relatively constant energy variations. Fig. 2 is a simple illustration of TSL identification. Processes A, B, and C are batch processes operating at different times, and renewable energy is available at only certain times. Different time intervals with very large variations in energy consumption could be identified. A detailed TSL specification methodology has been proposed by Nemet et al. [22].
3
2.3. Step 3: TS-HSCs construction The Total Site Heat Storage Cascade (TS-HSC) is developed in this work to determine the total amount of heat that can be recovered within a TSL, to be stored or letdown. This tool is represented by the horizontal arrows in Fig. 1. The TS-HSC is constructed for each level of utility equipped with a heat storage system in the TS system. The detailed methodology to construct the TS-HSC is described as follows: i. The TSLs are inputted in the first column of the TS-HSC in ascending order. The length of each TSL should be divided into time intervals according to the heat loss assumption. ii. The site MU requirement targeted by the TS-PTA is recorded according to the TSL in the second column. A negative requirement below the Pinch in the TS-PTA indicates that is excess heat to be rejected. A positive utility requirement above the Pinch indicates the need for an external heating utility. iii. The length of each time interval in TSLs is recorded in Column 3. iv. The energy requirement from the processes in the TS (Column 4) is formulated by multiplying the MU requirement with the length of the TSL. v. Additional heat sources available to be integrated into the TS, e.g., renewable energy sources including solar and biomass, are recorded in Column 5. vi. The Net Energy Available (NEA, Column 6) is calculated by deducting the targeted site utility requirement from the available extra utility (Column 6 ¼ Column 5 Column 4). Positive NEA shows energy excess from the processes to be stored in storage system, while negative NEA shows energy deficit at processes. vii. A cascade of available enthalpy for energy storage is performed for each time interval in Column 7 starting from the highest to the lowest time interval in the last TSL. a) The Heat Storage Cascade is the sum of the energy available from the previous time intervals, NEA (Column 6), Heat Losses (Column 8), the External Heating Utility (EHU Column 9) and the External Cooling Utility (ECU Column 10) as shown in Eq. (1). The stored heat is assumed to be unavailable in the storage system during process start-up. The storage cascade begins at zero loads. The heat losses, EHU and ECU are assumed to be zero at this step.
Cascadei ¼ Cascadei1 þNEAi þHeatLossesi þEHUi þECUi (1)
b) The heat losses (Column 8) are determined according to the amount of energy charged into or discharged from the heat
Solar (14 h) Process A (18 h) Process B (14 h)
2.2. Step 2: TS multiple utility target determination for all TSLs The TS multiple utility targets and the Site Pinch location for each TSL are then determined by using TS-PTA [8]. The multiple utility requirements could also determined by using a graphical methodology.
Process C (10 h) TSL 1 00:00 h
TSL 2
TSL 3
TSL 4
TSL 5 24:00 h
Fig. 2. Determining the TSL for integrated TS with variable energy supply and demand.
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
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P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
Cascadei for start-up process (Column 18, Table 6b). The total ECUi added should be the same amount as the smallest value in the Cascadei for continuous operation. This step would create a “Pinch” point in Cascadei for continuous operation with zero enthalpy (Column 19, Table 6b), which represents there would be no accumulation in the storage system. In order to reduce storage capacity, the ECUi should be added at the earliest TSL with energy charged into the storage system (positive value of NEAi). The amount of ECUi added should not more than the NEAi for that particular time interval. When the NEAi is not sufficient from the earliest TSL with energy charging, the next TSL with positive NEAi is considered. If the ECUi is not satisfied after the whole start-up operation, ECUi could be added for the TSLs with positive NEAi before the smallest value in Cascadei for continuous operation. c) The ECUi for continuous operation after the Pinch should be placed similar as in start-up process to ensure that the HS for continuous operation and start up process are the same. ix. If there are two storage facilities at different energy levels, energy surplus at higher level could be let-down to satisfy the energy deficit at lower level storage system. A new Heat Storage Cascade with an additional column for let-down. The cascade methodology is the same as Step 3-vii and 3-viii, the cascade formulation changed to Eq. (4).
storage system. The heat loss for charging and discharging process can be determined by Eqs. (2) and (3) with knowing the charging efficiency (hcharging) and discharging efficiency (hdischarging). Charging:
Heat Lossesi ¼ NEAi 1 hcharging
(2)
Discharging: Heat Lossesi ¼ NEAi 1 hdischarging
(3)
c) The EHUi (Column 9) is added when Cascadei shows a heat deficit (negative value) after every time interval. The Cascadei should reach a net zero enthalpy instead of a utility deficit after the EHUi is added. d) The amount of excess heat in the storage system by the end of the process start-up is termed as heat surplus (HS). viii. A Heat Storage Cascade for continuous operation is then performed based on the start-up cascade with following steps: a) The Heat Storage Cascade for continuous operation is initiated with the HS in the Heat Storage Cascade for process start-up. The cascade is built with the same method as in Step 3-vii-(a). The heat losses for energy conversion processes are determined using the method in Step 3-vii-(b). The EHUi is added with the same condition for continuous operation (Column 21, Table 7b) as in Step 3-vii-(c) b) If all the values in Cascadei for continuous operation are larger than zero (Column 11, Table 6a), add ECUi in the
Cascadei ¼ Cascadei1 þ NEAi þ HeatLossesi þ EHUi þ ECUi þ Letdowni
b) At TS-HSC for higher utility level, the amount of energy to be let-down is deducted at the same time interval that the energy is required at lower energy level (Column 31, Table 8c). In this case, energy will be stored at higher energy level storage system until it is needed. The energy also can be charged directly to the lower level storage system. c) The Heat Storage Cascade at lower energy level should include the let-down column to represent the let-down energy from higher energy level and reduce the amount of EHU (Column 30, Table 9c). However, the economics of this integration option would need to be study. It is not recommended to use this option when there is small amount of energy excess at higher level storage system.
Table 1 Streams data for processes A, B, C and D [21]. Stream
TS ( C) TT ( C) DH (kW) Cp/HL (kW/ C) TS0 ( C) TT0 ( C) TSL (h)
Process A A1 hot A2 hot A3 cold A4 cold A5 cold A6 hot Process B B1 hot B2 hot B3 hot B4 cold B5 hot B6 cold B7 hot B8 hot Process C C1 hot C2 hot C3 cold C4 cold C5 cold C6 cold C7 cold C8 cold C9 cold C10 cold C11 cold Process D D1 cold D2 cold D3 cold
110 150 50 85 62 92
80 149 135 100 100 55
120.00 180.00 104.40 73.30 130.00 233.84
1.3333 180.0000 1.2280 4.8870 3.4210 6.3200
104 144 56 91 68 86
74 143 141 106 106 49
00e24 00e24 00e24 00e24 00e24 00e24
200 20 50 100 150 80 95 191
195 54 85 120 40 95 25 182
160.0 10.0 19.8 130.0 83.5 48.0 80.0 360.0
32.0000 0.2941 0.5657 6.5000 0.7590 3.2000 1.1430 40.0000
194 26 56 106 144 86 89 185
189 60 91 126 34 101 19 176
06e20 06e20 20e06 06e20 06e17 06e20 06e17 06e17
85 80 25 55 33 25 82 25 80 18 21
40 40 55 85 60 60 121 28 100 25 121
0.5300 2.4100 0.5760 0.6000 0.4460 0.4290 0.5740 7.7000 1.6000 5.8640 0.0500
79 74 31 61 39 31 88 31 86 24 27
34 34 61 91 66 66 127 34 106 31 127
06e17 06e17 20e06 20e06 00e24 06e17 20e06 06e17 06e17 00e24 06e17
15 15 15
25 45 45
8.8000 0.8333 3.4500
21 21 21
31 51 51
00e24 00e24 06e20
23.85 96.40 17.30 18.00 12.00 15.00 22.38 23.10 32.00 41.10 5.00
Table 2 TS-PTA at Time Slice 1. 1
2
3
4
5
6
Initial Utility Net heat Net heat Net heat sink (kW) requirement cascade source (kW) (kW) (kW) MPS LPS HW
88.0 25.0 103.5
(4)
CW
0.00 0.00 107.18 44.24
1.72 49.11 305.54 0.00
7
8
MU MU Final cascade cascade targets (kW) (kW) (kW)
0.00 249.19
0.00
1.72 247.47
0.00
50.83 198.36
0.00
1.72
1.72
49.11
49.11
198.36 249.19
0.00
0.00
204.95
44.24
0.00
44.24
198.36 Pinch 44.24
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11 Table 3 TS-PTA at Time Slice 2. 1
2
Table 5 Multiple utility targets in all TSLs.
3
4
5
6
7
8
Utility
MU MU Initial Final Utility Net heat Net heat Net heat source (kW) sink (kW) requirement cascade cascade cascade targets (kW) (kW) (kW) (kW) (kW) MPS LPS
0.00
0.15
229.45
5
13.08
0.00
0.15 0.00
0.15
0.00 0.00
0.15 216.38
0.15 Pinch 216.38
TSL 1
MPS (kW)
1.72
LPS (kW)
49.11
HW (kW)
198.36 Pinch 44.24
CW (kW)
TSL 2
TSL 3
0.15 Pinch 216.38
0.00 23.27
99.50
45.91 Pinch 44.24
94.41
216.23 216.38 0.00 HW
189.51
90.00
99.50
99.51 315.73 315.88 0.00
CW
94.41
0.00
94.41
94.41 410.14 410.29 0.00
x. The EHUi and ECUi values in TS-HSC is revised for eliminating unnecessary heat losses accounted in the previous steps. Heat loss should not be considered when EHU and ECU are supplied to the processes directly. The final cascade in TSHSC should satisfy the requirements as listed below: a) Heat Storage Cascade for start-up and continuous operation should not consist of negative values. b) A time interval in the Heat Storage Cascade for continuous operation should show no load in the storage facility. c) The HS of the cascade for start-up and continuous operation should be the same to avoid unnecessary accumulation in the storage facility. For summarising the methodology above, Fig. 3 shows the flow diagram of the proposed methodology for targeting the energy requirement of a TS system dealing with variable supply and demand or batch processes using TS-HSC. 3. Case study The proposed procedure is illustrated using a case study involving four units: Processes A, B, C and D. Process A and B represent industrial plants, Process C is a hotel and Process D is a residential area. The stream table of these units is shown in Table 1. The data from Varbanov and Klemes [21] are modified to demonstrate different possible applications of the newly proposed tool. According to the availability of the streams indicated in Table 1, three TSLs can be identified for this case study: 20e06 h (TSL 1), 06e17 h (TSL 2), and 17e20 h (TSL 3). The minimum temperature difference, DTmin, was assumed to be 12 C. The utilities available on site are Medium Pressure steam (MPS) at 220 C, Low Pressure Steam (LPS) at 130 C, a hot water system (HW) at 80e50 C and the only cooling utility is cooling water (CW) at 15e30 C.
