CHEMICAL ENGINEERING TRANSACTIONS VOL. 76 2019 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Resource Integration and CO2 Conversion in Industrial Clusters Shaza I. Shehab, Razan O. Ahmad, Dhabia M. Al-Mohannadi*, Patrick Linke Department of Chemical Engineering, Texas A&M University at Qatar, Education City, PO Box 23874, Doha, Qatar dhabia.al-mohannadi@qatar.tamu.edu Countries around the globe seek ambitious CO2 emission reduction targets to avoid dangerous climate change effects. In this work, a tool was developed that produces a network of plants that can decrease emissions, maximize energy reuse and select products that maximize profit of an industrial cluster. The tool can integrate various plants and/or processes, such as industrial production plants, power plants etc., to enhance the potential of making financial profits. Recently, a focus has been put on integrating natural gas, energy, and CO2 to reduce emissions or energy demand, which fall within end-of-pipe carbon capture utilization and storage (CCUS) solutions. This work designs an industrial cluster on the utilization of key materials including emissions. This approach introduces a new representation of resources management in a cluster that systematically integrates H2O, N2, O2, CO2 and energy (power and heat) converting them to value added products under a reduction target. The approach develops a Linear Program (LP), which can be easily applied to explore different combinations of plants. The method was applied on an illustrative example where several scenarios were investigated. The industrial cluster converts CO2 by 76% and produces methanol, while making profit. 1. Introduction CO2 emissions have increased drastically from anthropogenic activity, mostly from stationary sources such as industrial activity. Individual Industrial process or plants have been optimized through energy and mass integration to increase profits by saving energy and at the same time, reduce emissions. However, integrating multiple industrial plants using an overall approach can achieve larger energy saving, bigger profits and larger emission reduction. This work falls under the umbrella of carbon capture utilization (CCU) where CO2 was used as raw material to make value added products while only utilizing renewable energy in aim of creating a green CO2 reducing cluster. Previous approaches have been developed to achieve footprint reduction in CO2 emissions in industrial clusters. Manan et al. (2017) have summarized CO2 emission reductions from an industrial cluster by energy reduction or using carbon capture utilization and sequestration (CCUS). Al-Mohanndi and Linke (2016) only looked into CO2 conversion processes, while Hassiba et al (2017) expanded the representation to include waste heat exchange. Al-Mohannadi et al. (2017) studied managing resources in industrial processes focusing on natural gas and CO2 material exchange. Panu et al. (2019) expanding on the representation of Noureldin and El-Halwagi (2015) discussed a bi-objective CO2 footprint reduction optimal design of Carbon-Hydrogen-Oxygen (CHO) symbiosis that integrate hydrocarbons streams between plants, while allowing the conversion of CO2 to produce value added products. However, the approach allowed only the integration of hydrocarbon streams and did not consider the energy needed for the plants in the city. The approach presented in this work allows the integration of any possible resource is necessary for a holistic optimization approach. This method will allow the user to study multiple materials (H2O, N2, O2, CO2) and energy (heat and power) across plants to analyze the potential of meeting emission targets without offsetting other demands. The optimization approach will optimize several processes and/or plants coexisting in industrial parks. Moreover, renewable energy sources can be incorporated, which will further reduce CO2 emissions. Several technologies exist that can convert CO2 to value added products examples include the process presented by Van-Dal and Bouallou (2013) which produced methanol from CO2, Pérez-Fortes et al. (2016), and more recently Alsayegh et al. (2019). This paper presents a systematic method that integrates several processes in a cluster DOI: 10.3303/CET1976201 Paper Received: 15/03/2019; Revised: 30/05/2019; Accepted: 30/05/2019 Please cite this article as: Shehab S.I., Ahmad R.O., Al-Mohannadi D.M., Linke P., 2019, Resource Integration and CO2 Conversion in Industrial Clusters, Chemical Engineering Transactions, 76, 1201-1206 DOI:10.3303/CET1976201 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 1201 to reduce CO2. The next section will outline the approach, any assumptions taken to develop the model, and is followed by an example where several cases were studied. 