DOI: 10.3303/CET25120004 Paper Received: 08 May 2025; Revised: 30 August 2025; Accepted: 27 September 2025 Please cite this article as: Ang T.J.N., Orosz Á., Friedler F., Andiappan V., How B.S., 2025, Enhanced Heat Recovery Network With Integrated Sensible Heat Storage Facilities for Energy Intensive Industry, Chemical Engineering Transactions, 120, 19-24 DOI:10.3303/CET25120004 CHEMICAL ENGINEERING TRANSACTIONS VOL. 120, 2025 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Bing Shen How, Viknesh Andiappan, Denny K.S. Ng, Hon Loong Lam, Petar S. Varbanov Copyright © 2025, AIDIC Servizi S.r.l. ISBN 979-12-81206-21-2; ISSN 2283-9216 Enhanced Heat Recovery Network with Integrated Sensible Heat Storage Facilities for Energy Intensive Industry Tiffany Jia Ning Anga, Ákos Oroszb, Ferenc Friedlerc, Viknesh Andiappana, Bing Shen Howa,* aResearch Centre for Sustainable Technologies, Faculty of Engineering, Computing and Science, Swinburne University of Technology, Jalan Simpang Tiga, 93350 Kuching, Malaysia bDepartment of Computer Science and Systems Technology, University of Pannonia, 8200, Veszprém, Egyetem u. 10, Hungary cSzéchenyi István University, 9026 Győr, Egyetem tér 1, Hungary bshow@swinburne.edu.my Energy-intensive industries contribute large amounts of greenhouse gas emissions. An effective strategy to decarbonise these industries is by applying process integration tools to enhance energy efficiency and reduce overall energy consumption. Recent studies showed that thermal energy storage offers significant benefits in energy efficiency enhancement, as it can amplify the energy recovery potential. Despite its potential, studies that applied process integration tools to address heat recovery problems with consideration of heat storage remain limited. This work develops an optimisation framework that aims to determine optimal heat storage type and size based on the total annualised cost (i.e., costs associated with storage facilities and utilities) to form a feasible heat recovery network between plants. The proposed framework is demonstrated through a case study that focuses on optimising the sensible heat storage selection for indirect heat integration between a mixed plastic waste treatment plant and a steel mill. By analysing the performance and effectiveness of the storage media studied, nitrate salt storage medium is selected due to its greatest energy and cost savings of 12.7 % and 20.7 %, when compared to direct Heat Integration. Insights from this provide information on the feasibility of implementing a storage-supported heat recovery network in the energy-intensive industry. 1. Introduction Energy-intensive industries account for nearly 25 % of the global carbon dioxide emissions (UNECE, 2022). Among various industries, chemicals and petrochemicals, cement, and iron and steel industries contribute the most significantly to the emissions. Process Integration tools are widely applied in energy efficiency enhancement to reduce overall energy consumption by forming a heat recovery network (Varbanov, 2023). Since the development of pinch analysis by Linnhoff and Flower (1978), the heat exchanger network synthesis has been extended from a single-plant analysis to an integrated analysis of multiple plants (Tian et al., 2020). Heat recovery can be achieved through direct integration between process