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Optimization of the Automotive Ammonia Fuel Cycle using P-graphs Donna Abrece Angeles, Kristian Ray Angelo Gonzales Are, Kathleen Aviso, Raymond R. Tan, and Luis Razon ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b01940 • Publication Date (Web): 14 Aug 2017 Downloaded from http://pubs.acs.org on August 15, 2017

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ACS Sustainable Chemistry & Engineering

Optimization of the Automotive Ammonia Fuel Cycle using P-graphs Donna A. Angeles1, Kristian Ray Angelo G. Are1, Kathleen B. Aviso1, Raymond R. Tan1, Luis F. Razon1* ¹ Chemical Engineering Department De La Salle University, 2401 Taft Avenue, 0922 Manila, the Philippines *corresponding author; e-mail: [email protected], ORCID: 0000-0002-0918-4411 Abstract Ammonia is a potential low-carbon alternative automotive fuel. However, it is produced commercially via the energy- and greenhouse gas-intensive Haber-Bosch process, and its nitrogen footprint may also detract from its environmental benefits. Thus, whether its use as an automotive fuel is sustainable from a life-cycle standpoint remains in question. In this study, a P-graph model is developed to determine the best well-to-wheel pathway for the use of ammonia as an automotive fuel, using carbon and nitrogen footprints as dual environmental criteria. Multiple fossil fuel-based and biomass-based ammonia production processes are considered, as well as different drivetrain configurations that include internal combustion engine vehicles (ICEVs) and fuel cell vehicles (FCVs). In the case of ICEVs, the model also considers the secondary fuels needed to allow ammonia use in existing engines. Solving the P-graph model identifies the optimal pathway as cyanobacteria-based ammonia production coupled with FCVs. This pathway has a carbon footprint of 4.96 g CO2 eq./km and a nitrogen footprint of 0.325 g reactive N/km. The model also identifies a cluster of near-optimal solutions, for which possible technology improvements are discussed. Keywords: Ammonia; life cycle optimization; P-graph; carbon footprint; nitrogen footprint

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Introduction Concerns about climate change and fossil fuel depletion have led to research interest in petroleum alternatives, particularly for use in transportation. Ammonia has been proposed as a low-carbon alternative fuel1. Unlike hydrogen, ammonia is relatively easy to store and handle, and has a higher energy density. In addition, the infrastructure for its production and distribution is well established. Ammonia may be used as a fuel either in internal combustion engine vehicles (ICEVs) or in fuel cell vehicles (FCVs). Interest in the use of ammonia in vehicles dates back to the mid-20th Century2,3. It has been tested extensively in ICEV in several countries4,5. In the literature, extensive data has been published for mixtures of ammonia with gasoline6, diesel fuel7, dimethyl ether (DME)8 and hydrogen9. Mixing ammonia with secondary fuels is necessary to overcome the low flame speed of ammonia, which creates combustion problems in conventional ICEVs. If the secondary fuel is carbon-based, then there are additional carbon emissions. Furthermore, increased NOx, N2O and NH3 emissions may result from the presence of additional reactive nitrogen during combustion. Alternatively, ammonia may be used in FCVs by inserting a reactor to crack or reform ammonia into N2 and H2 prior to utilization in the fuel cell10. Another major contributor to the environmental impact of ammonia fuel, on a life-cycle basis, is the upstream production process. Ammonia is produced commercially via the energy- and carbon-intensive Haber-Bosch process. The Haber-Bosch process requires nitrogen with hydrogen as inputs. Hydrogen gas is produced commercially via steam reforming or partial oxidation of natural gas or coal, both of which intrinsically produce CO2. Furthermore, the Haber-Bosch process requires high temperature and pressure. Because of this, and the rising demand for ammonia for use as agricultural fertilizer and for carbon capture, considerable research effort has been focused on alternative low-carbon methods for making ammonia11.


