- Email: [email protected]
Sustainable Process Design
CHAPTER
Sustainable Process Design: Sustainable Process Networks for Carbon Dioxide Conversion
7
Rebecca Frauzem*, Pichayapan Kongpannax, Kosan Roh{, Jay H. Lee{, Varong Pavarajarnjj, Suttichai Assabumrungratx, Rafiqul Gani*, 1 *Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark x Center of Excellence in Catalysis and Catalytic Reaction Engineering, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand { Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Republic of Korea jj Center of Excellence in Particle Technology, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand 1 Corresponding author: E-mail: [email protected]
7.1 INTRODUCTION With an increase in population and industry throughout the world, comes an increasing need for energy, electricity, fuel, and heat requirements (EIA, 2013). The amount of energy used around the world is expected to increase by 56% before 2040 (EIA, 2013). A complex network linking the initial energy carrier with the consumed product describes the energy consumption. At various stages of the energy network, there are processes which result in emissions; for example, burning fuel results in the emission of flue gas (US Department of State, 2010). Consequently, the amount of emissions continues to increase with the increased energy demand. Global warming is a primary environmental concern that is attributed to emissions (Dincer, 2010). Of these emissions, the compounds that are of concern are greenhouse gases (Dincer, 2010). Greenhouse gases are a selection of gases that trap heat that results in global warming; they include water vapor, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), ozone (O3), and various types of halogenated substances containing fluorine, chlorine, and bromine (such as chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs)) (Dincer, 2010). Of these compounds, carbon dioxide represents more than 85% of Computer Aided Chemical Engineering, Volume 36. ISSN 1570-7946. http://dx.doi.org/10.1016/B978-0-444-63472-6.00007-0 © 2015 Elsevier B.V. All rights reserved.
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greenhouse gas emissions (US Department of State, 2010). Therefore, the reduction of carbon dioxide emissions is a primary target of environmental policy and regulation (IPCC, 2007). There are two long-term methods of CO2 reduction that are under investigation and slowly being implemented: carbon capture and storage (CCS) and conversion (IPCC, 2007). As a result of the longer investigation time and the support of governments, CCS is the more prevalent of the two (IPCC, 2007). Recently, however, areas for improvement have arisen in the capture technology and the storage methods (Olah et al., 2009). There are a variety of capture technologies that exist: industrial, postcombustion, precombustion, and oxy-fuel (Wilcox, 2012). However, in order to achieve the desired separation, an input of energy, raw materials, or solvents is required. These inputs have a resulting environmental impact that should be avoided as much as possible (production of energy releases CO2). In addition to these issues, the storage methods are also controversial. Storage is done in geological formations or in vessels for further use. But, there is concern that the technology is immature and still needs to be further developed (IPCC, 2005). In contrast, conversion utilizes the emitted CO2 to produce other valuable products (Aresta, 2010). This is achieved through chemical reactions that are often performed catalytically. The selection of processes is limited by the need for processes with high conversion and yield. This is hindered by the stability of CO2 and the difficulty in selecting reactions that are favorable (Aresta, 2010). However, the processes that fulfill these requirements are desirable as they are able to not only produce desired products, but are also able to utilize some of the CO2 emitted into the atmosphere (Aresta et al., 2013). The amount of emissions can thereby be reduced if the amount utilized through the conversion process is greater than the emissions from consumed energy and utilities. Therefore, work focusing on conversion processes shows great promise; the transformation of carbon dioxide into other compounds could help reduce the emissions and mitigate the storage concerns. In order to describe conversion processes, the feed source (emissions) need to be detailed. Emissions are primarily the result of energy production and manufacturing processes; these emission sources represent 80% of the emissions globally (IPCC, 2007). The remaining sources are the result of natural phenomena such as decay and are therefore not considered as carbon dioxide sources for reduction methods. In addition to the amounts being emitted, the quality of the emissions needs to be described (Last and Schmick, 2011). Though high in quantity, flue gas emissions from energy production contain low concentrations of CO2. In order for these emissions to be utilized, they must be purified using carbon capture technology (Wilcox, 2012). Alternatively, high-purity emissions can also be used directly (Zakkour and Cook, 2010). The amounts and processes that produce these emissions are listed in Table 7.1. As Table 7.1 reveals, there is a large supply of high-purity emissions that can be used (approximately 300 million tons of CO2 annually (MtCO2/year)). However, in order to address the global issues, a combination of high-purity and low-purity emissions must be eliminated as high-purity emissions only represent 10% of all
7.2 Identifying More Sustainable Designs
Table 7.1 High-Purity Emission Sources (Frauzem, 2014)
CO2 Source
Nonutilized Emitted Amount (MtCO2/Year)
Natural gas production Ammonia production
150
Organic chemical production process Synthetic fuel production
6.3
119
27.6
Process Natural gas sweetening Ammonia production with on-site hydrogen production Ethylene oxide production FischereTropsch Methanol/DME
Emission Information Acid gas purge vent gas Purge gas from CO2 scrubbing Vent gas Vent gas Vent gas from CO2 scrubbing Vent gas
emissions (3500 MtCO2/year) (IPCC, 2007). Therefore, additional work is being implemented in feasible carbon capture methods; these are limited to those that emit less CO2 through energy consumption and processing than would be emitted without capture technology. Currently, amine stripping is one of the more prominent methods (Wilcox, 2012); this uses an amine, primarily monoethylamine (MEA), as a solvent to remove the carbon dioxide from the emission streams. This technology must be made more sustainable (emits less carbon dioxide in the process, both emission streams and energy consumption, than is captured through the process). Primarily, the reduction of the purity requirements in the separations is being investigated to help achieve more sustainable capture technology (Knudsen et al., 2007). With sustainable capture technology, it becomes possible to target a much higher amount of emissions for use in conversion processes. Environmental concerns are the motivation for discovering and implementing long-term carbon dioxide reduction methods. With a proper understanding of the emissions and the possible reactions, conversion is a promising aspect of this solution. This chapter focuses on the formulation of a network of various conversion pathways to help reduce CO2 and create valuable products. In the subsequent sections, the method implemented for superstructure generation and analysis, the network formulated, and case studies for methanol and DMC production are described.
7.2 IDENTIFYING MORE SUSTAINABLE DESIGNS Knowing the background and motivation for the use of conversion processes to help combat the CO2 problem, it is possible to follow a methodology to create a superstructure that networks various conversion processes. The objective of this chapter
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is the sustainable CO2 conversion process design for the production of valuable products. Sustainability is determined by finding a nontrade-off solution compared with a base case design. This is achieved by a reduction in energy consumption, increased profit and reduced environmental impact relative to this original base case design. In addition, there is a second aim of overall carbon dioxide reduction within the process, as defined by the subsequent equation. NET CO2 flow ¼
i X n
CO2 generated
i X
CO2 utilized
(7.1)
n
Here the amount input is that from the feed and the amount generated is the amount emitted through purge, for heating requirements and for electrical supply in the process. This is determined and compared by determining the net carbon dioxide relative to the amount of product produced (kgCO2,net/kgProduct) and should be negative (more input than output resulting in reduction). The methodology followed consists of three stages: first, superstructure-based identification of feasible processing networks and optimal networks; second, base case design of a CO2 conversion process; third, identification of more sustainable designs. The subsequent sections will detail the method followed for the formation of the network of sustainable CO2 conversion paths, their selection and design as a base case, and their improvement as more sustainable designs.