2. Approach In this paper, pre-existing plants in an industrial cluster are integrated to achieve maximum economic profit that can convert a given amount of CO2. Figure 1 shows an industrial cluster that consists of a number of plants, where each plant has different materials that can be exchanged within the city. In addition to materials, energy can be exchanged either as heat or power through the illustrated power grid. The purpose of the study is to design the optimum integration network that results in the maximum profit, while maintaining a user specified environmental footprint e.g. consuming a given CO2 flow within the city. A Linear Programming (LP) model is used to investigate the various route. User specified data are outlined below. Figure 1: General representation of the industrial cluster The formulation of the optimization problem takes the following steps: 1. The identification of a capacity requirement for each plant 2. The identification of the maximum environmental footprint allowed as constraints, the minimum and maximum flows of fresh feed and output of each flow to and from the industrial cluster. 3. The identification of an objective function, in which the profit of the industrial cluster is maximized 𝑀𝑎𝑥 (𝑝𝑟𝑜𝑓𝑖𝑡 𝑜𝑓 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑎𝑙 𝑐𝑙𝑢𝑠𝑡𝑒𝑟) (1) 𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝐸𝑞𝑢𝑎𝑙𝑖𝑡𝑦 𝑐𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠 ℎ(𝑥1, 𝑥2, . . , 𝑋𝑛) = 0 (2) 𝐼𝑛𝑞𝑢𝑎𝑙𝑖𝑡𝑦 𝑐𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠 𝑔(𝑥1, 𝑥2, . . , 𝑋𝑛) ≤ 0 (3) Materials integrated within the cluster must meet specification of the recipient plant, which entails meeting inlet conditions such as temperature, pressure, and purity. Each material in any plant has energy demand, emission footprint, capital and operating cost parameters. The exchange of materials including heat carriers such as steam is carried through piping systems, where the cost of connection, in this case is neglected as industrial clusters are often built with an existing infrastructure. When it comes to energy management, each plant has an integrated utility system that provides all heating and cooling requirements for its processes. Any additional demands, either hot or cold utilities, are bought for a price and their emission is accounted for by a CO2 footprint parameter that scales with the flow. The LP was solved using “What’sBest!16.0” optimization tool in Microsoft Excel 2016 (Lindo Systems, 2019) . 3. Case Study In this study, an industrial city has been explored, which consists of the plants illustrated in Table 1. The table shows the materials involved in each plant, along with the main material the plant is designed to produce referred 1202 to as Resource R, and the maximum capacity associated with the plant. The purpose of the study is to maximize the profit under controlled CO2 emissions. The capital cost for each is presented also in Table 1. Table 1: Industrial cluster plants and capacities Plant Products Reference Resource Max Capacity (t/y) Capex ($/t) Air Separation Water Splitting Methanol Production Ammonia Production O2, N2 O2, H2 CH3OH, H2O, Power NH3, Power O2 H2 CH3OH NH3 100,000 15,000 100,000 100,000 203 7,108 387 153 Figure 2 illustrates a schematic of the industrial cluster with the possible connections between the plants. Constraints specified for the fresh feed only allow air, water, CO2, and power to be imported, while the city allows ammonia, methanol, oxygen, and excess power to be sold. While waste streams include CO2 emissions and N2 waste stream. The cluster is required to convert imported 120,000 t CO2/y to value added products. The industrial city imported energy from renewable sources with no CO2 footprint. The main CO2 emission sources are the unconverted CO2, and CO2 emissions associated with water entering as fresh feed. The CO2 emission parameters associated with the methanol plant and the fresh feed water plant are 0.383 t CO2/t CH3OH and 0.0329 t CO2/t H2O. Figure 2: Schematic of the industrial cluster investigated Parameters relating flows of resources and energy demand in each of the four plants are presented in Table 2 and Table 3. The parameters were obtained through performing mass and energy balances on collected data. A negative parameter represents an input requirement and positive parameters represents an output. A zero parameter indicates that the resource is not involved in the process of that plant. Costs of the resources entering and leaving the industrial city are presented in Table 4. 