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In addition to carbon emissions in the ammonia life cycle, nitrogen footprint also needs to be considered as a major sustainability aspect11. The trade-offs between the reduction of tailpipe carbon emissions and the increased NOx and NH3 emission, coupled with the large emissions from the manufacture of ammonia, dictate the assessment of the environmental impact of fuel ammonia on a life-cycle basis. In other words, the impact of fuel ammonia must be assessed from “cradle-to-grave” to account for impacts that occur throughout the energy system . Such analysis can be done using life cycle assessment (LCA), an established methodological framework for the analysis of the environmental impacts of product systems. Its main components are: (1) Goal and scope definition; (2) Inventory analysis (LCI); (3) Impact assessment (LCIA) and (4) Interpretation. Cradle-to-gate LCA methodology has been applied to selected ammonia production methods12, without consideration of end use. In addition, the LCA concept has led to the development of footprint metrics to quantify specific sustainability aspects13. One limitation of LCA is its applicability only to systems of predefined configuration. Thus, life cycle optimization (LCO) has been proposed by combining LCA with mathematical programming; LCO is thus able to generate the optimal configuration of a system to achieve desirable levels of environmental performance. LCO initially made use of a linear programming (LP) formulation and was applied to the case of a boron-based product system14,15. A multi-objective LP (MOLP) extension was subsequently proposed16. A fuzzy LP (FLP) formulation was proposed by Tan et al.17 based on the computational framework of Heijungs and Suh18 to allow unique optima to be determined instead of Pareto optimal sets. This FLP approach was then modified using carbon, land and water footprints to optimize biofuel supply chains 19. A robust LCO model was proposed by Wang and Work20 to account for data uncertainties in novel technological systems. LCO models have been applied to renewable energy systems 21, shale gas supply chains22 and petrochemical complexes 23, etc. In practically all cases reported in the literature to date, LCO models have relied on equation-based mathematical programming methods. In this paper, an alternative approach based on a process graph (P-graph) framework is developed and applied to the 3 ACS Paragon Plus Environment


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optimization of ammonia-based automotive fuel systems. This is the first time the P-graph method has been applied to ammonia-based fuel systems. We are also unaware of any life-cycle analyses or lifecycle optimizations of ammonia-based fuel systems. P-graph methodology was developed to solve process network synthesis (PNS) problems typically encountered in the design of chemical engineering processes. P-graphs are bipartite graphs consisting of O-type vertices (depicted as horizontal bars) to represent process units and M-type vertices (depicted as dots) to represent streams. Arcs are used to link O-type and M-type vertices whenever process units are associated with streams. This framework allows unambiguous representation of relationships that exist in typical PNS problems. The P-graph framework is based on five axioms stated by Friedler et al.24.These axioms summarize fundamental information common to all PNS problems, and provide a basis for the development of rigorous and efficient solution techniques. P-graph methodology consists of the following algorithms: Maximal structure generation (MSG) – determination of the “maximal structure” (i.e., a mathematically rigorous superstructure)25; Solution structure generation (SSG) – determination of all combinatorially feasible solution structures that are subsets of the previously determined maximal structure Accelerated branch-and-bound (ABB) – determination of the optimal process network, given additional information (i.e., objective function and flowrate specifications).

P-graph methodology has some advantages over mathematical programming. Firstly, the algorithms described above enable the P-graph framework to be computationally efficient26. Secondly, it is possible for human error to occur when specifying a superstructure for a synthesis problem, leading to incorrectly defined problems. The MSG algorithm eliminates such errors27. Thirdly, SSG generates n-best solutions corresponding to the different network structures. This feature makes it easy to identify alternatives, which can facilitate decision-making. It is possible that the nominal optimum may only have negligible advantages over other near-optimal solutions28. Near-optimal solutions may also share general features that characterize good engineering solutions29, that are also robust30.


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Recent literature indicates a trend towards the use of P-graph methodology for problems that are structurally analogous to the traditional PNS problem31. In particular, the framework has been applied to the optimization of carbon management networks30 and supply chains32–34. P-graph based optimization of renewable energy supply chains considering multiple sustainability criteria has also been reported recently28,35.