The TS-PTAs for all TSLs are constructed as proposed by Liew et al. [8] to determine the minimum utility requirements at different TSLs (Tables 2e4). Table 5 summarises the amount of utilities required by the processes on a different TSL from Column 8 of each TS-PTA. For example, MPS, LPS and HW hot utilities are required from a centralised utility system in TSL 1 at 1.72 kW, 49.11 kW and 198.36 kW. The TS system required 44.24 kW of CW to cool hot streams. Chemical heat storage facilities are assumed available for LPS and HW storage at the site to allow heat transfer between TSLs. The energy density of this type of heat storage system is higher than that of other storage systems. The ZnCO3 and Fe(OH)2 are selected as the reaction materials for the LP and HW level storage systems in this case study. The energy storage density for LP and HW level are 694 kWh/m3and 611 kWh/m3 [23]. An independent TS-HSC was built for both the LPS and HW utility level, which both feature available heat storage facilities. The losses in the reactant storage tank for the thermo-chemical heat storage facility is neglected [23]. The charging and discharging efficiency for both the heat storage facilities are assumed at 80% and 58% [33]. In reality, the charging and discharging efficiencies for two different type of storage facilities could be different from each other. A solar farm is available as a renewable heat source for this TS system. Solar energy is assumed to be available at 60 C [22] and can be used to heat the HW. The integrated TS system analysis is based on two scenarios. The first scenario assumes that the renewable energy provides energy sources at a typical rate. The second scenario assumes renewable energy is at its lowest intensity for the system. In the first scenario, solar energy can generate 1,000 kWh at the HW level in TSL 2. The let-down option between storage systems is not shown in this scenario. Scenario 2 assumes cloudy weather. Solar energy can only deliver 20% of a typical harvesting rate in this case. The application of the proposed tool on let-down excess energy from higher utility level to lower energy level storage system is shown in this scenario. This analysis is important to show variations in the utility requirement as a function of changes in the energy supply.
Table 4 TS-PTA at Time Slice 3. 1
2
3
4
5
6
7
8
Utility
Net heat source (kW)
Net heat sink (kW)
Net heat requirement (kW)
Initial cascade (kW)
Final cascade (kW)
MU cascade (kW)
MU targets (kW)
0.00
69.18
0.00
0.00
69.18
0.00
23.27
45.91
0.00
78.18
0.00
0.00
33.94
44.24
0.00
MPS LPS HW CW
0.00 0.00 107.18 44.24
0.00 23.27 153.09 0.00
0.00
0.00
23.27
23.27
45.91 44.24
45.91 Pinch 53.53
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
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P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
Table 6 (a) TS-HSC for energy storage facility at LPS level for Scenario 1 e infeasible cascade. (b) TS-HSC for energy storage facility at LPS level for Scenario 1 e feasible cascade. 1
2
3
4
5
6
7
TSL
Process requirement (kW)
Time (h)
Process requirement (kWh)
Additional supply (kWh)
NEA (kWh)
Infeasible cascade
8
9
10
EHU (kWh)
ECU (kWh)
491
0
Start up Cascade (kWh)
49
10
491
0 0
2
216
2,380
11
0
0
23
3
70
70
0
29
15
TSL
Feasible cascade
16
17
18
0 491
19
Start up
ECU (kWh)
206
0
0
476
0
0
0
29
0
0
0
0
712
0
0
20
21
22
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
206
0
0
476
0
1,108
29
0
0
712
0
1,108
Continuous
Cascade (kWh)
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
Cascade (kWh)
0
697
1
0
491
0
0
0 476
2
1,108
0
673
796 29
3 HS
EHU (kWh)
2,913 505
1
Heat losses (kWh)
3,012
1,805
Total
Cascade (kWh)
0
1,904 3 HS
14
1,108 476
2,380
13
1,805
491
0
12
Continuous Heat losses (kWh)
0 1
11
0
0
697
697 505
Total
1,108
491
3.1. Scenario 1 In Scenario 1, Table 6 shows the TS-HSC for the heat storage system at the LPS level. The Heat Storage Cascade begins at the first time interval in TSL 1 with a positive energy requirement at the LPS
level. The amount of LPS required at 491 kWh in TSL 1 has to be satisfied by the EHU during process start-up. The cascade for continuous operation is built. All values in the cascade for continuous operation are found to be positive values (Column 11, Table 6a). This represents that there are energy surplus in the
Table 7 a) TS-HSC for energy storage facility at HW level for Scenario 1 e infeasible cascade. (b) TS-HSC for energy storage facility at HW level for Scenario 1 e feasible cascade. 1
2
3
4
5
6
7
TSL
Process requirement (kW)
Time (h)
Process requirement (kWh)
Additional supply (kWh)
NEA (kWh)
Infeasible cascade
8
9
10
Start up Cascade (kWh)
198
10
1,984
1,984
0
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
100
11
1,095
1,000
1,984
0
0
46
3
138
138
0
58
15
TSL
Feasible cascade
Cascade (kWh)
18
19
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
Cascade (kWh)
1,984
0
833
0
0
419
0
0
0
58
0
0
0
1310
0
20
21
22
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
833
1,336
0
419
0
0
58
0
0
1,310
1,336
0
1,480
0
0 419
0
0
1,676
Total
ECU (kWh)
Continuous
0
2
1,984
17
0 1
3 HS
0
16
Start up
EHU (kWh)
144 477
Total
Heat losses (kWh)
339
1,480
1
Cascade (kWh)
0
1,676 3 HS
14
1,336 419
2,095
13
1,480 0
0 2
12
Continuous
0 1
11
1,675 58
0
0
1,480
1,480 477
1,984
0
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P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
7
Table 8 (a) TS-HSC for energy storage facility at LPS level for Scenario 2 e infeasible cascade. (b) TS-HSC for energy storage facility at LPS level for Scenario 2 e feasible cascade. (c) TSHSC for energy storage facility at LPS level for Scenario 2 e feasible cascade with letdown. 1
2
3
4
5
6
7
TSL
Process requirement (kW)
Time (h)
Process requirement (kWh)
Additional supply (kWh)
NEA (kWh)
Infeasible cascade
8
9
10
11
EHU (kWh)
ECU (kWh)
491
0
Start up Cascade (kWh)
49
10
491
0 0
2
216
2,380
11
0
0
0
1,904 3 HS
23
3
70
29
15
16
TSL
Feasible cascade
17
18
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
Cascade (kWh)
0 0
491
0
0 476
1, 108
0
673
0
476
0
0
29
0
0
712
0
0 0
0
20
21
22
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
e206
0
0
e476
0
e1,108
e29
0
0
796 e29
0
0
697
697 e505
Total 1
23
TSL
Feasible cascade with let down
24
e1,108
491
e712
25
26
27
EHU (kWh)
ECU (kWh)
Cascade (kWh)
491
0
Start up
e1,108
0
28
29
30
31
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
Let down (kWh)
e206
0
0
e1,108
e476
0
0
0
e29
0
0
0
e712
0
0
e1,108
Continuous Heat losses (kWh)
0
1,805 0
0
0 e476
0
0
1,904
Total
0
0
2
2
206
697
1
1
ECU (kWh)
Continuous
Cascade (kWh)
Cascade (kWh)
491
19
Start up
3 HS
EHU (kWh)
2,913 505
Total
3 HS
0
1,805
1
Heat losses (kWh)
3,012
70
0
Cascade (kWh)
1,108 476
2,380
14
1,805
491
0
13
Continuous Heat losses (kWh)
0 1
12
1,904 e29
0
0
1,805
1,805 e505
491
0
system, which needs to be let-down to HW storage system or cool by ECU. This amount of heat surplus should not supply to the continuous process operation in order to prevent accumulation in the heat storage system. The excess heat (1,108 kWh) is cooled by the ECU at TSL 2 in start-up operation (Column 18, Table 6b). Energy is stored in the heat storage system by the end of the start-up operation (HS ¼ 697 kWh) and brought to the continuous operation. With the ECU supplied to the system during start-up operation, the amount of HS supplied to the continuous operation is sufficient to avoid the EHU requirement (Column 21, Table 6b). The minimum heat storage design capacity needed for the LPS is determined by the highest value in the heat storage cascade, which is 796 kWh, which is equivalent to 1.15 m3. The ECU represents the heat excess for the TS system. This heat could be let-down to satisfy the heat deficit at the HW storage level. The amount of excess energy to be let-down is based on the requirement targeted for the HW level. This integration option is demonstrated in Scenario 2. Heat losses during charging and discharging of energy from the storage are incorporated in this demonstration. Heat loss for discharging energy from storage is not
accounted in TSL 1 on process start-up, because the EHU is directly sent to the processes. The TS-HSC for the HW utility level is shown in Table 7a and b. The amount of EHU required at HW level in process start up is 1,984 kWh, while the HS available is 1,480 kWh. Negative values are found in the Heat Storage Cascade for continuous operation (Column 11, Table 7a), which is initiated with the HS available in startup operation. This represents heat deficit in the system in this energy level even energy storage is considered. This heat deficit has to be satisfied by providing EHU. A new cascade with proper EHU inclusion for continuous operation is performed (Columns 19e22, Table 7b). The EHU required is reduced to 1,336 kWh for continuous operation. The minimum storage capacity of the HW level is 1,675 kWh (2.74 m3). 1,272 kWh (2,380e1,108 kWh) of LPS and 3,095 kWh of HW (2,095 kWh from processes and 1,000 kWh from solar energy) are recovered through the storage systems for multiple utility levels in this TS system. By implementing heat storage facilities and considering its heat losses in TS system, the heating utility requirement of the LPS and HW were reduced by 100% and 56.15% during a smooth operation after the start-up cycle.