7.2.1 STAGE 1dSUPERSTRUCTURE-BASED OPTIMAL PROCESSING NETWORK The first aspect is the description of the generic form of the superstructure and its generation. Figure 7.1 shows the generic form of the superstructure interface. The intervals shown are the steps linking raw materials and products. This linkage is formed through a variety of processing paths shown as intervals. This superstructure needs to be described mathematically to provide a resulting problem formulation that can be solved. The framework employs a systematic approach for solving both the economic evaluation and the engineering aspects of problems simultaneously so that a large number of alternatives can be compared at their optimal points by using mixed integer nonlinear programming (MINLP) (Quaglia et al., 2012; Zondervan et al., 2011). This mathematical description becomes nonlinear as a result of the complexity of the process models involved; for example, processes with recycle loops create a nonlinear set of equations. The result from this part is the optimal process technology together with the optimal material flows through the network, from which the corresponding performance and sustainability matrices can be calculated. The process superstructure consists of processing steps and process intervals. A process interval presented in Figure 7.2 is defined as a process section, which can perform a certain processing task, e.g., mixing, reaction, separation, and transportation.
7.2 Identifying More Sustainable Designs
FIGURE 7.1 The generic form of superstructure and GAMS user interface for superstructure generation. In the blue (gray in print versions) boxes, the optimal network is indicated (Quaglia et al., 2012).
The important data for each of the process intervals are collected and organized in a predefined knowledge structure. At this step, collection of available data (in database as well as in literature) is prepared. Information on raw materials, main products, side products, reactions, catalysts, processing steps, reaction conversion,
FIGURE 7.2 Process interval of superstructure (Quaglia et al., 2012).
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energy requirements, and operating conditions is collected. The chemical and physical properties of substances related to the process, such as solubility, vapor pressure, azeotropes, heat of vaporization, boiling point, critical properties, and molar volume, are also important for generation of the process superstructure. Then, the entire superstructure has to be described mathematically and formulated as a mixed integer problem. This is described by the set of equations shown in Table 7.2. The combination of all this creates a mixed integer linear programming or MINLP model (the superstructure), which can be solved to find the feasible network of paths, including the optimal path or paths (Quaglia et al., 2012; Zondervan et al., 2011).
7.2.2 STAGE 2dBASE CASE DESIGN The network of optimal or most promising processing paths found from the superstructure (Stage 1) contains various process steps linking the raw materials and products. Each of the processing steps contains a combination of unit operations. The next step is the detailed description of processing steps within the optimal path or most promising paths; this is done through creation of a flowsheet and then the subsequent analysis. From the results of the simulation, information about the process operation is obtained; the streams flow and composition are fully described and the operating conditions of the processing units are complete. This data is then implemented for analysis. This analysis is performed to determine the sustainability of the process via economic, environmental, and life cycle assessment (LCA) factors. With these factors, it is possible to set targets for improvements.
7.2.3 STAGE 3dIDENTIFICATION OF MORE SUSTAINABLE DESIGNS Each of the targeted processing steps have been described in detail and areas for improvement are formed; this third step then takes these areas and generates improvements to the base case design. Tailor-made designs are obtained in a similar way as designing molecular chemical products with desired properties (Gani, 2004). The end result is a more sustainable design for the CO2 conversion path.
7.3 METHOD AND TOOLS The stages that are followed in the superstructure generation each require the additional support of methods and tools. A description of the workflow (described in Sections 7.2.1e7.2.3) and the dataflow are shown in Figure 7.3. The workflow follows the vertical stages, while the data flows horizontally as input and output of each stage.
7.3 Method and Tools
Table 7.2 Mathematical Formulation of the Superstructure Network (Quaglia et al., 2012) Objective function
Max profit ¼
P i;kk
out ðP3i;kk $Fi;kk Þ
P
i;kk
ðP2i;kk $Ri;kk Þ
(7.2)
out ðP1i;kk $Fi;kk Þ W Price
P
$
P i;kk
i;kk
R ðFi;kk $SWi;kk Þ CAPEX #year
Raw material assignment Utility consumption Reaction
out ¼ f Fi;kk i;kk
Waste separation
out ¼ F R $ð1 SW Fi;kk i;kk Þ i;kk
(7.6)
WASTE ¼ F R F out Fi;kk i;kk i;kk
(7.7)
out1 ¼ F out $s out2 out Fi;kk i;kk ; Fi;kk ¼ Fi;kk $ð1 si;kk Þ i;kk
(7.8)
1 out1 $xP ; F 2 out2 s Fi;kk Fi;k;kk k;kk i;k;kk Fi;kk $xk;kk
(7.9)
Producte product separation Superstructure flow model
M Fi;kk
k
Superstructure logic model
k
R ¼ FM þ Fi;kk i;kk
P
(7.3)
P P ¼ ðFi;k;kk Þ þ ai;kk $mi;kk $ ðFi;k;kk Þ P rr;react
1 out1 ; Fi;k;kk ¼ Fi;kk
(7.4)
i;k
M ðgi;kk;rr $qreact;kk;rr $Freact;kk Þ
P k
2 out2 Fi;k;kk ¼ Fi;kk
(7.5)
(7.10)
R M$y Fi;kk kk
(7.11)
P ðyk $vst;k Þ 1
(7.12)
j
Throughput limitation Piece-wise linear capital cost model
P
R F MAX Fi;kk KK i PhP CAPEX ¼ ðaj;kk $wj;kk þ bj;kk $Qj;kk Þ
(7.13)
j
kk
Thr Fkk ¼
P j
Qj;kk ;
(7.14)
j
P j
wj;kk ¼ 1;
(7.15)
Q0j;kk $wj;kk Qj;kk Q0jþ1;kk $Wj;kk
7.3.1 STAGE 1 In order to describe the superstructure and to solve the superstructure-based optimization, tools are required. First, a thorough collection of data and information from literature and databases is required. This is necessary to determine the raw materials, products, and the processing steps that link them. Then, this must be further defined mathematically using the equations described in Table 7.2. With this, the problem is
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FIGURE 7.3 Description of workflow (vertical flow) and dataflow (horizontal flow) for formation of a network of sustainable carbon dioxide conversion processes.
defined and must be solved. In order to solve the problem formulated in this stage, GAMS is implemented (Quaglia et al., 2012). The result is a network of processes from the superstructure.
7.3.2 STAGE 2 The base case simulations for detailed description and analysis of the processing steps require the implementation of various tools to obtain the flowsheets and analysis factors. In order to achieve this, implementation of process simulators and analysis software is necessary. This includes simulators such as Aspen PlusÒ and PROII, and software tools developed at CAPEC-PROCESS such as ICAS, ECON, LCSoft, and SustainPro. With these tools, it is possible to describe the individual process steps in detail and determine the hot spots and points for improvement in the process.