The central premise of our work, which is based on life cycle principles, is that sustainability is a function of system configuration, and not just the local properties of component processes. Furthermore, the use of P-graph allows the process selection and system design to be automated based on a predefined sustainability target function. The systems chosen include the two most widely used commercial processes and two proposed process system for utilizing biomass. While there are other processes in the early stages of development, there is insufficient information to allow the assessment of carbon and nitrogen footprints on a life-cycle basis. This work focuses only on carbon and nitrogen footprints as the sustainability indicators, since these aspects are as the most critical aspects of the fuel ammonia supply chain as these are where trade-offs can be expected. Carbon footprint is closely linked to energy-intensive activities, while nitrogen footprint issues arise from the presence of reactive nitrogen in the energy system under consideration. The rest of this paper is organized as follows. The modelling framework is discussed in the next section. Next, the ammonia fuel life cycle system is described. Finally, the model solution is discussed in detail. Finally, conclusions and prospects for future work are given.



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System Definition An optimal system for the production, transport and use of ammonia involves the selection of individual processes from a number of process options. In this study, alternatives exist for the ammonia production and distribution processes, as well as the end-user vehicle type. The four ammonia production processes considered are two existing, fossil-fuel-based commercial processes and two proposed novel biomassbased processes. The two commercial processes are Haber-Bosch processes which use steam reforming (labeled as SMRF) and partial oxidation (labeled as PROX) for the production of synthesis gas. Steam reforming is commonly used when natural gas is the feedstock while partial oxidation is used when the available feedstock is coal. The biomass-based processes are based on those proposed by Razon36 and Ahlgren et al.37. The process in Razon36 (labeled as ANAB) proposes to cultivate the cyanobacteria Anabaena ATCC 33047 in raceway ponds, produce biogas from the cyanobacterial biomass and recover ammonium sulfate from the biogas digestate. The ammonium sulfate is subsequently decomposed to recover pure ammonia38. The other biomass-based process is an alternative means of syngas production which uses Salix (short rotation willow coppice) to produce ammonium nitrate37. We removed the ammonium nitrate production step from their process and replaced it with compression and liquefaction of gaseous ammonia. The ammonia produced is transported via conventional freight transport (APMT).

To use ammonia in an ICEV, a secondary fuel is necessary and the production and transport of these fuels are also necessary for the completion of the life-cycle assessment. We considered three secondary fuels that were tested by Kong and his co-workers: gasoline, diesel and dimethyl ether, produced by existing commercial processes. The fuels are distributed via freight transport. The fuels are used with ammonia in light-duty ICEV. Emissions profiles are obtained from Reiter and Kong7 for ammonia-diesel, Gross and Kong8 for ammonia-DME and Ryu et al.6 for ammonia-gasoline. In cases where a secondary fuel is required, we select the upper limit of ammonia blending that still allows reasonable engine performance. The emissions considered were CO2, CH4, N2O, NOx and NH3. The different emissions are translated into


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carbon and nitrogen footprints using the characterization factors in Table S1 in the Supporting Information.

The life cycle system is shown in Figure 1. For simplicity, the processes within the system are divided into three major stages. The relevant energy and material flows for all key processes are given in Tables S2 and S3 in the Supporting Information.

Figure 1. The superstructure of the system. The task is to find the optimal path to obtain 1 km of transport service from fuel production, transport and use alternative combinations. ANAB = Anaebana process from Razon36,38, SALI = Salix process from Ahlgren37, SMRF = steam reforming, GAPR = gasoline production, DIPR = diesel production, DMPR = dimethyl ether (DME) production, APMT = ammonia transport, GATR = gasoline transport, DITR = diesel transport, DMTR = DME transport, FCV = fuel cell vehicle operation, GAVO = gasoline vehicle operation, DIVO = diesel vehicle operation, DMVO = DME vehicle operation

Any given process within the system can be represented in conventional block diagram form. Figure 2a represents the ammonia-gasoline vehicle operation (GAVO) process, which can then be translated into Pgraph form as shown in Figure 2b. The main process of the ammonia-gasoline vehicle operation is



