CHEMICAL ENGINEERING TRANSACTIONS VOL. 76, 2019 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Guest Editors: Petar S. Varbanov, Timothy G. Walmsley, Jiří J. Klemeš, Panos Seferlis Copyright © 2019, AIDIC Servizi S.r.l. ISBN 978-88-95608-73-0; ISSN 2283-9216 Total Site Heat Integration Considering Optimum Pressure Drops Simin Faramarzi, Nassim Tahouni*, M. Hassan Panjeshahi School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran ntahuni@ut.ac.ir In this study, a new methodology is developed for Total Site Minimum Energy Requirement (MER) targeting, in which optimum stream pressure drops (Ps) are considered for each individual process. To validate the accuracy of the methodology, two Total Site (TS) systems comprising processes A and B and processes C and D are selected and evaluated using two different approaches; I) considering streams’ assumed heat transfer coefficient (conventional method) and II) considering streams’ allowable Ps. In the second approach, the possibility of pumps/compressors replacement is first investigated for each individual process, through exploring a 3-way trade-off between energy consumption, area requirement and hydraulic system. The resulting data are then used to construct a Total Site profiles (TSPs) for MER targeting. These profiles depict that the hot and cold utility values, when Ps are optimized, are much less than those obtained in conventional method using assumed coefficients. Moreover, the TS energy targets that are estimated in this method are more realistic and compatible with the results of detailed design stage. 1. Introduction Total Site Heat Integration (TSHI) is a method for integrating heat recovery through different processes in an industrial complex. The aim of this technique is optimum design of a central utility system for the entire site within the framework of multiple processes. It has been proved that TSHI can lead to applicable designs. Many researchers have focused on this subject for improving conventional TSHI methods to achieve more accurate results. Nemet et al. (2012) studied the conventional TSHI method suggested by Klemes et al. (1997) and considered the capital costs for utility generation and consumption levels. Fodor et al. (2012) presented a TS targeting method by considering stream specific minimum temperature difference (Tmin) for individual processes and between process streams and utilities. The distinction of the suggested method is considering the effect of heat transfer coefficients of streams and utilities during TS targeting. Tarighaleslami et al. (2017) demonstrated a TSHI targeting method with more precise targets requiring isothermal and non-isothermal utilities. P is an important parameter for feasible synthesis of heat exchanger networks (HENs) and its consideration can be extended to TS problem. Limited researchers have considered the Ps in utility transmission lines between processes in TS problem, i.e. elevation Ps, frictional Ps, and Ps related to distances between plants. Chew et al. (2013) has considered the equipment pressure rating for HENs and selected the appropriate pressures for industrial implementation of TSHI. Wang et al. (2013) studied the P arising from distance between plants by economic analysis of TSHI. An improved algorithm for defining TS minimum utilities was extended by Liew et al (2014) by considering P impact on utility temperatures. Synthesis of TS by employing a stochastic multi-period mixed-integer nonlinear programming model was carried out by Nemet et al. (2015). The purpose of their work is to enhance TS modelling by including proper pressure levels for intermediate utilities. Another study by Chew et al. (2015) investigated the significance of Ps due to elevation changes and frictional losses in utility