Targeting for cogeneration potential and steam allocation for steam distribution network

Targeting for cogeneration potential and steam allocation for steam distribution network

Accepted Manuscript Research Paper Targeting for Cogeneration Potential and Steam Allocation for Steam Distribution Network Rex T.L. Ng, Jaslyn S.W. L...

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Accepted Manuscript Research Paper Targeting for Cogeneration Potential and Steam Allocation for Steam Distribution Network Rex T.L. Ng, Jaslyn S.W. Loo, Denny K.S. Ng, Dominic C.Y. Foo, Jin-Kuk Kim, Raymond R. Tan PII: DOI: Reference:

S1359-4311(16)32642-4 http://dx.doi.org/10.1016/j.applthermaleng.2016.10.132 ATE 9334

To appear in:

Applied Thermal Engineering

Received Date: Revised Date: Accepted Date:

13 July 2016 24 September 2016 21 October 2016

Please cite this article as: R.T.L. Ng, J.S.W. Loo, D.K.S. Ng, D.C.Y. Foo, J-K. Kim, R.R. Tan, Targeting for Cogeneration Potential and Steam Allocation for Steam Distribution Network, Applied Thermal Engineering (2016), doi: http://dx.doi.org/10.1016/j.applthermaleng.2016.10.132

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Targeting for Cogeneration Potential and Steam Allocation for Steam Distribution Network Rex T. L. Nga*, Jaslyn S. W. Loob, Denny K. S. Ngb,c, Dominic C. Y. Foob,d, Jin-Kuk Kime, Raymond R. Tanf a Department

of Chemical and Biological Engineering, University of Wisconsin–Madison, Madison, WI 53706, USA b Department of Chemical & Environmental Engineering, c Centre of Sustainable Palm Oil Research, d Centre of Excellence for Green Technologies, The University of Nottingham, Malaysia Campus, Broga Road, 43500 Semenyih, Selangor, Malaysia. e Department of Chemical Engineering, Hanyang University, 222 Wangsimni-ro, Haengdang-dong, Seongdong-gu, Seoul 133-791, South Korea. f Chemical Engineering Department, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines. Email: [email protected]*, [email protected], [email protected], [email protected], [email protected], [email protected] Abstract Cogeneration systems are recognized as an efficient means of supplying heat and power for industrial processes. Due to the rising awareness on environmental sustainability, much effort has been seen to improve the efficiency of cogeneration systems. This calls for the systematic synthesis of steam distribution networks. This paper first presents a novel algebraic technique – steam cascade analysis (SCA) to determine the targets for steam flowrates (single and multiple steam sources), and cogeneration potential for a steam distribution network, prior to detailed system design. The concept of SCA is then extended into an optimization framework based on the concept of the established automated targeting model. The latter allows different objective functions to be solved based on various constraints set by process and design engineers, prior to detailed design exercises. To illustrate the proposed approaches, an integrated palm oil processing complex case study is solved. Keywords: Process integration; resource conservation; automated targeting; pinch analysis; cascade analysis; process optimization.

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1. Background Cogeneration or combined heat and power (CHP) systems are one of the wellestablished means to provide thermal and electrical or mechanical energy simultaneously. Such systems offer the advantage of higher thermal efficiency, and hence, low emissions per unit of power output. In addition, they can be configured to supply thermal energy at different levels of quality. Figure 1 illustrates a typical steam distribution network in a cogeneration system. As shown, very high pressure steam (VHPS) is generated in steam boiler or heat recovery steam generator (HRSG), before being fed to the VHPS header. Note that VHPS is normally used for power generation rather than for process heating [1]. The VHPS is then expanded to lower levels to generate high (HPS), medium (MPS) and low pressure steam (LPS) via various steam turbines. The pressure change between the header levels will drive the steam turbines to produce cogeneration potential (electricity via turbogenerators or shaftpower via direct machine drivers). In normal practice, the excess or exhaust steam (ES) is condensed as hot water (HW) and be mixed with make-up water and excess LPS in deaerator before being fed to the boiler. Steam headers are used to distribute steam of various pressure levels to the processes in the plant. In addition, for cases where a demand exists for low-grade heat, HW can also be generated as an output for the system. [Figure 1] In cogeneration systems, major improvements in efficiency and cost reduction may be achieved by extracting the cogeneration potential from HPS via pressure reduction in the steam turbine. In order to achieve a cost effective and technically optimized system, proper design and operation has to be carried out. In general, the designs of cogeneration systems in the industry can be divided into grassroots and retrofit. A new design of steam distribution network will be established in grassroots design, while in retrofit study, the existing processes will be redesigned based on current operating data. Over the past few decades, various process integration tools have been developed to increase the accuracy in estimating the cogeneration potential and optimal allocation of steam in the steam distribution network. Process integration tools such as pinch analysis techniques were originally developed to address various energy-related problems. In the late 1970s, pinch analysis was dedicated for the synthesis of heat exchanger network, as comprehensively described by Linnhoff and Hindmarsh [2]. 2

Dhole and Linnhoff [3] introduced the concept of Total Sites Integration (TSI), where processing sites are serviced and linked through a central utility system. They proposed an exergetic model of site-wide targeting for cogeneration potential using the Site Source-Sink Profiles. Raissi [4] then proposed the idea of Site Utility Grand Composite Curve (SUGCC) and temperature-enthalpy model based on Salisbury approximation, in which the power produced by the turbine is approximately proportional to the difference of saturation temperature. Klemeš et al. [5] also adopted pinch analysis for TSI, which known as Total Site Heat Integration (TSHI) in order to determine the minimum fuel and power consumption, as well as CO2 emission targets. Comprehensive reviews of TSHI have been provided by Liew et al. [6]. Sorin and Hammache [7] later proposed an improved shaftwork targeting model with modified SUGCC by using average thermodynamic temperatures, instead of saturation temperature as presented in [4]. Mohan and El-Halwagi introduced an algebraic targeting approach to estimate cogeneration potential for grassroots cogeneration system with minimal fuel requirement [8]. However, it is limited to single steam source targeting. El-Halwagi et al. [9] then developed a systematic procedure to identify cogeneration targets of combustible wastes with heat and mass integration via surplus and deficit composite curves. Later, a linear algebraic approach based on Salisbury approximation was proposed by Bandyopadhyay et al. [10] and it was applied to target the cogeneration potential at the total site level from Site Grand Composite Curve (SGGC). Medina-Flores and Picón-Núñez [11] introduced a modified thermodynamic model to determine power generation target ahead of design and select actual turbine to be implemented in the cogeneration system. Besides, Chen and Lin [12] proposed a superstructural approach to design a flexible steam distribution network that satisfies period-varying demands. Ghannadzadeh et al. [13] introduced an iterative bottom-to-top model based on a simple steam turbine expansion model with a constant isentropic efficiency to target the shaft power of the steam turbines. A targeting algorithm that utilizes the relationship of entropy with enthalpy and isentropic efficiency for estimating cogeneration potential via the SUGCC was proposed [14]. In their later work, total annualized cost and total annualized sale of product were included in the targeting of cogeneration potential [15]. Sun et al. [16] proposed a SGGC to target steam mains and cogeneration potential.