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represented by the horizontal bar labelled GAVO. The input streams corresponding to ammonia and gasoline are represented by nodes T_NH3 and T_GAS respectively, with the flow rates shown beside the arcs directed from these nodes towards process GAVO. Consequently, the output stream of 1 km transport service is represented by a stream coming out of process GAVO and terminating in the node labelled Transport Service. In addition, two nodes located at the top of Figure 2b are included to account for the carbon and nitrogen footprints of the gaseous emissions. Since the emissions contribute to the carbon and nitrogen footprints at varying degrees, the characterization factors, as given in Table S1 in the Supporting Information, are associated with the arcs stemming from the footprint nodes and leading to fictitious process units representing each type of emission. For example, each kg of nitrous oxide (N2O) is equivalent to 298 kg of CO2. Thus, the arc from the carbon footprint node leading to the nitrous oxide horizontal bar has a rate of 298 kg. Note that this approach can be readily extended to include more sustainability indicators as needed.

The nodes corresponding to each type of emission (e.g. CO2, CH4, N2O, etc.) are then linked to the process units (e.g. GAVO) with flow rates based on the emission generated at each phase of the life cycle, as indicated in Tables S2 and S3 in the Supporting Information. GAVO for example, generates 0.053 kg of CO2 for each km of transport service. Similar representation can be done for all other individual process units. Once these components are specified, the MSG algorithm can be used to generate a maximal structure that contains all possible network structures for the optimization problem. Due to space constraints, the P-graph maximal structure corresponding to Figure 1 is given in the Supporting Information (Figure S1). The task then is to determine the optimal system configuration, such that the carbon and nitrogen footprints per km travelled by a representative vehicle are minimized. This was done using P-graph Studio Version 5.2.0.739. The resulting P-graph model is equivalent to the MILP formulation given in the Tables S4 and S5 in the Supporting Information.


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(a)

(b) Figure 2. Ammonia-gasoline vehicle shown in (a) conventional block diagram form and (b) P-graph representation

Results and Discussion The simultaneous minimization of carbon and nitrogen footprints requires handling two potentially conflicting objectives. The resulting bi-objective problem can then be solved using ε-constraint method40 to trace the Pareto frontier of non-dominated solutions. This method transforms a vector optimization problem into a single-objective problem by selecting one key objective function, and converting the other objectives into constraints. The resulting problem is then solved repeatedly for different numerical values 9 ACS Paragon Plus Environment


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for each new constraint. Such an approach can be implemented within the P-graph framework28. The system is optimized with the objective of minimizing the nitrogen footprint at a specified carbon footprint limit, which results in 16 feasible solution structures with the minimum nitrogen footprint of 0.325 g of reactive N/km of transport service. The detailed description of all 16 solutions is in Table S6 in the Supporting Information (Figures S2-S17). Two solution structures result in this minimal nitrogen footprint, with both solutions selecting the FCV option. One solution (solution 5), however, uses ammonia produced from the Haber-Bosch process with partial oxidation, while the other solution (solution 2) uses ammonia produced from the Anabaena process. Among all solutions, solution 2 also has the lowest carbon footprint of 4.96 g of CO2 eq./km of transport service. This optimal solution is illustrated in Figure 3.

Figure 3. Optimal solution with lowest carbon and nitrogen footprint

A plot of the performance of all 16 solutions with respect to carbon footprint and nitrogen footprint is shown in Figure 4. In Figure 4, the “best’ solutions combining both low nitrogen and low carbon footprints would be those nearest the origin, on the lower left. Figure 4 shows that there are three different clusters of solutions: Cluster A are solutions which exhibit low nitrogen and low carbon 10 ACS Paragon Plus Environment

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footprints, Cluster B consists of solutions with high carbon footprints and moderate nitrogen footprints while Cluster C are solutions with low carbon footprints but high nitrogen footprints. All structures in Cluster B make use of the ammonia-DME ICEV while those in Cluster C make use of the ammoniadiesel ICEV. The former cluster has a high average carbon footprint due to an increase in life cycle emissions of CH4 and N2O, which offsets any benefits gained from displacement of fossil fuel. The solution structures within clusters B and C differ only in the type of ammonia production process. Cluster A, on the other hand, consists of solutions that make use of either the FCV (points 1, 2, 5 and 6) or the gasoline-ammonia ICEV (points 3, 4, 7 and 8).

