Greenhouse emission pinch analysis (GEPA) for evaluation of emission reduction strategies

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1 Clean Techn Environ Policy DOI /s ORIGINAL PAPER Greenhouse emission pinch analysis (GEPA) for evaluation of emission reduction strategies Minhyun Kim 1 MinJeong Kim 1 SeHee Pyo 1 SeungChul Lee 1 Payam Ghorbannezhad 2 Dominic Chwan Yee Foo 2 ChangKyoo Yoo 1 Received: 24 August 2015 / Accepted: 27 October 2015 Springer-Verlag Berlin Heidelberg 2015 Abstract Wastewater treatment plants (WWTPs) are one of the important sources of greenhouse gas (GHG) emission. There is a need for systematical tool that can be used to analyze GHG emission from WWTPs, and to evaluate the associated reduction strategies. In this paper, a systematic analysis methodology, called the greenhouse emission pinch analysis (GEPA), is developed for this purpose. GEPA is graphical in nature, and can be used to analyze the on-site and off-site GHG emissions of the WWTP. Furthermore, three GHG reduction strategies, i.e., increased aeration capacity, external carbon source controller, and reuse of biogas, are evaluated for their environmental load and operational cost reduction using the & ChangKyoo Yoo ckyoo@khu.ac.kr Minhyun Kim minhyunman@gmail.com MinJeong Kim mg11242@hanmail.net SeHee Pyo sehee_0726@naver.com SeungChul Lee dori0524@naver.com Payam Ghorbannezhad p.ghorbannezhad@gmail.com Dominic Chwan Yee Foo Dominic.Foo@nottingham.edu.my 1 2 Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University, Yongin-Si, Gyeonggi-Do , Korea Department of Chemical and Environmental Engineering/ Centre of Excellence for Green Technologies, University of Nottingham Malaysia, Broga Road, Semenyih, Selangor, Malaysia GEPA. A case study is used to elucidate the proposed method. In this study, the third strategy which reuses biogas from anaerobic digestion shows the largest reduction of GHG emissions. Keywords Process integration Greenhouse gases (GHGs) Pinch analysis On-site emissions Off-site emissions Wastewater treatment plant Introduction There has been growing interest in the reduction of greenhouse gases (GHGs) emission from wastewater treatment plants (WWTPs) in recent years, since WWTPs have been recognized as one of the GHG emission sources (Eggleston et al. 2006). This is mainly due to many advanced treatment processes that have greater potential in emitting GHGs than conventional activated sludge treatment techniques (Barton and Atwater 2002). During the last decade, many studies have been conducted primarily related to carbon dioxide (CO 2 ), nitrous oxide (N 2 O), and methane (CH 4 ) emitted from aerobic/anaerobic reactor, anaerobic digestion, and effluent; they may be considered as on-site GHG emissions from the WWTPs (Eggleston et al. 2006; Rassamee et al. 2011; Corominas et al. 2012). In addition, some studies have been reported to estimate GHG emissions from the production of electricity and external carbon source that is required for WWTPs operation; there are considered as off-site GHG emissions (Shahabadi et al. 2009, 2010; Ashrafi 2012). In denitrification stage, carbon sources play the role as electron donors by denitrifying bacteria. If the ratio of chemical oxygen demand (COD) to nitrogen (N), i.e., COD/N in the influent is not sufficient, external carbon source such as

2 M. Kim et al. methanol is required to enhance the denitrification process. Note that COD/N ratio is a function of the available organic carbon (Peng et al. 2007). Recently, several works on GHG emissions modeling in WWTPs have been reported, focusing on the analysis and comparison of the total amount of GHG emissions (Ashrafi 2012; Peng et al. 2007; Flores-Alsina et al. 2014). Ashrafi (2012) investigated the contributions of on- and off-sites GHG emissions depending on the types of WWTP systems (e.g., aerobic, anaerobic, and hybrid bioreactors). The results were illustrated with the total amount of GHG emissions with respect to each unit of processes. Flores- Alsina et al. (2014) evaluated the performances of four control and operational strategies for WWTPs, by comparing the effluent quality index (EQI), GHG reduction, and operational cost index (OCI) of each control strategy. The EQI is an aggregated weighted index of effluent pollution loads, i.e., total suspended solid (TSS), COD, biochemical oxygen demand (BOD), total Kjeldahl nitrogen (TKN), and the oxidized forms of nitrogen (NO x ). The OCI is the economic assessment index which consists of the sum of operating