Strategic Planning for the Supply Chain of Aviation Biofuel with

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Strategic Planning for the Supply Chain of Aviation Biofuel with Consideration of Hydrogen Production Saul Domínguez-García, Claudia Gutiérrez-Antonio, Julio Armando De Lira-Flores, José María Ponce-Ortega, and Mahmoud M El-Halwagi Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b02632 • Publication Date (Web): 24 Oct 2017 Downloaded from http://pubs.acs.org on October 28, 2017

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Industrial & Engineering Chemistry Research

Strategic Planning for the Supply Chain of Aviation Biofuel with Consideration of Hydrogen Production Saúl Domínguez-García, 1 Claudia Gutiérrez-Antonio, 2 Julio Armando De Lira-Flores, 2 José María Ponce-Ortega 1* Mahmoud M. El-Halwagi 3,4 1

Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, 58060, México.

2

Faculty of Chemistry, Universidad Autónoma de Querétaro, Querétaro, Querétaro, 76010, México.

3

Chemical Engineering Department, Texas A&M University, College Station, TX, 77843, USA. 4

Adjunct Faculty at the Chemical and Materials Engineering Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding author: J.M. Ponce-Ortega E-mail: [email protected]

Tel. +52 443 3223500 ext. 1277 Fax. +52 443 3273584

ACS Paragon Plus Environment


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Abstract Substitution of petro-based aviation fuel with biomass-derived aviation fuel is an emerging strategy to reduce the carbon footprint associated with the aviation sector. There are several pathways for the production of aviation biofuel, and most of them require the use of hydrogen. Therefore, the analysis of the aviation biofuel supply chain must incorporate the production of hydrogen. This paper presents an optimization approach for the strategic planning of aviation fuel supply chains, which considers hydrogen production from fossil and renewable raw materials. The approach also considers extraction of fossil materials, growth of biomass, selection and several processing routes of the feedstock, along with the distribution of products. As case study, the strategic planning of aviation biofuels in Mexico considering the generation of biomass and the hydrogen production is selected. The results show that significant decreases in producing costs and CO2 emissions can be obtained if aviation fuel is generated from renewable raw materials. This finding is quite important, since in Mexico 90% of the consumed energy proceed from fossil sources. Several scenarios are addressed to assess the key factors in the design of the supply chain, reconciling the economic and environmental objectives; and also an analysis for the integration of the infrastructures of the fossil fuels and biorefineries is presented. Keywords: Aviation biofuel, biohydrogen, supply chain, optimization.


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1. Introduction The climate change has caused a number of natural disasters around the world, which includes floods, tsunamis, and droughts. Several scientific studies have indicated that the main cause of climate change is the excessive emission of greenhouse gases (GHGs), particularly carbon dioxide. In order to address the climate change crisis, the Paris Agreement (or Paris Climate Accord) sets the objective of substantial reduction in GHG emissions.1 One of the main sources of CO2 emissions is the use of fossil fuels for transportation. While automotive transportation is the largest source of emissions, aviation transport of passengers and goods account for 2% and 3% of the global CO2 emissions, respectively.2-4 Furthermore, a forecast indicates that by 2050, the aviation sector (domestic, international and shipping) is expected to contribute between 10%–32% of the total CO2 emissions.4 In this context, the relatively large contribution of fossil-based aviation fuels to CO2 emissions has promoted a growing trend to identify other fuels with lower carbon footprints. Towards this objective, it has been recommended to use blends of conventional jet fuels with other fuels. For instance, Al-Nuaimi et al.5 proposed an optimization-based framework for blending jet fuel with synthetic fuel derived from gas-toliquid (GTL) operation. Hong et al.6 suggested the use a mixture with different ratios of conventional aircraft fuel with a biofuel, which satisfies the physicochemical properties required for the aviation fuel. There are several pathways for converting biomass to aviation fuel, and most of them require the use of hydrogen. Since the objective of substituting fossil-based fuel with biofuel is to reduce the carbon footprint, it is important to identify hydrogen production routes with low carbon footprints. The hydrogen is usually production through reforming of natural gas; however, this processing route is associated with substantial CO2 emissions.7-9 On the other hand, the use of biomass to produce hydrogen offers a lower carbon footprint. Different processes have been proposed to produce biomass-derived hydrogen or “biohydrogen”.10,11 Pan et al.12 investigated the production of biohydrogen through the fermentation of wheat bran, while Fan et al.13 analyzed the conversion of corn to biohydrogen by anaerobic cultures.



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In order to produce renewable aviation fuel with low carbon footprint, all the required raw materials must to be generated through production processes with low carbon footprint. Here lies the importance of considering the production of hydrogen, along with the biomass generation in the aviation fuel supply chain. Thus, an effective supply chain for the aviation biofuel, and the assessment of the economic, environmental, and safety metrics can be obtained.14-16 Regarding the determination of the optimal supply chain for biofuels production several works have been reported in the literature. An analysis of several raw material crops for the production of biofuels was carried out by Sepulveda-González.17 Later, Moraes et al.18 carried out a comparative analysis of different types of raw materials to offer a methodical basis for screening and selection. El-Halwagi et al.19 used a multiobjective optimization framework for designing hydrogen production biorefineries and supply chains, while accounting for safety and economic objectives. Also, El-Halwagi20 proposed a metric for reconciling economic and other sustainability metrics in the design of processing facilities. The exploration of various processing routes for the production of aviation biofuel was reported by Chiaramonti et al.21 Santibañez-Aguilar et al.22-24 presented a study on the strategic planning of bioethanol and biodiesel supply chains. Moreover, several studies related to production of hydrogen have been carried out. Guillén-Gonsálbez et al.29,30 developed an optimization model for the supply chain of hydrogen production considering the market uncertainty. Martín et al.28 analyzed the production of hydrogen from switch grass, in Spain, using an optimization model. Also, Martín and Grossman28,31 analyzed the integrated production of biohydrogen and liquid biofuels. All the previously presented works are valuable research efforts; however, they lack in at least one of the following issues: •

The strategic planning for the supply chain associated with the aviation biofuels does not account for the needed hydrogen in the production process. It should be noticed that most of the reported processes for the aviation biofuel production require significant usage of hydrogen. This hydrogen can be fossil-based hydrogen or biohydrogen. This synergic relationship has not been properly addressed in the previous works.


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•

Minimization of the carbon footprint on a life-cycle basis for the whole supply chain for aviation biofuels.

• The consideration of feedstock type, availability, selection and processing routes for the production of biohydrogen. In order to address the foregoing limitations, the present paper introduces an optimization approach for the strategic planning of the aviation biofuel supply chain while incorporating the use and production of renewable and fossil hydrogen. The supply chain to produce hydrogen is integrated with the aviation biofuel supply chain. Several biomass sources and processing routes are considered for the production of biohydrogen. Finally, economic and environmental objectives are used to optimize the design of the supply chain.

2. Problem statement Given a certain region with the following known information: •

Demands for biomass-based aviation fuel. Production over the minimum demand may be exported and shortages to meet the minimum demand may be compensated through importation.

•

Amount of hydrogen required for the production of aviation fuels.

•

Available supply of fossil aviation fuels in the region.

•

Available land for the production of different types of biomass feedstock that can be used for aviation biofuel and biohydrogen production.

•

Candidate pathways for the processing of biomass to produce aviation fuels.

•

A transportation roadmap which can facilitate the transfer of feedstock, intermediates, byproducts, and main product.

The objective is to design a supply chain that utilizes biomass and fossil materials to produce cost-effective and environmentally friendly solutions.