7.3.3 STAGE 3 Within the final stage, the more sustainable design alternatives are found and evaluated. In order to achieve this, further tools and methods are required (tools from
7.4 More Sustainable CO2 Conversion Process Designs
ICAS). The goal of this final stage is the determination of more sustainable design alternatives to the base case for the processing steps. Therefore, tools and methods are necessary that aid in finding the alternatives that match targets determined upon analyzing the results from the base case simulations and software tools. Methods of process intensification, integration, and optimization are utilized. Process integration and optimization are common chemical engineering tools; heat and work are analyzed and integrated to minimize losses and operating conditions are manipulated to determine optimal operation according to various parameters. Process intensification uses target properties to determine more sustainable alternatives systematically (Babi et al., 2014). The flowsheet is defined by the tasks performed and then manipulated accordingly. Comparing the performance criteria then yields intensified alternatives to the base case. Each processing step can be analyzed for sustainability resulting in a network of more sustainable processes.
7.4 MORE SUSTAINABLE CO2 CONVERSION PROCESS DESIGNS The workflow is applied using the methods and tools described above (see Sections 7.3.1e7.3.3) for more sustainable carbon dioxide conversion paths.
7.4.1 CO2 PROCESSING PATHS (STAGE 1) Data and information is collected on the carbon dioxide feed source, feasible processing steps and possible products. The carbon dioxide feed can either be highpurity emissions directly or purified low-purity emissions. The low-purity emissions are purified using MEA stripping; the purity of the product is reduced to ensure the sustainability of the capture process. Then, a representative composition is described (Table 7.3). From literature and available data, the proposed processes include the conversion to several chemicals such as polyurethane, salicylic acid, formic acid, methanol, dimethyl ether (DME), glycerol carbonate, styrene carbonate, urea, ethylene carbonate, propylene carbonate, and dimethyl carbonate (DMC) (Aresta et al., 2013). Combining all the information collected, the superstructure-based network for sustainable carbon dioxide conversion is described by Figure 7.4.
Table 7.3 Representative High-Purity CO2 Emission Composition (Frauzem, 2014) Mol%
CO2
H2
CH4
H2O
C2H6
Other
CO2-rich
0.95
0
0.015
0.024
0.01
0.001
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FIGURE 7.4 CO2 conversion superstructure (Frauzem et al., 2015).
7.4 More Sustainable CO2 Conversion Process Designs
These steps can either take CO2 to produce a desired product or to produce intermediates. In addition, various products can be used both as raw materials for other process steps and final product. There are two products (dimethyl carbonate (DMC) and methanol) that are highlighted in the following stage; for each there are numerous processing paths.
7.4.2 DETAILED FLOWSHEETS (STAGE 2) DMC production is analyzed with a base case using the urea route. The bireforming route for methanol synthesis via synthesis gas (syngas) is also detailed. Finally, the production of methanol via direct hydrogenation of carbon dioxide is studied.
7.4.2.1 Dimethyl carbonate production The DMC production pathways are all in the secondary processing steps of the superstructure. These processing steps involve previously produced inputs in addition to CO2. Using thermodynamic analysis, DMC production via synthesis from urea is defined as the base case (Kongpanna et al., 2014). The detailed flowsheet of this process is shown in Figure 7.5. With the flowsheet and the detailed simulations, the details of the conversion process to DMC are known.
7.4.2.2 Methanol synthesis via synthesis gas One of the promising thermochemical CO2 conversion reactions is methane reforming. This is a conventional processing way to prepare the syngas (mixture of hydrogen and carbon monoxide mainly, and small portion of carbon dioxide) at high temperature and pressure for producing a variety of chemicals such as methanol, gasoline, or diesel. There are four different kinds of CO2-joining reforming pathways to produce the syngas: bireforming (or combined/mixed steam and dry reforming of methane), trireforming, dry reforming, and dry reforming with partial oxidation of methane. For this study, the base case is an industrial bireforming process (Holm-Larsen, 2001). This is simulated in detail using simulation software resulting in the flowsheet in Figure 7.6.
7.4.2.3 Methanol production via direct hydrogenation Finally, the methanol production via direct carbon dioxide hydrogenation is analyzed. The current standard of production of methanol is via synthesis gas. However, often this process requires large amounts of energy resulting in high amounts of CO2 emissions. Therefore, a direct method, which does not result in these emissions, is desirable. Direct hydrogenation is a conversion process that has been suggested as it uses carbon dioxide as a feed in a single reactor. In this process, there are three units: reactor, flash column, and distillation column. The reaction is performed in a packed bed reactor containing pellets of a copper/ zinc oxide (Cu/ZnO) multicomponent catalyst (Saito et al., 1996). Using
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FIGURE 7.5 The DMC production process via urea route (base case design) (Kongpanna et al., 2014).
Flowsheet for the bireforming base case for methanol synthesis (Holm-Larsen, 2001).
7.4 More Sustainable CO2 Conversion Process Designs
FIGURE 7.6
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thermodynamic and kinetic information collected from literature and data, the base case process is simulated resulting in Figure 7.7 (Kubota et al., 2001; Ushikoshi et al., 2000).
7.4.3 MORE SUSTAINABLE SOLUTIONS (STAGE 3) Each of the base cases is examined thoroughly using economic factors, environmental parameters, and LCA factors. These reveal the hot spots of the processes and the locations for improvements. Using intensification methods, improvements are determined and compared for each base case.
7.4.3.1 Dimethyl carbonate production From the base case simulation, sustainability factors show that the net CO2 is low because this process is energy intensive in downstream separation. So, the process intensification by phenomena-based synthesis has been used in this work for generating other process candidates, which meet the target for improvement (Babi et al., 2014). From this, the base case is manipulated in three ways: incorporation of a pervaporation membrane, use of a membrane reactor, and the use of reactive distillation (Kongpanna et al., 2014). Using targets for the costs and environmental factors, it is possible to find a nontrade-off solution. The following radar chart (Figure 7.8) shows these factors for these improved designs compared with the urea base case design. Additionally, the net carbon dioxide flow in the process is calculated for the various designs (Figure 7.9). The radar chart and the net CO2 calculations reveal that DMC production can be intensified with reactive distillation resulting in a more sustainable process. In addition, this reveals that conversion to DMC can reduce carbon dioxide emissions while producing a desirable product.
7.4.3.2 Methanol synthesis via synthesis gas The second case, methanol synthesis via syngas, shows targets for improvement because it requires large amounts of energy to heat the reformer. This conventionally releases high amounts of carbon dioxide. An optimization is performed by manipulating the operating conditions and then comparing the net CO2 with the base case. The result is a nontrade-off solution compared with conventional methanol production facilities as shown in the radar chart (Figure 7.10). Then, the net CO2 of the process is calculated. Relative to the current methanol production technology and the base case, the sustainability of the process is improved (Figure 7.11). As these values show, the optimization of the bireforming-based methanol plant will result in a reduction compared with the conventional case. However, the process is still releasing CO2 to the atmosphere (positive net CO2). These results obtain
Flowsheet of the base case simulation for methanol production via carbon dioxide hydrogenation (Frauzem, 2014).