transmission lines within TS. It is concluded that considering pressure drop by this method leads to more realistic targeting results in utility distribution network estimation. None of the current works have addressed the Ps arising from heat exchange in Total Site Heat Exchanger Networks, when targeting for MER. It was verified that use of assumed values for film heat transfer coefficient in targeting stage of HENs leads to inconsistency between targeting, synthesis, and detailed design results for DOI: 10.3303/CET1976205 Paper Received: 22/03/2019; Revised: 17/05/2019; Accepted: 14/06/2019 Please cite this article as: Faramarzi S., Tahouni N., Panjeshahi M.H., 2019, Total Site Heat Integration Considering Optimum Pressure Drops, Chemical Engineering Transactions, 76, 1225-1230 DOI:10.3303/CET1976205 1225 both retrofit and grass-root design (Polley et al, 1990; Polley and Panjeshahi, 1991). To ensure that the results of synthesis stage are compatible with those finally achieved after exchangers detailed design, it is needed to apply the area targeting algorithm based on allowable stream Ps. 2. Methodology The design of heat recovery networks is generally concluded in a number of stages. First, there is a targeting stage where the economics of heat recovery are evaluated in order to set the recovery level. This involves the trading-off energy and network capital costs. The second stage involves network synthesis. Here the topography of the heat recovery system required to realize the targeted energy recovery is determined. Finally, the detailed design of the exchangers within the recovery system is undertaken. Delay in considering P in above procedure can lead to inconsistent results between network synthesis and detailed design, with respect to surface area of the exchangers as well as incorrect capital-energy trade-off and network optimization in both grass-root and retrofit designs. Consequently, for decreasing difference in results of HEN synthesis and detailed design, we need a network area algorithm based on streams Ps (not assumed heat transfer coefficients) leading to consistent results. As a result, for the first part of the methodology employed in this research, area algorithm based on streams P (Polley and Panjeshahi, 1991), is used in targeting of individual processes of TS to get close to the optimum point of energy and area consumption. The equation (1) states the economic objective function. Total cost = f (P1, P1, … , Pn) = CCExch + CCPump/Comp + OCPower + OCEnergy (1) Where CCExch is the capital cost for all exchangers, CCPump/Comp is the capital cost for purchasing pumps/compressors, OCPower is operating cost for consumed electricity in pumps/compressors and CE is the cost of utilities. This is a 3-way trade-off between energy consumption, area requirement and hydraulic system. The objective cost function is minimized and then the optimum Tmin for intra process streams (∆Tmin,pp,opt), optimum stream Ps, minimum utility requirements and minimum area for each process are obtained. The P values are not the allowable pressure for each stream. Instead, the application of smaller/bigger pumps/compressors is considered (Panjeshahi and Tahouni, 2008). The results are obtained using PILOT software (Panjeshahi, 2012) programmed for targeting and design of HENs. Equation (2) states the conventional way to calculate minimum area for a HEN, where Aj is area contribution of stream j. Given the relationship between P and film heat transfer coefficient in equation (3), we can calculate the network area predictions on stream pressure drop rather than assumed heat transfer coefficients. In equation (3), Pj, Kj, Acj and hcj are specified P of stream j, constant value relating to stream j, exchanger area installed on stream j (contact