streams or indirect heat integration via an intermediate fluid (Er et al., 2022). Although direct heat integration can maximise energy recovery, practical issues (e.g., safety concerns, pipeline complexity, operational issues) are often encountered in its implementation (Liu et al., 2015). Direct heat integration often requires a more complicated design for process flowsheet, plant layout, and control (Zhang et al., 2016). Hence, indirect heat integration is usually recommended for heat recovery between plants (Chang et al., 2015). Recently, heat storage facilities (or thermal energy storage (TES)) have emerged as a key technology in amplifying the energy recovery potential for heat integration between plants (Liew et al., 2018). The cost and energy-saving benefits of considering TES were demonstrated in studies such as the integration of a hydrogen storage system using the P-graph approach for an energy system by Ji et al. (2023) and the integration of heat storage tanks of different temperatures by Wang et al. (2020). However, they merely focused on integrating a pre-determined heat storage option without optimising through a spectrum of heat storage options. In addition, there is a lack of a comprehensive framework that optimises the storage size based on the economic feasibility 19 and suitability for a given industry. Möhren et al. (2022) adopted an iterative approach to determine the storage size for a single plant by reducing a predefined maximum storage volume and studying its impact on cost. Jamaluddin et al. (2020) optimised the sizing for a thermochemical heat storage integrated into a trigeneration system. However, they did not explore the performance of other heat storage options and the feasibility of integrating them for multiple plants. This leads to limitations in achieving maximal energy recovery, as the pre- determined storage facility may not necessarily be the most suitable option for industrial applications. Thus, this work aims to develop an optimisation model that can determine optimal heat storage type and its respective sizing for the indirect heat integration based on the total annualised cost (TAC). 2. Problem Statement Given a set 𝑃 of plants, and for each plant 𝑝 ∈ 𝑃 a set of hot streams 𝐻𝑆 and a set of cold streams 𝐶𝑆 with their respective heat to be released (Qℎ𝑠, ℎ𝑠 ∈ 𝐻𝑆) and absorbed (Q𝑐𝑠 , 𝑐𝑠 ∈ 𝐶𝑆). The heat duties are determined based on the stream properties, including the heat capacities (𝐶𝑃ℎ𝑠 and 𝐶𝑃𝑐𝑠), supply temperatures (𝑇ℎ𝑠 𝐼𝑁 and 𝑇𝑐𝑠 𝐼𝑁) and target temperatures (𝑇ℎ𝑠 𝑂𝑈𝑇 and 𝑇𝑐𝑠 𝑂𝑈𝑇). With the integration of heat storage facilities, the heat transferred into storage medium 𝑠𝑡 ∈ 𝑆𝑇 is denoted by 𝑄𝑠𝑡 𝑂𝑈𝑇 , whereas the heat transferred out from storage medium 𝑠𝑡 is denoted by 𝑄𝑠𝑡 𝐼𝑁 . The determination of the hot and cold utility consumption is based on the problem table algorithm (PTA) which consists of 𝑘 stages of temperature intervals with 𝑇ℎ𝑠 𝐼𝑁, 𝑇𝑐𝑠 𝐼𝑁, 𝑇ℎ𝑠 𝑂𝑈𝑇, and 𝑇𝑐𝑠 𝑂𝑈𝑇 shifted by half the minimum approach temperature (∆𝑇𝑚𝑖𝑛) and arranged in descending order (Linnhoff and Flower, 1978). The start temperatures of interval 𝑘 ∈ {0,1,2, … , 𝐾} in each plant 𝑝 for charging and discharging are denoted as 𝑇𝑘,𝑝 𝑈𝑝𝑝𝑒𝑟,𝐶ℎ𝑎𝑟𝑔𝑒 and 𝑇𝑘,𝑝 𝑈𝑝𝑝𝑒𝑟,𝐷𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒 . The end temperatures of interval 𝑘 in each plant 𝑝 for charging