Figure 4. Plot of carbon and nitrogen footprint of 16 feasible solutions. The detailed description of the all of the solutions is in Table S6 in the Supporting Information.

The optimal solution given by solution 2 is superior to all other solutions in the cluster in terms of nitrogen footprint, although the relative magnitudes are comparable. On the other hand, this solution has 11 ACS Paragon Plus Environment


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an extremely low carbon footprint that is orders of magnitude smaller than those of competing options. A summary of the flows and the P-graph figures for the 16 solution structures is provided in the Supporting Information. The best solution using a commercially available ammonia production technology is solution 3, which uses steam reforming and an FCV. The best solution using an ICEV is solution 3, which uses the ANAB process for ammonia and gasoline as the secondary fuel. The distributions of the carbon and nitrogen footprints across the different stages of the life cycle for the eight solution structures in Cluster A were also calculated. The results for the carbon footprint are shown in Figure 5 while those for nitrogen footprint are in Figure 6. The carbon footprint primarily occurs during the fuel production stage of the life cycle while the contribution of the fuel transport phase is negligible. The nitrogen footprint, on the other hand, is primarily attributed to the fuel use phase of the life cycle. It should be noted that, in many cases, such emissions result from emissions of traces of unburned ammonia from engines designed and optimized to run on petroleum-based fuels. Since poor combustion at high ammonia ratios also causes an increase in other critical emissions (i.e., CH4, N2O, and NOx), this result suggests that improvements in engine technology will be needed to reduce emissions generated during ammonia combustion. Conclusions In this work, the automotive ammonia fuel cycle has been optimized based on carbon and nitrogen footprints using a P-graph modeling approach. The LCO identifies the production of ammonia using the biomass-based Anabaena process, coupled with its use in FCVs, is the optimal pathway for transportation use given currently available information. Further analysis with the P-graph model also identifies clusters of fuel cycle pathways with comparable levels of performance. An analysis of the non-optimal solutions shows that vehicle emissions resulting from poor combustion conditions, occurring within engines designed to run on petroleum-based fuels, are major contributors to both carbon and nitrogen footprints.


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Future work can focus on utilizing the same modeling framework developed here, using additional data on process and vehicle performance as new results are published in experimental literature. Furthermore, the inherent uncertainty that occurs in modeling novel and speculative technological systems can be addressed using methods such as Monte Carlo simulation or possibilistic fuzzy sets, which can allow for a more nuanced comparison of competing technologies. Also, while this work has focused only on carbon and nitrogen footprints, a more comprehensive sustainability assessment can be done to include additional environmental footprint metrics13. In particular, land and water footprint indices for biomass-based ammonia production systems may result in alternative optima, depending on the scarcity of land and water resources in specific geographic areas and the high land and water inputs necessary for biomassbased systems. Also, occupational health and safety risks associated with storing, handling and transporting ammonia can also be integrated within this LCO

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. Including more sustainability metrics

would then naturally require addressing the appropriate weights given to each sustainability metric to arrive at a single score.

Figure 5. Distribution of carbon footprint between life cycle phases for solutions in Cluster A.



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Figure 6. Distribution of nitrogen footprint between life cycle stages

Supporting Information P-graph superstructures and solution structures. Tables of data used for models. Table containing the equivalent MILP formulation to the P-graph model. Description of the 16 feasible solutions. Acknowledgements The authors thank Prof Song-Charng Kong of Iowa State University for information about ammonia fuel in internal combustion engines. K. Are and D. Angeles acknowledge financial support from the Engineering Research and Development for Technology Program of the Philippines’ Department of Science and Technology.

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Synopsis: Optimum pathway for production and use of ammonia as automotive fuel is determined using a P-graph life-cycle optimization model.


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Optimization of the Automotive Ammonia Fuel ... - ACS Publications

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