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Different works have also been conducted to retrofit the existing facilities. For instances, a top-down and bottom-up procedures to target cogeneration potential using another iterative procedure was developed [17]. Liew et al. [18] proposed a framework that integrates both top–down retrofit concept and the Plus–Minus Principle in order to identify the energy recovery opportunities and cost-effective retrofit options. A graphical method for steam and cogeneration potential targeting that accounting for boiler feed water preheating, steam superheating, desuperheating and condensate recycle was developed [19]. A methodology based on pinch analysis was proposed to integrate gas turbine and regenerator and thermodynamic analysis was performed to minimize fuel consumption [20]. R-curve tool was used to evaluate the performance of the cogeneration system which integrate renewable energies into the total site utility system [21]. Automated targeting method (ATM), on the other hand, can be applied to address energy-related problems. It is an optimization framework that was originally developed for minimum flowrate targeting of mass separating agent for the synthesis of mass exchange network [22]. It utilizes the benefits of the insight-based pinch analysis technique to determine various network targets prior to detailed design. This ATM was extended for the synthesis of concentration-based resource conservation network (RCN). The ATM was adopted to determine the minimum fresh resource flowrate for direct reuse/recycle RCN [23], as well as for material interception network [24]. Apart from concentration-based RCN, the ATM was also extended for property-based RCN, in which the stream quality of the systems are dependent on functional properties. Ng et al. presented the ATM for bilateral property integration problem, where fresh resources with lowest and highest property operators are minimized simultaneously [25]. Waste treatment consideration was also included in the synthesis of RCN [26]. In later works, the developed ATM for single network [23–26] was also extended for resource conservation across different process plants (inter-plant RCN in short)[27], as well as various batch process integration problems, including for heat, mass and water recovery problems [28]. A later work that incorporates fuzzy model was then proposed to solve the multiple-objective optimization problems for a batch water network [29], where trade-off between fresh water consumption and water storage capacity was explored.

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Apart from the various RCN problems, the ATM was also extended for carbonconstrained energy planning (CCEP) problems [30–32], the synthesis of integrated biorefineries [33,34], the synthesis of heat exchanger network (HEN) with utility system [35] and supply chains planning [36]. For example, Lee et al. performed the targeting of CO2-neutral and low-carbon energy planning problems for different geographic regions, by meeting national or regional carbon emission limits [30]. The ATM was extended to determine the minimum extent of retrofit when carbon capture and storage technologies is installed for power plants [31]. A hybrid model which combines both graphical approach and ATM was developed for the multi-period CCEP problem [32]. For the synthesis of integrated biorefineries, Ng proposed the basic framework of ATM to determine the maximum biofuel production and revenue levels of an integrated biorefinery [33]. Later, multiple process parameters in the synthesis of a gasificationbased integrated biorefinery were considered [34]. They constructed material and energy cascades to simultaneously optimize syngas production in the gasification process, and the allocation of syngas to various processes. Goh et al. performed the synthesis of HEN and trigeneration utility system simultaneously via the ATM [35] by minimizing the cost. Most recently, the ATM was extended for the planning of aggregate production in energy supply chains [36]. Note that it is common to encounter cases where multiple steam sources are available for use. Even though conventional graphical methods are used for the targeting of multiple steam sources, they are bound with some limitations, such as inaccuracy, cumbersome and tedious. Besides, targeting for cogeneration potential and steam allocation may lead to contradicting objectives (e.g., minimize cost, maximize cogeneration potential, etc.). The aim of this paper is to fill the above research gaps by introducing an overall framework where both algebraic and automated targeting approaches may be used effectively for grassroots design of steam distribution network. Specifically, an algebraic targeting approach known as steam cascade analysis (SCA) is first developed. It The SCA is an extension of material cascade analysis that was originally developed for the targeting of external resources in material resource conservation networks [37,38]. In addition to single feed steam flowrate and cogeneration potential, the proposed algebraic targeting approach can also be used to target the flowrates of multiple steam levels, with the use of steam cascade table (SCT).

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Apart from the SCA, the ATM was extended for targeting cogeneration potential and steam allocation. Apart from minimizing the flowrates of steam sources (the main objective of the algebraic targeting approach), the ATM has the flexibility of optimizing different objective functions, and with added process constraints. Additional aspects (e.g., cost) can also be included in the ATM, which allows the optimization to be performed prior to detailed design of the cogeneration system. The rest of this paper is organized as follows. The formal problem statement is outlined in the next section. The description of the SCA algebraic targeting approach is given. Next, an industrial case study on integrated palm oil processing complex (POPC) is given to illustrate the applicability of the proposed SCA algebraic targeting technique. In the second part, a detailed description of the ATM and its formulation are then given. The case study is revisited and solved to illustrate the proposed ATM. Finally, conclusion and future works are given. 2. Problem Statement The problem for the synthesis of cogeneration system is formally stated as follows: Consider a set of steam sources i, each with flowrate of Fi,kSR are fed into different steam header levels k at specific temperature (Tk), pressure (Pk) and specific enthalpy (Hk). The pressure of steam source i can be reduced to lower level via steam turbine in order to fulfil the requirement of process sink j, in which steam is required for operation. Each sink j has a fixed flowrate of Fj,kSK. Steam turbines are used to reduce steam pressure as well as to generate power with specific isentropic efficiency (ηk). It is desired to determine the flowrate of the highest pressure steam (FS), the total cogeneration potential (ETOTAL) or the total annual cost (TAC). 3. Steam Cascade Analysis (SCA) Cascade analysis is an established insight-based approach based on pinch analysis that is commonly used for resource targeting purposes. Cascade analysis has been widely used in various applications, such as water minimization [39], utility gas network [40], solvent recovery [41], carbon-constrained energy planning [42], distributed energy generation system [43], etc. In this work, SCA is extended from the targeting procedure for multiple sources in a resource conservation network [37].