and discharging are denoted as 𝑇𝑘,𝑝 𝐿𝑜𝑤𝑒𝑟,𝐶ℎ𝑎𝑟𝑔𝑒 and 𝑇𝑘,𝑝 𝐿𝑜𝑤𝑒𝑟,𝐷𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒 . The cascaded heat at stage of temperature interval 𝑘 is denoted as 𝑑𝑘. This work aims to (i) evaluate the performance (i.e., TAC) of indirect heat integration compared to other heat integration scenarios; and (ii) determine the optimal storage option based on the TAC. 3. Methodology A mixed-integer linear programming model that aims to optimise the heat storage selection and sizing is developed. The objective function is set to minimise the TAC associated with the energy and storage medium (𝐶𝑠𝑡 𝑇𝑜𝑡𝑎𝑙), as given in Eq(1). The energy cost for each plant 𝑝 includes the annualised hot utility cost and cold utility cost, which are denoted as CHU and CCU . The storage size is represented by 𝑉𝑠𝑡 𝑆𝑡𝑜𝑟𝑎𝑔𝑒 , whereas the associated unit storage cost is denoted as Cst. 𝑀𝑖𝑛 𝐶𝑠𝑡 𝑇𝑜𝑡𝑎𝑙 = CHU ∑ 𝑄𝐻𝑈,𝑝 𝑀𝑖𝑛 + CCU ∑ 𝑄𝐶𝑈,𝑝 𝑀𝑖𝑛 𝑝∈𝑃 + 𝑝∈𝑃 Cst𝑉𝑠𝑡 𝑆𝑡𝑜𝑟𝑎𝑔𝑒 (1) Using the PTA, the heat is cascaded down the temperature interval 𝑘 in plant 𝑝. The model constraint for the energy balance equations at each temperature interval 𝑘 is given in Eq(2). This minimises the utilities required for the process, as represented by Eq(3) and Eq(4). For each plant 𝑝, the cascaded heat at stage of temperature interval 𝑘 = 0 shows the minimum hot utility required (𝑄𝐻𝑈,𝑝 𝑀𝑖𝑛 ), whereas the cascaded heat at the last stage of temperature interval (𝑘 = 𝐾) shows the minimum cold utility required (𝑄𝐶𝑈,𝑝 𝑀𝑖𝑛 ) for the process. 𝑑𝑘,𝑝 = 𝑑𝑘−1,𝑝 + ∑ Qℎ𝑠,𝑘,𝑝 ℎ𝑠∈𝐻𝑆 + ∑ 𝑄𝑠𝑡,𝑘,𝑝 𝐼𝑁 𝑠𝑡∈𝑆𝑇 − ∑ Q𝑐𝑠,𝑘,𝑝 𝑐𝑠∈𝐶𝑆 − ∑ 𝑄𝑠𝑡,𝑘,𝑝 𝑂𝑈𝑇 𝑠𝑡∈𝑆𝑇 , ∀𝑘{0,1, … , 𝐾}, 𝑝 ∈ 𝑃 (2) 𝑑𝑘=0,𝑝 = 𝑄𝐻𝑈,𝑝 𝑀𝑖𝑛 , ∀𝑝 ∈ 𝑃 (3) 𝑑𝑘=𝐾,𝑝 = 𝑄𝐶𝑈,𝑝 𝑀𝑖𝑛 , ∀𝑝 ∈ 𝑃 (4) The total heat to be released by the hot streams (Qℎ𝑠,𝑘,𝑝) and the total heat to be absorbed by the cold streams (Q𝑐𝑠,𝑘,𝑝) at stage of temperature interval 𝑘 in plant 𝑝 are given in Eq(5) and Eq(6). ∑ Qℎ𝑠,𝑘,𝑝 ℎ𝑠∈𝐻𝑆 = ∑ 𝐶𝑃ℎ𝑠,𝑘,𝑝(𝑇𝑘,𝑝 𝑈𝑝𝑝𝑒𝑟 − 𝑇𝑘,𝑝 𝐿𝑜𝑤𝑒𝑟) ℎ𝑠∈𝐻𝑆 , ∀𝑘 ∈ {0,1, … , 𝐾}, 𝑝 ∈ 𝑃 (5) ∑ Q𝑐𝑠,𝑘,𝑝 𝑐𝑠∈𝐶𝑆 = ∑ 𝐶𝑃𝑐𝑠,𝑘,𝑝(𝑇𝑘,𝑝 𝑈𝑝𝑝𝑒𝑟 − 𝑇𝑘,𝑝 𝐿𝑜𝑤𝑒𝑟) 𝑐𝑠∈𝐶𝑆 , ∀𝑘 ∈ {0,1, … , 𝐾}, 𝑝 ∈ 𝑃 (6) The heat transferred into ( 𝑄𝑠𝑡,𝑘,𝑝 𝑂𝑈𝑇 ) and transferred out ( 𝑄𝑠𝑡,𝑘,𝑝 𝐼𝑁 ) from the storage medium 𝑠𝑡 at stage of temperature interval 𝑘 in plant 𝑝 are shown in Eq(7) and Eq(8). The heat capacity flowrate of the storage (𝐶𝑃𝑠𝑡) 20 is determined by the model. The feasible temperature ranges for the charging and discharging processes are between the minimum and maximum operating temperatures of the storage 𝑠𝑡 with consideration of ∆𝑇𝑚𝑖𝑛 compared to the process streams. 𝑄𝑠𝑡,𝑘,𝑝 𝑂𝑈𝑇 = 𝐶𝑃𝑠𝑡,𝑝(𝑇𝑘,𝑝 𝑈𝑝𝑝𝑒𝑟.𝐶ℎ𝑎𝑟𝑔𝑒 − 𝑇𝑘,𝑝 𝐿𝑜𝑤𝑒𝑟,𝐶ℎ𝑎𝑟𝑔𝑒 ), ∀𝑘 ∈ {0,1, … , 𝐾}, 𝑝 ∈ 𝑃, 𝑠𝑡 ∈ 𝑆𝑇 (7) 𝑄𝑠𝑡,𝑘,𝑝 𝐼𝑁 = 𝐶𝑃𝑠𝑡,𝑝(𝑇𝑘,𝑝 𝑈𝑝𝑝𝑒𝑟,𝐷𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒 − 𝑇𝑘,𝑝 𝐿𝑜𝑤𝑒𝑟,𝐷𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒 ), ∀𝑘 ∈ {0,1, … , 𝐾}, 𝑝 ∈ 𝑃, 𝑠𝑡 ∈ 𝑆𝑇 (8) The binary constraint is included to ensure that the storage facility is not charged and discharged simultaneously, as represented by Eq(9). 𝐵𝑠𝑡,𝑝 𝐼𝑁 is the binary variable that indicates if heat is transferred out from storage medium 𝑠𝑡 and 𝐵𝑠𝑡,𝑝 𝑂𝑈𝑇 is the binary variable that indicates if heat is transferred into storage medium 𝑠𝑡. The constraint is activated using the big-M method in Eq(10) and Eq(11), where 𝑀 is an arbitrarily large constant and 𝑚 is an arbitrarily small value. When there is a heat flow into the storage medium, 𝐵𝑠𝑡,𝑝 𝑂𝑈𝑇 is forced to be “1”. When there is a heat flow out of the storage medium, 𝐵𝑠𝑡,𝑝 𝐼𝑁 is forced to be “1” instead. Both 𝐵𝑠𝑡,𝑝 𝐼𝑁 and 𝐵𝑠𝑡,𝑝 𝑂𝑈𝑇 will remain as “0” if otherwise. 