3. Methodology Figure 1 shows a schematic representation of the addressed problem, which shows the reserves of fossil energy and the production of biomass in the considered region (e.g., Mexico). This representation considers the possibility of installing new biorefineries and biohydrogen production plants. The excess production of aviation biofuel can be exported, whereas the deficiency in meeting the regional demand can be overcome through



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importation. The following section describes the key building blocks of the optimization model. 3.1 Use of land for cultivation Area of cultivation The total used area ( Ai ,m ) for cultivation of any raw material ( m) in the site ( i) is equal to the sum of the existing area used for cultivation ( AiExist plus the new area ( AiNew ,m ) ,m ) needed to produce the biomass demand in each biorefinery. New Ai ,m = AiExist , m + Ai , m , ∀ i ∈ I , m∈ M

(1)

Production of biomass The amount of produced biomass (seeds) ( Pi , m ) in the cultivation sites ( i) for the raw material ( m) depends on the production factor ( βi ,m ) and the used area

(A ) i ,m

as

follow:

Pi , m = β i , m Ai , m , ∀ i ∈ I , m ∈ M The production factor for the biomass

(2)

(β ) i ,m

is determined by environmental

conditions and land composition. Delivery of biomass to biorefineries and biohydrogen plants The amount of produced biomass ( Pi , m ) is equal to the sum of the raw materials sent to the biorefineries ( FMBTi ,i1,m ) and biohydrogen plants ( FMBH i ,i 2,m ) : Pi ,m = ∑ FMBTi ,i1, m + ∑ FMBH i ,i 2,m , ∀ i ∈ I , m ∈ M i1

(3)

i2

3.2 Production and distribution of biohydrogen Balance of biomass sent to each biohydrogen production plant The raw material delivered to each biohydrogen production plant ( FMBHtotali 2,m ) is equal to the sum of the amount of biomass ( FMBH i ,i 2,m ) sent to the installation site of the plants. FMBHtotali 2, m = ∑ FMBH i ,i 2, m , ∀ i 2∈ I 2, m∈ M i


(4)

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Balance of biomass delivered to each processing route in any biohydrogen production plant The total amount of raw material processed in each biohydrogen plant ) processed in each route (rbh) . ( FMBHtotali 2,m ) is equal to the sum of biomass ( fmiroute 2,m ,rbh FMBHtotali 2, m = ∑ fmiroute , ∀ i1∈ I1, m∈ M 2,m ,rbh

(5)

rbh

Production of biohydrogen The amount of biohydrogen produced in each plant ( PBHi 2 ) is equal to the sum of processed biomass ( fmiroute ) multiplied by a conversion factor (γ i 2,m ,rbh ) . 2,m ,rbh PBH i 2 = ∑∑ fmiroute γ , ∀ i 2∈ I 2 2,m ,rbh i 2, m , rbh m

(6)

rbh

Distribution of the produced biohydrogen The biohydrogen produced in each plant is equal to the sum of the biohydrogen sent to biorefineries ( FBHBi 2,i1 ) and refineries ( FBHRi 2,k1 ) : PBH i 2 = ∑ FBHBi 2,i1 + ∑ FBHRi 2,k1 , ∀ i 2∈ I 2 i1

(7)

k1

Delivery of fossil fuels to refineries and hydrogen plants The amount of fossil fuels ( Fk ,n ) obtained in each extraction site is equal to the sum of the raw material sent to the refineries ( FfossilTk ,k1,n ) and hydrogen plants ( FfossilHk ,k 2,n ) : Fk ,n = ∑ FfossilTk ,k1,n + ∑ FfossilH k ,k 2,n , ∀ k ∈ K , n ∈ N k1

(8)

k2

3.3 Production and distribution of hydrogen Balance of fossil raw materials sent to each hydrogen production plants The raw material delivered to each hydrogen production plant ( FfossilHtotalk 2,n ) is equal to the sum of biomass ( FfossilH k,k 2,n ) sent to the site of the plant. FfossilHtotalk 2,n = ∑ FfossilH k,k 2,n , ∀ k 2∈ K 2, n ∈ N

(9)

k

Balance of fossil fuel delivered to each processing route in any hydrogen production plant



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The total amount of raw material processed in each hydrogen plant ( FfossilHtotalk 2,n ) is equal to the sum of all fossil mass ( fhkroute 2,n, rh ) processed in each route

(rh) . FfossilHtotalk 2,n = ∑ fhkroute 2,n, rh , ∀ k 2 ∈ K 2, n ∈ N

(10)

rh

Production of hydrogen The amount of hydrogen produced in each plant ( PH k 2 ) is equal to the sum of the fossil mass processed ( fhkroute 2, n , rh ) multiplied by a conversion factor (λk 2, n , rh ) : PH k 2 = ∑∑ fhkroute 2, n , rh λk 2, n , rh , ∀ k 2∈ K 2 n

(11)

rh

Distribution of produced hydrogen The hydrogen produced in each plant is equal to the sum of all flows of hydrogen sent to biorefineries ( FHBk 2,i1 ) and refineries ( FHRk 2,k1 ) . PH k 2 = ∑ FHRk 2,k 1 + ∑ FHBk 2,i1 , ∀ k 2∈ K 2 k1

(12)

i1

3.5 Total hydrogen supplied to biorefineries Biohydrogen supplied to each biorefinery The total amount of biohydrogen sent to each biorefinery ( BHBi1 ) is equal to the sum of hydrogen produced from all biohydrogen plants ( FBHBi 2,i1 ) . BHBi1 = ∑ FBHBi 2,i1 , ∀ i1 ∈ I1

(13)

i2

Hydrogen supplied to each biorefinery The total amount of hydrogen sent to each biorefinery ( HBi1 ) is equal to the sum of hydrogen flows from all hydrogen plants ( FHBk 2,i1 ) : HBi1 = ∑ FHBk 2,i1 , ∀ i1∈ I1

(14)

k2

Total hydrogen supplied to each biorefinery The total amount of hydrogen that arrives to each biorefinery ( HBtotali1 ) is equal to the sum of flows of biohydrogen ( BHBi1 ) and hydrogen of fossil origin ( HBi1 ) .

HBtotali1 = BHBi1 + HBi1 , ∀ i1∈ I1


(15)

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Balance of total hydrogen consumed in each biorefinery The total amount of hydrogen that arrives to each biorefinery must be consumed to avoid accumulation; then it is equal to the sum of all flows of biomass transformed into aviation biofuel ( f mroute , r ,i1 ) multiplied by a conversion factor (φm , r , i1 ) and a stoichiometric coefficient (υ m ,r ,i1 ) . HBtotali1 = ∑∑ υm , r ,i1 f mroute , r ,i1φm , r ,i1 , ∀ i1∈ I 1 m

(16)

r

3.6 Total hydrogen supplied to refineries Biohydrogen supplied to each refinery The total amount of biohydrogen sent to each refinery ( BHRk1 ) is equal to the sum of hydrogen flows from all hydrogen plants ( FBHRi 2,k1 ) . BHRk 1 = ∑ FBHRi 2,k1 , ∀ k ∈ K

(17)

i2

Hydrogen supplied to each refinery The total amount of hydrogen sent to each refinery ( HRk1 ) is equal to the sum of hydrogen flows coming from all hydrogen plants ( FHRk 2,k1 ) . HRk 1 = ∑ FHRk 2,k1 , ∀ k ∈ K

(18)

k2

Total hydrogen supplied to each refinery The total amount of hydrogen that arrives to each refinery ( HRtotalk1 ) is equal to the sum of flows of biohydrogen ( BHRk1 ) and hydrogen of fossil origin ( HRk1 ) .