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Figure 2 show the generic framework for SCA, which summarizes the overall methodology for targeting of single and multiple sources. The targeting results may be conveniently presented in the steam cascade table (SCT), which take the generic form in Table 1. The details of the algebraic targeting technique for single and multiple steam sources are next outlined in the following sub-sections. [Table 1] [Figure 2] 3.1

SCA targeting for single steam source

In step 1 of the targeting procedure, temperature (Tk) and pressure (Pk) for each steam header level k are first identified. The SCT can then be set up following the generic structure in Table 1. In column 1, the steam header level k are arranged in descending order based on their pressure (Pk) levels. Specific enthalpy (Hk) of each header level k (column 2) can then be determined based on the Tk and Pk from the steam table. The main assumption here is that, specific enthalpy at higher level are of much higher quality. Next, the total flowrates of steam sources (Σi Fi,kSR) and sinks (Σj Fj,kSK) are calculated in columns 3 and 4 for each header level k, respectively. The net steam flowrate (column 5) is determined by the difference between the sources and sinks at each level k. Note that a positive flowrate indicates a surplus of steam at steam header level k, whereas a negative value indicates a deficit . Next, the cumulative steam flowrate, δk is obtained by cascading the net steam flowrate (column 6) across all Hk levels, with the first entry assumed as zero. This is to facilitate the rigorous determination of the minimum flowrate of the highest pressure steam, FS that fulfils all steam demands in the entire steam distribution network. The FS value shall remain as zero if all δk values are positive (i.e. a feasible SCT with surplus of steam at all levels). A negative cumulative steam flowrate indicates that insufficient steam source is being supplied to the sinks, which is infeasible. From the infeasible SCT, the absolute value of the largest negative δk is then used to replace the earlier-assumed zero flowrate in the first entry of column 6. All values in the SCT is then re-calculated. Note that the last entry in column 6 (i.e. δn) represents the excess steam generated (FXS) from the network.

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Based on the targeted values of FS and FXS, the extractable power of each turbine at header level k (Ek) can be determined from its steam flow rate, enthalpy change and isentropic efficiency. This is carried out in column 7 using Equation (1):



   =  ∑ , − ∑  ,   −  

(1)

where ηk is the isentropic efficiency. The cumulative extractable power (EkC, column 8) is obtained by cascading Ek across all steam levels. The total cogeneration potential (ETOTAL) of the steam distribution network is determined from the final entry of column 8. 3.1.1

Example

An illustrative example on biomass cogeneration system from Mohan and El-Halwagi [8] is demonstrated with the SCA targeting procedure. Steam source and sink data for the example are listed in Table 2. Note that this steam distribution network has an excess steam (FXS) of 21,772 kg/h. The objective of this example is to completely remove the excess steam. This can be done by reducing either of the steam source of VHPS or HPS. If the secondary aim is to maximize the cogeneration potential (without excess steam), one should reduce the HPS instead of the VHPS; this will be illustrated in Scenario A. [Table 2] Following the SCA procedure, targeting for Scenario A is performed for single steam source, with the SCT shown in Table 3. For given VHPS flowrates, the first entry of cumulative steam flowrate (FS) is given as zero. Following the proposed technique, the HPS flowrate is now reduced to 25,855 kg/h from its original flowrate of 47,627 kg/h, after the removal of 21,772 kg/h of excess steam flowrate. We next proceed to determine the cogeneration potential of the steam distribution network. In consistent with previous work [8], the isentropic efficiency is set to 70%. The extractable power for all intervals are determined using Equation (1), and summarized in Table 3 (column 7). Summation of these extractable power values gives the total cogeneration potential (ETOTAL) of 18.22 GJ/h, identical to that reported in Mohan and El-Halwagi (2007). [Table 3] A different scenario is next analyzed. For Scenario B, it is aimed to reduce the VHPS flowrate at a constant HPS flowrate. Table 4 shows the targeting result for Scenario B,

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where the VHPS flowrate is reduced to 32,659 kg/h (= 54,431 kg/h – 21,772 kg/h). The new total cogeneration potential is calculated as 14.93 GJ/h, which is lower than that in Table 3, where HPS is minimized. [Table 4] 3.2

SCA targeting for multiple steam sources

There are cases where multiple external steam sources exist. This calls for a revised targeting procedure, in which additional steps are needed to determine the optimum use of steam sources. The targeting procedure for this case follows those of the multiple fresh resources [37] and regeneration system [44] of material resource conservation network. Similar to the procedure of single source targeting, Tk, Pk and Hk of all steam headers k are first identified. The SCT is then set up by arranging the Pk values in descending order. The sinks and sources of all Hk levels in the steam network are then categorized into specific enthalpy region based on the level of steam source i (SERi), that are to be targeted. For example, assuming that two steam sources where their flowrates are to be determined (e.g. levels k and k+2 as in Table 1). The levels beneath that of level k is referred to as region SER1. The sinks in this region are to be supplied by steam of the highest pressure in this region. Similarly, the region below level k+2 is referred to as SER2, etc. Note that steam of lower pressure should be maximized before the use of higher pressure steam, assuming that the latter is always more expensive. Thus, steam flowrate targeting is first performed for SER of lower level, by first setting the source flowrate to zero. Following the same procedures for single source targeting, the rigorous steam flowrate to be supplied to the SER of the lowest level is then obtained. Steam flowrate targeting is then performed for SER of higher level. The targeting procedure is repeated until the targeting at all SERs are completed. Ek and EkC are then calculated to determine the ETOTAL in the last stage, following the same procedure used for single source targeting approach. The applicability of this targeting method is illustrated in the next section. 4. Industrial Case Study In this case study, the minimum steam source flowrate and cogeneration potential of the palm oil processing complex (POPC) are to be targeted. The POPC that consists of the palm oil mill (POM), palm oil refinery (POR), palm oil-based biorefinery (POB) and CHP