𝐵𝑠𝑡,𝑝 𝐼𝑁 + 𝐵𝑠𝑡,𝑝 𝑂𝑈𝑇 ≤ 1, ∀𝑝 ∈ 𝑃, 𝑠𝑡 ∈ 𝑆𝑇 (9) 𝑚𝐵𝑠𝑡,𝑝 𝐼𝑁 ≤ ∑ 𝑄𝑠𝑡,𝑘,𝑝 𝐼𝑁 K k=0 ≤ 𝑀𝐵𝑠𝑡,𝑝 𝐼𝑁 , ∀𝑝 ∈ 𝑃, 𝑠𝑡 ∈ 𝑆𝑇 (10) 𝑚𝐵𝑠𝑡,𝑝 𝑂𝑈𝑇 ≤ ∑ 𝑄𝑠𝑡,𝑘,𝑝 𝑂𝑈𝑇 K k=0 ≤ 𝑀𝐵𝑠𝑡,𝑝 𝑂𝑈𝑇 , ∀𝑝 ∈ 𝑃, 𝑠𝑡 ∈ 𝑆𝑇 (11) 𝑄𝑠𝑡 𝑆𝑡𝑜𝑟𝑎𝑔𝑒 is taken to be the maximum 𝑄𝑠𝑡,𝑝 𝑆𝑡𝑜𝑟𝑎𝑔𝑒 so that storage size is sufficiently large, as indicated in Eq(12). With 𝑄𝑠𝑡 𝑆𝑡𝑜𝑟𝑎𝑔𝑒 , the volume of the storage required (𝑉𝑠𝑡) can be determined using Eq(13), where tp and ρE,st represent storage duration and energy density of the storage medium (Möhren et al., 2022). 𝑄𝑠𝑡 𝑆𝑡𝑜𝑟𝑎𝑔𝑒 = ∑ ∑ 𝑄𝑠𝑡,𝑘,𝑝 𝑂𝑈𝑇 K k=0 𝑝∈𝑃 , ∀𝑠𝑡 ∈ 𝑆𝑇 (12) 𝑉𝑠𝑡 = 𝑄𝑠𝑡 𝑆𝑡𝑜𝑟𝑎𝑔𝑒 × tp ρE,st , ∀𝑠𝑡 ∈ 𝑆𝑇 (13) 4. Case Study A case study is presented to demonstrate the proposed methodology. It comprises two plants from the energy- intensive industry: a mixed plastic waste treatment plant from Yadav et al. (2023) and steel mill from McBrien et al. (2016). The objective is to optimise the sensible heat storage selection for indirect heat integration. The model developed is used to optimise energy recovery under three scenarios. The first scenario is direct heat integration, where the heat exchange occurs between the streams in both plants. The second scenario focuses on the heat integration of each plant independently (termed as intraplant heat integration). For the third scenario, the heat storage facility is introduced as an intermediate platform that stores and transfers the heat between the participating plants (termed as indirect heat integration between plants). In this work, three commonly used sensible heat storage media are considered for the selection (see Table 1). Note that the developed model can be easily modified to incorporate other heat storage options. The operating temperature ranges are taken from Platzer and Stieglitz (2024). The energy density and cost are determined using data (i.e., cost per unit mass, density, average heat capacity, operating temperature) provided by Platzer and Stieglitz (2024). This work focuses on the storage media cost, as it is the dominant aspect of the overall system cost for TES construction. Table 1: Data for sensible heat storage facilities. Storage material Operating temperature range (°C) Energy density (kWh/m3) Cost ($/(m3y)) Synthetic oil 250–350 57.5 90.00 Nitrate salt 265–565 249.3 31.17 Cast iron 200–400 224.0 240.00 21 The lifetime of all storage options is assumed to be 30 y (Mitali et al., 2022). This work assumes a storage period of 24 h and a ∆𝑇𝑚𝑖𝑛 of 10 °C (Möhren et al., 2022). The hot utility cost is taken to be $100/kWy, and the cold utility cost is $10/kWy (Ziyatdinov et al., 2020). Table 2 summarises the data of the hot and cold streams involved in both plants. This work focuses on steady-state conditions to investigate the potential of excess heat to be stored and used by another plant. Table 2: Stream data of mixed plastic waste treatment plant and steel mill (H: hot stream; C: cold stream). Plants Streams Supply temperature (°C) Target temperature (°C) Heat capacity flowrate (kW/°C) Mixed plastic waste treatment plant H1 670 594 23.098 H2 594 90 19.391 H3 90 25 17.261 H4 43 23 5.076 H5 50 –15 3.940 H6 –18 –37 2.598 H7 –37 –98 0.123 H8 50 –31 0.512 H9 –34 –37 0.347 H10 233 170 7.181 H11 232 90 8.289 H12 100 37 0.00162 C1 –126 12 0.163 C2 22 170 3.180 Steel mill H1 700 20 0.334 H2 1,100 20 0.636 H3 1,100 20 0.021 H4 250 20 0.523 H5 700 20 1.333 H6 350 20 1.036 H7 180 20 1.726 H8 250 20 0.239 H9 1,500 20 0.296 H10 1,700 20 0.117 H11 1,700 20 0.034 H12 1,700 1,200 0.608 H13 1,200 700 0.608 H14 700 20 0.676 H15 900 20 0.590 C1 20 1,100 0.693 C2 60 1,100 0.081 C3 20 1,100 0.462 C4 20 1,300 0.109 C5 20 1,300 0.898 C6 20 1,300 0.869 C7 20 1,200 0.53 C8 20 1,200 0.132 C9 20 1,200 1.238 C10 20 1,180 1.717 C11 1,500 1,700 0.952 C12 20 1,700 0.055 C13 20 1,700 0.106 C14 700 1,200 0.59 C15 20 1,200 0.792 C16 20 1,200 0.058 5. Results and Discussion Figure 1(a) depicts the energy required for different scenarios of heat integration. The results showed that the direct heat integration between two plants can achieve a minimal energy consumption of 15.99 MW. On the 22 other hand, intraplant heat integration leads to 20.1 % higher energy consumption (i.e., 19.21 MW). As heat recovery is confined to the process streams within a single plant, the mixed plastic waste treatment plant requires 14.24 MW of energy, and the steel mill requires 4.96 MW of energy. Although direct heat integration provides the lowest energy consumption, it may not necessarily be suitable for actual implementation due to the practical issues in process retrofit, safety, flexibility and space. In addition, the mixed plastic waste treatment plant has excess heat that may be stored and used to fulfil the heating requirement of the steel mill. This highlights the significance of considering TES for indirect heat integration to improve heat recovery. This work also explores the effectiveness of integrating various storage options for the presented case study. Figure 1(b) illustrates the charging and discharging of the storage options and energy cost required for implementing the TES-supported heat integration. For all storage options considered, the surplus heat from the mixed plastic waste treatment can be stored and transferred to the steel mill. It can be observed that all stored heat is fully consumed in the steel mill to reduce the energy cost. While the integration of TES incurs additional storage cost, it contributes to energy savings, accounting for 4.4 % to 12.7 % lower than that of intraplant heat integration (see Figure 1(a)). This results in overall TAC reduction of between 4.9 % and 20.7 % compared to the intraplant heat integration (i.e., 632 k$/y). Based on the comparisons in Figure 1(b), nitrate salt offers superior performance in energy and cost savings, given its cost effectiveness and high energy density (see Table 2). This increases the amount of heat stored in the nitrate salt during charging and the heat available to be transferred to the steel mill during discharging. Consequently, the steel mill relies 25 % less on the external hot utility than on the intraplant direct heat integration, resulting in the lowest utility cost. By storing the excess heat, the cold utility needed by the mixed plastic waste treatment plant can also be reduced by 8.6 %. Despite having the second- largest storage volume, the storage cost of integrating nitrate salt is still the lowest (80.8 % and 77.0 % lower than that of using cast iron and synthetic oil) due to its lowest cost per unit