HRtotalk1 = BHRk1 + HRk1 , ∀ k ∈K

(19)

Balance of total hydrogen consumed in each refinery The total amount of hydrogen that arrives to each refinery ( HRtotalk1 ) must be consumed to avoid accumulation; then it is equal to the sum of all flows of fossil materials route transformed into aviation fuel ( ftn,rt ,k1 ) multiplied by a conversion factor (δn,rt ,k1 ) and a

stoichiometric coefficient (τ n,rt ,k1 ) . HRtotalk 1 = ∑∑ τ n,rt ,k1 ftn,route rt ,k1δ n, rt ,k1 , ∀ k1∈ K 1 n

rt


(20)


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3.7 Production and distribution of aviation biofuel Balance of biomass sent to each biorefinery The total amount of raw material delivered to each biorefinery ( FMBTtotali1,m ) is equal to the sum of the amount of biomass ( FMBTi ,i1,m ) sent to the processing plant. FMBTtotali1,m = ∑ FMBTi ,i1,m , ∀ i1 ∈ I1, m∈ M

(21)

i

Balance of biomass delivered to each processing route in any biorefinery The total amount of raw material processed in each biorefinery ( FMBTtotali1,m ) is equal to the sum of the biomass ( fmroute ,r ,i1 ) processed through each route ( r ) . FMBTtotali1,m = ∑ f mroute , r ,i1 , ∀ i1∈ I 1, m ∈ M

(22)

r

Production of aviation biofuel The amount of aviation biofuel produced in each biorefinery ( Bi1 ) is equal to the route sum of all biomass processed ( fm,r.i1 ) multiplied by a conversion factor (φm,r ,i1 ) .

Bi1 = ∑∑ f mroute , r .i1 φm , r ,i1 , ∀ i1∈ I 1 m

(23)

r

Distribution of produced aviation biofuel The aviation biofuel produced in each biorefinery is equal to the sum of the flows of the biofuel sent to the national market (bi1,a ) and international market ( FBNIi1 ) . Bi1 = ∑ bi1, a + FBNI i1 , ∀ i1∈ I1

(24)

a

3.8 Production and distribution of aviation fuel of fossil origin Balance of fossil raw materials sent to each refinery The total amount of raw material delivered to each refinery ( FfossilTtotalk1,n ) is equal to the sum of the amount of fossil raw materials ( FfossilTk,k1,n ) sent to the processing plants. FfossilTtotalk 1,n = ∑ FfossilTk,k1, n , ∀ k1∈ K1, n ∈ N k

Balance of fossil raw materials delivered to each processing route in any refinery


(25)

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The total amount of raw material processed in each refinery (FfossilTtotalk1,n ) is route equal to the sum of the fossil raw material ( ftk1,n,rt ) processed in each route ( rt ) .

FfossilTtotalk1,n = ∑ ftkroute 1, n , rt , ∀ k1∈ K1, n ∈ N

(26)

rt

Production of aviation fuel of fossil origin The amount of aviation fuel produced in each refinery (Tfossilk1 ) is equal to the sum route of all fossil raw materials processed ( ftk1,n,rt ) multiplied by a conversion factor (δ n,rt ) .

Tfossilk 1 = ∑ ∑ ftkroute 1, n , rt δ n, rt , ∀ k1 ∈ K 1 n

(27)

rt

Distribution of produced aviation fuel The aviation fuel produced in each refinery is equal to the sum of all the flows sent to the national ( FTk1,a ) and international markets (FTNIk1 ) . Tfossilk 1 = ∑ FTk 1,a + FTNI k 1 , ∀ k1 ∈ K1

(28)

a

3.9 Production and distribution of byproducts Production of byproducts The amount of byproduct ( j ) produced in each biorefinery (Si1, j ) is equal to the sum of the processed biomass ( fi1,route m , r ) multiplied by a conversion factor (α m , r , j ) . Si1, j = ∑∑ fi1,route m , r α m , r , j , ∀ i1 ∈ I 1, j ∈ J m

(29)

r

Distribution of byproducts The amount of byproduct ( j ) produced in each refinery (Si1, j ) is equal to the sum of the flows of this product sent to the national market ( si1, j ,a ) and international market

( FSNIi1, j ) . Si1, j = ∑ si1, j ,a + FSNI i1, j , ∀ i1∈ I1, j ∈ J a

3.10 Balances of aviation biofuel and aviation fuel in the markets Balance of aviation biofuel in the national market


(30)


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The amount of aviation biofuel received in the national market ( BTNa ) must be equal to the sum of the flows sent from biorefineries (bi1, a ) . BTN a = ∑ bi1, a , ∀ a ∈ A

(31)

i1

Balance of aviation fuel of fossil origin in the national market The amount of aviation fuel of fossil origin received in the national market (TN a ) must be equal to the sum of the flows sent from refineries ( FTk1,a ) and international market

( FTINa ) . TN a = ∑ FTk 1,a + FTIN a , ∀ a ∈ A

(32)

k1

Total balance of aviation fuel received in the national market The total amount of aviation fuel received in the national market (TBa ) is equal to the sum of aviation biofuel ( BTNa ) and aviation fuel of fossil origin (TN a ) received in the national market.

TBa = BTN a + TN a , ∀ a ∈ A

(33)

Balance of aviation biofuel sent to international market The amount of aviation biofuel sent to international marker (BTNItotal ) is equal to the sum of all aviation biofuel sent to international market from each biorefinery ( FBNIi1 ) . BTNItotal = ∑ FBNI i1

(34)

i1

Balance of aviation fuel of fossil origin sent to international market The amount of aviation fuel of fossil origin sent to international marker (TNItotal ) is equal to the sum of the aviation fuel of fossil origin sent to the international market from each refinery ( FTNI k1 ) . TNItotal = ∑ FTNI k1

(35)

k1

Total balance of aviation fuel sent to international market The total amount of aviation fuel sent to international market (TI ) is equal to the sum of aviation biofuel ( BTNItotal ) and aviation fuel of fossil origin (TNItotal ) sent to international market.


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TI = BTNItotal + TNItotal

(36)

3.11Balance of byproducts in national and international markets Balance of byproducts received in the national market The amount of byproduct ( j ) received in the national market (TS j ,a ) is equal to sum of amounts of byproduct coming from each biorefinery (si1, j , a ) and those from international market ( FSIN j ,a ) . TS j , a = ∑ si1, j ,a + FSIN j ,a , ∀ a ∈ A, j ∈ J

(37)

i1

Balance of byproducts sent to international market The amount of byproduct ( j ) sent to international market ( SI j ) is equal to the sum of all the amount of this byproduct sent from each biorefinery ( FSNI j ,i1 ) . SI j = ∑ FSNI j ,i1 , ∀ j ∈ J

(38)

i1

3.12 Constraints Area constraint The used area for the production of biomass ( Ai ,m ) is constrained by the available area ( Aimax , m ) . It means that only it is possible to consider a limited area of land for the cultivation of raw material ( m) without a high environmental impact.

Ai ,m ≤ Aimax ,m , ∀ i ∈ I , m ∈ M

(39)

Aviation fuel demand The amount of aviation fuel received in the national market (TBa ) must be lower or equal than the total demand of this fuel in the national market ( DBamax ) .

TBa = DBamax , ∀ a ∈ A

(40)

Byproduct demand restrictions The amount of byproduct ( j ) received in the national market (TS j ,a ) must be lower or equal than the total demand of this byproduct in the national market ( DS max j ,a ) .



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TS j ,a ≤ DS max j ,a , ∀ j ∈ J , a ∈ A

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(41)

Constraint for fossil reserves The amount of fossil raw material ( n ) extracted in each site ( Fk ,n ) is limited by the available reserves in the site ( Fkmax ,n ) .

