Microsoft Word - PRES22_0064.docx DOI: 10.3303/CET2294035 Paper Received: 15 April 2022; Revised: 20 May 2022; Accepted: 25 May 2022 Please cite this article as: Ti W., Ng D.K.S., Ng L.Y., Andiappan V., 2022, Optimal Blue Hydrogen Process with CO₂ Capture,Utilisation and Storage, Chemical Engineering Transactions, 94, 211-216 DOI:10.3303/CET2294035 A publication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 94, 2022 The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Petar S. Varbanov, Yee Van Fan, Jiří J. Klemeš, Sandro Nižetić Copyright © 2022, AIDIC Servizi S.r.l. ISBN 978-88-95608-93-8; ISSN 2283-9216 Optimal Blue Hydrogen Process with CO2 Capture, Utilisation and Storage Weiee Tia, Denny K S Nga, Lik Yin Ngb, Viknesh Andiappana,c,* aSchool of Engineering and Physical Sciences, Heriot-Watt University Malaysia, 62200, Putrajaya, Wilayah Persekutuan Putrajaya, Malaysia bDepartment of Chemical & Petroleum Engineering, Faculty of Engineering, Technology and Built Environment, UCSI University (Kuala Lumpur Campus), UCSI Heights, 1, Jalan Puncak Menara Gading, Taman Connaught, 56000 Cheras, Kuala Lumpur, Malaysia. cFaculty of Engineering, Computing and Science, Swinburne University of Technology, Jalan Simpang Tiga, 93350 Kuching, Sarawak, Malaysia. vmurugappan@swinburne.edu.my Hydrogen (H2) energy has a high potential to become a source of future sustainable fuel to replace fossil fuel. At present, natural gas conversion is the conventional process to produce H2. However, this process produces carbon dioxide (CO2) emissions as a by-product. CO2 capture, utilisation, and storage (CCUS) technologies can be integrated in conventional H2 plants to address this issue. By doing so, conventional H2 plants can be retrofitted to produce cleaner H2 called blue H2. This work presents a mathematical model to optimise blue H2 processes integrated with CCUS technologies. The research objectives are to determine optimal and feasible decarbonisation systems for H2 production and optimal storage technologies for the produced H2 with minimum cost. The developed optimisation-based model considers different grey H2 processes, CO2 capture technologies, CO2 transportation, utilisation, storage, and H2 storage. The model factors technology efficiency, costing and overall energy consumption. The developed model is demonstrated with a blue H2 production case study. The optimised blue H2 process with CCUS was obtained with the optimisation objective of minimising total annualised cost (TAC). 1. Introduction Approximately 40 % of the global CO2 emission is due to the combustion of fossil fuel for electricity generation on residential, commercial, and industrial scales (Abdul Latif et al., 2021). These detrimental effects have alerted policymakers and researchers to decarbonise the energy sector. Hydrogen (H2) energy is an alternative fuel resource of the future and does not produce CO2 emissions when utilised. Steam methane reforming (SMR) is the most used technology to convert natural gas to H2. This H2 production path is categorised as grey H2. To achieve net-zero CO2 emissions, grey H2 processes need to be retrofitted with carbon capture, utilisation, and storage (CCUS). This turns the previously mentioned grey H2 to blue H2 production, where no CO2 is expected to be emitted into the environment (ATCO, 2022). The implementation of blue H2 processes can become realisable when optimisation is done on the economic aspect. In literature, research work can be found on H2 processes and carbon capture systems. For instance, Li et al. (2020) proposed a multi-criterion decision-making model (MCDM) to study the sustainability assessment of grey H2 production. Santibanez-Gonzalez (2017) used a stochastic MILP model to minimise the carbon capture system's investment and construction cost. Many scholars have done optimisation-based research on the integration of H2 production processes and carbon capture systems. Cormos et al. (2018) investigated the technical and economic performances of H2 production from SMR and autothermal reforming integrated with pre-combustion carbon capture. Besides, Roussanaly et al. (2020) compared the H2 production cost through SMR without and with carbon emissions capture and storage. However, several research gaps are identified. The available carbon capture research discussed above targets the cement industry instead of H2 production plants. In addition, the blue H2 research work above considered limited technological options in their analysis. In other words, these works focused only on one grey 211 H2 production route and one carbon capture technology. Such focus may lead to restricted possibilities of achieving higher H2 production performance as no alternative technologies were considered. 