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facilities, has been introduced to promote the integration of various processing plants [45,46]. Palm oil is one of the major edible oils globally, with South East Asia being the main exporter [47]. In an integrated POPC, fresh fruit bunches (FFB) is processed in the POM to generate crude palm oil; which is further processed into various palm refined products (e.g., stearin and olein) via refining and fractionation processes in the POR. Palm-based biomass are gradually being utilized, instead of disposed of as waste [48], or left to decompose in the plantations. These palm-based biomasses are either converted into value-added products (e.g., palm pellet, compost and dried long fiber) via various technology in the POB [49,50] or heat and power in the CHP. A well designed CHP unit in an integrated POPC can supply sufficient steam and power to various processing sites (i.e. POM, POR and POB), thus reducing the overall production and utility costs. In this work, the POM will have a capacity of 80,000 kg/h FFB, and it requires 24,000 kg/h of LPS at 4 bar(a) for its FFB sterilization process. Meanwhile, the POB requires 17,500 kg/h of MPS at 12 bar(a) and 3,500 kg/h LPS at 4 bar(a), for the production of pellet, dried long fiber and compost. Besides, 1,200 kg/h of VHPS at 65 bar(a), 43,840 kg/h of MPS at 12 bar(a) and 12,800 kg/h of LPS at 4 bar(a) are required by the POR with a capacity of 16,000 kg/h crude palm oil for its fractionation and refining processes. It is assumed that the palm mesocarp fiber (PMF) generated in the POM is taken as biomass boiler fuel. The isentropic efficiency of all turbines is assumed as 70%. Specific enthalpy of superheated steam may be determined from the steam tables [51]. In this case, steam distribution network for a POPC is to be synthesized. Two scenarios are analyzed, with the steam data given in Table 5. In Scenario I, steams and electricity generated from the CHP plant are supplied to the POB. The minimum HPS flowrate and cogeneration potential are to be targeted. In Scenario II, utilities generated from the CHP plant are integrated with the POM, POB and POR. Multiple source targeting procedure is applied to determine the minimum requirements of VHPS and HPS, as well as the cogeneration potential in this integrated POPC. [Table 5] 4.1

Scenario I: Single Source Targeting

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Following the proposed algebraic targeting approach, the SCT for this scenario is shown in Table 6, with Figure 3 shows the steam distribution network. In this scenario, the minimum HPS flowrate (FS) is targeted at 21,000 kg/h, with no excess steam generation (i.e. FXS = 0 kg/h). The total cogeneration potential (ETOTAL) is then determined as 0.83 GJ/h, contributed by the extractable power in the HPS (0.38 GJ/h) and MPS (0.45 GJ/h) turbines. [Table 6] [Figure 3] 4.2

Scenario II: Multiple Sources Targeting

In this scenario, VHPS and HPS are available for use. The multiple steam sources targeting procedure is followed to determine their minimum usage. First, the region that consists the VHPS header is designated as SER1 whereas the region that consists of HPS header and its lower level headers is designated as SER2. Following the proposed technique, steam targeting is first performed for SER2, with results shown in Table 7 (a), the minimum HPS source steam flowrate (FHPS) is first determined as 101,640 kg/h. It is then followed by the targeting at SER1. As shown in Table 7 (b), the minimum VHPS supply flowrate (FVHPS) is targeted at 1,200 kg/h. Note that there is no excess steam generated from the steam network (i.e. FXS = 0 kg/h). Next, the extractable power is calculated for each steam level, which contributes to the ETOTAL of 7.01 GJ/h (see last entry in Table 7 (b)). Figure 4 shows the steam distribution network for Scenario II. Boiler B produces 101,640 kg/h of HPS, which is then converted to MPS and LPS that are required in the POM, POR and POB. On the other hand, Boiler A will supply 1,200 kg/h VHPS to POR. As shown, 1.85 GJ/h of electricity is generated via steam turbine II and generator II. In addition, the remaining 40,300 kg/h of MPS is converted to LPS and 5.16 GJ/h of electricity is generated with steam turbine III and generator III. [Table 7] [Figure 4] 5. Automated Targeting Model (ATM) The ATM for steam distribution network is developed based on the SCA concept presented in the earlier section. A generic framework of the ATM is presented using the steam cascade diagram (SCD) in Figure 5. Similar to the algebraic targeting approach, 11

temperature (Tk), pressure (Pk) and specific enthalpy (Hk) of all steam headers k are first identified. The sources and sinks are then arranged in descending order of Pk, i.e. from highest (k = 1) to the lowest level (k = n) in the steam cascade. Apart from the latter, an extractable power cascade is also needed to determine cogenerate potential. Note that cogeneration potential is cascaded down in the extractable power cascade from higher to lower levels. [Figure 5] Flowrate cascading across all specific enthalpy levels of the steam cascade is first performed. As described by Equation (2), the steam flowrate of level k (δk) is a result of summation of steam flowrate cascaded from earlier level, i.e. k − 1 (δk-1), with the net steam flowrate balance at level k.



  =  + ∑ , − ∑  , 

∀k

(2)

Equation (2) is added to ensure no negative stream flowrates on the first (k = 1) and final (k = n) levels of the steam cascade.  ≥ 0

k = 1, n

(3)

The extractable power cascade is next performed. Within each enthalpy interval, the extractable power is given by the product of δk and the difference between two adjacent specific enthalpy levels. The cumulative extractable power balance at the k-th level (ECk) is hence given by Equation (4).   =  +    −  

∀k

(4)

where ECk-1 is the extractable power cascaded from level k – 1. The total cogeneration potential (ETOTAL) of the steam distribution network is located at the final entry of the extractable power cascade. In order to achieve a feasible extractable power cascade, ECk must take a positive value. This constraint is given by Equation (5).  ≥ 0

∀k

(5)

The basic formulations of ATM in Equations 2 – 5 are linear in nature, and hence can be solved in any linear programming software. Different objective functions (e.g. minimum TAC) or process constraints can be included in the optimization model. Note however

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that the addition of non-linear constraints or objectives may impose computational difficulties in determining global optimal solutions. 6. Revisited Case Study The industrial case study of an integrated POPC in Section 4 is revisited here. To incorporate the economic evaluation of the grassroots CHP design, the generation cost (CGEN), capital costs of boiler (CBOILER) and non-condensing turbine (CTURBINE) are included in calculating the TAC [52], which is given as in Equation 6 that follows.  =  

!

+ " #$%&



+  '( #%!  ∀k

(6)

where α is the annual operating time and β is the annualized factor. β can be calculated based on the operating lifespan of the CHP (t) and interest rate (r) in the following equation: )*)+

" = *)+

(7)



According to U.S. Department of Energy [53], the generation cost is determined by the following equation that includes the fuel cost: 

!