costs: aeration energy, pumping energy, mixing energy, sludge production, external carbon addition, methane production, and net heating energy (Nopens et al. 2010). On the other hand, there have been several studies on GHG reduction strategies at on- and off-site locations (Corominas et al. 2012; Ashrafi 2012; Snip 2009). Corominas et al. (2012) evaluated the effects of anaerobic digester (AD) volume on off-site GHG emissions, when biogas generated from the AD is used as energy source for WWTP operations. Ashrafi (2012) investigated the variations in operating conditions of the reactor (e.g., temperature and solid retention time) on GHG emissions. The author also suggested optimal operational conditions in order to reduce GHG emissions (Ashrafi 2012). Snip (2009) studied the effects of set-point change of dissolved oxygen (DO), ammonia controller, and nitrate controller on GHG emission from on- and off-site locations of a WWTP. Some of these studies proposed GHG reduction strategies without the consideration of operational cost and environmental loads of the WWTPs (Corominas et al. 2012; Ashrafi 2012). Otherwise, most of these studies have shortcomings when analyzing GHG emissions results. For example, they cannot quantify how much GHG is reduced by the strategy at each process or at the on- and off-site locations of a WWTP (Ashrafi 2012; Flores-Alsina et al. 2014; Snip 2009). Therefore, a new method is needed for the evaluation of GHG emission reduction strategies, for on- and off-site locations. Additionally, the newly developed method should also be able to quantify the total changes in GHG emissions at each unit processes. This calls for the development for greenhouse emission pinch analysis (GEPA) in this study. Pinch analysis was first developed in the 1970s for the systematic synthesis of heat exchanger network (Hohmann 1971, Linnhoff and Flower 1978), and was then extended into various heat integration problems in the 1980s (Linnhoff et al. 1982, 1994; Smith 1995). Since then, there were various extensions of pinch analysis, which include mass exchange network synthesis (El-Halwagi and Manousiouthakis 1989), mass integration (El-Halwagi 1997, 2006), as well as the special cases of material resource conservation network, such as water network synthesis (Wang and Smith 1994), oxygen integration (Zhelev and Ntlhakana 1999), hydrogen integration (Alves and Towler 2002), and property integration (Kazantzi and El-Halwagi 2005). More recently, novel applications of pinch analysis for energy supply chain design (Lam et al. 2010), carbonconstrained energy planning (Tan and Foo 2007; Lee et al. 2009), production carbon footprint reduction (Tjan et al. 2010), carbon capture and storage (Tan et al. 2009a; Diamante et al. 2014), and water footprint reduction (Tan et al. 2009b; Jia et al. 2015) have also been reported in the literature. The newly developed GEPA in this work is an extension of carbon emission pinch analysis (CEPA), which was originally developed for the optimum allocation of energy sources to the energy demands, subject to CO 2 emission constraint (Tan and Foo 2007). Graphical tool known as carbon emission pinch diagram was developed to determine the minimum carbon-neutral (Tan and Foo 2007) and low-carbon sources (Lee et al. 2009) needed for an energy allocation problem. An equivalent algebraic tool was also developed for the same objective (Foo et al. 2008). A later extension of CEPA was carbon-constrained power generation sector planning (Tan et al. 2009a), where CO 2 emission is to be reduced to an acceptable limit. Various graphical and algebraic pinch analysis tools were developed to address both CO 2 capture (Tan et al. 2009a, Sahu et al. 2014; Ooi et al. 2014) and CO 2 storage planning problems (Ooi et al. 2013; Diamante et al. 2013, 2014). Another extension of the work was later reported for carbon footprint reduction strategies for manufacturing processes by Tjan et al. (2010), who takes into account of external (mainly by raw material supplies) and internal (generated in-plant) carbon footprints with economic value. A similar concept was also reported recently for the analysis of water footprint for water-intensive manufacturing processes, where the overall water footprint was broken down into external and internal elements (Jia et al. 2015). In both of these works, carbon (Tjan et al. 2010) and water (Jia et al. 2015) footprint composite curves were used to analyze the various footprint reduction strategies. The