Fk ,n ≤ Fkmax ,n , ∀ k ∈ K , n∈ N

(42)

3.13 Capacity constraints of processing plants Binary variables for the activation of biorefineries The capacity of any biorefinery ( Bi1 ) has to be greater or equal than the minimum capacity of aviation biofuel production ( Bimin 1 ) , and lower or equal than the maximum capacity of aviation biofuel production ( Bimax 1 ) . The binary variable ( yi1 ) is used to associate the capital cost with the dimension of biorefinery. max Bimin 1 yi1 ≤ Bi1 ≤ Bi1 yi1 , ∀ i1∈ I 1

(43)

Binary variables for the existence of processing routes in each biorefinery The capacity of any processing route in each biorefinery ( fi1,route m , r ) has to be greater or min ) , and lower or equal than the minimum capacity of processing of raw material ( fi1,route m,r

) . The binary equal than the maximum capacity of processing of raw material ( fi1,routemax m,r variable ( zi1, m ,r ) is used to associate the capital cost with the capacity of biorefinery. min route max fi1,route zi1,m,r ≤ fi1,route zi1,m, r ∀ i1∈ I1, m∈ M , r ∈ R m,r m , r ≤ f i1, m , r

(44)

Binary variables for existence of refineries The capacity of any refinery (Tfossilk1 ) has to be greater or equal than the minimum capacity of aviation fuel production (Tfossilkmin 1 ) , and lower or equal than the maximum capacity of aviation fuel production (Tfossilkmax 1 ) . The binary variable (uk1 ) is used to associate the capital cost with the capacity of refinery. max Tfossilkmin 1 uk 1 ≤ Tfossilk1 ≤ Tfossilk1 uk 1 , ∀ k1∈ K1

Binary variables for the activation of processing routes in each refinery


(45)

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route The capacity of any processing route in each refinery ( ftk1,n, rt ) has to be greater or routemin equal than the minimum capacity of processing of raw material ( ftk1,n, rt ) , and lower or route max equal than the maximum capacity of processing of raw material ( ftk1,n, rt ) . The binary

variable (vk1,n,rt ) is used to associate the capital cost with the dimension of refinery. routemin route routemax ftk1,n, vk1,n,rt ∀ k1∈K1, n∈N , rt ∈RT rt vk1,n,rt ≤ ftk1,n,rt ≤ ftk1,n,rt

(46)

Binary variables for activation of the hydrogen production plants The capacity of any hydrogen plant ( PH k 2 ) has to be greater or equal than the minimum capacity of hydrogen of fossil origin ( PH kmin 2 ) , and lower or equal than the maximum capacity of hydrogen of fossil origin ( PH kmax 2 ) . The binary variable ( d k 2 ) is used to associate the capital cost with the capacity of process plant. max PH kmin 2 d k 2 ≤ PH k 2 ≤ PH k 2 d k 2 , ∀ k 2∈ K 2

(47)

Binary variables for activation of processing routes in each hydrogen production plant The capacity of any processing route in each hydrogen plant ( fhkroute 2,n, rh ) has to be greater or equal than the minimum capacity of processing of raw material ( fhkroutemin 2,n, rh ) , and max lower or equal than the maximum capacity of processing of raw material ( fhkroute 2,n, rh ) . The

binary variable (ek1,n,rh ) is used to associate the capital cost with the dimension of process plant. route routemax fhkroutemin 2,n, rh ek 2,n, rh ≤ fhk 2,n,rh ≤ fhk2,n,rh ek1,n, rh ∀ k 2∈K 2, n∈ N , rh∈RH

(48)

Binary variables for activating the biohydrogen production plants The capacity of any biohydrogen plant ( PBH i 2 ) has to be greater or equal than the minimum capacity of biohydrogen production ( PBH imin 2 ) , and lower or equal than the maximum capacity of biohydrogen production ( PBH imax 2 ) . The binary variable ( wi 2 ) is used to associate the capital cost with the dimension of process plant. max PBH imin 2 wi 2 ≤ PBH i 2 ≤ PBH i 2 wi 2 , ∀ i 2∈ I 2


(49)


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Binary variables for activation of processing routes in each production plant of biohydrogen The capacity of any processing route in each biohydrogen plant ( fmiroute 2, m, rbh ) has to be min greater or equal than the minimum capacity of processing of raw material ( fmiroute 2, m, rbh ) , and max lower or equal than the maximum capacity of processing of raw material ( fmiroute 2, m, rbh ) . The

binary variable ( xi 2,m,rh ) is used to associate the capital cost with the dimension of process plant. route routemax fmiroutemin 2,m,rbh xi 2,m,rbh ≤ fmi 2, m, rbh ≤ fmi 2,m,rbh xi 2, m, rh ∀ i 2∈I 2, m∈M , r ∈RBH

(50)

3.13 Costs Feedstock cost The total feedstock cost (Cost feedstock ) is equal to the sum of all biomass processed

( Pi , m ) multiplied by the unitary cost of biomass (UCi feedstock ) , plus the sum of all fossil mass ,m ). processed ( Fk ,n ) multiplied by the unitary cost of fossil mass (UCfossilkfeedstoks ,n

Cost feedstock = ∑∑ Pi ,m UCi ,feedstock + ∑∑ Fk ,nUCfossilkfeedstoks m ,n i

m

k

(51)

n

Processing cost of biomass for producing aviation biofuel proces sin g ) is The processing cost of biomass for aviation biofuel production (Costavitionbiofuel

equal to the sum of the processed biomass ( fi1,route m , r ) multiplied by the unitary processing cost sin g (UCbtiproces ). 1.m.r proces sin g proces sin g Costaviationbiofuel = ∑∑∑ fi1,route m, rUCbti1.m.r i1

m

(52)

r

Processing cost of biomass for biohydrogen production proces sin g ) is equal The processing cost of biomass for biohydrogen production (Costbiohydrogen

to the sum of the processed biomass ( fmiroute 2, m, rbh ) multiplied by the unitary cost of processing sin g (UCbhiproces ). 2.m.rbh


Page 17 of 61

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proces sin g proces sin g Costbiohydrogen = ∑∑∑ fmiroute 2, m, rbhUCbhi 2.m.rbh i2

(53)

m rbh

Processing cost of fossil mass for producing aviation fuel proces sin g The processing cost of fossil mass for aviation fuel production (Costaviation fuel ) is

equal to the sum of the processed fossil mass ( ftkroute 1,n, rt ) multiplied by the unitary cost of sin g ). processing (UCtkproces 1.n.rt proces sin g route proces sin g Costaviation fuel = ∑∑∑ ftk1,n, rtUCtk 1.n.rt k1

n

(54)

rt

Processing cost of fossil mass for hydrogen production proces sin g ) is equal The processing cost of biomass for biohydrogen production (Costhydrogen

to the sum of the processed biomass ( fhkroute 2,n, rh ) multiplied by the unitary cost of processing sin g (UChkproces ). 1.n.rh proces sin g proces sin g Costhydrogen = ∑∑∑ fhkroute 2,n, rhUChk 2,n, rh k2

n

(55)

rh

Total cost of processing proces sin g ) is equal to the sum of biomass The total cost of feedstock processing (Costtotal

processing cost and fossil mass processing cost for hydrogen and aviation fuel production. proces sin g proces sin g proces sin g proces sin g proces sin g Costtotal = Cost jet + Costhydrogen + Costbiohydrogen + Costbiojet fuel fuel

(56)

Transportation cost for biomass to biorefineries The transportation cost of biomass to biorefineries (CTMB) is equal to the sum of the biomass sent to biorefineries ( FMBTi ,i1,m ) multiplied by the unitary transportation cost between the cultivation site and processing sites (UCTMBi ,i1,m ) . CTMB = ∑∑∑ FMBTi ,i1, mUCTMBi ,i1, m i

i1

(57)

m

Transportation cost of biomass to biohydrogen production plants The transportation cost of biomass to biohydrogen production plants (CTMH ) is equal to the sum of the biomass sent to the hydrogen production plants ( FMBH i ,i 2, m )



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multiplied by the unitary transportation cost between the cultivation site and installation site of hydrogen production plants (UCTMH i ,i 2,m ) .

CTMH = ∑∑∑ FMBHi ,i 2,mUCTMH i ,i 2,m i

i2

(58)

m

Transportation cost for fossil mass to refineries The transportation cost of fossil resources to refineries (CTNR) is equal to the sum of all fossil mass sent to all refineries ( FfosilTk ,k1,n ) multiplied by the unitary transportation cost between the extraction site and installation site of refineries (UCTNRk ,k1,n ) .