2. Research novelty To address these research gaps, an optimisation-based mathematical model is employed in this research to enhance blue H2 performance with various technologies being considered. In addition, optimisation of the blue H2 process can be investigated from many perspectives, which include total annualised production cost, annual energy consumption and annual CO2 production/emissions. Therefore, this research focus on the optimisation objective of achieving minimum TAC for the blue H2 process. 3. Methodology The first step of this work focused on technology compilation through a literature review. The compiled technologies are then put into a single diagram called a superstructure. Superstructure illustrates all the possible interconnections between technologies considered. For this work, the superstructure developed is shown in Figure 1. In addition, performance parameters for each technology in the superstructure were collected, which include grey H2 yield, carbon capture efficiency, energy consumption for grey H2 production, carbon capture and H2 storage and economic data for each technology. Figure 1: Superstructure of blue H2 process with CCUS After collecting useful performance data for the relevant technologies, the next step was to formulate mathematical equations based on the developed superstructure. These equations include relationships like material mass flowrates, the energy consumption of the process, annualised capital, and the operating cost of the process. Figure 2 illustrates the general principle used to formulate equations for this work. It serves as a guiding framework for how the equations were developed for this work. As shown, three main sections are identified throughout the process, namely feed (f), technology (g), and product (h). For instance, in grey H2 production, feed (f) refers to natural gas, heavy oil, coal, and naphtha fed into various technologies. The considered technologies (g) are steam methane reforming (SMR) and natural gas partial oxidation (POX). Then, the product formed from the technology (h) is the syngas containing mainly H2 and CO2. Figure 2: Generic superstructure Figure 2 starts with feed f ε F. The available mass flowrate of feed is denoted as Ff while the actual mass flowrate of feed flow taken by particular technology is denoted as Ffg, as shown in Eq(1). The inequality sign is used in Eq(1) to provide flexibility to the model to decide how much feed is necessary (Ffg) based on the available feed input (Ff). Next, the product mass flowrate for particular technology (Fgh) can be determined based on the technology efficiency (Xfgh), as shown in Eq(2). 212 Ff ≥ 𝐺 𝑔=1 𝐹𝑓𝑔 (1) 𝐹𝑔ℎ = 𝐹 𝑓=1 𝐹𝑓𝑔Xfgh ∀𝑔∀ℎ (2) The energy consumption of the overall process is expressed as Eq(3). The energy consumption of each technology (Eg) is obtained by multiplying the product mass flowrate (Fgh) with the energy factor of each technology (Wg). Energy factors are typically expressed as the energy required per unit flow of product. 𝐸𝑔 = 𝐻 ℎ=1 𝐹𝑔ℎWg ∀𝑔 (3) Furthermore, the annualised capital and operating cost of the process are defined in Eq(4) and Eq(5), respectively. The costs of the process can be determined based on either the inflow of raw material (Ffg) or the total product produced from the technology (Fgh). The cost factors compiled include the capital cost per unit input, capital cost per unit output, operating cost per unit input and operating cost per unit output as expressed by C input g ,C product g ,O input g and O product g . The objective of minimising TAC is shown in Eq(6) where H2 sale and CO2 sale represent the expected profit gained from selling H2 and captured CO2 𝐵𝐶𝑔 = 𝐹 𝑓=1 𝐹𝑓𝑔C input g + 𝐻 ℎ=1 𝐹𝑔ℎC product g × 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑦 𝑓𝑎𝑐𝑡𝑜𝑟(𝑟) ∀𝑔 (4) 𝐵𝑂𝑔 = 𝐹 𝑓=1 𝐹𝑓𝑔O input g + 𝐻 ℎ=1 𝐹𝑔ℎO product g ∀𝑔 (5) 𝑀𝑖𝑛 𝑇𝐴𝐶 = 𝐺 𝑔=1 𝐵𝐶𝑔 + 𝐺 𝑔=1 𝐵𝑂𝑔 ― 𝐻2 𝑠𝑎𝑙𝑒 ― 𝐶𝑂2 𝑠𝑎𝑙𝑒 (6) As mentioned previously, the generic equations above serve as a guiding framework to model the interactions in Figure 1. The following section discusses a case study where the developed equations are coded in LINGO software to optimise the process shown in Figure 1. 