= ∑/, 1.3/,

(8)

where Cf,k is the fuel cost ($/kg). Please note that 30% additional fuel cost is added to account for other maintenance costs, e.g. raw water supply, boiler feed water treatment, feed water pumping power, combustion air fan power, sewer charges for boiler blowdown, ash disposal, environmental emission control and maintenance materials and labor [53]. The fuel cost can be calculated as given: #$%& /, = 0.429923 × 4/ LHV/ 8/,



(9)

where γf is the unit price of fuel ($/kg), LHVf is the low heating value of the fuel (kJ/kg), Qf,k BOILER is the amount of heat transferred from the combustion of fuel to the steam at level k (Btu/h). Note that 0.43 is the conversion of the unit of Btu to kJ. Qf,k can be determined as follows: 8#$%&



#$%& = ∑/ /,



#$%& 8/,



(10)

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where Qk BOILER is heat required in the boiler (Btu/h) and ηf BOILER is the efficiency in the boiler based on the type of fuel. The total capital cost for a water-tube boiler ($) is given by [52]:  #$%&



;.<<

#$%& = ∑ 39: 9' 8/, 

(11)

where NkP and NkT are the factor to account for operation pressure, Pk (psig) and superheated temperature, Tk (°F) at different level, which can be determined as follows: 9: = 7 × 10> ?

(12)

9' = 1.5 × 10A TC + 1.13 × 10D  + 1

(13)

The total capital cost for a non-condensing turbine ($) is given as by [52]:  '( #%! = ∑ 475  

;.>E

(14)

The new CHP is designed based on an operating lifespan of 25 years and the interest rate is taken as 10%. The CHP plant has an annual operating time of 8,000 hours. Boiler efficiency is assumed as 75%. The isentropic efficiency of all turbines is assumed as 70%. Specific enthalpy of superheated steam is determined from steam tables [51]. It is assumed that the maximum availability of PMF is 25,000 kg/h. Four scenarios are demonstrated for the illustration of the ATM. In Scenario 1, ATM is used to target the minimum total cost of an integrated POPC without considering self-supplied electricity. In Scenario 2, additional constraints are imposed where the generations of steams and electricity are sufficient for steam and electricity demands in the POM, POR and POB. This is served as base case for Scenarios 3 and 4. In Scenario 3, the objective function is set to maximize cogeneration potential. In Scenario 4, the model is extended into multiobjective optimization to trade-off between TAC and cogeneration potential. Due to non-linear terms in the capital cost functions (Equations 11, 13 and 14), a nonlinear programming (NLP) model is resulted for these scenario. All NLP models are solved via LINGO 13.0 with a branch-and-bound based NLP [54], in Dell Inspiron 3542 with Intel® Core™ i5-4210U Processor (1.7 GHz) and 8 GB DDR3 RAM within few seconds. 6.1

Scenario 1: Minimum Total Annual Cost

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In this scenario, targeting of cogeneration potential and steam allocations of the integrated POPC. ATM for Scenario 1 is solved by minimizing TAC in Equations (6), subject to the constraints in Equations (2) – (14). A minimum TAC of USD 5.11 million/y is resulted, with the SCD shown in Figure 6. As shown, two steam boilers are to be installed, to generate VHPS and HPS at flowrates of 1,200 kg/h and 101,640 kg/h, respectively. Note that the VHPS produced is only sufficient for process demand. Therefore, there is no extra VHPS expanded to lower pressure (δ1 = 0). Total cogeneration potential, ETOTAL is targeted at 7.01 GJ/h. There is no excess steam generated in this scenario (F4SK = 0). Note that the results match those obtained using algebraic targeting as presented earlier, where the objective was set to minimize steam flowrate. Note that it is possible to have different results when different cost parameters are used or additional constraints are added in the ATM. [Figure 6] 6.2

Scenario 2: Minimum Total Annual Cost (Energy Self-sustained)

In this scenario, it is desired that the POPC to form an energy self-sustained complex, where the generation of energy (steams and electricity) must be sufficient for use in the POM, POR and POB. In a previous work [45], based on 16,000 kg/h of crude palm oil production in the POPC, the cogeneration potential (electricity) requirements for the POM, POR and POB were reported as 1,040 kW, 1,600 kW and 1,500 kW, respectively for hourly production. Hence, additional constraint in Equation (15) is added in the model.  '$'F& ≥ 

G

(15)

where EREQ is total cogeneration potential required in the integrated POPC (4,140 kW or 14.90 GJ/h in this case). The model resulted with a minimum TAC of USD 6.77 million/y. Figure 7 illustrates the SCD of the energy self-sustained integrated POPC. One steam source – VHPS (F1SK = 118,229 kg/h, therefore only one boiler is required to target minimum excess steam. The TAC of this scenario is higher (USD 6.77 million/y). This is because additional steam turbines and generators at steam header interval of VHPS−HPS are needed to supply a total cogeneration potential of 14.90 GJ/h. For this case, a total of 15,389 kg/h of excess steam is generated. This excess steam can be used by another nearby plants or else will 15

be treated as an unavoidable by-product for producing sufficient power for the integrated POPC. [Figure 7] 6.3

Scenario 3: Maximum Cogeneration Potential (Energy Self-sustained)

In this scenario, it is assumed that excess cogeneration potential generated can be sold to the nearest grid. The objective of this scenario is set as maximizing cogeneration potential and subjected to the availability of PMFs. As compared to previous scenarios, the TAC of this scenario is the highest (USD 7.82 million/y). All available PMFs are fully utilized to generate a total 138,020 kg/h of VHPS in the boiler and it is further expanded to lower pressure steams (Figure 8). From the extractable power cascade, the total cogeneration potential is targeted as 18.75 GJ/h. Note that much larger amount of excess LPS is produced in this scenario (δ4 = 35,180 kg/h). [Figure 8] 6.4

Scenario 4: Multi-Objective Optimization to Trade-Off between Minimum TAC and Maximum Cogeneration Potential

Fuzzy optimization approach [55,56] is adopted to trade-off various objectives in this scenario. A degree of satisfaction (λ), which is a continuous interdependence variable is introduced. Based on “max-min” aggregation, every fuzzy constraint will be satisfied partially at least to λ. In order to satisfy the set fuzzy goals of multiple-objectives simultaneously, λ is maximized and subject to the following equations: Obj( − KLM ≥ NObj( − Obj& 