CTNR = ∑∑∑ FfosilTk ,k1,nUCTNRk , k1,n k1

k

(59)

n

Transportation cost for fossil resources to hydrogen production plants The transportation cost of fossil resources to hydrogen production plants (CTNH ) is equal to the sum of all fossil mass sent to all hydrogen production plants ( FfossilH k .k 2.n ) , multiplied by the unitary transportation cost between the extraction site and installation site of hydrogen production plant (UCTNH k ,k 2,n ) .

CTNH = ∑∑∑ FfossilH k .k 2.nUCTNH k ,k 2,n k2

k

(60)

n

Transportation cost for aviation biofuel to national market The transportation cost for the aviation biofuel to national market (CTB) is equal to the sum of the aviation biofuel sent to national market (bi1, a ) multiplied by the unitary transportation cost between the installation site of biorefineries and the market (UCTBi1,a ) .

CTB = ∑∑ bi1,aUCTBi1,a i1

(61)

a

Transportation cost for byproducts to national market The transportation cost of byproducts to national market (CTS ) is equal to the sum of the byproducts sent to the national market (si1, j , a ) multiplied by the unitary


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transportation cost between the installation site of biorefineries and the final site of consumption (UCTSi1, j ,a ) .

CTS = ∑∑∑ si1, j ,aUCTSi1, j ,a i1

j

(62)

a

Transportation cost of aviation fuel of fossil origin to national market The transportation cost of aviation fuel of fossil origin to national market (CTT ) is equal to the sum of the aviation fuel sent to national market ( FTk1,a ) multiplied by the unitary transportation cost between the refineries and the market (UCTTk1, a ) .

CTT = ∑∑ FTk1,aUCTTk1,a k1

(63)

a

Transportation cost of aviation fuel from international to national market The transportation cost of aviation fuel from international market to national market

(CTBI ) is equal to the sum of all aviation fuel received in each national market ( FTINa ) multiplied by unitary transportation cost (UCTTI a ) .

CTBI = ∑ FTINaUCTTI a

(64)

a

Transportation cost of byproducts from international to national market The transportation cost of byproducts from international to national market (CTSI ) is equal to the sum of all byproducts received in each national market ( FSIN j ,a ) multiplied by unitary cost of transportation (UCTSI j ,a ) .

CTSI = ∑∑ FSIN j ,aUCTSI j ,a j

(65)

a

Transportation cost of aviation biofuel sent to international market The transportation cost of aviation biofuel sent to international market (CTBE ) is equal to the sum of the aviation biofuel sent from biorefineries to international market

( FBNIi1 ) multiplied by unitary transportation cost (UCTBEi1 ) .



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CTBE = ∑ FBNIi1UCTBEi1

Page 20 of 61

(66)

i1

Transportation cost of aviation fuel of fossil origin sent to international market The transportation cost of aviation fuel of fossil origin sent to international market

(CTTE ) is equal to the sum of the aviation fuel sent from each refinery to international market ( FTNI k1 ) multiplied by unitary transportation cost (UCTTEk1 ) .

CTTE = ∑ FTNI k1UCTTEk1

(67)

k1

Transportation cost of byproducts sent to international market The transportation cost of byproducts sent to international market (CTSE ) is equal to the sum of all byproducts sent from each biorefinery to international market ( FSNI j ,i1 ) multiplied by the unitary transportation cost (UCTSE j ,i1 ) .

CTSE = ∑∑ FSNI j ,i1UCTSE j ,i1 j

(68)

i1

Transportation cost of biohydrogen to biorefineries The transportation cost of biohydrogen to biorefineries (CTBHB) is equal to the sum of the biohydrogen sent to biorefineries ( FBHBi 2,i1 ) multiplied by the unitary transportation cost (UCTBHBi 2,i1 ) .

CTBHB = ∑∑ FBHBi 2,i1UCTBHBi 2,i1 i2

(69)

i1

Transportation cost of biohydrogen to refineries The transportation cost of biohydrogen to refineries (CTBHR) is equal to the sum of the biohydrogen sent to refineries ( FBHRi 2,k1 ) multiplied by the unitary transportation cost

(UCTBHRi 2,k1 ) . CTBHR = ∑∑ FBHRi 2,k1UCTBHRi 2,k1 i2

k1

Transportation cost of hydrogen of fossil origin to biorefineries


(70)

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The transportation cost of hydrogen of fossil origin to biorefineries (CTHB) is equal to the sum of the hydrogen sent to the biorefineries ( FHBk 2,i1 ) multiplied by the unitary transportation cost (UCTHBk 2,i1 ) .

CTHB = ∑∑ FHBk 2,i1UCTHBk 2,i1 k2

(71)

i1

Transportation cost of hydrogen of fossil origin to refineries The transportation cost of hydrogen of fossil origin to refineries (CTHR) is equal to the sum of the hydrogen sent to the refineries ( FHRk 2,k1 ) multiplied by the unitary transportation cost (UCTHRk 2,k1 ) .

CTHR = ∑∑ FHRk 2, k1UCTHRk 2, k1 k2

(72)

k1

Total transportation cost transport ) is equal to the sum of costs of raw The total transportation cost (Costtotal

material, hydrogen, aviation fuel and byproduct transportation as follows: transport Costtotal = CTMB + CTMH + CTNR + CTNH + CTB + CTS + CTT + CTBI + CTSI

(73)

CTBE + CTTE + CTSE + CTBHB + CTBHR + CTHB + CTHR Capital cost by installation of biorefineries The capital cost for installing biorefineries (CCB) is equal to the sum of binary variables of activation ( yi1 ) multiplied by the installation base cost (CBiB1 ) , plus the sum of the binary variables of activation for each processing route ( zi1, m.r ) multiplied by the installation base cost of each route (CRiB1,m ,r ) , and plus the sum of the processed raw route ) multiplied by a sizing parameter (CDiB1,m,r ) . material to produce aviation biofuel ( fi1,m.r

route CCB = ∑ CBiB1 yi1 + ∑∑∑ CRiB1,m,r zi1,m.r + ∑∑∑ CDiB1,m,r fi1,m.r i1

i1

m

r

i1

m

(74)

r

Capital cost for installing biohydrogen production plants The capital cost for installing biohydrogen production plants (CCBH ) is equal to the sum of binary variables of activation ( wi 2 ) multiplied by the installation base cost



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(CBiBH 2 ) , plus the sum of the binary variables of activation for each processing route ( xi 2,m,rbh ) multiplied by the installation base cost of each route (CRiBH 2,m, rbh ) , plus the sum of the processed raw material for producing biohydrogen ( fmiroute 2, m.rbh ) multiplied by a sizing parameter (CDiBH 2, m.rbh ) . BH BH route CCBH = ∑ CBiBH 2 wi 2 + ∑∑∑ CRi 2, m , rbh xi 2, m , rbh + ∑∑∑ CDi 2, m.rbh fmi 2, m.rbh i2

i2

i2

m rbh

(75)

m rbh

Capital cost for installing refineries The capital cost for installing refineries (CCR) is equal to the sum of binary variables of activation (uk1 ) multiplied by the installation base cost (CBkR1 ) , plus the sum of the binary variables of activation for each processing route (vk1,n,rt ) multiplied by the installation base cost of each route (CRkR1,n, rt ) , plus the sum of the processed raw material to R produce aviation fuel ( ftkroute 1, n , rt ) multiplied by a sizing parameter (CDk 1, n , rt ) .

CCR = ∑ CBkR1uk1 + ∑∑∑ CRkR1,n,rt vk1,n,rt + ∑∑∑ CDkR1,n, rt ftkroute 1, n , rt k1

k1

n

k1

rt

n

(76)

rt

Capital cost for installing hydrogen production plants The capital cost for installing hydrogen production plants (CCH ) is equal to the sum of binary variables of activation ( d k 2 ) multiplied by the installation base cost (CBkH2 ) , plus the sum of the binary variables of activation for each processing route (ek 2,n,rh ) multiplied by the installation base cost of each route (CRkH2, n, rh ) , plus the sum of the processed raw material to produce hydrogen ( fhkroute 2, n , rh ) multiplied by a sizing parameter

(CDkH2,n,rh ) .