4. Case study background This case study focuses on recommending the optimal blue H2 process with minimum TAC. A linear model was developed to perform process optimisation using the information below. The U.S. Energy Information Administration (2021) claims that Malaysia holds approximately 1.18 trillion cubic metre of proved natural gas reserves as of January 2020. The information on the available natural gas supply in Malaysia provides a sound way to estimate the feed flow of fossil fuel in this case study. The feed flow estimated is 10Mt/year for each raw material feed flow involved in Figure 1. Other general information is listed in Table 1. Table 1: General information for case study Parameter Value Unit H2 production rate (Zapantis, 2021) 1.3 kt/d Annual operating period 351 d Annual H2 production rate 456.3 kt Plant lifespan 30 y Interest rate (Eria, 2020) 3% - Capital recovery factor (r) 0.51 - In addition, some assumptions have been made throughout the case study as follows:  100% blue H2 sale with consistent price in all cases  100% CO2 storage and/or utilisation  99% purification stage in grey H2 production 213  No capacity limitation set for all technologies In this case study, the minimum H2 purity requirement is 40 % based on its use in the power generation sector (Thyer et al., 2009) who investigated the flame stability of H2/CO2 mixture with different concentration ratios and the results revealed that stable combustion is achieved starting at an H2/CO2 concentration ratio of 40:60. In other words, H2 with a purity of 40 % can be safely combusted to generate energy for power generation purposes. Therefore, the lowest H2 purity requirement to be sold to the power generation sector is set at 40% in this case study to ensure a safe power generation process using H2. Furthermore, the performance data of technologies considered are listed in Table 2, Table 3 and Table 4. Table 2: Performance data for grey H2 processes, carbon capture technologies and H2 storage Performance data Process efficiency (%) Energy consumption (kWh/y) Capital cost ($/t flow/y) Operating cost ($/t flow) Grey H2 processes SMR 84 8.24 8.45 729 POX 90 56.2 30.6 1,632 TRIR 99 72.7 1,020,000 589 Carbon capture technologies Membrane separation 85 62.6 653,000 54 Selexol absorption 90 488 26.66 Rectisol absorption 85 159 602,000 26.6 MEA absorption 89 11,708 70 NH3 absorption 95 3,186 855,000 55 CaO looping 85 486 40 Zeolite 13X adsorption 70 1,900 1,068,000 30 H2 storage Liquid H2 organic carrier - 16 /t H2 - 0.0678 Liquefaction - 12,500 /t H2 - 8 Compression - 4,500 /t H2 - 7.55 Table 3: Performance data for carbon transportation and storage CO2 transportation CO2 storage Pipe Ships Truck Oilwell Ocean Saline Operating cost ($/t CO2) 3 1,640 0.025 0.65 14 15.2 Table 4: Performance data for CO2 utilisation CO2 utilisation Enhanced oil recovery (EOR) Enhance gas recovery (EGR) Coalbed methane (CBM) MeOH industry Building industry Sale price ($/t CO2) 20 10 15 300 467 5. Result and discussion 5.1 Optimal production route The optimisation objective studied using the developed model was minimising the TAC of blue H2 production. No constraint was defined for the target purity of H2 in the model to provide flexibility on the technology selection. Instead, the obtained H2 purity from the model will be analysed later to confirm that the purity is above the minimum requirement of 40 %. The results are shown in Figure 3. Figure 3: Preferred production route to achieve minimum TAC 214 Figure 3 presents the selected production path for blue H2 production to meet the objective function of minimising production annualised cost without constraint being set on H2 purity. Note that in grey H2 production, only the mass flowrate of natural gas is presented, while other raw materials such as steam are not illustrated in Figure 3. This is because natural gas is the non-renewable element identified among the raw materials in SMR which will eventually contribute the carbon emissions. The H2 yield from SMR is dependent on the feed flow of natural gas and thus only natural gas flow is presented. The results show that SMR is preferred over partial oxidation (POX) to produce grey H2. This is explained by the lower annualised capital expenditure (CAPEX), and operating expenditure (OPEX) observed in SMR as compared to POX. The other aspects, such as H2 yield, and energy consumption, are factored in the optimisation model however do not play a significant role in this case as the objective function only targets the annualised cost. Next, the produced grey H2 is sent to CO2 capture treatment by Rectisol absorption. Prior to carbon capture section, the syngas produced from SMR is subjected to purification stage with assumed efficiency of 99% to obtain a product stream with major components of CO2 and H2 gas only, as mentioned under Section 4. Rectisol absorption is the most favoured CO2 capture technology due to the lowest annualised CAPEX and OPEX. Plus, the CO2 capture efficiency of Rectisol absorption is reported to be 85 %. This will yield the blue H2 with a purity of 55 % with the remaining impurities being the uncaptured CO2. As discussed in the previous section, the minimum required purity of H2 to be sold to the power generation sector is 40 %. Therefore, the blue H2 produced (55 %) fulfils the purity requirement and is allowed to be used for power generation purposes. Next, the captured CO2 is transported by truck as this transportation route is the most economical