7.4 More Sustainable CO2 Conversion Process Designs
FIGURE 7.7
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FIGURE 7.8 Radar chart comparing the various targeted values for the improved designs (Kongpanna et al., 2014).
FIGURE 7.9 Net CO2 for the base case compared with process intensification for a sustainable design of DMC production (Kongpanna et al., 2014).
7.4 More Sustainable CO2 Conversion Process Designs
FIGURE 7.10 Radar chart comparing targeted factors for the conventional methanol plant with the base case and the optimization.
lower net CO2 emission level as well as lower operating cost, but are still not a negative net CO2 process. Therefore, the improvements are sustainable compared with the current production methods, but should be further optimized and intensified to yield a negative net CO2 process.
7.4.3.3 Methanol production via direct hydrogenation The third case study shows a hot spot in the energy consumption. Carbon dioxide hydrogenation to form methanol has low conversions to ensure that the selectivity is high. Therefore, there is a large recycle stream that results. Using heat integration it is possible to improve this hot spot to a net CO2 of 1.125 kg CO2/kg MeOH. In addition, the influence of the operating conditions and feed conditions is observed. These show the benefits of increasing the pressure, decreasing the temperature, and having pure feeds (Figures 7.12 and 7.13). The economic factors for analysis show improvements compared with the base case with a reduction in the capital and operating costs. As this is new technology,
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FIGURE 7.11 Comparison of sustainability of base case and improvements for methanol synthesis.
FIGURE 7.12 Indicators of the sustainability for various operating conditions (Frauzem, 2014).
7.5 Conclusions
FIGURE 7.13 Indicators of the sustainability for various feed purities (Frauzem, 2014).
there is no reference point other than the base case design. Using heat integration, the base case is improved to make a more sustainable conversion process for the production of methanol.
7.5 CONCLUSIONS In order to combat global warming and the influence of greenhouse gases, long-term solutions are required. Since carbon dioxide is the largest contributor to these phenomena, methods for reducing the emissions are necessary. This network of more sustainable conversion processes aims at utilizing carbon dioxide to create valuable products. The target is a nontrade-off solution that also shows an overall reduction of the emissions; the economic costs are reduced, environmental factors are improved, and the amount of carbon dioxide fed into the process is greater than the amount emitted or generated to ensure sustainability. Utilizing the method described, it is possible to generate a network based on superstructure optimization. This network is further analyzed and optimized using process integration, optimization, and intensification. The three paths, DMC production, methanol synthesis from syngas, and methanol production via direct hydrogenation, are used as case studies for the detailed flowsheet creation, analysis, and improvement. Following the steps described, it is possible to create a network of more sustainable carbon dioxide conversion processes that yield a portfolio of valuable products.
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REFERENCES Aresta, M., 2010. Carbon Dioxide as Chemical Feedstock, Carbon Dioxide as Chemical Feedstock. http://dx.doi.org/10.1002/9783527629916. Aresta, M., Dibenedetto, A., Angelini, A., 2013. The changing paradigm in CO2 utilization. J. CO2 Util. 3e4, 65e73. http://dx.doi.org/10.1016/j.jcou.2013.08.001. Babi, D.K., Woodley, J.M., Gani, R., 2014. Achieving more sustainable designs through a process synthesis-intensification framework. Comput. Aided Chem. Eng. 33, 31e36. http://dx.doi.org/10.1016/B978-0-444-63456-6.50006-5. Dincer, I., 2010. Global Warming, Green Energy and Technology. Springer, US, Boston, MA. http://dx.doi.org/10.1007/978-1-4419-1017-2. EIA, 2013. International Energy Outlook 2013. US Energy Information Administration (EIA), Washington, DC. Frauzem, R., 2014. Techno-economic Evaluation of a CO2-conversion Process. Frauzem, R., Kongpanna, P., Roh, K., Lee, J.H., Pavarajarn, V., Assbumrungrat, S., Gani, R., 2015. Formulation of a Network and the Study of Reaction Paths for the Sustainable Reduction of CO2 Emissions (In progress). Gani, R., 2004. Chemical product design: challenges and opportunities. Comput. Chem. Eng. 28, 2441e2457. http://dx.doi.org/10.1016/j.compchemeng.2004.08.010. Holm-Larsen, H., 2001. CO2 reforming for large scale methanol plants e an actual case. Stud. Surf. Sci. Catal. 441e446. IPCC, 2005. Carbon Dioxide Capture and Storage, Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, New York. IPCC, 2007. Climate Change 2007: Mitigation of Climate Change, Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, New York. Knudsen, J.N., Jensen, J.N., Vilhelmsen, P., 2007. First year operation experience with a 1 t/h CO2 absorption pilot plant at Esbjerg coal-fired power plant. In: European Congress of Chemical Engineering (ECCE-6). Copenhagen. Kongpanna, P., Pavarajarn, V., Gani, R., Assabumrungrat, S., 2014. Techno-economic evaluation of different CO2-based processes for dimethyl carbonate production. Chem. Eng. Res. Des. 93, 496e510. http://dx.doi.org/10.1016/j.cherd.2014.07.013. Kubota, T., Hayakawa, I., Mabuse, H., Mori, K., Ushikoshi, K., Watanabe, T., Saito, M., 2001. Kinetic Study of Methanol Synthesis from Carbon Dioxide and Hydrogen, pp. 121e126. Last, G., Schmick, M., 2011. Identification and Selection of Major Carbon Dioxide Stream Compositions. Pacific Northwest National Laboratory, Oak Ridge. Olah, G.A., Goeppert, A., Prakash, G.K.S., 2009. Beyond Oil and Gas: The Methanol Economy. Wiley-VCH, Weinheim. Quaglia, A., Sarup, B., Sin, G., Gani, R., 2012. Integrated business and engineering framework for synthesis and design of enterprise-wide processing networks. Comput. Chem. Eng. 38, 213e223. http://dx.doi.org/10.1016/j.compchemeng.2011.12.011. Saito, M., Fujitani, T., Takeuchi, M., Watanabe, T., 1996. Development of copper/zinc oxidebased multicomponent catalysts for methanol synthesis from carbon dioxide and hydrogen. Appl. Catal. A Gen. 138, 311e318. http://dx.doi.org/10.1016/0926-860X(95) 00305-3. US Department of State, 2010. U.S. Climate Action Report. US Department of State (US DOS). US DOS, Washington, DC.
References
Ushikoshi, K., Mori, K., Kubota, T., Watanabe, T., Saito, M., 2000. Methanol synthesis from CO2 and H2 in a bench-scale test plant. Appl. Organomet. Chem. 14, 819e825. http:// dx.doi.org/10.1002/1099-0739(200012)14, 12<819::AID-AOC90>3.0.CO;2-#. Wilcox, J., 2012. Carbon Capture. Springer, New York. http://dx.doi.org/10.1007/978-1-46142215-0. Zakkour, P., Cook, G., 2010. CCS Roadmap for Industry: High-purity CO2 Sources. Carbon Counts Company (UK) Ltd. Zondervan, E., Nawaz, M., de Haan, A.B., Woodley, J.M., Gani, R., 2011. Optimal design of a multi-product biorefinery system. Comput. Chem. Eng. 35, 1752e1766. http://dx.doi.org/ 10.1016/j.compchemeng.2011.01.042.