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P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
Table 9 (a) TS-HSC for energy storage facility at HW level for Scenario 2 e infeasible cascade. (b) TS-HSC for energy storage facility at HW level for Scenario 2 e feasible cascade. (c) TSHSC for energy storage facility at HW level for Scenario 2 e feasible cascade with letdown. 1
2
3
4
5
6
7
TSL
Process requirement (kW)
Time (h)
Process requirement (kWh)
Additional supply (kWh)
NEA (kWh)
Infeasible cascade
8
9
10
11
Heat loss (kWh)
EHU (kWh)
ECU (kWh)
0
1,984
0
Start up Cascade (kWh)
198
10
1,984
Cascade (kWh)
e100
e1,095
11
200
e259
1,295
0
0
46
3
138
e138
0
e58
15
TSL
Feasible cascade
16
17
18
Heat loss (kWh)
EHU (kWh)
ECU (kWh)
Cascade (kWh)
0 0
1,984
0
0 e259
0
0
1,036
e259
0
0
e58
0
0
e1150
0
0 0
0
20
21
22
Heat loss (kWh)
EHU (kWh)
ECU (kWh)
e833
1,977
0
e259
0
0
e58
0
0
1,036 e58
0
0
840
840 e317
Total 1
23
TSL
Feasible cascade with let down
24
1,984
e1,150
0
25
26
27
EHU (kWh)
ECU (kWh)
Cascade (kWh)
1,984
0
Start up
1,977
0
28
29
30
31
Heat loss (kWh)
EHU (kWh)
Let down (kWh)
ECU (kWh)
e833
869
1,108
0
e259
0
0
0
e58
0
0
0
1,108
0
Continuous Heat loss (kWh)
0
840 0
0
0 e259
0
0
1,036
1,036 e58
0
0
840
Total
0
0
2
2
0
840
1
1
e833
Continuous
Cascade (kWh)
Cascade (kWh)
1,984
19
Start up
3 HS
ECU (kWh)
e1,137 e317
Total
3 HS
0
840
1
EHU (kWh)
e941
1,036 3 HS
Heat loss (kWh)
e1,977
0 2
14
840
e1,984
0
13
Continuous
0 1
12
840 e317
1,984
0
3.2. Scenario 2 Scenario 1 considered good operating conditions for good solar energy harvesting for the TS. However, there may be a period of time with bad weather. This may possibly cover the whole week with cloudy weather. These conditions would directly affect the energy collection system for solar panels, as less energy would be collected for that period. In this scenario, it is assumed that the weather is cloudy with no wind blowing, and that only 20% of the energy is assumed to be delivered by the solar panels in the TS system. This scenario also showed the possibility for using the proposed TS-HSC in targeting TS energy requirement with consideration of heat recovery between energy storage systems at different utility level. Table 8 shows the TS-HSC for the LP level storage system in this scenario is almost the same as in Scenario 1, since there is no changes on the energy availability. However, the excess energy represented by ECU is let-down for satisfying EHU in HW level. Referring to Table 6, there are 1,108 kWh of energy excess in LP level, which is cooled by ECU. This amount of energy could be stored in the system to be let-down to satisfy the energy requirement in HW level. The TS-HSC for HW is required to be built before
e1,150
869
the timing and the amount of energy could be let-down to HW level are determined. The trend in TS-HSC for the energy storage system at the HW level for Scenario 2 does not significantly deviate from Scenario 1 because solar energy only appears in TSL 2. The TS requires 1,984 kWh of heating utility to start the process at TSL 1. The startup storage cascade shows a total of 840 kWh HS in the HW storage system. This heat is supplied to the continuous operation and reduced the EHU to 1,977 kWh (869 þ 1,108 kWh) for the TS system. With the findings in Table 9, it is obvious that the energy excess at LP level should be extracted in TSL 1 for letting down to HW level. The EHU at HW level reduced from 1,977 kWh to 869 kWh (56.04%) with the contribution of letting down excess heat from LP level. In this case, the minimum storage capacity for LP and HW levels are 1,904 kWh (2.74 m3) and 1,036 kWh (1.70 m3). 4. Conclusion An algorithmic targeting method has been proposed for TSHI with variable supply (renewable energy) and demand (urban energy consumptions) to save energy in a TS system. The newly proposed TS-HSC has several advantages:
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Fig. 3. Flow diagram of the proposed methodology.
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P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
1. The proposed methodology is used to evaluate the utility requirement for various scenarios, which is very important to integrate renewable energy sources with variable supply. 2. The TS-HSC considers start-up and normal process operation for the TS system. 3. The proposed method considers TES facility for multiple utility levels in TS. 4. The proposed tool incorporates the heat loss during energy conversion process for energy charging or discharging in thermo-chemical energy storage. 5. The novel methodology considers the integration option for letting down the heat excess from a higher utility storage level to the lower level. 6. The TS-HSC can determine the quantity of heat recovered via the heat storage system for subsequent time intervals. 7. The algorithmic tool is advantageous from the perspective of efficiency and precision as compared to graphical TSHI approaches. Based on the typical case study scenario (Scenario 1), the minimum storage capacities of the LPS and HW storage systems should be 673 kWh and 1,675 kWh. The external heating utilities requirement at the LPS level is completely eliminated with contributions from the heat storage system. The heating requirement at HW level is reduced by 56.15% with the energy harvested from solar energy and energy storage system. The energy recovery between heat storage systems is studied in Scenario 2. The hot utility requirement for HW level is reduced by 56.04%. However, the investment cost increased due to the increment in storage capacity for LP level. Future research on TSHI should consider the long-term variations in energy availability and process operation schedules. The work can also be extended by exploring the trade-off between the potential power generation from excess heat and the thermal storage between Time Slices. The detailed piping systems, pressure drop and design of the storage facility should be studied, which influence the capital and operating costs. Another group of important issues, such as operability, reliability and maintenance should be analysed to ensure this technology is realistic from an operational point of view. Acknowledgements The authors would like to thank the Universiti Teknologi Malaysia (UTM) for providing the research funding for this project under Vote No. Q.J130000.7125.03H44. The first author gratefully acknowledges the PhD scholarship provided by the Ministry of Education (MOE) of Malaysia. The support from the Hungarian project Társadalmi Megújulás Operatív Program “TÁMOP e 4.2.2.A11/1/KONV-2012-0072 e Design and optimisation of modernisation and efficient operation of energy supply and utilisation systems using renewable energy sources and ICTs” and the EC supported project “Energye2011-8-1 Efficient Energy Integrated Solutions for Manufacturing Industries (EFENIS) e ENER/FP7/ 296003/EFENIS” significantly contributed to the completion of this analysis. Nomenclature CC Composite Curve ECU external cooling utility [kWh] EHU external heating utility [kWh] DTmin minimum temperature differences [ C] GCC Grand Composite Curve HS heat surplus [kWh] HEN heat exchangers network LIES locally integrated energy sector
DTmin,pp minimum temperature differences between process and process [ C]
DTmin,up minimum temperature differences between process and MU NEA NG SCC SUGCC TES TSL T TSHI TS-HSC TS-PTA TSP TSST TSUD
utility [ C] multiple utility [kWh] Net Energy Available [kWh] natural gas Site Composite Curve Site Utility Grand Composite Curve Thermal Energy Storage Time Slice Total Site Total Site Heat Integration Total Site Heat Storage Cascade Total Site Problem Table Algorithm Total Site Profiles Total Site Sensitivity Table Total Site Utility Distribution
References [1] E. Taibi, D. Gielen, M. Bazilian, The potential for renewable energy in industrial applications, Renew. Sust. Energ. Rev. 16 (2012) 735e744. [2] J. Klemes (Ed.), Handbook of Process Integration (PI): Minimisation of Energy and Water Use, Waste and Emissions, Woodhead/Elsevier, Cambridge, United Kingdom, 2013. [3] M. Bendig, F. Maréchal, D. Favrat, Defining “waste heat” for industrial processes, Appl. Therm. Eng. 61 (2013) 134e142. [4] V.R. Dhole, B. Linnhoff, Total site targets for fuel, co-generation, emissions, and cooling, Comput. Chem. Eng. 17 (1993) 101e109. [5] K. Raissi, Total Site Integration (PhD Thesis), UMIST, Manchester, UK, 1994. [6] J. Klemes, V.R. Dhole, K. Raissi, S.J. Perry, L. Puigjaner, Targeting and design methodology for reduction of fuel, power and CO2 on total sites, Appl. Therm. Eng. 17 (1997) 993e1003. [7] P.S. Varbanov, Z. Fodor, J.J. Klemes, Total Site targeting with process specific minimum temperature difference (DTmin), Energy 44 (2012) 20e28. [8] P.Y. Liew, S.R. Wan Alwi, P.S. Varbanov, Z.A. Manan, J.J. Klemes, A numerical technique for Total Site sensitivity analysis, Appl. Therm. Eng. 40 (2012) 397e408. [9] P.Y. Liew, S.R. Wan Alwi, P.S. Varbanov, Z.A. Manan, J.J. Klemes, Centralised utility system planning for a total site heat integration network, Comput. Chem. Eng. 57 (2013) 104e111. [10] A. Kapil, I. Bulatov, R. Smith, J. Kim, Process integration of low grade heat in process industry with district heating networks, Energy 44 (2012) 11e19. [11] R. Hackl, S. Harvey, Applying exergy and total site analysis for targeting refrigeration shaft power in industrial clusters, Energy 55 (2013) 5e14. [12] R. Hackl, E. Andersson, S. Harvey, Targeting for energy efficiency and improved energy collaboration between different companies using total site analysis (TSA), Energy 36 (2011) 4609e4615. [13] K. Matsuda, Y. Hirochi, H. Tatsumi, T. Shire, Applying heat integration total site based pinch technology to a large industrial area in Japan to further improve performance of highly efficient process plants, Energy 34 (2009) 1687e1692. [14] K. Matsuda, S. Tanaka, M. Endou, T. Iiyoshi, Energy saving study on a large steel plant by total site based pinch technology, Appl. Therm. Eng. 43 (2012) 14e19. [15] M.J. Atkins, M.R.W. Walmsley, J.R. Neale, Process integration between individual plants at a large dairy factory by the application of heat recovery loops and transient stream analysis, J. Clean. Prod. 34 (2012) 21e28. [16] J. Varghese, S. Bandyopadhyay, Fired heater integration into total site and multiple fired heater targeting, Appl. Therm. Eng. 42 (2012) 111e118. [17] M.H. Khoshgoftar Manesh, M. Amidpour, S. Khamis Abadi, M.H. Hamedi, A new cogeneration targeting procedure for total site utility system, Appl. Therm. Eng. 54 (2013) 272e280. [18] N.D. Chaturvedi, S. Bandyopadhyay, Indirect thermal integration for batch processes, Appl. Therm. Eng. 62 (2014) 229e238. [19] K.H. Chew, J.J. Klemes, S.R. Wan Alwi, Z. Abdul Manan, Industrial implementation issues of Total Site Heat Integration, Appl. Therm. Eng. 61 (2013) 17e25. [20] S. Perry, J. Klemes, I. Bulatov, Integrating waste and renewable energy to reduce the carbon footprint of locally integrated energy sectors, Energy 33 (2008) 1489e1497. [21] P.S. Varbanov, J.J. Klemes, Integration and management of renewables into Total Sites with variable supply and demand, Comput. Chem. Eng. 35 (2011) 1815e1826. [22] A. Nemet, J.J. Klemes, P.S. Varbanov, Z. Kravanja, Methodology for maximising the use of renewables with variable availability, Energy 44 (2012) 29e37. [23] J. Xu, R.Z. Wang, Y. Li, A review of available technologies for seasonal thermal energy storage, Sol. Energy (2013), http://dx.doi.org/10.1016/ j.solener.2013.06.006.