volume (see Figure 1(c)). Figure 1: (a) Energy consumption and TAC of different heat integration scenarios; (b) Heat stored, heat discharged, and energy cost of integrating storage options; (c) Volume and cost of storage options integrated. Apart from that, the operating temperature ranges of the storage facilities affect the selection. A higher maximum operating temperature of nitrate salt allows it to supply heat at higher temperature to the steel mill with target temperatures of cold streams ranging between 1,100 °C and 1,700 °C. Thus, it is more suitable for the illustrated case study, as the cold streams can be heated from 20 °C up to 555 °C given a ∆𝑇𝑚𝑖𝑛 of 10 °C. Contrarily, synthetic oil and cast iron can only heat the cold streams up to 340 °C and 390 °C. This restricts the amount of heat that can be stored and transferred to the steel mill and increases the reliance on external energy (i.e., higher utility requirement). Another key observation is that the cold utility requirement of the steel mill with integrated storage facilities is identical to the intraplant heat integration (i.e., 75.61 kW) except for the case where cast iron is used as the storage medium. This is because the heating requirement of the steel mill within the feasible operating range of cast iron (i.e., 200 °C – 400 °C) is only 608.81 kW. To return the temperature of storage to its initial temperature state for continuous periodic operation, this additional heat has to be removed by the cold utility. Hence, the cast iron is less suitable for the illustrated case study. Since the objective of this work is to minimise the TAC (i.e., energy cost and storage cost), nitrate salt is selected as the most optimal storage option to be integrated. 6. Conclusion This work proposed an optimisation model to integrate the TES and select the optimal storage option for the indirect heat integration between plants. Among the three sensible heat storage options considered, nitrate salt is determined to be the most optimal option to be integrated for the illustrated case study. It contributes to the 23 greatest energy and cost savings of 12.7 % and 20.7 %, compared to the indirect heat integration. The TAC required is merely 4.9 % higher than the direct heat integration case, highlighting the potential of integrating TES as a trade-off solution that can offer plausible energy recovery potential between the participating plants while addressing practicability issues associated with direct heat integration. Future work can extend the developed model to integrate TES under multi-period operations (e.g., with varied flowrate and supply temperature). Other cost factors (e.g., infrastructure development cost) and design aspects (e.g., efficiency, heat dissipation loss) can be incorporated to better reflect the actual situations. Acknowledgments The authors would like to acknowledge the financial support provided by Swinburne University of Technology Sarawak Campus under the SUTS Postgraduate Research Scholarship. References Chang C., Wang Y., Feng X., 2015, Indirect heat integration across plants using hot water circles. Chinese Journal of Chemical Engineering, 23, 992-997. Er H.A., Wan Alwi S.R., Manan Z.A., Klemeš J.J., 2022, Simultaneous retrofit of direct and indirect Heat Exchanger Storage Network (HESN) via individual batch process stream