3 Greenhouse emission pinch analysis (GEPA) for evaluation of emission reduction strategies concept is now extended into GEPA to graphically analyze the GHG emission from the on- and off-sites of a WWTP, and its associated reduction strategies. There were other extensions related to carbon and environmental footprint problems. Priya and Bandyopadhyay (2012) investigated parameters to decide the priority of various power plants with emission, in which prioritized cost is introduced to allow a tradeoff between cost and emission. This paper is structured as follows. In the following section, data for GHG emissions and operational costs in the on- and off-site locations of WWTPs were estimated using a layout of the benchmark simulation model no. 2 (BSM2). BSM2 is a plant-wide detailed protocol for implementing, diagnosing, and evaluating control strategies (Nopens et al. 2010). Next, GEPA is used to graphically analyze the sources of GHG emissions in the plants, for both on- and off-site locations. Three strategies were then implemented to assess their potential in reducing onand off-site GHG emissions. Methodology The GHG emissions from WWTP are defined as problem of this study. Inputs of WWTP are default influent condition provided to benchmark simulation model no. 1 (BSM1) (Alex et al. 2008). On the other hand, we can suppose that desired GHG emissions of WWTP applying the reduction strategies might be lower than base case model. Figure 1 shows the proposed framework for GEPA. The proposed framework consists of three main steps: (1) data collection from on- and off-site locations of the WWTP, (2) application of GEPA for analyzing GHG emissions, and (3) evaluation of GHG reduction strategies. These steps will be discussed in the following sub-sections. As mentioned earlier, the GHG emissions from WWTPs may be divided into on- and off-site sources in general. The on-site emissions of WWTPs are originated during biological nutrient removal processes such as substrate oxidation, biomass decay, nitrification, denitrification, and sludge digestion. On the other hand, the off-site GHG emissions are originated from production of electricity and methanol in order to operate various WWTP processes (e.g., aeration, mixing, pumping, heating of ADs). In order to estimate the amount of on-site GHG emissions, an activated sludge model using two-step nitrification and four-step denitrification processes (ASM2N4DN) was used. The ASM2N4DN model describes N 2 O emission mechanism in greater detail than the conventional ASM models (Snip 2009). Figure 2 indicates a finalized layout of BSM2 composed of a primary clarifier, activated sludge reactors, a secondary clarifier, and anaerobic digester. The on- and off-site GHG emissions were estimated based on the ASM2N4DN and anaerobic digestion model no. 1 (ADM1) (Batstone et al. 2002). The amount of carbon dioxide gas (CO 2 ) generated from the biological process units of WWTPs is calculated using stoichiometric equations developed by Ashrafi (2012). In general, AD which converts solid-waste sludge into methane (CH 4 ) and CO 2 - rich biogas is one of the major CH 4 emission points of WWTPs (Eggleston et al. 2006). Therefore, ADM1 was used to estimate the amount of CH 4 emitted from the AD (Batstone et al. 2002). The amount of off-site GHGs emitted from the electricity generation were calculated based on the assumption that oil with an emission factor of 604 g CO 2 /kwh was used as fuel for power generation (Sahely et al. 2006). The amount of GHG emitted from the production of the external carbon source was estimated using the emission factor of 1.54 g CO 2 /g methanol as COD (Dong and Steinberg 1997). The cost required for operating WWTPs consists that for on-site and off-site processes. The on-site cost includes that for biological nutrient removal process in the WWTPs. In order to calculate the on-site cost, the sludge production (SP), and water quality index (WQI) of each reactor were estimated. SP was calculated as a product of total suspended solids (TSS) per unit waste sludge (kg TSS/m 3 ) with waste sludge flowrate (m 3 /day) (Jeppsson et al. 2007). A unit cost of 75 /kg is used to convert into daily production cost for the sludge treatment ( /day) (Vanrolleghem and Gillot 2002). On the other hand, we developed WQI which is the performance index of amount of removed pollutants, using Eq. 1 that follows: WQI i ¼ b TSS TSS i;re þ b COD COD i;re þ b TKN TKN i;re þ b NO NO x;i;re þ b BOD BOD i;re ; ð1þ Fig. 1 The proposed GEPA scheme of the GHG footprint composite analysis and the GHG reduction at on- and off-site locations of the WWTP where i is the number of biological reactors, the subscript i,re represents the amount of components removed in the ith biological reactor, b is the weighting factor of pollutants