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Rebecca Frauzem*, Pichayapan Kongpannax, Kosan Roh{, Jay H. Lee{, Varong Pavarajarnjj, Suttichai Assabumrungratx, Rafiqul Gani*, 1 *Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark x Center of Excellence in Catalysis and Catalytic Reaction Engineering, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand { Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Republic of Korea jj Center of Excellence in Particle Technology, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand 1 Corresponding author: E-mail: [email protected]
7.1 INTRODUCTION With an increase in population and industry throughout the world, comes an increasing need for energy, electricity, fuel, and heat requirements (EIA, 2013). The amount of energy used around the world is expected to increase by 56% before 2040 (EIA, 2013). A complex network linking the initial energy carrier with the consumed product describes the energy consumption. At various stages of the energy network, there are processes which result in emissions; for example, burning fuel results in the emission of flue gas (US Department of State, 2010). Consequently, the amount of emissions continues to increase with the increased energy demand. Global warming is a primary environmental concern that is attributed to emissions (Dincer, 2010). Of these emissions, the compounds that are of concern are greenhouse gases (Dincer, 2010). Greenhouse gases are a selection of gases that trap heat that results in global warming; they include water vapor, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), ozone (O3), and various types of halogenated substances containing fluorine, chlorine, and bromine (such as chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs)) (Dincer, 2010). Of these compounds, carbon dioxide represents more than 85% of Computer Aided Chemical Engineering, Volume 36. ISSN 1570-7946. http://dx.doi.org/10.1016/B978-0-444-63472-6.00007-0 © 2015 Elsevier B.V. All rights reserved.
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greenhouse gas emissions (US Department of State, 2010). Therefore, the reduction of carbon dioxide emissions is a primary target of environmental policy and regulation (IPCC, 2007). There are two long-term methods of CO2 reduction that are under investigation and slowly being implemented: carbon capture and storage (CCS) and conversion (IPCC, 2007). As a result of the longer investigation time and the support of governments, CCS is the more prevalent of the two (IPCC, 2007). Recently, however, areas for improvement have arisen in the capture technology and the storage methods (Olah et al., 2009). There are a variety of capture technologies that exist: industrial, postcombustion, precombustion, and oxy-fuel (Wilcox, 2012). However, in order to achieve the desired separation, an input of energy, raw materials, or solvents is required. These inputs have a resulting environmental impact that should be avoided as much as possible (production of energy releases CO2). In addition to these issues, the storage methods are also controversial. Storage is done in geological formations or in vessels for further use. But, there is concern that the technology is immature and still needs to be further developed (IPCC, 2005). In contrast, conversion utilizes the emitted CO2 to produce other valuable products (Aresta, 2010). This is achieved through chemical reactions that are often performed catalytically. The selection of processes is limited by the need for processes with high conversion and yield. This is hindered by the stability of CO2 and the difficulty in selecting reactions that are favorable (Aresta, 2010). However, the processes that fulfill these requirements are desirable as they are able to not only produce desired products, but are also able to utilize some of the CO2 emitted into the atmosphere (Aresta et al., 2013). The amount of emissions can thereby be reduced if the amount utilized through the conversion process is greater than the emissions from consumed energy and utilities. Therefore, work focusing on conversion processes shows great promise; the transformation of carbon dioxide into other compounds could help reduce the emissions and mitigate the storage concerns. In order to describe conversion processes, the feed source (emissions) need to be detailed. Emissions are primarily the result of energy production and manufacturing processes; these emission sources represent 80% of the emissions globally (IPCC, 2007). The remaining sources are the result of natural phenomena such as decay and are therefore not considered as carbon dioxide sources for reduction methods. In addition to the amounts being emitted, the quality of the emissions needs to be described (Last and Schmick, 2011). Though high in quantity, flue gas emissions from energy production contain low concentrations of CO2. In order for these emissions to be utilized, they must be purified using carbon capture technology (Wilcox, 2012). Alternatively, high-purity emissions can also be used directly (Zakkour and Cook, 2010). The amounts and processes that produce these emissions are listed in Table 7.1. As Table 7.1 reveals, there is a large supply of high-purity emissions that can be used (approximately 300 million tons of CO2 annually (MtCO2/year)). However, in order to address the global issues, a combination of high-purity and low-purity emissions must be eliminated as high-purity emissions only represent 10% of all
7.2 Identifying More Sustainable Designs
Table 7.1 High-Purity Emission Sources (Frauzem, 2014)
CO2 Source
Nonutilized Emitted Amount (MtCO2/Year)
Natural gas production Ammonia production
150
Organic chemical production process Synthetic fuel production
6.3
119
27.6
Process Natural gas sweetening Ammonia production with on-site hydrogen production Ethylene oxide production FischereTropsch Methanol/DME
Emission Information Acid gas purge vent gas Purge gas from CO2 scrubbing Vent gas Vent gas Vent gas from CO2 scrubbing Vent gas
emissions (3500 MtCO2/year) (IPCC, 2007). Therefore, additional work is being implemented in feasible carbon capture methods; these are limited to those that emit less CO2 through energy consumption and processing than would be emitted without capture technology. Currently, amine stripping is one of the more prominent methods (Wilcox, 2012); this uses an amine, primarily monoethylamine (MEA), as a solvent to remove the carbon dioxide from the emission streams. This technology must be made more sustainable (emits less carbon dioxide in the process, both emission streams and energy consumption, than is captured through the process). Primarily, the reduction of the purity requirements in the separations is being investigated to help achieve more sustainable capture technology (Knudsen et al., 2007). With sustainable capture technology, it becomes possible to target a much higher amount of emissions for use in conversion processes. Environmental concerns are the motivation for discovering and implementing long-term carbon dioxide reduction methods. With a proper understanding of the emissions and the possible reactions, conversion is a promising aspect of this solution. This chapter focuses on the formulation of a network of various conversion pathways to help reduce CO2 and create valuable products. In the subsequent sections, the method implemented for superstructure generation and analysis, the network formulated, and case studies for methanol and DMC production are described.
7.2 IDENTIFYING MORE SUSTAINABLE DESIGNS Knowing the background and motivation for the use of conversion processes to help combat the CO2 problem, it is possible to follow a methodology to create a superstructure that networks various conversion processes. The objective of this chapter
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is the sustainable CO2 conversion process design for the production of valuable products. Sustainability is determined by finding a nontrade-off solution compared with a base case design. This is achieved by a reduction in energy consumption, increased profit and reduced environmental impact relative to this original base case design. In addition, there is a second aim of overall carbon dioxide reduction within the process, as defined by the subsequent equation. NET CO2 flow ¼
i X n
CO2 generated
i X
CO2 utilized
(7.1)
n
Here the amount input is that from the feed and the amount generated is the amount emitted through purge, for heating requirements and for electrical supply in the process. This is determined and compared by determining the net carbon dioxide relative to the amount of product produced (kgCO2,net/kgProduct) and should be negative (more input than output resulting in reduction). The methodology followed consists of three stages: first, superstructure-based identification of feasible processing networks and optimal networks; second, base case design of a CO2 conversion process; third, identification of more sustainable designs. The subsequent sections will detail the method followed for the formation of the network of sustainable CO2 conversion paths, their selection and design as a base case, and their improvement as more sustainable designs.