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11 [24] T. Kousksou, P. Bruel, A. Jamil, T. El Rhafiki, Y. Zeraouli, Energy storage: applications and challenges, Sol. Energy Mater. Sol. Cells 120 (Part A) (2014) 59e 80. [25] M.K. Rathod, J. Banerjee, Thermal stability of phase change materials used in latent heat energy storage systems: a review, Renew. Sust. Energ. Rev. 18 (2013) 246e258. [26] C.W. Chan, J. Ling-Chin, A.P. Roskilly, A review of chemical heat pumps, thermodynamic cycles and thermal energy storage technologies for low grade heat utilisation, Appl. Therm. Eng. 50 (2013) 1257e1273. [27] D. Fernandes, F. Pitié, G. Cáceres, J. Baeyens, Thermal energy storage: “how previous findings determine current research priorities”, Energy 39 (2012) 246e257. [28] S. Stoltze, B. Lorentzen, P.M. Petersen, B. Qvale, A simple technique for analyzing waste-heat recovery with heat-storage in batch processes, in:
[29] [30] [31]
[32] [33]
11
P.A. Pilavachi (Ed.), Energy Efficiency in Process Technology, Springer, Netherlands, 1993, pp. 1063e1072, http://dx.doi.org/10.1007/978-94-0111454-7_94. S.-H. Jung, I.-B. Lee, D. Yang, K. Chang, Synthesis of maximum energy recovery networks in batch processes, Korean J. Chem. Eng. 11 (1994) 162e171. O. Pourali, M. Amidpour, D. Rashtchian, Time decomposition in batch process integration, Chem. Eng. Process. Process Intensif. 45 (2006) 14e21. D.C.Y. Foo, Y.H. Chew, C.T. Lee, Minimum units targeting and network evolution for batch heat exchanger network, Appl. Therm. Eng. 28 (2008) 2089e 2099. A. Hauer, Thermal Energy Storage, International Environment Agency e Energy Technology Systems Analysis Program, Paris, France, 2013. A.H. Abedin, M.A. Rosen, Assessment of a closed thermochemical energy storage using energy and exergy methods, Appl. Energy 93 (2012) 18e23.
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Contents lists available at ScienceDirect
Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng
Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand Peng Yen Liew a, Sharifah Rafidah Wan Alwi a, *, Jirí Jaromír Klemes b, Petar Sabev Varbanov b, Zainuddin Abdul Manan a a
Process Systems Engineering Centre (PROSPECT), Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia } Centre for Process Integration and Intensification e CPI, Research Institute of Chemical and Process Engineering e MUKKI, Faculty of Information Technology, University of Pannonia, Egyetem u. 10, H-8200 Veszprém, Hungary b
Temp. (°C)
Temp. (°C)
g r a p h i c a l a b s t r a c t
Temp. (°C)
TSL 3 5h
Energy Storage
TSL 1 4h
TSL 2 8h
DH (MW)
Temp. (°C)
TSL 4 6h
DH (MW)
DH (MW)
DH (MW)
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 December 2013 Received in revised form 11 February 2014 Accepted 5 March 2014 Available online xxx
Fluctuating renewable energy supply presents a challenge for applying energy-saving methodologies such as Process Integration. Graphical targeting procedures based on the Time Slices (TSLs) have been proposed in previous works to handle the energy supply/demand variability in TSHI. The targeting procedures for TSHI with TSLs include the construction of Composite Curves, Grand Composite Curve and Total Site Profile for each time interval. Heat Integration analysis utilising a numerical algorithm typically offers higher precision and more rapid calculations as compared to the graphical approach. This paper introduces an algorithm to efficiently perform utility targeting for a large-scale TSHI system involving renewable energy and variable energy supply/demand to include TSL. The presented tool is an extension of the Total Site Problem Table Algorithm (TS-PTA), which has been previously used for processes with steady energy supply/demand. Due to its algorithmic nature, the technique presented enables the accurate and rapid determination of the stream origins, and can be embedded into larger algorithms. Optimised heat storage facilities are used to manage the variable energy supply and demand. The Total Site Heat Storage Cascade (TS-HSC) is the core of the algorithm. The new developed tool is incorporated with the heat losses for the thermo-chemical energy storage systems. The process start-up and continuous operations are considered in the novel methodology. The tool is featured to analyse the heat excess in specific TSLs that can be cascaded to the next TSL via energy storage system during start-up and operation. The proposed tool can be also used to estimate the required heat storage capacity. Ó 2014 Elsevier Ltd. All rights reserved.
Keywords: Total Site Heat Integration Thermo-chemical heat storage Heat loss Utility system Pinch Analysis Cascade analysis Variable supply and demand
* Corresponding author. E-mail addresses: [email protected], [email protected] (S.R. Wan Alwi). http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014 1359-4311/Ó 2014 Elsevier Ltd. All rights reserved.