option as compared to pipeline and ship. Moreover, the captured CO2 can either be stored or utilised. Since the storing of captured CO2 will add up to the overall expenditure of the process, CO2 utilisation appears to be an ideal option. Among all utilisation opportunities, the construction industry has the highest CO2 sale value and thus became the most advantageous route to minimise the overall production cost of blue H2. Lastly, the produced blue H2 is recommended to be stored by a liquid H2 organic carrier (LOHC), which has the lowest storing cost as compared to liquefaction and compression. In this case study, H2 purity has no effect on the performance of H2 storage. The total annualised production cost obtained is 88,953 M USD with a total annual energy consumption of 800GWh. The detailed cost breakdown is listed in Table 5. Table 5: Cost breakdown for optimised process Production path Selected technology Annualised cost CAPEX OPEX Profit Grey Hydrogen SMR $ 197,000 $ 361 M - CO2 capture Rectisol absorption $ 91,000 M $ 57 M - CO2 transportation Truck - $ 53,000 - CO2 utilisation (sale) Building material - - $ 955 M H2 storage LOHC - $ 56,000 - H2 sale - - - $ 1,580 M Total: $ 88,953 M 4.2 Effect of CCUS on CO2 emission reduction Verification steps are conducted to benchmark the result obtained. According to the EU Commission (2022), the definition of low-carbon H2 (blue H2) is H2 that is produced from non-renewable sources and meets a GHG emissions reduction threshold of 70 % as compared to grey H2 (fossil-based H2). Thus, a CO2 emissions reduction analysis is carried out to study the validity of blue H2 in each case. The analysis began with obtaining the total CO2 production from the blue H2 process. This was done by summing up the CO2 production from grey H2 as a by-product and carbon emission due to energy consumption concerning three major sections which include grey H2 production, carbon capture and H2 storage. On the other hand, the introduction of CO2 emissions from CO2 transportation and utilisation are not factored in this model. According to the U.S. Energy Information Administration (2022), the CO2 emission factor is around 385 t of CO2 per GWh of energy consumed. Thus, the CO2 produced due to energy consumption can be determined by multiplying the total energy consumption of the process with the reported CO2 emission factor. In this case study, CO2 production from grey H2 will be sent for CO2 capture treatment while no further treatment is available for the CO2 production from energy consumption. Since the implementation of CCUS is the key element to retrofit the grey H2 into blue H2 process, the CO2 emission reduction is calculated using Eq(7). The results are listed in Table 6. 𝐶𝑂2 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝐶𝑂2 𝑐𝑎𝑝𝑡𝑢𝑟𝑒 𝑇𝑜𝑡𝑎𝑙 𝑎𝑛𝑛𝑢𝑎𝑙 𝐶𝑂2 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 × 100 % (7) 215 Table 6: CO2 emission reduction result From grey H2 production From energy consumption Total CO2 emissions CO2 capture rate CO2 emissions reduction CO2 flow (kt/y) 2,510 308 2,818 2,133 76 % The outcome in Table 6 concludes that implementation of CCUS has effectively reduced primary greenhouse gas (CO2) emission to a good level (>70 %). Also, this achievement has direct effect on relieving the pressure and stress of global warming caused by the massive growth of CO2 emissions in H2 generation sector. 6.0 Conclusion In conclusion, this research covers the implementation of CCUS into grey H2 production to produce blue H2. An optimisation-based mathematical model was developed to determine the ideal blue H2 process with minimum cost, and energy consumption. The case study has shown that a minimum annualised cost of 88,953 M USD can be achieved with H2 purity of 55 %. There are limitations in this work that serve as motivation for future work. Firstly, carbon capture was not included for carbon emission due to energy consumption. Besides, the emissions generated from CO2 transportation and utilisation was not considered for this work. In future work, the proposed model can be improved by performing CO2 capture on the CO2 emissions due to energy consumption, transportation and utilisation. Moreover, detailed information concerning the products' market value and demand could be determined accordingly to the interested country so that the process TAC can be resolved more precisely. Lastly, technology limitations such as maximum capacity can be factored in to analyse the system's flexibility. Acknowledgements The support from the School of Engineering and Physical Sciences (EPS) of Heriot Watt University Malaysia through the Empower Research Grant Scheme (Project Code: EPS/EmRGS/2021/02) is acknowledged. Reference Abdul Latif, S. N., Chiong, M. 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