7.2.1 STAGE 1dSUPERSTRUCTURE-BASED OPTIMAL PROCESSING NETWORK The first aspect is the description of the generic form of the superstructure and its generation. Figure 7.1 shows the generic form of the superstructure interface. The intervals shown are the steps linking raw materials and products. This linkage is formed through a variety of processing paths shown as intervals. This superstructure needs to be described mathematically to provide a resulting problem formulation that can be solved. The framework employs a systematic approach for solving both the economic evaluation and the engineering aspects of problems simultaneously so that a large number of alternatives can be compared at their optimal points by using mixed integer nonlinear programming (MINLP) (Quaglia et al., 2012; Zondervan et al., 2011). This mathematical description becomes nonlinear as a result of the complexity of the process models involved; for example, processes with recycle loops create a nonlinear set of equations. The result from this part is the optimal process technology together with the optimal material flows through the network, from which the corresponding performance and sustainability matrices can be calculated. The process superstructure consists of processing steps and process intervals. A process interval presented in Figure 7.2 is defined as a process section, which can perform a certain processing task, e.g., mixing, reaction, separation, and transportation.
7.2 Identifying More Sustainable Designs
FIGURE 7.1 The generic form of superstructure and GAMS user interface for superstructure generation. In the blue (gray in print versions) boxes, the optimal network is indicated (Quaglia et al., 2012).
The important data for each of the process intervals are collected and organized in a predefined knowledge structure. At this step, collection of available data (in database as well as in literature) is prepared. Information on raw materials, main products, side products, reactions, catalysts, processing steps, reaction conversion,
FIGURE 7.2 Process interval of superstructure (Quaglia et al., 2012).
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energy requirements, and operating conditions is collected. The chemical and physical properties of substances related to the process, such as solubility, vapor pressure, azeotropes, heat of vaporization, boiling point, critical properties, and molar volume, are also important for generation of the process superstructure. Then, the entire superstructure has to be described mathematically and formulated as a mixed integer problem. This is described by the set of equations shown in Table 7.2. The combination of all this creates a mixed integer linear programming or MINLP model (the superstructure), which can be solved to find the feasible network of paths, including the optimal path or paths (Quaglia et al., 2012; Zondervan et al., 2011).
7.2.2 STAGE 2dBASE CASE DESIGN The network of optimal or most promising processing paths found from the superstructure (Stage 1) contains various process steps linking the raw materials and products. Each of the processing steps contains a combination of unit operations. The next step is the detailed description of processing steps within the optimal path or most promising paths; this is done through creation of a flowsheet and then the subsequent analysis. From the results of the simulation, information about the process operation is obtained; the streams flow and composition are fully described and the operating conditions of the processing units are complete. This data is then implemented for analysis. This analysis is performed to determine the sustainability of the process via economic, environmental, and life cycle assessment (LCA) factors. With these factors, it is possible to set targets for improvements.
7.2.3 STAGE 3dIDENTIFICATION OF MORE SUSTAINABLE DESIGNS Each of the targeted processing steps have been described in detail and areas for improvement are formed; this third step then takes these areas and generates improvements to the base case design. Tailor-made designs are obtained in a similar way as designing molecular chemical products with desired properties (Gani, 2004). The end result is a more sustainable design for the CO2 conversion path.
7.3 METHOD AND TOOLS The stages that are followed in the superstructure generation each require the additional support of methods and tools. A description of the workflow (described in Sections 7.2.1e7.2.3) and the dataflow are shown in Figure 7.3. The workflow follows the vertical stages, while the data flows horizontally as input and output of each stage.
7.3 Method and Tools
Table 7.2 Mathematical Formulation of the Superstructure Network (Quaglia et al., 2012) Objective function
Max profit ¼
P i;kk
out ðP3i;kk $Fi;kk Þ
P
i;kk
ðP2i;kk $Ri;kk Þ
(7.2)
out ðP1i;kk $Fi;kk Þ W Price
P
$
P i;kk
i;kk
R ðFi;kk $SWi;kk Þ CAPEX #year
Raw material assignment Utility consumption Reaction
out ¼ f Fi;kk i;kk
Waste separation
out ¼ F R $ð1 SW Fi;kk i;kk Þ i;kk
(7.6)
WASTE ¼ F R F out Fi;kk i;kk i;kk
(7.7)
out1 ¼ F out $s out2 out Fi;kk i;kk ; Fi;kk ¼ Fi;kk $ð1 si;kk Þ i;kk
(7.8)
1 out1 $xP ; F 2 out2 s Fi;kk Fi;k;kk k;kk i;k;kk Fi;kk $xk;kk
(7.9)
Producte product separation Superstructure flow model
M Fi;kk
k
Superstructure logic model
k
R ¼ FM þ Fi;kk i;kk
P
(7.3)
P P ¼ ðFi;k;kk Þ þ ai;kk $mi;kk $ ðFi;k;kk Þ P rr;react
1 out1 ; Fi;k;kk ¼ Fi;kk
(7.4)
i;k
M ðgi;kk;rr $qreact;kk;rr $Freact;kk Þ
P k
2 out2 Fi;k;kk ¼ Fi;kk
(7.5)
(7.10)
R M$y Fi;kk kk
(7.11)
P ðyk $vst;k Þ 1
(7.12)
j
Throughput limitation Piece-wise linear capital cost model
P
R F MAX Fi;kk KK i PhP CAPEX ¼ ðaj;kk $wj;kk þ bj;kk $Qj;kk Þ
(7.13)
j
kk
Thr Fkk ¼
P j
Qj;kk ;
(7.14)
j
P j
wj;kk ¼ 1;
(7.15)
Q0j;kk $wj;kk Qj;kk Q0jþ1;kk $Wj;kk
7.3.1 STAGE 1 In order to describe the superstructure and to solve the superstructure-based optimization, tools are required. First, a thorough collection of data and information from literature and databases is required. This is necessary to determine the raw materials, products, and the processing steps that link them. Then, this must be further defined mathematically using the equations described in Table 7.2. With this, the problem is
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FIGURE 7.3 Description of workflow (vertical flow) and dataflow (horizontal flow) for formation of a network of sustainable carbon dioxide conversion processes.
defined and must be solved. In order to solve the problem formulated in this stage, GAMS is implemented (Quaglia et al., 2012). The result is a network of processes from the superstructure.
7.3.2 STAGE 2 The base case simulations for detailed description and analysis of the processing steps require the implementation of various tools to obtain the flowsheets and analysis factors. In order to achieve this, implementation of process simulators and analysis software is necessary. This includes simulators such as Aspen PlusÒ and PROII, and software tools developed at CAPEC-PROCESS such as ICAS, ECON, LCSoft, and SustainPro. With these tools, it is possible to describe the individual process steps in detail and determine the hot spots and points for improvement in the process.