CCH = ∑ CBkH2 d k 2 + ∑∑∑ CRkH2,n, rh ek 2,n, rh + ∑∑∑ CDkH2,n ,rh fhkroute 2, n , rh k2

k2

n

rh

k2

n

rh

Total capital cost


(77)

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The total capital cost (CapCost ) is equal to the sum of the capital cost for installing of biorefineries, biohydrogen production plants, refineries and hydrogen production plants as follows:

CapCost = CCB + CCBH + CCR + CCH

(78)

Import charges for aviation fuel and byproducts Import charges for aviation fuel and byproducts ( InternationalCost ) is equal to the sum of all aviation fuel received in the national market from the international one ( FTINa ) multiplied by the international price of aviation fuel and byproducts (UCTI ) , plus the sum of all amounts of byproducts received in the national market from the international one

( FSIN j ,a ) multiplied by the international price of each byproduct (UCSI j ) .

InternationalCost = ∑∑ FSIN j ,aUCSI j + ∑ FTINaUCTI j

a

(79)

a

3.14 Sales Domestic sales Domestic sales ( DomesticSales) are the result of the sum of the amounts of aviation biofuel distributed to national market from all biorefineries (bi1.a ) , and the sum of the amounts of aviation fuel of fossil origin distributed to national market from all refineries

( FTk1,a ) ; both sums multiplied by the national price of aviation fuel (UCaaviationbiofuel ) plus the sum of the amounts of byproducts distributed to national market from all biorefineries

(si1, j , a ) multiplied by the national price of the byproducts (UC byproduc ) as follow: j ,a

DomesticSales = (∑∑ bi1.a + ∑∑ FTk1,a )UCaaviationbiofuel + ∑∑∑ si1, j ,aUC byproduc j ,a a

i1

a

k1

i1

j

(80)

a

International sales International sales ( InternationalSales) are the result of the sum of the amounts of aviation biofuel distributed to international market from all biorefineries ( FBNIi1 ) and the sum of the amounts of aviation fuel of fossil origin distributed to international market from all refineries ( FTNI k1 ) ; both sums multiplied by the international price of aviation fuel



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(UCTI ) , plus the sum of the amounts of byproducts distributed to international market from all biorefineries ( FSNI j ,i1 ) multiplied by the international price of the byproducts

(UCSI j ) as follows:   InternationalSales =  ∑ FTNI k1 + ∑ FBNI i1  UCTI + ∑∑ FSNI j ,i1UCSI j i1 j i1  k1 

(81)

Total sales The total sales (TotalSales) are equal to domestic sales plus international sales.

TotalSales = DomesticSales + InternationalSales

(82)

Economic objective function The total profit (Pr ofit ) is equal to the total sales minus all the costs involved in the supply chain for production of aviation fuel and hydrogen as follow: proces sin g Pr ofit = TotalSales − Cost feedstock − Costtotal

(83)

transport −Costtotal − CapCost − InternationalCost

3.15 Emissions of carbon dioxide CO2 captured in the cultivation fields The CO2 captured in the cultivation fields (CMPCO2 ) is equal to the sum of cultivated areas ( Ai ,m ) multiplied by the conversion factor to produce biomass (βi ,m ) and the parameter for carbon dioxide capture (CMPi , m ) . CMPCO2 = ∑∑ Ai ,m CMPi , m β i ,m i

(84)

m

CO2 released by the raw material transport The CO2 released by the raw material transportation (TMPCO2 ) is equal to the sum of all biomass transported to all biorefineries from all cultivation sites ( FMBTi ,i1,m ) multiplied by a unitary parameter of CO2 emissions (TMPBi ,i1, m ) , plus the sum of the biomass transported to all biohydrogen production plants from all cultivation sites

( FMBH i ,i 2,m ) multiplied by a unitary parameter of CO2 emissions (TMPBH i ,i 2,m ) , plus the


Page 25 of 61

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sum of the all fossil mass transported to all refineries from all extraction sites

( FfossilTk ,k1,n ) multiplied by a unitary parameter of CO2 emissions (TNPRk ,k1,n ) , plus the sum of all fossil mass transported to all hydrogen production plants from all extraction sites

( FfossilH k ,k 2,n ) multiplied by a unitary parameter of CO2 emissions (TNPH k ,k 2,n ) as follows:

TMPCO2 = ∑∑∑ FMBTi ,i1,mTMPBi ,i1,m + ∑∑∑ FMBH i ,i 2,m TMPBH i ,i 2,m i

i1

m

i

i2

(85)

m

+ ∑∑∑ FfossilTk , k1,nTNPRk , k1,n + ∑∑∑ FfossilH k , k 2,nTNPH k ,k 2, n k

k1

n

k

k2

n

CO2 released by the aviation fuel transportation The CO2 released by the aviation fuel transportation (TBTCO2 ) is equal to the sum of all aviation biofuel transported to all national markets from all biorefineries (bi1,a ) multiplied by a unitary parameter of CO2 emissions (TBTi1,a ) , plus the sum of the aviation fuel transported to the national market from all refineries ( FTk1,a ) multiplied by a unitary parameter of CO2 emissions (TTk1,a ) , plus the sum of the aviation fuel transported to national market from international market ( FTINa ) multiplied by a unitary parameter of CO2 emissions (TTINa ) , plus the sum of the aviation biofuel transported to the international market from the national market ( FBNIi1 ) multiplied by a unitary parameter of CO2 emissions (TBNI i1 ) , plus the sum of the aviation fuel transported to the international market from the national market ( FTNI k1 ) multiplied by a unitary parameter of CO2 emissions (TTNI k1 ) as follows:

TBTCO2 = ∑∑ bi1,a TBTi1,a + ∑∑ FTk1,aTTk1,a + ∑ FTIN aTTIN a i1

a

k1

a

(86)

a

+ ∑ FBNI i1TBNI i1 + ∑ FTNI k1TTNI k1 i1

k1

CO2 released for the transportation of byproducts The CO2 released by the byproducts transportation (TSPCO2 ) is equal to the sum of the byproducts transported to the national market from biorefineries (si1, j,a ) multiplied by a



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Page 26 of 61

unitary parameter of CO2 emissions (TSPi1, j ,a ) , plus the sum of the byproducts transported to the national market from the international market ( FSIN j ,a ) multiplied by a unitary parameter of CO2 emissions (TSIN j ,a ) , plus the sum of the byproducts received in the national market form the international market ( FSNI j ,i1 ) multiplied by a unitary parameter of CO2 emissions (TSNI j ) .

TSPCO2 = ∑∑∑ si1, j,a TSPi1, j ,a + ∑∑ FSIN j ,aTSIN j ,a + ∑∑ FSNI j ,i1TSNI j i1

j

a

j

a

j

(87)

i1

CO2 released by the raw material processing The CO2 released by the raw material processing ( PMPCO2 ) is equal to the sum of the biomass processed to produce aviation biofuel ( fi1,route m , r ) multiplied by a unitary parameter of CO2 emissions ( PMPi1, m, r ) , plus the sum of biomass processed to produce

( fmiroute 2, m, rbh )

biohydrogen

multiplied by a unitary parameter of CO2 emissions

( PMPBH i 2,m,rbh ) , plus the sum of fossil mass processed to produce aviation fuel ( ftkroute 1, n , rt ) multiplied by a unitary parameter of CO2 emissions ( PMPTk1,n, rt ) , and plus the sum of fossil mass processed to produce hydrogen of fossil origin ( fhkroute 2, n , rh ) multiplied by a unitary parameter of CO2 emissions ( PMPH k 2,n ,rh ) . route PMPCO2 = ∑∑∑ f i1,route m , r PMPi1, m , r + ∑∑∑ fmi 2, m , rbh PMPBH i 2, m , rbh i1

m

r

i2

m

(88)

rbh

route + ∑∑∑ ftkroute 1, n , rt PMPTk 1, n , rt + ∑∑∑ fhk 2, n , rh PMPH k 2, n , rh k1

n

rt

k2

n

rh

CO2 released by aviation fuel consumption The CO2 released by aviation fuel consumption ( BTCO2 ) is equal to the sum of all aviation fuel consumed in the national market (TBa ) multiplied by a unitary parameter of CO2 emissions ( BTa ) , plus the sum of total amount of aviation fuel sent to international market (TI ) multiplied by unitary parameter of CO2 emissions ( BTT ) .