4 M. Kim et al. Fig. 2 Plant layout of BSM2 (Nopens et al. 2010) in the effluent, TSS is the total suspended solids, COD is the chemical oxygen demand, TKN is the Kjeldahl nitrogen, NO x is the nitrate plus nitrite nitrogen, and BOD is the biological oxygen demand. Development of this equation was referred to the EQI, which represents the environmental load of the discharged effluent. In this study, the EQI was calculated with Eq. 2 (Nopens et al. 2010): Z tend EQI ¼ 1 ½b 1000T TSS TSS e ðþþb t COD COD e ðþ t t start þb TKN TKN e ðþþb t NO NO Xe ðþþb t BOD BOD e ðþ Q t e ðþdt; t ð2þ where T is the total simulation time, b is the weighting factor of pollutants in the effluent, TSS e, COD e, TKN e, NOx e, and BOD e are the concentrations of each of the pollutants in the effluent, and Q e is the effluent flow rate. In Eqs. 1 and 2, the weighting factors are b TSS = 2, b COD = 1, b TKN = 20, b NO = 10, b BOD = 2, which were generally used in WWTP (Nopens et al. 2010). The factors were determined on empirical effluent component weightings by Vanrolleghem et al. (1996) and revised for better agreement with ecological aspects related to effluent of nitrogen by Jeppsson et al. (2007). The amount of each pollutant removed in the ith reactor comes from the ASM2N4DN model. In order to convert the WQI into cost of each cost, a contribution factor to the environmental load (i.e., effluent quality) was defined. The contribution factor of each reactor could be calculated using an inverse number of each coefficient of WQI i as follows: / i ¼ WQI 1 i P n ; ð3þ i¼1 WQI 1 i where / is contribution factor in the ith biological reactor, n is total number of biological reactors. Since the WQI is a function of the amount of pollutants removed at each reactor, a high value of WQI indicates better biological nutrient removal efficiency and a low contribution factor to the effluent quality. Therefore, the biological treatment cost ( /day) of each reactor was calculated using the following simple Eq. 4: ð4þ where EQI is the performance index of the WWTP (Eq. 2) and 50 ( /kg) is the factor used to convert the EQI into the cost unit (Vanrolleghem and Gillot 2002). Here, EQI is included in the OCI as measure of pollutants loads that operators must pay (inspired in the Flemish legislation). Hence, the more pollution goes to the water bodies, the

5 Greenhouse emission pinch analysis (GEPA) for evaluation of emission reduction strategies more needs to be paid. In summary, the cost required for treating the load is distributed among the reactors, as the biological treatment cost depends on the removal efficiency of each process. The cost of the off-site location comes from power consumption and external carbon source required for WWTP operation. The electricity consumption and the amount of external carbon source for operating the WWTP were calculated from equations suggested in the BSM2 report (Nopens et al. 2010). The unit cost for electricity is taken as 25 /kwh (Vanrolleghem and Gillot 2002). In case of the external carbon, it was assumed that the cost factor for added external carbon source was 75 /kg COD as same with that of sludge disposal since they have same weightings when calculating the OCI (Jeppsson et al. 2007; Vanrolleghem and Gillot 2002). Analysis of GHG emission using GEPA As discussed earlier, GEPA is an extension of the CEPA methodology developed for carbon (Tjan et al. 2010), and water footprint reduction (Jia et al. 2015). The GEPA explains graphically how much GHGs are emitted from each unit of the process and how much the GHGs can be reduced based on various energy reduction strategies that may lead to reduced environmental loads. Figure 3 shows the graphical tool that is proposed for GEPA in this work, i.e., greenhouse gas composite curve (GGCC). To plot the GGCC, the operational costs of on- and off-site elements are plotted on the x-axis, while their total GHG emissions are plotted on the y-axis. Note that the on- and off-site elements were plotted in ascending order of GHG intensity, which is indicated by the slope of the segment (i.e., GHG emissions amount/total cost). For more detailed analysis, the individual elements of the on- and off-sites can also be included in the GGCC (see case study section for illustration). In