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
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P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
1. Introduction Widespread utilisation of renewable energy sources can help to reduce the dependency on fossil fuels and mitigate the environmental emissions. However, renewable energy has been mostly utilised for power generation and for buildings. It has been targeted that 21% of the energy feedstock for the manufacturing industry by the year 2050 to comprise of renewable energy [1]. In order to meet this target, more research emphasis needs to be given to the use of renewable energy in the industrial sector. The Pinch Analysis is an established tool for Heat and Power Integration to reduce the energy usage in the industry [2]. By maximising the heat recovery within processes, Heat Integration minimises the energy requirement and reduces the wasted energy as well as environmental emissions [3]. The Heat Pinch Analysis tool has been extended to consider energy integration across several plants or processes using indirect heat transfer [4], termed as Total Site Heat Integration (TSHI). The methodology has been extended by Raissi [5] and developed further by Klemes et al. [6]. The established tools involved in the analysis include the Grand Composite Curves (GCC), the Total Site Profiles (TSP), the Site Composite Curves (SCC), and the Site Utility Grand Composite Curves (SUGCC). These tools have been developed to enable visualisation of the heat availability and consumptions on the TS. Varbanov et al. [7] proposed to replace the global minimum temperature difference (DTmin) with the minimum temperature difference for process to process (DTmin,pp) and for utility to process (DTmin,up) in Heat Integration analysis. Numerical algorithms have been introduced for TS analysis by Liew et al. [8]. The numerical tools, such as the Total Site Problem Table Algorithm (TS-PTA), the Multiple Utility Problem Table Algorithm (MU-PTA), and the Total Site Utility Distribution (TSUD) table, have been extended from the graphical approaches. Total Site Sensitivity Table (TSST) is proposed by Liew et al. [9] for assessing the utility requirement sensitivity towards the process operational changes. The algebraic/algorithmic approach vitally complements the graphical approaches as it can provide an efficient base algorithm for site-wide utility targeting, pinch point determination and even network design (covering single, multiple and total site process as well as utility systems). Kapil et al. [10] introduced a mathematical optimisation method for integrating the low grade heat to satisfy the district heating requirement. Hackl and Harvey [11] applied the TS analysis and exergy analysis on a sub-ambient refrigeration system. Hackl et al. [12] analysed the TS energy integration of a chemical cluster in Sweden. The cluster consists of five chemical companies producing a variety of products, including polyethylene, polyvinyl chloride, amines, ethylene, oxygen/nitrogen and plasticisers. The TS analysis result shows that up to 129 MW of energy can be potentially saved via TS centralised utility system. The TS methodology also has been implemented to heavy chemical complex [13], steel plant [14], and large dairy factory [15]. Varghese and Bandyopadhyay [16] proposed a systematic methodology for integrating the fired heater into the site utility system. A new model for estimating the cogeneration potential in a site utility system is published by Khoshgoftar Manesh et al. [17]. Chaturvedi and Bandyopadhyay [18] proposed a new targeting methodology using indirect heat transfer in TS integration for batch processes. Chew et al. [19] summarised the issues to be considered to ensure the practical industrial application of TS system. The TS concept was initially proposed as a systematic tool for energy conservation among industrial processes. The Locally Integrated Energy System (LIES) is a TS problem addressed by Perry et al. [20] where batch processes, renewable energy and urban energy consumptions are considered. Varbanov and Klemes [21]
proposed a TS heat cascade, which has shown the relation between process, steam system, renewable energy and heat storage. However, the intermittent renewable energy sources (e.g. solar for heat generation) typically varies with time and location [22]. Similar to the methodology for individual Batch Process Heat Integration, the Time Slice (TSL) methodology is introduced to handle the variable nature of the renewable energy supply and urban energy demands [21]. Fluctuations in energy supply/demand in a TS system are addressed by installing a Thermal Energy Storage (TES) facility and by implementing the TSL methodology in the system. The concept of TS energy consumption and generation can be understood from the perspective of a centralised utility system. Fig. 1 shows the TS Heat Cascade proposed by Varbanov and Klemes [21]. TSP targets multiple utility requirements, which are indicated by black arrows in Fig. 1 as steam generation and consumption. Heat storage facilities can be available for any type of utility, represented by horizontal arrows. The storage systems connect the utility from one TSL to another. The heat excess within a cycle of operation has to be discarded to prevent the waste of energy, which cannot be accumulated in the system. It is important to maintain the energy storage facility capacity at the optimal condition. Three types of TES facility among those available are: the sensible heat, latent heat, and chemical energy storage systems [23]. The energy capacity and storage volume depends on the material and temperature. Sensible heat storage is based on liquid media storage (e.g., water, oil-based fluids, and molten salts) or solid media storage (e.g., rocks and metal) [24]. Latent heat storage is more attractive compared to sensible heat storage because it requires less material weight and volume to store a certain amount of energy. Latent heat also requires large heat storage densities and capacities at constant temperatures. Numerous studies on latent heat storage materials, also known as phase change materials, have been reported. Phase change materials can be organic or inorganic depending on their temperature range, suitability for heating or cooling, thermophysical properties and long term stability [25]. The energy density provided by thermo-chemical storage systems is significantly higher than that of other storage technologies; as such, this system requires additional studies [26]. The high energy density of reversible chemical reactions/processes encounter the problem for other types of energy storage facility: a very large volume of media is required to store a certain amount of energy [27].
Fig. 1. Total Site Heat Cascade [21].
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P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
TES has been conceptually implemented in the Heat Integration of batch processes as a type of indirect heat recovery for heat transfer between TSLs [21]. Stoltze et al. [28] proposed an algorithm to formulate the number of storage tanks required to minimise the energy target. Jung et al. [29] found that direct heat transfer should be maximised before indirect heat transfer with a heat storage system is used and proposed a systematic reschedule methodology. Using a time decomposition strategy, Pourali et al. [30] analysed the combination of different TSLs to minimise the total cost for the Heat Integration of batch processes. Foo et al. [31] developed a methodology to estimate the minimum number of heat exchangers for Batch Heat Integration, considering cases with and without TES. Previous studies [21] on TS variable supply and demand targeting techniques still faced several limitations: i) The energy-targeting method using TSL only considers steady-state processes without start-up/shutdown and normal process operation. ii) Mostly TES system at one utility level in the TS system is considered. iii) The heat losses for storage facility are not incorporated in the methodology. iv) The possibility of using heat excess after charging sufficient energy into the TES facility at higher level of energy storage for lower level energy demand via letdown is not taken into account. This work overcomes these previous limitations. The TS-PTA algorithm [8] is used to improve the Total Site Heat Cascade [21]. The Total Site Heat Storage Cascade (TS-HSC) Table is a new tool introduced to represent the time dependent energy flows between processes, storage facility, and centralised utility system. The new tool is build based on the thermo-chemical energy storage system that can provide the highest efficiency of energy storage [32] with minimum losses compared to sensible and latent heat storage [23]. The tool is used to determine the external utilities and heat storage capacity required during start up and operation for batch TSHI. 2. Targeting methodology The proposed algorithmic targeting methodology for TS with variable energy supply and demand is described below: 2.1. Step 1: identification of TSLs In the first step, TSLs should be identified by observing the temporal variations of the process energy demands and the renewable sources. The energy demand at a processing site could be time-dependent when a batch process is integrated. These variations are more significant if service, leisure, and commercial buildings are integrated into the TS. A TSL is a time interval with relatively constant energy variations. Fig. 2 is a simple illustration of TSL identification. Processes A, B, and C are batch processes operating at different times, and renewable energy is available at only certain times. Different time intervals with very large variations in energy consumption could be identified. A detailed TSL specification methodology has been proposed by Nemet et al. [22].
3
2.3. Step 3: TS-HSCs construction The Total Site Heat Storage Cascade (TS-HSC) is developed in this work to determine the total amount of heat that can be recovered within a TSL, to be stored or letdown. This tool is represented by the horizontal arrows in Fig. 1. The TS-HSC is constructed for each level of utility equipped with a heat storage system in the TS system. The detailed methodology to construct the TS-HSC is described as follows: i. The TSLs are inputted in the first column of the TS-HSC in ascending order. The length of each TSL should be divided into time intervals according to the heat loss assumption. ii. The site MU requirement targeted by the TS-PTA is recorded according to the TSL in the second column. A negative requirement below the Pinch in the TS-PTA indicates that is excess heat to be rejected. A positive utility requirement above the Pinch indicates the need for an external heating utility. iii. The length of each time interval in TSLs is recorded in Column 3. iv. The energy requirement from the processes in the TS (Column 4) is formulated by multiplying the MU requirement with the length of the TSL. v. Additional heat sources available to be integrated into the TS, e.g., renewable energy sources including solar and biomass, are recorded in Column 5. vi. The Net Energy Available (NEA, Column 6) is calculated by deducting the targeted site utility requirement from the available extra utility (Column 6 ¼ Column 5 Column 4). Positive NEA shows energy excess from the processes to be stored in storage system, while negative NEA shows energy deficit at processes. vii. A cascade of available enthalpy for energy storage is performed for each time interval in Column 7 starting from the highest to the lowest time interval in the last TSL. a) The Heat Storage Cascade is the sum of the energy available from the previous time intervals, NEA (Column 6), Heat Losses (Column 8), the External Heating Utility (EHU Column 9) and the External Cooling Utility (ECU Column 10) as shown in Eq. (1). The stored heat is assumed to be unavailable in the storage system during process start-up. The storage cascade begins at zero loads. The heat losses, EHU and ECU are assumed to be zero at this step.
Cascadei ¼ Cascadei1 þNEAi þHeatLossesi þEHUi þECUi (1)
b) The heat losses (Column 8) are determined according to the amount of energy charged into or discharged from the heat
Solar (14 h) Process A (18 h) Process B (14 h)
2.2. Step 2: TS multiple utility target determination for all TSLs The TS multiple utility targets and the Site Pinch location for each TSL are then determined by using TS-PTA [8]. The multiple utility requirements could also determined by using a graphical methodology.
Process C (10 h) TSL 1 00:00 h
TSL 2
TSL 3
TSL 4
TSL 5 24:00 h
Fig. 2. Determining the TSL for integrated TS with variable energy supply and demand.
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P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
Cascadei for start-up process (Column 18, Table 6b). The total ECUi added should be the same amount as the smallest value in the Cascadei for continuous operation. This step would create a “Pinch” point in Cascadei for continuous operation with zero enthalpy (Column 19, Table 6b), which represents there would be no accumulation in the storage system. In order to reduce storage capacity, the ECUi should be added at the earliest TSL with energy charged into the storage system (positive value of NEAi). The amount of ECUi added should not more than the NEAi for that particular time interval. When the NEAi is not sufficient from the earliest TSL with energy charging, the next TSL with positive NEAi is considered. If the ECUi is not satisfied after the whole start-up operation, ECUi could be added for the TSLs with positive NEAi before the smallest value in Cascadei for continuous operation. c) The ECUi for continuous operation after the Pinch should be placed similar as in start-up process to ensure that the HS for continuous operation and start up process are the same. ix. If there are two storage facilities at different energy levels, energy surplus at higher level could be let-down to satisfy the energy deficit at lower level storage system. A new Heat Storage Cascade with an additional column for let-down. The cascade methodology is the same as Step 3-vii and 3-viii, the cascade formulation changed to Eq. (4).
storage system. The heat loss for charging and discharging process can be determined by Eqs. (2) and (3) with knowing the charging efficiency (hcharging) and discharging efficiency (hdischarging). Charging:
Heat Lossesi ¼ NEAi 1 hcharging
(2)
Discharging: Heat Lossesi ¼ NEAi 1 hdischarging
(3)
c) The EHUi (Column 9) is added when Cascadei shows a heat deficit (negative value) after every time interval. The Cascadei should reach a net zero enthalpy instead of a utility deficit after the EHUi is added. d) The amount of excess heat in the storage system by the end of the process start-up is termed as heat surplus (HS). viii. A Heat Storage Cascade for continuous operation is then performed based on the start-up cascade with following steps: a) The Heat Storage Cascade for continuous operation is initiated with the HS in the Heat Storage Cascade for process start-up. The cascade is built with the same method as in Step 3-vii-(a). The heat losses for energy conversion processes are determined using the method in Step 3-vii-(b). The EHUi is added with the same condition for continuous operation (Column 21, Table 7b) as in Step 3-vii-(c) b) If all the values in Cascadei for continuous operation are larger than zero (Column 11, Table 6a), add ECUi in the
Cascadei ¼ Cascadei1 þ NEAi þ HeatLossesi þ EHUi þ ECUi þ Letdowni
b) At TS-HSC for higher utility level, the amount of energy to be let-down is deducted at the same time interval that the energy is required at lower energy level (Column 31, Table 8c). In this case, energy will be stored at higher energy level storage system until it is needed. The energy also can be charged directly to the lower level storage system. c) The Heat Storage Cascade at lower energy level should include the let-down column to represent the let-down energy from higher energy level and reduce the amount of EHU (Column 30, Table 9c). However, the economics of this integration option would need to be study. It is not recommended to use this option when there is small amount of energy excess at higher level storage system.