mapping. Energy, 261, 125052. Jamaluddin K., Wan Alwi S.R., Hamzah K., Klemeš J.J., 2020, A Numerical Pinch Analysis Methodology for Optimal Sizing of a Centralized Trigeneration System with Variable Energy Demands. Energies, 13, 2038. Ji M., Zhang W., Xu Y., Liao Q., Klemeš J.J., Wang B., 2023, Optimisation of multi-period renewable energy systems with hydrogen and battery energy storage: A P-graph approach. Energy Conversion and Management, 281, 116826. Liew P.Y., Wan Alwi S.R., Ho W.S., Abdul Manan Z., Varbanov P.S., Klemeš J.J., 2018, Multi-period energy targeting for Total Site and Locally Integrated Energy Sectors with cascade Pinch Analysis. Energy, 155, 370-380. Linnhoff B., Flower J.R., 1978, Synthesis of heat exchanger networks: II. Evolutionary generation of networks with various criteria of optimality. AIChE Journal, 24, 642-654. Liu X., Klemeš J., Varbanov P., Qian Y., Yang S., 2015, Safety Issues Consideration for Direct and Indirect Heat Transfer on Total Sites. Chemical Engineering Transactions, 45, 151-156. McBrien M., Serrenho A.C., Allwood J.M., 2016, Potential for energy savings by heat recovery in an integrated steel supply chain. Applied Thermal Engineering, 103, 592-606. Mitali J., Dhinakaran S., Mohamad A.A., 2022, Energy storage systems: a review. Energy Storage and Saving, 1, 166-216. Möhren S., Schäfer C., Meyer J., Krause H., 2022, A simultaneous approach for integration of thermal energy storages in industrial processes using multiperiod heat integration. Energy Storage and Saving, 1, 117-128. Platzer W., Stieglitz R., 2024, Solar Thermal Energy Systems: Fundamentals, Technology, Applications. Springer International Publishing, Switzerland. Tian Y., Wang S., Liu K., Li S., 2020, Two-plant direct heat integration with safety redundancy based on a bilevel algorithm. Chemical Engineering Science, 222, 115662. UNECE, 2022, Technology Brief: Carbon Neutral Energy Intensive Industries, United Nations Economic Commission for Europe (UNECE) , accessed 28.06.2024. Varbanov P.S., 2023, Basic Process Integration Terminology, Chapter In: JJ Klemeš (Ed.) Handbook of Process Integration (PI): Minimisation of Energy and Water Use, Waste and Emissions, Woodhead Publishing, Cambridge, UK, 25-72. Wang X., Tian H., Yan F., Feng W., Wang R., Pan J., 2020, Optimization of a distributed energy system with multiple waste heat sources and heat storage of different temperatures based on the energy quality. Applied Thermal Engineering, 181, 115975. Yadav G., Singh A., Dutta A., Uekert T., DesVeaux J.S., Nicholson S.R., Tan E.C.D., Mukarakate C., Schaidle J.A., Wrasman C.J., Carpenter A.C., Baldwin R.M., Román-Leshkov Y., Beckham G.T., 2023, Techno- economic analysis and life cycle assessment for catalytic fast pyrolysis of mixed plastic waste. Energy & Environmental Science, 16, 3638-3653. Zhang B.J., Li J., Zhang Z.L., Wang K., Chen Q.L., 2016, Simultaneous design of heat exchanger network for heat integration using hot direct discharges/feeds between process plants. Energy, 109, 400-411. Ziyatdinov N.N., Emel'yanov I.I., Chen Q., Grossmann I.E., 2020, Optimal heat exchanger network synthesis by sequential splitting of process streams. Computers & Chemical Engineering, 142, 107042. 24 0020.pdf Enhanced Heat Recovery Network with Integrated Sensible Heat Storage Facilities for Energy Intensive Industry