area) and clean heat transfer coefficient of stream j. By simultaneously solving the equations (2) and (3), the heat transfer coefficients will be omitted. Then equation (4) estimates the network area based on Ps, where Acj, K, CPK and AK are contact area of stream j, number of opposing streams to stream j within that interval, specific heat of stream k and area contribution of stream k (Polley and Panjeshahi, 1991). A min = A j j=1 J å Þ Aj = q ji DT LM,i æ è çç ö ø ÷÷ 1 h j æ è çç ö ø ÷÷i=1 I å (2) DP j =K j A cj h cj( ) m (3) A cj = A j + CP k CP kå opposingstreams å (4) By implementing the algorithm in process level, stream Ps are optimised and the output of this step is used for energy targeting within the processes of TS. The new proposed method for TS targeting is summarized in Figure 1. Step 1: Data extraction including process, physical property and cost data for each process. Step 2: Targeting the intra-process heat recovery for each individual process based on area algorithm (Polley and Panjeshahi, 1991). Thus, by implementation a 3-way trade-off between area, energy and P, the optimum process to process ∆Tmin (∆Tmin,pp,opt) is identified. Also, the Pinch location, heating and cooling utility demands, total annualized cost, minimum area and Grand Composite Curve (GCC) for each process are known. 1226 Afterwards the heat source/sink segments from the GCC of individual processes are extracted and heat recovery pockets on the GCCs are removed due to the potential for internal process heat recovery. It is noted that the sink / source parts in GCC have been shifted by 1/2 ∆Tmin,pp,opt (T*) in each individual process. Step 3: Targeting the utility generation and consumption for TS. To do this, the Tmin between process and utilities (∆Tmin,pu) is selected based on the method suggested by varbanov (2012). The utility temperatures are sketched based on heat source and sink segments. This stage prepares TS Problem Table Algorithm (PTA), which is in the similar way of individual processes PTA. The shifted temperatures, T*, are first shifted back to their original values, and then shifted again by ∆Tmin,pu to make sure Tmin between process and utilities is maintained (equation 5). Then, TSP are illustrated by combining site heat source and sink parts. In the final part, utility generation and consumption is analysed from highest temperature level of hot utility and moves toward the lowest temperature level for maximization of utility generation. 𝑆𝑖𝑡𝑒 𝑆𝑖𝑛𝑘: 𝑇 ∗∗ = 𝑇 ∗ − 0.5∆𝑇𝑚𝑖𝑛,𝑝𝑝,𝑜𝑝𝑡 + ∆𝑇𝑚𝑖𝑛,𝑝𝑢 (𝑎𝑛𝑑) 𝑆𝑖𝑡𝑒 𝑆𝑜𝑢𝑟𝑐𝑒: 𝑇 ∗∗ = 𝑇 ∗ + 0.5∆𝑇𝑚𝑖𝑛,𝑝𝑝,𝑜𝑝𝑡 − ∆𝑇𝑚𝑖𝑛,𝑝𝑢 (5) STEP 2 STEP 3STEP 1 Extract Stream Data/ Cost Data/ Physical Property Data Extract Stream Data/ Cost Data/ Physical Property Data Target Intra-Process Heat Recovery based on Optimum Pressure Drops Tmin,pp,opt, Optimum Ps, PTA, GCC Tmin,pp,opt, Optimum Ps, PTA, GCC Remove Heat Pockets and Specify the Utility Data Start Plot TSP Total Site TargetingTotal Site Targeting End Figure 1. Flowchart of the modified TSHI method 3. Case Study In this research, two TS examples have been studied applying the new proposed procedure. Each case study is TS consisted of two individual processes. The stream data of both processes are shown in Tables 1 and 2The first case study includes two processes A and B., which are the modified examples from literature (Canmet ENERGY, 2003 and Kemp, 2007). Also, the cost data is represented in Table 3. Table 1. Stream data for process A Stream Ts (°C) Tt (°C) CP (kW/°C) h (W/m2.°C) Allowable ∆P (kPa) A1 Hot 200 100 20 500 120 A2 Hot 150 60 40 250 80 A3 Cold 50 120 70 500 20 A4 Cold 50 220 15 250 30 Table 2. Stream data for process B Stream Ts (°C) Tt (°C) CP (kW/°C) h (W/m2.