Table 1 Streams data for processes A, B, C and D [21]. Stream
TS ( C) TT ( C) DH (kW) Cp/HL (kW/ C) TS0 ( C) TT0 ( C) TSL (h)
Process A A1 hot A2 hot A3 cold A4 cold A5 cold A6 hot Process B B1 hot B2 hot B3 hot B4 cold B5 hot B6 cold B7 hot B8 hot Process C C1 hot C2 hot C3 cold C4 cold C5 cold C6 cold C7 cold C8 cold C9 cold C10 cold C11 cold Process D D1 cold D2 cold D3 cold
110 150 50 85 62 92
80 149 135 100 100 55
120.00 180.00 104.40 73.30 130.00 233.84
1.3333 180.0000 1.2280 4.8870 3.4210 6.3200
104 144 56 91 68 86
74 143 141 106 106 49
00e24 00e24 00e24 00e24 00e24 00e24
200 20 50 100 150 80 95 191
195 54 85 120 40 95 25 182
160.0 10.0 19.8 130.0 83.5 48.0 80.0 360.0
32.0000 0.2941 0.5657 6.5000 0.7590 3.2000 1.1430 40.0000
194 26 56 106 144 86 89 185
189 60 91 126 34 101 19 176
06e20 06e20 20e06 06e20 06e17 06e20 06e17 06e17
85 80 25 55 33 25 82 25 80 18 21
40 40 55 85 60 60 121 28 100 25 121
0.5300 2.4100 0.5760 0.6000 0.4460 0.4290 0.5740 7.7000 1.6000 5.8640 0.0500
79 74 31 61 39 31 88 31 86 24 27
34 34 61 91 66 66 127 34 106 31 127
06e17 06e17 20e06 20e06 00e24 06e17 20e06 06e17 06e17 00e24 06e17
15 15 15
25 45 45
8.8000 0.8333 3.4500
21 21 21
31 51 51
00e24 00e24 06e20
23.85 96.40 17.30 18.00 12.00 15.00 22.38 23.10 32.00 41.10 5.00
Table 2 TS-PTA at Time Slice 1. 1
2
3
4
5
6
Initial Utility Net heat Net heat Net heat sink (kW) requirement cascade source (kW) (kW) (kW) MPS LPS HW
88.0 25.0 103.5
(4)
CW
0.00 0.00 107.18 44.24
1.72 49.11 305.54 0.00
7
8
MU MU Final cascade cascade targets (kW) (kW) (kW)
0.00 249.19
0.00
1.72 247.47
0.00
50.83 198.36
0.00
1.72
1.72
49.11
49.11
198.36 249.19
0.00
0.00
204.95
44.24
0.00
44.24
198.36 Pinch 44.24
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11 Table 3 TS-PTA at Time Slice 2. 1
2
Table 5 Multiple utility targets in all TSLs.
3
4
5
6
7
8
Utility
MU MU Initial Final Utility Net heat Net heat Net heat source (kW) sink (kW) requirement cascade cascade cascade targets (kW) (kW) (kW) (kW) (kW) MPS LPS
0.00
0.15
229.45
5
13.08
0.00
0.15 0.00
0.15
0.00 0.00
0.15 216.38
0.15 Pinch 216.38
TSL 1
MPS (kW)
1.72
LPS (kW)
49.11
HW (kW)
198.36 Pinch 44.24
CW (kW)
TSL 2
TSL 3
0.15 Pinch 216.38
0.00 23.27
99.50
45.91 Pinch 44.24
94.41
216.23 216.38 0.00 HW
189.51
90.00
99.50
99.51 315.73 315.88 0.00
CW
94.41
0.00
94.41
94.41 410.14 410.29 0.00
x. The EHUi and ECUi values in TS-HSC is revised for eliminating unnecessary heat losses accounted in the previous steps. Heat loss should not be considered when EHU and ECU are supplied to the processes directly. The final cascade in TSHSC should satisfy the requirements as listed below: a) Heat Storage Cascade for start-up and continuous operation should not consist of negative values. b) A time interval in the Heat Storage Cascade for continuous operation should show no load in the storage facility. c) The HS of the cascade for start-up and continuous operation should be the same to avoid unnecessary accumulation in the storage facility. For summarising the methodology above, Fig. 3 shows the flow diagram of the proposed methodology for targeting the energy requirement of a TS system dealing with variable supply and demand or batch processes using TS-HSC. 3. Case study The proposed procedure is illustrated using a case study involving four units: Processes A, B, C and D. Process A and B represent industrial plants, Process C is a hotel and Process D is a residential area. The stream table of these units is shown in Table 1. The data from Varbanov and Klemes [21] are modified to demonstrate different possible applications of the newly proposed tool. According to the availability of the streams indicated in Table 1, three TSLs can be identified for this case study: 20e06 h (TSL 1), 06e17 h (TSL 2), and 17e20 h (TSL 3). The minimum temperature difference, DTmin, was assumed to be 12 C. The utilities available on site are Medium Pressure steam (MPS) at 220 C, Low Pressure Steam (LPS) at 130 C, a hot water system (HW) at 80e50 C and the only cooling utility is cooling water (CW) at 15e30 C.
The TS-PTAs for all TSLs are constructed as proposed by Liew et al. [8] to determine the minimum utility requirements at different TSLs (Tables 2e4). Table 5 summarises the amount of utilities required by the processes on a different TSL from Column 8 of each TS-PTA. For example, MPS, LPS and HW hot utilities are required from a centralised utility system in TSL 1 at 1.72 kW, 49.11 kW and 198.36 kW. The TS system required 44.24 kW of CW to cool hot streams. Chemical heat storage facilities are assumed available for LPS and HW storage at the site to allow heat transfer between TSLs. The energy density of this type of heat storage system is higher than that of other storage systems. The ZnCO3 and Fe(OH)2 are selected as the reaction materials for the LP and HW level storage systems in this case study. The energy storage density for LP and HW level are 694 kWh/m3and 611 kWh/m3 [23]. An independent TS-HSC was built for both the LPS and HW utility level, which both feature available heat storage facilities. The losses in the reactant storage tank for the thermo-chemical heat storage facility is neglected [23]. The charging and discharging efficiency for both the heat storage facilities are assumed at 80% and 58% [33]. In reality, the charging and discharging efficiencies for two different type of storage facilities could be different from each other. A solar farm is available as a renewable heat source for this TS system. Solar energy is assumed to be available at 60 C [22] and can be used to heat the HW. The integrated TS system analysis is based on two scenarios. The first scenario assumes that the renewable energy provides energy sources at a typical rate. The second scenario assumes renewable energy is at its lowest intensity for the system. In the first scenario, solar energy can generate 1,000 kWh at the HW level in TSL 2. The let-down option between storage systems is not shown in this scenario. Scenario 2 assumes cloudy weather. Solar energy can only deliver 20% of a typical harvesting rate in this case. The application of the proposed tool on let-down excess energy from higher utility level to lower energy level storage system is shown in this scenario. This analysis is important to show variations in the utility requirement as a function of changes in the energy supply.