order to reduce the GHG emission, segments with higher slope (for both on- and off-sites) should be targeted. This is similar to the strategies adopted for carbon (Tjan et al. 2010), and water footprint reduction (Jia et al. 2015). The amount of GHG emissions and the total operational cost will then be calculated for each of the reduction technologies. The adjusted GGCC (with segments of reduced slopes) represents the targeted benchmark when the GHG reduction strategy was implemented. The GEPA gives a valuable graphical aid to the decision-makers who need to understand the GHG sources and decide the most suitable strategy for GHG reduction. Case study: base case model The GHG emissions for the on- and off-site locations for the base case model were estimated in the configuration of the BSM2. Besides, the operating cost of each process was calculated by combining the EQI with the OCI, with results shown in Table 1. In the BSM2, kg CO 2 eq/day of GHGs was emitted from the WWTP, and their total operating cost was estimated as /day. In the GHG emission sources at both the on- and off-site locations, the largest sources of GHGs came from sludge processing ( kg CO 2 eq/day), followed by the biological processes ( kg CO 2 eq/day), consisting of five individual units of anoxic and aerobic processes. Note that both sludge processing and biological processes are on-site operations. On the other hand, off-site emissions were contributed by chemical production and power generation, which contribute and kg CO 2 eq/day, respectively. The operational costs are also summarized in Table 1. The GGCC for the base case model is shown in Fig. 4, plotted using the data in Table 1. As shown, the on-site segment has higher GHG intensity (indicated by higher slope). However, this GGCC only considers the total value of the on- and off-site elements. A more detailed GGCC that includes all individual elements (i.e., unit processes that have CO 2 emission) is hence plotted in Fig. 5. Note that these elements are also arranged in ascending order of their slope which indicates the GHG intensity. GHG reduction scheme for case study Fig. 3 Greenhouse gas composite curve of the GEPA revised from CEPA (Tjan et al. 2010) In order to reduce GHG emissions from the WWTPs, three GHG reduction strategies are proposed, i.e., increase of aeration capacity in the aerobic reactors (strategy 1-S1), installation of external carbon source controller in the first

6 M. Kim et al. Table 1 GHGs emission and cost consumed for a WWTP (base case model) Source Process units Cost (10 5 Euro/d) GHGs emission (10 4 kg CO 2 eq/d) On-site Biological process 1st anoxic nd anoxic st aerobic nd aerobic rd aerobic Total Sludge processing (anaerobic digester) Total emission from on-site Off-site Production of external carbon source Generation of power Total emission from off-site Total emission from WWTP Fig. 4 GHG footprint composite curves for total value of on- and offsite element of base case model Fig. 6 GHG footprint composite curves of the BSM2 with the increase in the aeration capacity in the aeration basins (S1) Fig. 5 Detailed GHG footprint composite curves of base case model including all individual elements anoxic reactor (S2), and biogas reuse from AD (S3). All strategies were evaluated in BSM2 for their performance in GHG reduction. Their results are discussed next. For the first GHG reduction strategy (S1), we look into the increase of aeration capacity in the aerobic reactors. Doing this would supply DO concentration that is sufficient for nitrification. Note however that doing so will increase the GHG emissions at the off-site locations due to the increased aeration, but the improved nitrification efficiency lowered the EQI and reduced N 2 O gas in the denitrification process. The default value of the oxygen transfer rate (K L a) in the aerators in the BSM2 was not sufficient for the complete nitrification, which will result in insufficient nitrification performance (Jeppsson et al. 2007). Hence, it is logical to increase the values of oxygen transfer rates of