(16)

KLM − Obj& ≥ NObj( − Obj& 

(17)

where Obj is objective function and ObjU/ObjL are predefined upper/lower bounds of the objective considered. Equation (16) is the constraint for minimization case (e.g., minimum TAC) whereas the latter is used for maximization case (e.g., maximum ETOTAL). The upper and lower bounds are defined based on the maximum and minimum values obtained from Scenarios 2 and 3. Upper bounds for TAC and ETOTAL are set to USD 7.82 million/y and 18.75 GJ/h, respectively, while lower bounds for TAC and ETOTAL are set to USD 6.77 million/y and 14.90 GJ/h, respectively. The model is solved and subjected to Equations (2) – (17). The maximum λ of 0.5 is obtained and the corresponding TAC and 16

ETOTAL are determined as USD 7.29 million/y and 16.83 GJ/h, respectively. Figures 9 and 10 illustrate the SCD and steam distribution network for this scenario. As shown, 128,133 kg/h of VHPS are to be produced from one biomass boiler. After supplying all steam demands to the integrated POPC, 25,273 kg/h of excess steam is generated. [Figure 9] [Figure 10] 7. Conclusion In this paper, two targeting methods are proposed to target minimum steam flowrates and cogeneration potential for a steam distribution network, i.e. SCA and ATM. The SCA is an algebraic targeting technique that can be used to target multiple steam sources and cogeneration potential without detailed information. On the other hand, the ATM has the advantage over algebraic approach when economic analysis or process constraints are to be considered. The proposed methods may be extended for cogeneration system retrofit or flexible designs, to enable the retrofitting of existing design or the handling of various uncertainties, such as fuel availability and additional process demands. In addition, sustainability assessment using environmental, social and economic evaluations of cogeneration system can also be included in future works using appropriate quantitative metrics. Abbreviation ATM

Automated targeting method

CCEP

Carbon-constrained energy planning

CHP

Combined heat and power

ES

Exhaust steam

FFB

Fresh fruit bunch

HEN

Heat exchanger network

HPS

High pressure steam

HW

Hot water

LPS

Low pressure steam

MPS

Medium pressure steam

NLP

Non-linear programming

PMF

Palm mesocarp fiber 17

POB

Palm oil-based biorefinery

POM

Palm oil mill

POPC

Palm oil processing complex

POR

Palm oil refinery

RCN

Resource conversation network

SCA

Steam cascade analysis

SCD

Steam cascade diagram

SCT

Steam cascade table

SER

Specific enthalpy region

SGGC

Site Grand Composite Curve

SUGCC

Site Utility Grand Composite Curve

TSI

Total Sites Integration

TSHI

Total Site Heat Integration

VHPS

Very high pressure steam

Nomenclature Parameter Hk

Specific enthalpy level

LHVf

Lower heating value of fuel f

ObjU/ObjL

Predefined upper/lower bounds of objective

Pk

Pressure at level k

r

Interest rate

t

Operating lifespan of CHP

Tk

Temperature at level k

α

Annual operating time

β

Annualized factor

γf

Unit price of fuel f

ηb

Boiler efficiency

ηf BOILER

Boiler efficiency based on the type of fuel f

ηk

Isentropic efficiency at level k

Variable CBOILER

Capital cost of boiler

18

Cf,k

Fuel cost at level k

CGEN

Generation cost

CTURBINE

Capital cost of non-condensing turbine

ECk

Cumulative extractable power at level k

Ek

Extractable power at interval k

EkC

Cumulative extractable power at level k

EREQ

Total cogeneration potential required in an integrated POPC

ETOTAL

Total cogeneration potential

FHPS

Flowrate of HPS

Fi,kSR

Flowrate of source i at level k

Fj,kSK

Flowrate of sink j at level k

FS

Rigorous highest pressure steam flowrate

FVHPS

Flowrate of VHPS

FXS

Flowrate of excess steam generated

NkP

Factor for operation pressure

NkT

Factor for superheated temperature

Obj

Objective function

Qf,k BOILER

Amount of heat transferred from the combustion of fuel to the steam at level k

TAC

Total cost

δk

Cumulative steam flowrate at interval k

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23

List of Figure Figure 1: Typical steam distribution network Figure 2: Framework of SCA algebraic targeting technique Figure 3: Steam distribution network for Scenario I Figure 4: Steam distribution network for Scenario II Figure 5: Generic steam cascade diagram (SCD) Figure 6: SCD for Scenario 1 Figure 7: SCD for Scenario 2 Figure 8: SCD for Scenario 3 Figure 9: SCD for Scenario 4 Figure 10: Steam distribution network for Scenario 4

24

VHPS

Fuel

to Process Boiler

Cogeneration potential E

G Steam Turbine

Generator

HPS

Boiler feed water

to Process

Stack gas

Air

MPS to Process

Heat recovery steam generator (HRSG)

G Compressor

E

G

Natural gas

E

G

Gas turbine

LPS

E

to Process Vented steam

LPS

G

E ES

Make-up water

Deaerator

Hot water

Figure 1: Typical steam distribution network

25

Condenser

HW

Start Identification of Steam Header

• Temperature (Tk) and pressure (Pk) at Steam header level k, are identified • Steam header level karranged in descending order based on Pk • Specific enthalpy of each steam header level k (Hk) is determined Calculation of Steam Flowrate

• Total steam flowrates of source (Σi Fi,kSR) and sink (Σj Fj,kSK) are calculated • The net steam flowrate (Σi Fi,kSR– Σj Fj,kSK) is obtained Multiple

Single

Single source or multiple sources? Determination of Specific Enthalpy Region

Single Source Steam Cascade Analysis • The cumulative steam flowrate (δk) is calculated by cascading Σi Fi,kSR– Σj Fj,kSK with the assumption of zero at the first row of δk

• Specific enthalpy region (SER) is first identified • Steam flowrate targeting is first performed at the lowest SER Multiple Sources Steam Cascade Analysis

• The source flowrate at the targeted SER is initially

Yes

All δk values in surplus?

assumed as zero

• δk is obtained by first cascading Σi Fi,kSR– Σj Fj,kSK

No Rigorous highest pressure steam flowrate (FS) is zero

• The earlier-assumed zero flowrate

The steam supply flowrate of targeted SER is zero

is replaced with absolute value of the largest negative in δk • Revised values of δk are obtained by re-cascading Σi Fi,kSR– Σj Fj,kSK

Yes

All δk values in surplus? No

The earlier-assumed zero flowrate is replaced with absolute value of the largest negative in δk

Calculation of Cogeneration Potential

• Extractable energy (Ek) is calculated

• Cumulative extractable energy (EkC) is obtained by cascading Ek • Total cogeneration potential (ETOTAL) is obtained

No

Is higher SER available?