Table 4 TS-PTA at Time Slice 3. 1
2
3
4
5
6
7
8
Utility
Net heat source (kW)
Net heat sink (kW)
Net heat requirement (kW)
Initial cascade (kW)
Final cascade (kW)
MU cascade (kW)
MU targets (kW)
0.00
69.18
0.00
0.00
69.18
0.00
23.27
45.91
0.00
78.18
0.00
0.00
33.94
44.24
0.00
MPS LPS HW CW
0.00 0.00 107.18 44.24
0.00 23.27 153.09 0.00
0.00
0.00
23.27
23.27
45.91 44.24
45.91 Pinch 53.53
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P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
Table 6 (a) TS-HSC for energy storage facility at LPS level for Scenario 1 e infeasible cascade. (b) TS-HSC for energy storage facility at LPS level for Scenario 1 e feasible cascade. 1
2
3
4
5
6
7
TSL
Process requirement (kW)
Time (h)
Process requirement (kWh)
Additional supply (kWh)
NEA (kWh)
Infeasible cascade
8
9
10
EHU (kWh)
ECU (kWh)
491
0
Start up Cascade (kWh)
49
10
491
0 0
2
216
2,380
11
0
0
23
3
70
70
0
29
15
TSL
Feasible cascade
16
17
18
0 491
19
Start up
ECU (kWh)
206
0
0
476
0
0
0
29
0
0
0
0
712
0
0
20
21
22
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
206
0
0
476
0
1,108
29
0
0
712
0
1,108
Continuous
Cascade (kWh)
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
Cascade (kWh)
0
697
1
0
491
0
0
0 476
2
1,108
0
673
796 29
3 HS
EHU (kWh)
2,913 505
1
Heat losses (kWh)
3,012
1,805
Total
Cascade (kWh)
0
1,904 3 HS
14
1,108 476
2,380
13
1,805
491
0
12
Continuous Heat losses (kWh)
0 1
11
0
0
697
697 505
Total
1,108
491
3.1. Scenario 1 In Scenario 1, Table 6 shows the TS-HSC for the heat storage system at the LPS level. The Heat Storage Cascade begins at the first time interval in TSL 1 with a positive energy requirement at the LPS
level. The amount of LPS required at 491 kWh in TSL 1 has to be satisfied by the EHU during process start-up. The cascade for continuous operation is built. All values in the cascade for continuous operation are found to be positive values (Column 11, Table 6a). This represents that there are energy surplus in the
Table 7 a) TS-HSC for energy storage facility at HW level for Scenario 1 e infeasible cascade. (b) TS-HSC for energy storage facility at HW level for Scenario 1 e feasible cascade. 1
2
3
4
5
6
7
TSL
Process requirement (kW)
Time (h)
Process requirement (kWh)
Additional supply (kWh)
NEA (kWh)
Infeasible cascade
8
9
10
Start up Cascade (kWh)
198
10
1,984
1,984
0
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
100
11
1,095
1,000
1,984
0
0
46
3
138
138
0
58
15
TSL
Feasible cascade
Cascade (kWh)
18
19
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
Cascade (kWh)
1,984
0
833
0
0
419
0
0
0
58
0
0
0
1310
0
20
21
22
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
833
1,336
0
419
0
0
58
0
0
1,310
1,336
0
1,480
0
0 419
0
0
1,676
Total
ECU (kWh)
Continuous
0
2
1,984
17
0 1
3 HS
0
16
Start up
EHU (kWh)
144 477
Total
Heat losses (kWh)
339
1,480
1
Cascade (kWh)
0
1,676 3 HS
14
1,336 419
2,095
13
1,480 0
0 2
12
Continuous
0 1
11
1,675 58
0
0
1,480
1,480 477
1,984
0
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7
Table 8 (a) TS-HSC for energy storage facility at LPS level for Scenario 2 e infeasible cascade. (b) TS-HSC for energy storage facility at LPS level for Scenario 2 e feasible cascade. (c) TSHSC for energy storage facility at LPS level for Scenario 2 e feasible cascade with letdown. 1
2
3
4
5
6
7
TSL
Process requirement (kW)
Time (h)
Process requirement (kWh)
Additional supply (kWh)
NEA (kWh)
Infeasible cascade
8
9
10
11
EHU (kWh)
ECU (kWh)
491
0
Start up Cascade (kWh)
49
10
491
0 0
2
216
2,380
11
0
0
0
1,904 3 HS
23
3
70
29
15
16
TSL
Feasible cascade
17
18
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
Cascade (kWh)
0 0
491
0
0 476
1, 108
0
673
0
476
0
0
29
0
0
712
0
0 0
0
20
21
22
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
e206
0
0
e476
0
e1,108
e29
0
0
796 e29
0
0
697
697 e505
Total 1
23
TSL
Feasible cascade with let down
24
e1,108
491
e712
25
26
27
EHU (kWh)
ECU (kWh)
Cascade (kWh)
491
0
Start up
e1,108
0
28
29
30
31
Heat losses (kWh)
EHU (kWh)
ECU (kWh)
Let down (kWh)
e206
0
0
e1,108
e476
0
0
0
e29
0
0
0
e712
0
0
e1,108
Continuous Heat losses (kWh)
0
1,805 0
0
0 e476
0
0
1,904
Total
0
0
2
2
206
697
1
1
ECU (kWh)
Continuous
Cascade (kWh)
Cascade (kWh)
491
19
Start up
3 HS
EHU (kWh)
2,913 505
Total
3 HS
0
1,805
1
Heat losses (kWh)
3,012
70
0
Cascade (kWh)
1,108 476
2,380
14
1,805
491
0
13
Continuous Heat losses (kWh)
0 1
12
1,904 e29
0
0
1,805
1,805 e505
491
0
system, which needs to be let-down to HW storage system or cool by ECU. This amount of heat surplus should not supply to the continuous process operation in order to prevent accumulation in the heat storage system. The excess heat (1,108 kWh) is cooled by the ECU at TSL 2 in start-up operation (Column 18, Table 6b). Energy is stored in the heat storage system by the end of the start-up operation (HS ¼ 697 kWh) and brought to the continuous operation. With the ECU supplied to the system during start-up operation, the amount of HS supplied to the continuous operation is sufficient to avoid the EHU requirement (Column 21, Table 6b). The minimum heat storage design capacity needed for the LPS is determined by the highest value in the heat storage cascade, which is 796 kWh, which is equivalent to 1.15 m3. The ECU represents the heat excess for the TS system. This heat could be let-down to satisfy the heat deficit at the HW storage level. The amount of excess energy to be let-down is based on the requirement targeted for the HW level. This integration option is demonstrated in Scenario 2. Heat losses during charging and discharging of energy from the storage are incorporated in this demonstration. Heat loss for discharging energy from storage is not
accounted in TSL 1 on process start-up, because the EHU is directly sent to the processes. The TS-HSC for the HW utility level is shown in Table 7a and b. The amount of EHU required at HW level in process start up is 1,984 kWh, while the HS available is 1,480 kWh. Negative values are found in the Heat Storage Cascade for continuous operation (Column 11, Table 7a), which is initiated with the HS available in startup operation. This represents heat deficit in the system in this energy level even energy storage is considered. This heat deficit has to be satisfied by providing EHU. A new cascade with proper EHU inclusion for continuous operation is performed (Columns 19e22, Table 7b). The EHU required is reduced to 1,336 kWh for continuous operation. The minimum storage capacity of the HW level is 1,675 kWh (2.74 m3). 1,272 kWh (2,380e1,108 kWh) of LPS and 3,095 kWh of HW (2,095 kWh from processes and 1,000 kWh from solar energy) are recovered through the storage systems for multiple utility levels in this TS system. By implementing heat storage facilities and considering its heat losses in TS system, the heating utility requirement of the LPS and HW were reduced by 100% and 56.15% during a smooth operation after the start-up cycle.
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
8
P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
Table 9 (a) TS-HSC for energy storage facility at HW level for Scenario 2 e infeasible cascade. (b) TS-HSC for energy storage facility at HW level for Scenario 2 e feasible cascade. (c) TSHSC for energy storage facility at HW level for Scenario 2 e feasible cascade with letdown. 1
2
3
4
5
6
7
TSL
Process requirement (kW)
Time (h)
Process requirement (kWh)
Additional supply (kWh)
NEA (kWh)
Infeasible cascade
8
9
10
11
Heat loss (kWh)
EHU (kWh)
ECU (kWh)
0
1,984
0
Start up Cascade (kWh)
198
10
1,984
Cascade (kWh)
e100
e1,095
11
200
e259
1,295
0
0
46
3
138
e138
0
e58
15
TSL
Feasible cascade
16
17
18
Heat loss (kWh)
EHU (kWh)
ECU (kWh)
Cascade (kWh)
0 0
1,984
0
0 e259
0
0
1,036
e259
0
0
e58
0
0
e1150
0
0 0
0
20
21
22
Heat loss (kWh)
EHU (kWh)
ECU (kWh)
e833
1,977
0
e259
0
0
e58
0
0
1,036 e58
0
0
840
840 e317
Total 1
23
TSL
Feasible cascade with let down
24
1,984
e1,150
0
25
26
27
EHU (kWh)
ECU (kWh)
Cascade (kWh)
1,984
0
Start up
1,977
0
28
29
30
31
Heat loss (kWh)
EHU (kWh)
Let down (kWh)
ECU (kWh)
e833
869
1,108
0
e259
0
0
0
e58
0
0
0
1,108
0
Continuous Heat loss (kWh)
0
840 0
0
0 e259
0
0
1,036
1,036 e58
0
0
840
Total
0
0
2
2
0
840
1
1
e833
Continuous
Cascade (kWh)
Cascade (kWh)
1,984
19
Start up
3 HS
ECU (kWh)
e1,137 e317
Total
3 HS
0
840
1
EHU (kWh)
e941
1,036 3 HS
Heat loss (kWh)
e1,977
0 2
14
840
e1,984
0
13
Continuous
0 1
12
840 e317
1,984
0
3.2. Scenario 2 Scenario 1 considered good operating conditions for good solar energy harvesting for the TS. However, there may be a period of time with bad weather. This may possibly cover the whole week with cloudy weather. These conditions would directly affect the energy collection system for solar panels, as less energy would be collected for that period. In this scenario, it is assumed that the weather is cloudy with no wind blowing, and that only 20% of the energy is assumed to be delivered by the solar panels in the TS system. This scenario also showed the possibility for using the proposed TS-HSC in targeting TS energy requirement with consideration of heat recovery between energy storage systems at different utility level. Table 8 shows the TS-HSC for the LP level storage system in this scenario is almost the same as in Scenario 1, since there is no changes on the energy availability. However, the excess energy represented by ECU is let-down for satisfying EHU in HW level. Referring to Table 6, there are 1,108 kWh of energy excess in LP level, which is cooled by ECU. This amount of energy could be stored in the system to be let-down to satisfy the energy requirement in HW level. The TS-HSC for HW is required to be built before
e1,150
869
the timing and the amount of energy could be let-down to HW level are determined. The trend in TS-HSC for the energy storage system at the HW level for Scenario 2 does not significantly deviate from Scenario 1 because solar energy only appears in TSL 2. The TS requires 1,984 kWh of heating utility to start the process at TSL 1. The startup storage cascade shows a total of 840 kWh HS in the HW storage system. This heat is supplied to the continuous operation and reduced the EHU to 1,977 kWh (869 þ 1,108 kWh) for the TS system. With the findings in Table 9, it is obvious that the energy excess at LP level should be extracted in TSL 1 for letting down to HW level. The EHU at HW level reduced from 1,977 kWh to 869 kWh (56.04%) with the contribution of letting down excess heat from LP level. In this case, the minimum storage capacity for LP and HW levels are 1,904 kWh (2.74 m3) and 1,036 kWh (1.70 m3). 4. Conclusion An algorithmic targeting method has been proposed for TSHI with variable supply (renewable energy) and demand (urban energy consumptions) to save energy in a TS system. The newly proposed TS-HSC has several advantages:
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
Fig. 3. Flow diagram of the proposed methodology.