Page 27 of 61

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BTCO2 = ∑ TBa BTa + BTT TI

(89)

a

CO2 released by the consumption of byproducts The CO2 released by the consumption of byproducts (SPCO2 ) is equal to the sum of all byproducts consumed in the national market (TS j,a ) multiplied by a unitary parameter of CO2 emissions ( SPj ,a ) , plus the sum of total amount of aviation fuel sent to international market ( SI j ) multiplied by a unitary parameter of CO2 emissions (SSPj ) .

SPCO2 = ∑∑TS j,a SPj ,a + ∑ SI j SSPj a

j

(90)

j

Environmental objective function The total CO2 released (TOTALCO2 ) is equal to the sum of emissions by transportation, raw material processing and consumption of products minus the carbon dioxide captured in the fields of cultivation plus the amount of CO2 remaining (CREM ) .

TOTALCO2 = TMPCO2 + TBTCO2 + TSPCO2 + PMPCO2

(91)

+ BTCO2 + SPCO2 − CMPCO2 + CREM The amount of CO2 remaining (CREM ) is now defined as the amount of CO2 that was captured in the biomass crops, but was not used to produce aviation biofuel or biohydrogen. It is important to mention that the parameter of CO2 captured during cultivation depends on the climatological conditions and biomass composition; in Mexico SAGARPA2 reports these parameters along with the productive potential of energy crops. On the other hand, the CO2 release parameter is constituted of emissions by transportation, processing and product consumption; the first one was calculated based on the consumed fuel per kilometer for each transported ton. The emissions by consumption were estimated with base on the carbon contain per ton of each product assuming that all products are used as fuels. The emissions by processing consider those associated to heating and cooling services along with those generated as byproducts for each processing route. All the CO2 emissions associated to processing were calculated using a chemical processes simulator. These parameters are available in supplementary material section.



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3.16 Mass conservation law Balance of CO2 captured in the biomass crops The amount of CO2 captured in the biomass crops must be equal to the sum of carbon dioxide released into the atmosphere by aviation and byproducts consumption, plus the amount of CO2 released by the transformation of biohydrogen plus the amount of CO2 remaining.

CMPCO2 = ∑ BTT Bi1 + ∑∑ si1, j SSPj + ∑∑∑ fmiroute 2, m, rbhCCMPi 2, m, rbh + CREM i1

j

i1

i2

(92)

m rbh

The amount of CO2 released by the transformation of biomass to biohydrogen

(∑∑∑ fmiroute 2, m , rbhCCMPi 2, m , rbh ) is the generated byproduct, the unitary parameter of CO2 i2

m rbh

emissions by biomass transformation to biohydrogen (CCMPi 2, m, rbh ) depends on the carbon content in the biomass. 3.17 Sustainability indicators The sustainability of an activity is a measure of its reversibility; it means how much feasible is that this activity is continued for the following generations. The sustainability is based on the following three aspects: the economic benefit, the environmental affectation and the social benefit associated with such activity. Nevertheless, it does not exist a measure patron to quantify the degree of sustainability of activities, being its interpretation relative and arbitrary. This research proposes two sustainability indicators which are defined below:

Economic-environmental density ( DS ) The economic-environmental density is the ratio between the economic benefit and the amount of greenhouse gases emissions (CO2). This indicator determines how much money is possible to gain by one ton of greenhouse emission in such activity. This indicator is represented by the symbol ( DS ) and it is calculated as follow:

DS =

Economic benefit ($) Greenhouse emissions (tons of CO2 released) Coefficient of mass integration in coupled supply chains (CIM )


(93)

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The coefficient of mass integration in coupled supply chains is the ratio between the amounts of recycled mass and the amount of processed mass. This indicator determines how efficient is the integration of coupled supply chains, and for the specific case of supply chain to produce aviation fuel from biomass and fossil mass it is represented by the symbol (CIM ) and calculated as follow:

CIM =

CO2 captured in the biomass crops ( ton ) − CREM (ton) CO2captured in the biomass crops ( ton )

(94)

If a large amount of biomass is cultivated, transported, and processed but it is not transformed into fuel, hydrogen or byproducts, this represents a passive system, which does not produce energy but it consumes energy and it produces pollution. On the other hand, sustainability is based on savings of economic cost, pollution and other issues; so, a passive system is not sustainable, in other words, a supply chain with CIM equal to 1 is more sustainable. Finally, the model formulation is stated as a multi-objective optimization approach, where the problem consists in maximizing equation (83) and minimizing equation (91) subject to the rest of equations.

4. Case studies In this paper, a case study from Mexico is presented. The problem considers the satisfaction of the aviation fuel in the national marker while accounting the use of aviation biofuel under several scenarios. The considered data were taken from Dominguez-García et al.23 and Gutiérrez-Antonio et al.24 (see Tables S1 - S4 available in the supporting information section). Then, the model is a Mixed-Integer Linear Programming (MILP) problem, which consists of 358,127 continuous variables, 22,520 binary variables and 108,477 constraints; this model was implemented in the software GAMS® and solved through CPLEX in a computer with an Intel processor at 3.0 GHz with 32 GB of RAM. The average CPU time required to solve each point of the Pareto curve using the epsilon constraint method was of 100 seconds. The supply chain to produce aviation fuel is analyzed from an integral point of view, involving the production and consumption of hydrogen to produce aviation biofuel.



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Thus, with the aim of determining the convenience of separating the hydrogen production from the aircraft fuel production plants along with the use of fossil and biomass raw material simultaneously, different scenarios are studied. Therefore, Scenario 1 considers the exclusive use of fossil raw materials to produce both hydrogen and aviation fuel, Scenario 2 considers the exclusive use of biomass to produce hydrogen and aviation fuels (also called biohydrogen and aviation biofuel). Scenario 3 considers the use of fossil mass and biomass in an integrated mode to produce hydrogen and aviation fuel; this scenario is quite interesting since it could be the starting case for the energetic transition in aviation sector. Finally, Scenario 4 considers the use of fossil mass and biomass in an integrated mode to produce hydrogen and aviation fuel, but involving environmental and economic aspects simultaneously. In the four scenarios, there is possible to import and export aviation fuel. Next, a more detailed description of each scenario is provided. Scenario 1. In this case the aim is determining the supply chain in Mexico that maximizes the profit with the following conditions. The demand of aviation fuel in each state of Mexico must be completely satisfied, and only the existing refineries can be used to produce hydrogen and aviation fuel from fossil resources (petroleum). The surplus of aviation fuel and byproducts can be exported; moreover it is possible import aviation fuel. Scenario 2. In this case, the goal is generating the supply chain in Mexico that maximizes the profit with the following conditions. The demand of aviation fuel in each state of Mexico must be completely satisfied. The cultivation sites are all the states of Mexico, in each state is possible to install one biorefinery; furthermore the needed hydrogen to produce the aviation fuel may be obtained from biomass. In each biorefinery is possible to install five different processing routes with different gains and byproducts; also, in each biohydrogen production plant is possible to install three processing routes to convert biomass to biohydrogen. The surplus of aviation biofuel and byproducts can be exported; moreover it is possible to import aviation fuel. Scenario 3. In this case, the focus is to determine the supply chain in Mexico that maximizes the profit with the following conditions. The demand of aviation fuel in each state of Mexico must be completely satisfied, and the resulting fuel must be a mixture with 50% from biomass and 50% from fossil mass. The cultivation sites are all the states of