7 Greenhouse emission pinch analysis (GEPA) for evaluation of emission reduction strategies the three aerators to 136 (1st aerobic), 136 (2nd aerobic), and 33/day (3rd aerobic), respectively, (from the original respective rates of 120, 120, and 30/day in the base case model). The adjusted GGCC of S1 (Fig. 6) shows that the electricity cost was increased by /day (= /day) from the base case model. This is mainly due to the larger K L a value, as the aerators spent more electricity for higher aeration rate in the three aerobic reactors. This results with an increase of GHG emissions at the off-site locations by kg CO 2 eq/day, i.e., to 0.42 CO 2 eq/day (from 0.39 CO 2 eq/day in the base case model). In addition, the DO concentrations of the three aerobic reactors were increased to 0.71, 2.00, and 0.20 mg/ l, respectively (from the respective default values of 0.44, 0.87, and 0.10 mg/l in the base case model). The increased DO concentration enhances the nitrification process due to the growth of nitrate oxidizing bacteria (Rassamee et al. 2011), where the average NO - 2 concentration in the aerobic reactors was decreased from 1.25 mg/l (base case model) to 0.68 mg/l (S1). The higher DO concentration and lower NO - 2 concentration in the aerobic reactors decreased the N 2 O production in the aerobic reactor (Flores-Alsina et al. 2014). As a result, the amount of N 2 O emitted from the aerobic reactors was decreased by kg CO 2 eq/day. With the decrease of N 2 O from the aerobic reactors, coupled with the increase of GHG emissions at the off-site locations, the net CO 2 reduction can be easily determined as kg CO 2 eq/day (= kg CO 2 eq/day). This results in the reduced GHG emissions for the entire process as to kg CO 2 eq/day (= kg CO 2- eq/day), with a small increase in the operational cost ( /day). This corresponds to GHG reduction of approximately 2 %. On the other hand, S2 was to implement an external carbon source controller that can manipulate the amount of external carbon sources to be injected to the first anoxic reactor (1st anoxic), based on its nitrate concentration. The reason for using the controller is that the concentration of substrate in denitrification process is generally not sufficient for the removal of oxidized nitrogen (Cherchi et al. 2009). On the other hand, excessive external carbon addition may cause a lack of sufficient DO for nitrification and a higher nitrate concentration, which may lead to an increase in N 2 O by incomplete nitrification (Rassamee et al. 2011; Flores-Alsina et al. 2014). Hence, using the controller will improve the nutrient removal process and result in the reduction of N 2 O generation with sustainable DO concentration. Figure 7 shows GGCC for the second GHG reduction strategy (S2). With the external carbon controller, the amount of external carbon added was decreased from kg COD/day (base case model) to kg COD/day. The corresponding GHG emissions at Fig. 7 GHG footprint composite curves of the BSM2 with the installation of the external carbon source controller (S2) the off-site locations and its operational cost were decreased by kg CO 2 eq/day and /day, respectively. The concentration of the heterotrophs in the biological processes was reduced slightly from mg/l to mg/l due to reduced external carbon feed. Therefore, the average amount of N 2 O produced by the heterotrophs was decreased slightly by kg CO 2 eq/day. In addition, the sludge production by the reduced external carbon was decreased slightly from kg/day to kg/day, which led to the decrease of GHG emissions and its associated cost in the anaerobic digester by kgco 2 eq/day and /day, respectively. In summary, the total GHG emissions and cost for S2 were reduced by kg CO 2 eq/day (= ? ? kg CO 2 eq/day) and /day (= ? /day), respectively. The total GHG emission reduction for this case is hence calculated as about 4.0 %, corresponding to the cost reduction of 4.7 %. For the third strategy (S3), methane-rich biogas in the AD was reused for plant operation. The electricity generation is to be carried out using a small-scale power system with methane-rich biogas as energy source (Jason et al. 2010). The electricity generated was enough to compensate for all the required electricity for the plant (Igoni et al. 2008). This is an energy self-sufficient scenario, hence the GHG emission at the off-site locations of kg CO 2 eq/day is completely removed, while the cost for its power generation was reduced by /day. Therefore, it can be seen from Fig. 8 that there is no segment of power generation at off-site in GGCC. At the onsite locations, there is no emission of CH 4 gas in air, because the CH 4 gas is used as the methane-rich biogas in AD. As a result, the GHG emissions are reduced by kg CO 2 eq/day. On the other hand, the cost for