End

Figure 2: Framework of SCA algebraic targeting technique

26

Yes

Flowrate targeting is at the higher SER

HPS 21,000 kg/h Boiler B

0.38 GJ/h

G Steam Turbine II

Generator II

3,500 kg/h

17,500 kg/h 0.45 GJ/h

G Steam Turbine III

MPS

Generator III

LPS 3,500 kg/h

Figure 3: Steam distribution network for Scenario I

27

1,200 kg/h VHPS VHPS 1,200 kg/h VHPS

Boiler A

HPS 101,640 kg/h Boiler B

1.85 GJ/h

G Steam Turbine II

Generator II

40,300 kg/h G Steam Turbine III

Generator III

MPS 61,340 kg/h 5.16 GJ/h LPS 40,300 kg/h

Figure 4: Steam distribution network for Scenario II

28

Steam Cascade

Extractable Power Cascade

δ0 = 0 E1C = 0 H1

Σi Fi,1

SR

Σj Fj,1

k=1

SK

δ1 = δ0 + (Σi Fi,1SR − Σj Fj,1SK) H2

Σi Fi,2SR

E2C = E1C + η2δ1 (H1 − H2)

Σj Fj,2SK

k=2

δ2 = δ1 + (Σi Fi,2SR − Σj Fj,2SK) H3

Σi Fi,3SR

k=1

k=2 E3C = E2C + η3δ2 (H2 − H3)

Σj Fj,3SK

k=3

k=3

Hn-1

Hn

Σi Fi,n-1SR

Σi Fi,nSR

k = n–1

En-1C = En-2C + ηn-1δn-2 (Hn-2 − Hn-1)

Σj Fj,n-1SK

δn-1 = δn-2 + (Σi Fi,n-1SR − Σj Fj,n-1SK) k = n–1 ETOTAL =EnC = En-1C + ηnδn-1 (Hn-1 − Hn)] k=n Σ F SK j

j,n

δn = δn-1 + (Σi Fi,nSR − Σj Fj,nSK)

Figure 5: Generic steam cascade diagram (SCD)

29

Steam Cascade (kg/h) Specific Enthalpy (kJ/kg) H1 = 3,030.60

H2 = 2,961.70

H3 = 2,935.70

H4 = 2,752.80

Extractable Power Cascade (GJ/h)

δ0 = 0 F1SR = 1,200

F2

SR

E1C = 0 k=1

F1

SK

= 1,200 k=1

δ1 = 0 = 101,640

F3SR = 0

F4

SR

δ2 = 101,640

k=2 E3C = 1.85

F3SK = 61,340

k=3

δ3 = 40,300 =0

E2C = 0

F2SK = 0

k=2

F4SK = 40,300

k=4 δ4 = 0

Figure 6: SCD for Scenario 1

30

k=3 ETOTAL = 7.01

Steam Cascade (kg/h) Specific Enthalpy (kJ/kg) H1 = 3,030.60

H2 = 2,961.70

H3 = 2,935.70

H4 = 2,752.80

Extractable Power Cascade (GJ/h)

δ0 = 0 F1SR = 118,229

F2

SR

F4

k=1

F1

= 1,200 k=1

δ1 = 0 =0

F3SR = 0

SR

E1C = 0 SK

δ2 = 117,029

k=2 E3C = 7.77

F3SK = 61,340

k=3

δ3 = 55,689 =0

E2C = 5.64

F2SK = 0

k=2

F4SK = 40,300

k=4

δ4 = 15,389

Figure 7: SCD for Scenario 2

31

k=3 ETOTAL = 14.90

Steam Cascade (kg/h) Specific Enthalpy (kJ/kg) H1 = 3,030.60

H2 = 2,961.70

H3 = 2,935.70

H4 = 2,752.80

Extractable Power Cascade (GJ/h)

δ0 = 0 F1SR = 138,020

F2

SR

F3

SR

E1C = 0 F1SK = 1,200

k=1

δ1 = 136,820 =0

F4SR = 0

E2C = 6.60

F2SK = 0

k=2

δ2 = 136,820 =0

k=1

k=2 E3C = 9.09

F3SK = 61,340

k=3

δ3 = 75,480 F4SK = 40,300

k=4

δ4 = 35,180

Figure 8: SCD for Scenario 3

32

k=3 ETOTAL = 18.75

Steam Cascade (kg/h) Specific Enthalpy (kJ/kg) H1 = 3,030.60

H2 = 2,961.70

H3 = 2,935.70

H4 = 2,752.80

Extractable Power Cascade (GJ/h)

δ0 = 0 F1SR = 128,113

F2

SR

F3

SR

E1C = 0 F1SK = 1,200

k=1

δ1 = 126,913 =0

F4SR = 0

E2C = 6.12

F2SK = 0

k=2

δ2 = 126,913 =0

k=1

k=2 E3C = 8.43

F3SK = 61,340

k=3

δ3 = 65,573 F4SK = 40,300

k=4

δ4 = 25,273

Figure 9: SCD for Scenario 4

33

k=3 ETOTAL = 16.83

128,113 kg/h VHPS VHPS Boiler

126,913 kg/h VHPS

23,205 kg/h PMF

1,200 kg/h VPS 6.12 GJ/h

G Steam Turbine I

Generator I

HPS

126,913 kg/h 2.31 GJ/h

G Steam Turbine II

Generator II

65,573 kg/h G Steam Turbine III

25,273 kg/h excess

Generator III

MPS 61,340 kg/h 8.40 GJ/h LPS 40,300 kg/h

Figure 10: Steam distribution network for Scenario 4

34

List of Table Table 1: Generic steam cascade table (SCT) Table 2: Data for illustrative example [8] Table 3: SCT for Scenario A of illustrative example Table 4: SCT for Scenario B of illustrative example Table 5: Data for case study: (a) Scenario I and (b) Scenario II Table 6: SCT for Scenario I Table 7: SCT for Scenario II: (a) Targeting at SER2 (b) Targeting at SER1