7.3.3 STAGE 3 Within the final stage, the more sustainable design alternatives are found and evaluated. In order to achieve this, further tools and methods are required (tools from
7.4 More Sustainable CO2 Conversion Process Designs
ICAS). The goal of this final stage is the determination of more sustainable design alternatives to the base case for the processing steps. Therefore, tools and methods are necessary that aid in finding the alternatives that match targets determined upon analyzing the results from the base case simulations and software tools. Methods of process intensification, integration, and optimization are utilized. Process integration and optimization are common chemical engineering tools; heat and work are analyzed and integrated to minimize losses and operating conditions are manipulated to determine optimal operation according to various parameters. Process intensification uses target properties to determine more sustainable alternatives systematically (Babi et al., 2014). The flowsheet is defined by the tasks performed and then manipulated accordingly. Comparing the performance criteria then yields intensified alternatives to the base case. Each processing step can be analyzed for sustainability resulting in a network of more sustainable processes.
7.4 MORE SUSTAINABLE CO2 CONVERSION PROCESS DESIGNS The workflow is applied using the methods and tools described above (see Sections 7.3.1e7.3.3) for more sustainable carbon dioxide conversion paths.
7.4.1 CO2 PROCESSING PATHS (STAGE 1) Data and information is collected on the carbon dioxide feed source, feasible processing steps and possible products. The carbon dioxide feed can either be highpurity emissions directly or purified low-purity emissions. The low-purity emissions are purified using MEA stripping; the purity of the product is reduced to ensure the sustainability of the capture process. Then, a representative composition is described (Table 7.3). From literature and available data, the proposed processes include the conversion to several chemicals such as polyurethane, salicylic acid, formic acid, methanol, dimethyl ether (DME), glycerol carbonate, styrene carbonate, urea, ethylene carbonate, propylene carbonate, and dimethyl carbonate (DMC) (Aresta et al., 2013). Combining all the information collected, the superstructure-based network for sustainable carbon dioxide conversion is described by Figure 7.4.
Table 7.3 Representative High-Purity CO2 Emission Composition (Frauzem, 2014) Mol%
CO2
H2
CH4
H2O
C2H6
Other
CO2-rich
0.95
0
0.015
0.024
0.01
0.001
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184 CHAPTER 7 Sustainable Process Design
FIGURE 7.4 CO2 conversion superstructure (Frauzem et al., 2015).
7.4 More Sustainable CO2 Conversion Process Designs
These steps can either take CO2 to produce a desired product or to produce intermediates. In addition, various products can be used both as raw materials for other process steps and final product. There are two products (dimethyl carbonate (DMC) and methanol) that are highlighted in the following stage; for each there are numerous processing paths.
7.4.2 DETAILED FLOWSHEETS (STAGE 2) DMC production is analyzed with a base case using the urea route. The bireforming route for methanol synthesis via synthesis gas (syngas) is also detailed. Finally, the production of methanol via direct hydrogenation of carbon dioxide is studied.
7.4.2.1 Dimethyl carbonate production The DMC production pathways are all in the secondary processing steps of the superstructure. These processing steps involve previously produced inputs in addition to CO2. Using thermodynamic analysis, DMC production via synthesis from urea is defined as the base case (Kongpanna et al., 2014). The detailed flowsheet of this process is shown in Figure 7.5. With the flowsheet and the detailed simulations, the details of the conversion process to DMC are known.
7.4.2.2 Methanol synthesis via synthesis gas One of the promising thermochemical CO2 conversion reactions is methane reforming. This is a conventional processing way to prepare the syngas (mixture of hydrogen and carbon monoxide mainly, and small portion of carbon dioxide) at high temperature and pressure for producing a variety of chemicals such as methanol, gasoline, or diesel. There are four different kinds of CO2-joining reforming pathways to produce the syngas: bireforming (or combined/mixed steam and dry reforming of methane), trireforming, dry reforming, and dry reforming with partial oxidation of methane. For this study, the base case is an industrial bireforming process (Holm-Larsen, 2001). This is simulated in detail using simulation software resulting in the flowsheet in Figure 7.6.
7.4.2.3 Methanol production via direct hydrogenation Finally, the methanol production via direct carbon dioxide hydrogenation is analyzed. The current standard of production of methanol is via synthesis gas. However, often this process requires large amounts of energy resulting in high amounts of CO2 emissions. Therefore, a direct method, which does not result in these emissions, is desirable. Direct hydrogenation is a conversion process that has been suggested as it uses carbon dioxide as a feed in a single reactor. In this process, there are three units: reactor, flash column, and distillation column. The reaction is performed in a packed bed reactor containing pellets of a copper/ zinc oxide (Cu/ZnO) multicomponent catalyst (Saito et al., 1996). Using
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FIGURE 7.5 The DMC production process via urea route (base case design) (Kongpanna et al., 2014).
Flowsheet for the bireforming base case for methanol synthesis (Holm-Larsen, 2001).
7.4 More Sustainable CO2 Conversion Process Designs
FIGURE 7.6
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thermodynamic and kinetic information collected from literature and data, the base case process is simulated resulting in Figure 7.7 (Kubota et al., 2001; Ushikoshi et al., 2000).
7.4.3 MORE SUSTAINABLE SOLUTIONS (STAGE 3) Each of the base cases is examined thoroughly using economic factors, environmental parameters, and LCA factors. These reveal the hot spots of the processes and the locations for improvements. Using intensification methods, improvements are determined and compared for each base case.
7.4.3.1 Dimethyl carbonate production From the base case simulation, sustainability factors show that the net CO2 is low because this process is energy intensive in downstream separation. So, the process intensification by phenomena-based synthesis has been used in this work for generating other process candidates, which meet the target for improvement (Babi et al., 2014). From this, the base case is manipulated in three ways: incorporation of a pervaporation membrane, use of a membrane reactor, and the use of reactive distillation (Kongpanna et al., 2014). Using targets for the costs and environmental factors, it is possible to find a nontrade-off solution. The following radar chart (Figure 7.8) shows these factors for these improved designs compared with the urea base case design. Additionally, the net carbon dioxide flow in the process is calculated for the various designs (Figure 7.9). The radar chart and the net CO2 calculations reveal that DMC production can be intensified with reactive distillation resulting in a more sustainable process. In addition, this reveals that conversion to DMC can reduce carbon dioxide emissions while producing a desirable product.
7.4.3.2 Methanol synthesis via synthesis gas The second case, methanol synthesis via syngas, shows targets for improvement because it requires large amounts of energy to heat the reformer. This conventionally releases high amounts of carbon dioxide. An optimization is performed by manipulating the operating conditions and then comparing the net CO2 with the base case. The result is a nontrade-off solution compared with conventional methanol production facilities as shown in the radar chart (Figure 7.10). Then, the net CO2 of the process is calculated. Relative to the current methanol production technology and the base case, the sustainability of the process is improved (Figure 7.11). As these values show, the optimization of the bireforming-based methanol plant will result in a reduction compared with the conventional case. However, the process is still releasing CO2 to the atmosphere (positive net CO2). These results obtain
Flowsheet of the base case simulation for methanol production via carbon dioxide hydrogenation (Frauzem, 2014).
7.4 More Sustainable CO2 Conversion Process Designs
FIGURE 7.7
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FIGURE 7.8 Radar chart comparing the various targeted values for the improved designs (Kongpanna et al., 2014).