°C) Allowable ∆P (kPa) B1 Hot 200 50 3 250 120 B2 Hot 240 100 1.5 250 80 B3 Hot 200 119 23 250 90 B4 Cold 30 200 4 250 20 B5 Cold 50 250 2 250 20 Table 3. Cost data for process A and B Hot utility ($/y) Cold utility ($/y) Heat exchanger ($) Plant life time (y) Payback time (y) Interest rate % 30 7 0+1000(A) 20 3 30 1227 The related data for the second Total Site case study including two processes C and D as well as cost data are shown in Tables 4, 5 and 6. Process C is a modified example by Nie and Zhu (1999) and process D is an aromatics plant studied by Polley and Panjeshahi (1991). Table 4. Stream data for process C Stream Ts (°C) Tt (°C) CP (kW/°C) h (W/m2.°C) Allowable ∆P (kPa) A1 Hot 180 30 59.8 1282 200 A2 Hot 270 40 114.4 2066 25 A3 Hot 350 30 33.8 895 6 A4 Hot 380 50 145.6 1115 13 A5 Hot 150 100 657.8 1217 26 A6 Hot 290 190 384.8 1076 34 A7 Cold 20 390 520 1300 350 Table 5. Stream data for process D Stream Ts (°C) Tt (°C) CP (kW/°C) h (W/m2.°C) Allowable ∆P (kPa) A1 Hot 327 40 100 500 120 A2 Hot 220 160 160 500 80 A3 Hot 220 60 60 500 90 A4 Hot 160 45 400 500 60 A5 Cold 100 300 100 500 20 A6 Cold 35 164 70 500 20 A7 Cold 85 138 350 500 30 A8 Cold 60 170 60 500 15 A9 Cold 140 300 200 500 80 Table 6. Cost data for process C and D Hot utility ($/y) Cold utility ($/y) Heat exchanger ($) Plant life time (y) Payback time (y) Interest rate % 40 7 0+800(A)0.83 20 3 15 4. Results and discussion The targeting results for individual processes A and B are demonstrated in Table 7, separated for two different area algorithms based on fixed-heat transfer coefficients (conventional method) and the optimum Ps. Next, the results for TS MER targeting are shown in Table 8. The ∆Tmin,pu for this TS is selected of 20C. Also, the net amount of utilities is shown in Table 9. Moreover, TSPs for targeting based on fixed-heat transfer coefficients and optimum Ps are illustrated in Figures 2 and 3, which clearly shows the results. Table 7. Results of targeting for individual process A and B Process A Process B Fixed h Optimum ∆P Fixed h Optimum ∆P Hot utility (kW) 3,870 2,410 418 208 Cold utility (kW) 2,020 560 1,861 1,651 Amin (m2) 515.53 201.97 142.02 50.66 Total annualized cost ($/y) 285,717 151,368 68,399 33,076 ∆Tmin,pp,opt (°C) 57 24 63 28 Table 8. TS targeting for case study I (process A and B) Fixed h Optimum ∆P % of change CO2 Reduction (t/y) (EPA, 2019) Hot utility (kW) 1,707.95 1,328.6 23 1094.6 Cold utility (kW) 2,591.04 921.39 70 4817.6 1228 Table 9. Utilities for MER targeting of first TS (processes A & B) Utilities fixed h (kW) Optimum ∆P(kW) HPS (300 °C) 820 772 MPS (200 °C) 647.95 96 LPS (120 °C) 240 460 CW (10-30 °C) 2591.04 921.39 Figure 2: TSP for case study I (fixed h) Figure 3: TSP for case study I (optimum ∆P) The relevant results for targeting of second case study are summarized in Tables 10 and 11 (∆Tmin,pu =20C). Table 10. Results for targeting of individual process C and D Process C Process D Fixed h Optimum ∆P Fixed h Optimum ∆P Hot utility (kW) 44,148 51,428 24,480 21,680 Cold utility (kW) 17,264 24,543 32,200 29,400 Amin (m2) 10,456 20,699 12,889 8,639 Total annualised cost ($/y) 2,396,375 3,234,659 1,831,839 1,511,927 ∆Tmin,pp,opt (°C) 14 28 25 20 0 50 100 150 200 250 300 -3000 -2000 -1000 0 1000 2000 3000 T * * (° C ) H(kW) shifted Site Heat Sink Site Heat Source site sink composite site source composite 0 50 100 150 200 250 300 350 -3000 -2000 -1000 0 1000 2000 3000 T * * (° C ) H(kW) shifted Site Heat Sink Site Heat Source site sink composite site source composite 1229 Table 11. TS targeting for case study II (process C and D) Fixed h Optimum ∆P % of change Hot utility (kW) 64,677.32 59,069.72 9 % Cold utility (kW) 45,512.12 39,912.30 13 % 5. Conclusions This study developed a new method for targeting of TSHI, considering the streams optimum Ps. 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