35

Table 1: Generic steam cascade table (SCT)

Hk

Σi Fi,kSR

Σj Fj,kSK

Σi Fi,kSR – Σj Fj,kSK

Hk

Σi Fi,kSR

Σj Fj,kSK

Σi Fi,kSR

Σj Fj,k+1SK





Σi Fi,k+2SR

Σj Fj,k+2SK

⋮ ⋮

⋮ ⋮

n–1

Hk+1 ⋮ Hk+2 ⋮ ⋮ Hn-1

Σi Fi,k+1SR

Σi Fi,n-1SR

Σj Fj,n-1SK

– ⋮ SR Σi Fi,k+2 – Σj Fj,k+2SK ⋮ ⋮ SR Σi Fi,n-1 – Σj Fj,n-1SK

n

Hn

Σi Fi,nSR

Σj Fj,nSK

Σi Fi,nSR – Σj Fj,nSK

k

δk

Ek

ECk

δk-1 = FS k



Σj Fj,kSK

ECk

δk

SER1 k+1

k+2 SER2

Σi Fi,k+1SR

Ek

Σj Fj,k+1SK

δk+1

Ek+1

δk+2 ⋮ ⋮ δn-1

Ek+2 ⋮ ⋮ En-1

ECk+1 ⋮ C E k+2 ⋮ ⋮ C E n-1 ECn

δn = FXS

En ETOTAL

*The allocation of SER as shown in Table 1 is only for example illustration purpose

36

Table 2: Data for illustrative example [8] Flowrate, Temperature, Pressure, Specific Enthalpy, kg/h °C bar(a) kJ/kg Source VHPS HPS Sink MPS LPS

54,431 47,627

482.22 315.55

44.82 13.79

3,242.49 3,026.59

50,802 29,483

232.22 160.00

6.89 4.48

2,896.27 2,766.71

37

Table 3: SCT for Scenario A of illustrative example k (Pk) 1 (44.82) 2 (13.79) 3 (6.89) 4 (4.48)

Hk, kJ/kg

Σi Fi,kSR, kg/h

3242.49

54,431

3026.59

Σj Fj,kSK, kg/h

Σi Fi,kSR – Σj Fj,kSK , kg/h

δk, kg/h FS = 0

Ek, GJ/h

54,431

8.23

80,286

7.32

ECk, GJ/h

54,431

25,855

25,855

2896.27

50,802

-50,802

2766.71

29,484

-29,484

8.23 15.55 29,484

2.67

18.22 FXS

=0

0 ETotal = 18.22

38

Table 4: SCT for Scenario B of illustrative example k (Pk) 1 (44.82) 2 (13.79) 3 (6.89) 4 (4.48)

Hk, kJ/kg

Σi Fi,kSR, kg/h

3242.49

32,659

3026.59

Σj Fj,kSK, kg/h

Σi Fi,kSR – Σj Fj,kSK , kg/h

δk, kg/h FS = 0

Ek, GJ/h

32,659

4.94

80,286

7.32

ECk, GJ/h

32,659

47,627

47,627

2896.27

50,802

-50,802

2766.71

29,484

-29,484

4.94 12.26 29,484

2.67

14.93 FXS

=0

0 ETotal = 14.93

39

Table 5: Data for of case study: (a) Scenario I and (b) Scenario II (a) Scenario I Flowrate, kg/h Source To be HPS determined Sink MPS 17,500 LPS 3,500 (b) Scenario II Flowrate, kg/h Source To be VHPS determined To be HPS determined Sink VHPS 1,200 MPS 61,340 LPS 40,300

Temperature, °C

Pressure, bar(a)

Specific Enthalpy, kJ/kg

300.00

40.00

2,961.70

250.00 150.00

12.00 4.00

2,935.70 2,752.80

Temperature, °C

Pressure, bar(a)

Specific Enthalpy, kJ/kg

350.00

65.00

3,030.60

300.00

40.00

2,961.70

350.00 250.00 150.00

65.00 12.00 4.00

3,030.60 2,935.70 2,752.80

40

Table 6: SCT for Scenario I

k (Pk)

Hk, kJ/kg

Σi Fi,kSR, kg/h

Σj Fj,kSK, kg/h

Σi Fi,kSR – Σj Fj,kSK , kg/h

δk, kg/h

δk, kg/h

Ek, GJ/h

ECk, GJ/h

Fs = 21,000 2,961.70 1 (40.00) 2,935.70 2 (12.00) 2,752.80 3 (4.00)

0 17,500

0 0.38

3,500

0.45

-17,500

0.38

-17,500 3,500

21,000

ETotal = 0.83

-3,500 -21,000

41

Fxs = 0

0

Table 7: SCT for Scenario II: (a) Targeting at SER2 (b) Targeting at SER1 (a) Targeting at SER2 Hk, kJ/kg

k (Pk) SER1

SER2

1 (65.00)

3,030.60

2 (40.00)

2,961.70

3 (12.00) 4 (4.00)

2,935.70

Σi Fi,kSR, Σj Fj,kSK, Σi Fi,kSR – Σj Fj,kSK , kg/h kg/h kg/h 1,200

Σi Fi,kSR, kg/h

δk, kg/h

-1,200 FHPS = 101,640

0 61,340

-61,340

-61,340 2,752.80

40,300

-40,300 -101,640

(b) Targeting at SER1 Hk, Σi Fi,kSR, Σj Fj,kSK, Σi Fi,kSR – Σj k (Pk) kJ/kg kg/h kg/h Fj,kSK , kg/h

δk, kg/h 0

3,030.60 SER1 1 (65.00)

1,200

Ek, GJ/h

0

0

101,640

2,935.70 SER2 3 (12.00)

61,340

2,752.80 4 (4.00)

40,300

ECk, GJ/h

-1,200 -1,200

FHPS = 2,961.70 2 101,640 (40.00)

δk, kg/h FVHPS = 1,200

0 100,440 101,640

1.85

39,100

5.16

-61,340

1.85 40,300

ETotal = 7.01

-40,300 -1,200

42

Fxs = 0

0

Highlights: •

A novel concept of Steam Cascade Analysis (SCA) is proposed



Algebraic targeting approach for single and multiple sources is developed



Cogeneration potential are targeted with the use of steam cascade table



Automated targeting method (ATM) based on SCA is extended



ATM has the flexibility of optimizing different objectives with process constraints

43