FIGURE 7.9 Net CO2 for the base case compared with process intensification for a sustainable design of DMC production (Kongpanna et al., 2014).
7.4 More Sustainable CO2 Conversion Process Designs
FIGURE 7.10 Radar chart comparing targeted factors for the conventional methanol plant with the base case and the optimization.
lower net CO2 emission level as well as lower operating cost, but are still not a negative net CO2 process. Therefore, the improvements are sustainable compared with the current production methods, but should be further optimized and intensified to yield a negative net CO2 process.
7.4.3.3 Methanol production via direct hydrogenation The third case study shows a hot spot in the energy consumption. Carbon dioxide hydrogenation to form methanol has low conversions to ensure that the selectivity is high. Therefore, there is a large recycle stream that results. Using heat integration it is possible to improve this hot spot to a net CO2 of 1.125 kg CO2/kg MeOH. In addition, the influence of the operating conditions and feed conditions is observed. These show the benefits of increasing the pressure, decreasing the temperature, and having pure feeds (Figures 7.12 and 7.13). The economic factors for analysis show improvements compared with the base case with a reduction in the capital and operating costs. As this is new technology,
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FIGURE 7.11 Comparison of sustainability of base case and improvements for methanol synthesis.
FIGURE 7.12 Indicators of the sustainability for various operating conditions (Frauzem, 2014).
7.5 Conclusions
FIGURE 7.13 Indicators of the sustainability for various feed purities (Frauzem, 2014).
there is no reference point other than the base case design. Using heat integration, the base case is improved to make a more sustainable conversion process for the production of methanol.
7.5 CONCLUSIONS In order to combat global warming and the influence of greenhouse gases, long-term solutions are required. Since carbon dioxide is the largest contributor to these phenomena, methods for reducing the emissions are necessary. This network of more sustainable conversion processes aims at utilizing carbon dioxide to create valuable products. The target is a nontrade-off solution that also shows an overall reduction of the emissions; the economic costs are reduced, environmental factors are improved, and the amount of carbon dioxide fed into the process is greater than the amount emitted or generated to ensure sustainability. Utilizing the method described, it is possible to generate a network based on superstructure optimization. This network is further analyzed and optimized using process integration, optimization, and intensification. The three paths, DMC production, methanol synthesis from syngas, and methanol production via direct hydrogenation, are used as case studies for the detailed flowsheet creation, analysis, and improvement. Following the steps described, it is possible to create a network of more sustainable carbon dioxide conversion processes that yield a portfolio of valuable products.
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REFERENCES Aresta, M., 2010. Carbon Dioxide as Chemical Feedstock, Carbon Dioxide as Chemical Feedstock. http://dx.doi.org/10.1002/9783527629916. Aresta, M., Dibenedetto, A., Angelini, A., 2013. The changing paradigm in CO2 utilization. J. CO2 Util. 3e4, 65e73. http://dx.doi.org/10.1016/j.jcou.2013.08.001. Babi, D.K., Woodley, J.M., Gani, R., 2014. Achieving more sustainable designs through a process synthesis-intensification framework. Comput. Aided Chem. Eng. 33, 31e36. http://dx.doi.org/10.1016/B978-0-444-63456-6.50006-5. Dincer, I., 2010. Global Warming, Green Energy and Technology. Springer, US, Boston, MA. http://dx.doi.org/10.1007/978-1-4419-1017-2. EIA, 2013. International Energy Outlook 2013. US Energy Information Administration (EIA), Washington, DC. Frauzem, R., 2014. Techno-economic Evaluation of a CO2-conversion Process. Frauzem, R., Kongpanna, P., Roh, K., Lee, J.H., Pavarajarn, V., Assbumrungrat, S., Gani, R., 2015. Formulation of a Network and the Study of Reaction Paths for the Sustainable Reduction of CO2 Emissions (In progress). Gani, R., 2004. Chemical product design: challenges and opportunities. Comput. Chem. Eng. 28, 2441e2457. http://dx.doi.org/10.1016/j.compchemeng.2004.08.010. Holm-Larsen, H., 2001. CO2 reforming for large scale methanol plants e an actual case. Stud. Surf. Sci. Catal. 441e446. IPCC, 2005. Carbon Dioxide Capture and Storage, Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, New York. IPCC, 2007. Climate Change 2007: Mitigation of Climate Change, Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, New York. Knudsen, J.N., Jensen, J.N., Vilhelmsen, P., 2007. First year operation experience with a 1 t/h CO2 absorption pilot plant at Esbjerg coal-fired power plant. In: European Congress of Chemical Engineering (ECCE-6). Copenhagen. Kongpanna, P., Pavarajarn, V., Gani, R., Assabumrungrat, S., 2014. Techno-economic evaluation of different CO2-based processes for dimethyl carbonate production. Chem. Eng. Res. Des. 93, 496e510. http://dx.doi.org/10.1016/j.cherd.2014.07.013. Kubota, T., Hayakawa, I., Mabuse, H., Mori, K., Ushikoshi, K., Watanabe, T., Saito, M., 2001. Kinetic Study of Methanol Synthesis from Carbon Dioxide and Hydrogen, pp. 121e126. Last, G., Schmick, M., 2011. Identification and Selection of Major Carbon Dioxide Stream Compositions. Pacific Northwest National Laboratory, Oak Ridge. Olah, G.A., Goeppert, A., Prakash, G.K.S., 2009. Beyond Oil and Gas: The Methanol Economy. Wiley-VCH, Weinheim. Quaglia, A., Sarup, B., Sin, G., Gani, R., 2012. Integrated business and engineering framework for synthesis and design of enterprise-wide processing networks. Comput. Chem. Eng. 38, 213e223. http://dx.doi.org/10.1016/j.compchemeng.2011.12.011. Saito, M., Fujitani, T., Takeuchi, M., Watanabe, T., 1996. Development of copper/zinc oxidebased multicomponent catalysts for methanol synthesis from carbon dioxide and hydrogen. Appl. Catal. A Gen. 138, 311e318. http://dx.doi.org/10.1016/0926-860X(95) 00305-3. US Department of State, 2010. U.S. Climate Action Report. US Department of State (US DOS). US DOS, Washington, DC.
References
Ushikoshi, K., Mori, K., Kubota, T., Watanabe, T., Saito, M., 2000. Methanol synthesis from CO2 and H2 in a bench-scale test plant. Appl. Organomet. Chem. 14, 819e825. http:// dx.doi.org/10.1002/1099-0739(200012)14, 12<819::AID-AOC90>3.0.CO;2-#. Wilcox, J., 2012. Carbon Capture. Springer, New York. http://dx.doi.org/10.1007/978-1-46142215-0. Zakkour, P., Cook, G., 2010. CCS Roadmap for Industry: High-purity CO2 Sources. Carbon Counts Company (UK) Ltd. Zondervan, E., Nawaz, M., de Haan, A.B., Woodley, J.M., Gani, R., 2011. Optimal design of a multi-product biorefinery system. Comput. Chem. Eng. 35, 1752e1766. http://dx.doi.org/ 10.1016/j.compchemeng.2011.01.042.
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