8 M. Kim et al. GHG footprint composite curves. In this study, the third strategy which reuses biogas from AD shows the largest reduction of GHG emissions. It was possible to graphically evaluate the efficiencies of the three GHG reduction strategies in a benchmark simulation. Therefore, it is helpful to select GHG reduction strategy in WWTP considering economic aspect as well as GHG emissions. The methodology can be extended by incorporating life cycle assessment (LCA). On the other hand, optimization of each strategy is needed to reduce GHG emissions from WWTP and remain as future work. Furthermore, the use of LCA with pinch analysis enables the evaluation of total environmental impact of WWTPs, apart from GHGs. Fig. 8 GHG footprint composite curves of the BSM2 with AD biogas reuse (S3) Acknowledgments This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1A2A2A ). sludge treatment was the same as the base case model, since the S3 only used the byproduct of the AD and did not affect the operational cost of the WWTP. As shown in Fig. 8, the intensity of AD segment is decreased by reuse of methane-rich biogas. The segment of the biological process moved to the left side of the figure without any changes in the length and slope of the curves, which means that biological reaction processes were not affected at all. The GGCC in Fig. 7 shows that S3 has reduced GHG emissions and operational cost by kg CO 2- eq/day (= ? kg CO 2 eq/day) and /day, respectively. As compared to the base case model, the GHG emission reduction for S3 is calculated as 43 %, with a cost reduction corresponds to 18 %. Finally, note that all the above strategies show GHGs reduction. However, S1 and S2 have GHG emission reduction of less than 5 %. Moreover, S1 that increases aeration capacity shows an increase of operation cost too. Of these three strategies, reuse of methane-rich biogas in AD (S3) shows a decrease in both GHG emissions and operational cost as 43 and 18 %, respectively. Therefore, S3 is the best candidate for the reduction of GHG emissions as well as operational cost. Conclusions In this paper, a new systematic method was proposed to analyze the GHG sources and to evaluate the GHG reduction strategies using graphical pinch analysis. In particular, the performances of GHG reduction strategies at on-site (within the units of the WWTPs) and the off-site locations (outside of the WWTPs) were evaluated using the GEPA. The results showed that the GEPA can identify the sources of GHG emissions and their contribution to the References Alex J, Benedetti L, Copp J, Gernaey KV, Jeppsson U, Nopens I, Pons MN, Rieger L, Rosen C, Steyer J, Vanrolleghem PA, Winkler S (2008) Benchmark simulation model no. 1 (BSM1). Technical report, Lund University Alves JJ, Towler GP (2002) Analysis of refinery hydrogen distribution systems. Ind Eng Chem Res 41: Ashrafi O (2012) Estimation of greenhouse gas emissions in wastewater treatment plant of pulp & paper industry. Dissertation, Concordia University Barton PK, Atwater JW (2002) Nitrous oxide emissions and the anthropogenic nitrogen in wastewater and solid waste. J Environ Eng 128: Batstone DJ, Keller J, Angelidaki I et al (2002) The IWA anaerobic digestion model no 1 (ADM1). Water Sci Technol 45:65 73 Cherchi C, Onnis-Hayden A, El-Shawabkeh I, Gu AZ (2009) Implication of using different carbon sources for denitrification in wastewater treatments. Water Environ Res 81(8): Corominas L, Flores-Alsina X, Snip L, Vanrolleghem PA (2012) Comparison of different modeling approaches to better evaluate greenhouse gas emissions from whole wastewater treatment plants. Biotechnol Bioeng 109(11): Diamante JAR, Tan RR, Foo DCY, Ng DKS, Aviso KB, Bandyopadhyay S (2013) A graphical approach to pinch-based sourcesink matching and sensitivity analysis in carbon capture and storage systems. Ind Eng Chem Res 52(22): Diamante JAR, Tan RR, Foo DCY, Ng DKS, Aviso KB, Bandyopadhyay S (2014) Unified pinch approach for targeting of carbon capture and storage (CCS) systems with multiple time periods and regions. J Clean Prod 71:67 74 Dong Y, Steinberg M (1997) Hynol an economical process for methanol production from biomass and natural gas with reduced CO 2 emission. Int J Hydrog Energy 22(10): Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (2006) 2006 IPCC guidelines for national greenhouse gas inventories. IGES, Hayama El-Halwagi MM (1997) Pollution prevention through process integration: systematic design tools. Academic Press, San Diego El-Halwagi MM (2006) Process integration. Elsevier Inc, San Diego El-Halwagi MM, Manousiouthakis V (1989) Synthesis of mass exchange networks. AIChE J 35(8):3 1244

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