1203 Table 2: Parameters of flows in terms of reference products (R) Resource Air Separation (t R/t O2) Water Splitting (t R/t H2) Methanol Production (t R/t MeOH) Ammonia Production (t R/t NH3) Air -4.330 0.000 0.000 0.000 O2 1.000 8.000 0.000 0.000 N2 3.270 0.000 0.000 -0.849 H2O 0.000 -9.000 0.563 0.000 H2 0.000 1.000 -0.200 -0.182 CO2 0.000 0.000 -1.758 0.000 NH3 0.000 0.000 0.000 1.000 CH3OH 0.000 0.000 1.000 0.000 CO2 Emissions 0.000 0.000 0.396 0.000 Dilute N2 0.000 0.000 0.000 0.031 Ar 0.060 0.000 0.000 0.000 Table 3: Energy resources parameters in terms of reference products (R) Resource Unit Air Separation Water Splitting Methanol Production Ammonia Production Chilled Water GJ/t R 0 0 -0.885 0 Electricity kWh/t R -245 -5,370 0.111 458 Refrigeration kWh/t R 0 0 0 -65 Data for the CO2 conversion were obtained from (Demirel, 2015). Air separation energy consumption data were obtained (Alsultanny and Al-Shammari, 2014), and the H2O splitting energy consumption data were obtained from (Gambhir et al., 2017). Also, the data for Ammonia production was obtained from (Canada, 2009). Table 4: Prices of resources entering and leaving the industrial cluster Resource Price ($) Unit Air 0 $/t H2O 0.89 $/t NH3 548 $/t CH3OH 380 $/t O2 50 $/t Electricity 0.06 $/kWh Chilled water 4.5 $/GJ Refrigeration 0.0033 $/kWh The prices of methanol and ammonia were obtained from (Mevawala et al., 2019). The price of oxygen was obtained from (Ebrahimi et al. 2015) The refrigeration price was obtained from (Demirel, 2015). The chilled water price was obtained from (Towler & Sinnott, 2012). The price of electricity was obtained from (Kim et al., 2011). The cluster was assumed to have a lifetime of 15 years, with an interest rate of 8%. The model was used to design a network achieving a treatment of 120,000 tCO2/y as fresh feed while maximizing the profit. Several options scenarios to deal with emissions were investigated namely, CO2 can be imported to the cluster at no cost, the cluster can receive a monetary subsidy to convert CO2 or the cluster can buy CO2 feed at a cost. The breakdown of the two scenarios that were studied are shown in Table 5. Oxygen was sold as a byproduct while in Scenario 2 it was considered a waste product. Table 5: Scenarios studied Scenario O2 Trade Cases 1 On Case 1: CO2 imported at $0/t Case 2: CO2 bought at $20/t Case 3: CO2 purchased by the city $20/t 2 Off CO2 imported at $0/t 1204 All three cases emitted CO2 28,934 t /y, resulting in a net conversion of 75.9 % of the inlet and had the same plants activated. Figure 3 illustrates the optimized network including the capacity of each plant and the total profit in each case. Figure 3: Industrial city network and total profit for the three cases in scenario 1 The optimization resulted in minimizing the ammonia plant capacity to zero, due to its high operating cost. N2 produced from the air separation did not have any use and leaves the cluster as a waste stream. Power was imported into the industrial cluster, while H2 and H2O were exchanged and consumed within the cluster. This indicates that if CO2 was to be bought, sold, or obtained for free, profit can still be made from this industrial cluster. Results for the second scenario, where O2 is not allowed to be sold, are shown in Figure 4. The cluster had a total capital cost of $120 M, and generated a total profit of $6.78 M /y. The plant capacities remained the same as those found in Figure 3. Figure 4: Industrial cluster network without selling O2 and free of charge CO2 The air separation unit is excluded from the network, as its capacity was minimized to zero. In return, the power import requirement decreased, and the total profit of the industrial cluster decreased due to the O2 limitation trade. This result shows that even if O2 was not sold, the total CO2 footprint can still decrease with a profitable outcome. 4. Conclusion An integration network for an industrial cluster has been investigated through linear programming. An example was solved to illustrate the method. The cluster investigated converts CO2 into value added products, producing methanol, ammonia and oxygen to be sold. Requirements of CO2 treatment is 120,000 t/y of fed to the city. Three cases were studied with different CO2 pricing scenarios, each resulting in different profits and costs. In the three cases the same net conversion of CO2 (76%) was implemented, and the profits rendered ranged from 1205 $6.78 M /y to $15.79 M/y. Then, with the use of this tool, having CO2 as a feedstock and converting it to profitable products can render an overall profit while meeting environmental constraints. Multiple options can be further investigated such as, the addition of more plants within the cluster, which allows for further interactions, and/or the addition of power inputs from non-renewable energy sources, while investigating the influence of the environmental impact constrain on the economic performance of the cluster. References Al-Mohannadi, D.M., Alnouri, S., Linke, P., 2016, On the systematic carbon integration of industrial parks for climate footprint reduction, Journal of Cleaner Production, 112, 4053-4064, DOI: 10.1016/j.jclepro.2015.05.094 Al-Mohannadi, D.M., Abdulaziz, K., Alnouri, S., Linke, P., 2017, On the synthesis of carbon constrained natural gas monetization networks, Journal of Cleaner Production, 168, 735-745, DOI:10.1016/j.jclepro.2017.09.012 Alsayegh, S., Johnson, J. 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