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
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P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11
1. The proposed methodology is used to evaluate the utility requirement for various scenarios, which is very important to integrate renewable energy sources with variable supply. 2. The TS-HSC considers start-up and normal process operation for the TS system. 3. The proposed method considers TES facility for multiple utility levels in TS. 4. The proposed tool incorporates the heat loss during energy conversion process for energy charging or discharging in thermo-chemical energy storage. 5. The novel methodology considers the integration option for letting down the heat excess from a higher utility storage level to the lower level. 6. The TS-HSC can determine the quantity of heat recovered via the heat storage system for subsequent time intervals. 7. The algorithmic tool is advantageous from the perspective of efficiency and precision as compared to graphical TSHI approaches. Based on the typical case study scenario (Scenario 1), the minimum storage capacities of the LPS and HW storage systems should be 673 kWh and 1,675 kWh. The external heating utilities requirement at the LPS level is completely eliminated with contributions from the heat storage system. The heating requirement at HW level is reduced by 56.15% with the energy harvested from solar energy and energy storage system. The energy recovery between heat storage systems is studied in Scenario 2. The hot utility requirement for HW level is reduced by 56.04%. However, the investment cost increased due to the increment in storage capacity for LP level. Future research on TSHI should consider the long-term variations in energy availability and process operation schedules. The work can also be extended by exploring the trade-off between the potential power generation from excess heat and the thermal storage between Time Slices. The detailed piping systems, pressure drop and design of the storage facility should be studied, which influence the capital and operating costs. Another group of important issues, such as operability, reliability and maintenance should be analysed to ensure this technology is realistic from an operational point of view. Acknowledgements The authors would like to thank the Universiti Teknologi Malaysia (UTM) for providing the research funding for this project under Vote No. Q.J130000.7125.03H44. The first author gratefully acknowledges the PhD scholarship provided by the Ministry of Education (MOE) of Malaysia. The support from the Hungarian project Társadalmi Megújulás Operatív Program “TÁMOP e 4.2.2.A11/1/KONV-2012-0072 e Design and optimisation of modernisation and efficient operation of energy supply and utilisation systems using renewable energy sources and ICTs” and the EC supported project “Energye2011-8-1 Efficient Energy Integrated Solutions for Manufacturing Industries (EFENIS) e ENER/FP7/ 296003/EFENIS” significantly contributed to the completion of this analysis. Nomenclature CC Composite Curve ECU external cooling utility [kWh] EHU external heating utility [kWh] DTmin minimum temperature differences [ C] GCC Grand Composite Curve HS heat surplus [kWh] HEN heat exchangers network LIES locally integrated energy sector
DTmin,pp minimum temperature differences between process and process [ C]
DTmin,up minimum temperature differences between process and MU NEA NG SCC SUGCC TES TSL T TSHI TS-HSC TS-PTA TSP TSST TSUD
utility [ C] multiple utility [kWh] Net Energy Available [kWh] natural gas Site Composite Curve Site Utility Grand Composite Curve Thermal Energy Storage Time Slice Total Site Total Site Heat Integration Total Site Heat Storage Cascade Total Site Problem Table Algorithm Total Site Profiles Total Site Sensitivity Table Total Site Utility Distribution
References [1] E. Taibi, D. Gielen, M. Bazilian, The potential for renewable energy in industrial applications, Renew. Sust. Energ. Rev. 16 (2012) 735e744. [2] J. Klemes (Ed.), Handbook of Process Integration (PI): Minimisation of Energy and Water Use, Waste and Emissions, Woodhead/Elsevier, Cambridge, United Kingdom, 2013. [3] M. Bendig, F. Maréchal, D. Favrat, Defining “waste heat” for industrial processes, Appl. Therm. Eng. 61 (2013) 134e142. [4] V.R. Dhole, B. Linnhoff, Total site targets for fuel, co-generation, emissions, and cooling, Comput. Chem. Eng. 17 (1993) 101e109. [5] K. Raissi, Total Site Integration (PhD Thesis), UMIST, Manchester, UK, 1994. [6] J. Klemes, V.R. Dhole, K. Raissi, S.J. Perry, L. Puigjaner, Targeting and design methodology for reduction of fuel, power and CO2 on total sites, Appl. Therm. Eng. 17 (1997) 993e1003. [7] P.S. Varbanov, Z. Fodor, J.J. Klemes, Total Site targeting with process specific minimum temperature difference (DTmin), Energy 44 (2012) 20e28. [8] P.Y. Liew, S.R. Wan Alwi, P.S. Varbanov, Z.A. Manan, J.J. Klemes, A numerical technique for Total Site sensitivity analysis, Appl. Therm. Eng. 40 (2012) 397e408. [9] P.Y. Liew, S.R. Wan Alwi, P.S. Varbanov, Z.A. Manan, J.J. Klemes, Centralised utility system planning for a total site heat integration network, Comput. Chem. Eng. 57 (2013) 104e111. [10] A. Kapil, I. Bulatov, R. Smith, J. Kim, Process integration of low grade heat in process industry with district heating networks, Energy 44 (2012) 11e19. [11] R. Hackl, S. Harvey, Applying exergy and total site analysis for targeting refrigeration shaft power in industrial clusters, Energy 55 (2013) 5e14. [12] R. Hackl, E. Andersson, S. Harvey, Targeting for energy efficiency and improved energy collaboration between different companies using total site analysis (TSA), Energy 36 (2011) 4609e4615. [13] K. Matsuda, Y. Hirochi, H. Tatsumi, T. Shire, Applying heat integration total site based pinch technology to a large industrial area in Japan to further improve performance of highly efficient process plants, Energy 34 (2009) 1687e1692. [14] K. Matsuda, S. Tanaka, M. Endou, T. Iiyoshi, Energy saving study on a large steel plant by total site based pinch technology, Appl. Therm. Eng. 43 (2012) 14e19. [15] M.J. Atkins, M.R.W. Walmsley, J.R. Neale, Process integration between individual plants at a large dairy factory by the application of heat recovery loops and transient stream analysis, J. Clean. Prod. 34 (2012) 21e28. [16] J. Varghese, S. Bandyopadhyay, Fired heater integration into total site and multiple fired heater targeting, Appl. Therm. Eng. 42 (2012) 111e118. [17] M.H. Khoshgoftar Manesh, M. Amidpour, S. Khamis Abadi, M.H. Hamedi, A new cogeneration targeting procedure for total site utility system, Appl. Therm. Eng. 54 (2013) 272e280. [18] N.D. Chaturvedi, S. Bandyopadhyay, Indirect thermal integration for batch processes, Appl. Therm. Eng. 62 (2014) 229e238. [19] K.H. Chew, J.J. Klemes, S.R. Wan Alwi, Z. Abdul Manan, Industrial implementation issues of Total Site Heat Integration, Appl. Therm. Eng. 61 (2013) 17e25. [20] S. Perry, J. Klemes, I. Bulatov, Integrating waste and renewable energy to reduce the carbon footprint of locally integrated energy sectors, Energy 33 (2008) 1489e1497. [21] P.S. Varbanov, J.J. Klemes, Integration and management of renewables into Total Sites with variable supply and demand, Comput. Chem. Eng. 35 (2011) 1815e1826. [22] A. Nemet, J.J. Klemes, P.S. Varbanov, Z. Kravanja, Methodology for maximising the use of renewables with variable availability, Energy 44 (2012) 29e37. [23] J. Xu, R.Z. Wang, Y. Li, A review of available technologies for seasonal thermal energy storage, Sol. Energy (2013), http://dx.doi.org/10.1016/ j.solener.2013.06.006.
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014
P.Y. Liew et al. / Applied Thermal Engineering xxx (2014) 1e11 [24] T. Kousksou, P. Bruel, A. Jamil, T. El Rhafiki, Y. Zeraouli, Energy storage: applications and challenges, Sol. Energy Mater. Sol. Cells 120 (Part A) (2014) 59e 80. [25] M.K. Rathod, J. Banerjee, Thermal stability of phase change materials used in latent heat energy storage systems: a review, Renew. Sust. Energ. Rev. 18 (2013) 246e258. [26] C.W. Chan, J. Ling-Chin, A.P. Roskilly, A review of chemical heat pumps, thermodynamic cycles and thermal energy storage technologies for low grade heat utilisation, Appl. Therm. Eng. 50 (2013) 1257e1273. [27] D. Fernandes, F. Pitié, G. Cáceres, J. Baeyens, Thermal energy storage: “how previous findings determine current research priorities”, Energy 39 (2012) 246e257. [28] S. Stoltze, B. Lorentzen, P.M. Petersen, B. Qvale, A simple technique for analyzing waste-heat recovery with heat-storage in batch processes, in:
[29] [30] [31]
[32] [33]
11
P.A. Pilavachi (Ed.), Energy Efficiency in Process Technology, Springer, Netherlands, 1993, pp. 1063e1072, http://dx.doi.org/10.1007/978-94-0111454-7_94. S.-H. Jung, I.-B. Lee, D. Yang, K. Chang, Synthesis of maximum energy recovery networks in batch processes, Korean J. Chem. Eng. 11 (1994) 162e171. O. Pourali, M. Amidpour, D. Rashtchian, Time decomposition in batch process integration, Chem. Eng. Process. Process Intensif. 45 (2006) 14e21. D.C.Y. Foo, Y.H. Chew, C.T. Lee, Minimum units targeting and network evolution for batch heat exchanger network, Appl. Therm. Eng. 28 (2008) 2089e 2099. A. Hauer, Thermal Energy Storage, International Environment Agency e Energy Technology Systems Analysis Program, Paris, France, 2013. A.H. Abedin, M.A. Rosen, Assessment of a closed thermochemical energy storage using energy and exergy methods, Appl. Energy 93 (2012) 18e23.
Please cite this article in press as: P.Y. Liew, et al., Algorithmic targeting for Total Site Heat Integration with variable energy supply/demand, Applied Thermal Engineering (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.03.014