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Mexico. In each state is possible to install one biorefinery and one biohydrogen production plant; these plants can be linked together with refineries already installed in an integrated mode to produce aviation fuel and hydrogen from biomass and fossil mass (petroleum). In each biorefinery is possible to install five different processing routes with different gains and byproducts; furthermore in each biohydrogen production plant is possible to install three processing routes to convert biomass to biohydrogen. The surplus of aviation fuel and byproducts can be exported; moreover it is possible to import aviation fuel. Scenario 4. In this case the goal is to determine the supply chain in Mexico that maximizes the profit with the following conditions. The epsilon constrain method is used to determine a Pareto

solution considering

economic and

environmental aspects

simultaneously. The demand of aviation fuel in each state of Mexico must be completely satisfied, and the resulting fuel must be a mixture with 50% from of biomass and 50% from fossil mass. The cultivation sites are all states of Mexico; in each state it is possible to install one biorefinery and one biohydrogen production plant, these plants can be linked together with refineries already installed in an integrated mode to produce aviation fuel and hydrogen from biomass and fossil resources (petroleum). In each biorefinery, it is possible to install five different processing routes with different gains and byproducts; furthermore in each biohydrogen production plant is possible to install three processing routes to convert biomass to biohydrogen. The surplus of aviation fuel and byproducts can be exported; moreover it is possible import aviation fuel.

5. Results In this section, the obtained results are analyzed for each one of the defined scenarios. Scenario 1. This scenario corresponds to the maximum economic benefit using exclusively fossil resources to produce both, hydrogen and aviation fuel. The yearly production of aviation fuel and hydrogen is shown in Table 1, and the economic results for this case are shown in Table 2, which correspond to transportation cost of 225.45 M$/year, processing cost of 246.91 M$/year and the raw material cost of 2,490.50 M$/year. The export sales are 2,388.00 M$/year and the national sales are 3,087.00 M$/year, so that the profit is 2,512.10 M$/year. The total emissions and the sustainability indicators are shown



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in Table 3. The economic-environmental density has a value of 112.16 $/t, which means that for every ton of CO2 produced $112.16 are achieved. The coefficient of mass integration in coupled supply chains for this case is equal to zero, since the biomass is not used as raw material. Figure 2 shows the schematic representation of distribution of aviation fuel for this case. Scenario 2. In this case, there was considered the maximum economic benefit using exclusively biomass to produce biohydrogen and aviation biofuel. The yearly production of aviation biofuel and biohydrogen is shown in Table 1, and the economic results for this case are shown in Table 2, which correspond to transportation cost of 205.32 M$/year, processing cost of 2,652.30 M$/year and raw material cost of 21,530.00 M$/year. The export sales are 27,460.00 M$/year and the national sales are 6,629.80 M$/year, so that the profit is 7,269.70 M$/year; the economic benefit for this case is almost three times greater than the Scenario 1, it means the use of biomass could be more profitable than the exclusive use of fossil fuels. The total emissions and sustainability indicators are shown in Table 3. The economic-environmental density has a value of 107.75 $/ton, which means that for every ton of CO2 produced $107.75 are achieved, which is a smaller amount of money than the one of Scenario 1; it means the use of biomass could be more profitable but less sustainable. The coefficient of mass integration in coupled supply chains for this case is 0.60, it means 40% of biomass captured in the crops is returned to the atmosphere. Figure 3 shows the schematic representation of distribution of aviation biofuel for this case. Scenario 3. In this scenario, the consideration was given to the maximum economic benefit using fossil resources and biomass to produce hydrogen and aviation fuel. The yearly production of aviation biofuel and biohydrogen are shown in Table 1, and the economic results for this case are shown in Table 2, which correspond to transportation cost of 330.1 M$/year, processing cost of 2,894.90 M$/year and raw material cost of 24,030.00 M$/year. The export sales are 32,320.00 M$/year and the national sales are 7,259.50 M$/year. So, the profit is 9,908.70 M$/year, the economic benefit for this case is greater than the one of Scenario 2; it means using biomass and fossil mass in an integrated mode could be more profitable than the production of aviation fuel from exclusively biomass or exclusively fossil resources. The total emissions and sustainability indicators are shown in Table 3. The economic-environmental density has a value of 111.42 $/ton,


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which means that for each ton of CO2 produced $111.42 are achieved, which is a greater amount of money than the one of Scenario 2; it means using biomass combined with fossil resources could be more sustainable than using exclusively one of them. The coefficient of mass integration in coupled supply chains for this case is 0.61, 1% greater than the one of Scenario 2, and it means that using biomass combined with fossil mass can raise the efficiency of the supply chain. As was mentioned before, this scenario could represent the initial point to begin the energetic transition in the aviation sector. Figure 4 shows the schematic representation of distribution of aviation fuel for this case. Scenario 4. In this case, the economic benefit using fossil mass and biomass to produce hydrogen and aviation fuel is addressed with the epsilon constrain method, to determine a Pareto Solution considering economic and environmental aspects simultaneously. It should be noted that the Pareto curve represents the overall tradeoffs between the economic and environmental objectives. This way, any point along this curve can be seen as a trade-off solution. Furthermore, the solution presented in Tables 1-3 for scenario 4 is the one remarked in blue color in Table S5, and this was selected because this shows a good compromise between the considered objectives. In this solution the yearly production of aviation biofuel and biohydrogen are shown in Table 1, and the economic results for this case are shown in Table 2, which correspond to transportation cost of 196.45 M$/year, processing cost of 1,421.30 M$/year and raw material cost of 22,190.00 M$/year. The export sales are 11,310.00 M$/year and the national sales are 22,320.00 M$/year, so that the profit is 7,299.30 M$/year. The total emission and sustainability indicators are shown in Table 3. The economic-environmental density has a value of 346.77 $/ton, which means that for each ton of CO2 produced $ 311.76 are achieved; this is a greater amount of money than the one of Scenario 3, it means that using biomass combined with fossil mass could be more sustainable than using exclusively one of them, and the epsilon constrain method allows determining a more sustainable solution. The coefficient of mass integration in coupled supply chains for this case is 1.00, it means 100% of biomass captured is used to produce aviation fuel. Additional data of sustainable indicators for the analyzed points in the Pareto curve are presented in Table S5 as supporting information; while Figure 5 shows the location of



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a trade-off solution according with the epsilon constrain method, but this solution is not the most sustainable depending on the economic-environmental density. On the other hand, Figures 6 and 7 show that the economic-environmental density behavior as a function of CO2 emissions and economic benefit, which present a maximum point of this indicator of sustainability equal to 360.04 $/ton, while the economic-environmental density indicator for the trade-off solution has a value of 311.76 $/ton. Figures 8 and 9 show the schematic representation of distribution of aviation fuel for trade-off solution and maximum economic-environmental density solution, respectively. Also, Table S6 contains a summary of the production activity of each state of Mexico, which is marked with X.

6. Conclusions This paper has presented an optimization approach to design supply chains for the production of aviation fuels. The approach integrates the use of biomass and fossil resources to produce hydrogen and aviation fuel taking synergistic advantages of both types of feedstocks. The model incorporates several decision-making factors including the optimal selection of the bioresources and fossil resources, cultivation sites for biomass, extraction sites of fossil resources, processing routes and technologies, and integration of new biorefineries with existing infrastructures. Furthermore, the model incorporates the optimal distribution and transportation of materials through the supply chain. Two objective functions have been considered simultaneously: the maximization of the overall profit and the minimization of the associated emissions. A case study has been solved for Mexico with several scenarios and conditions. In this case study, it was determined that the transportation cost associated with the use of biomass to produce biohydrogen and aviation biofuel is cheaper than the use of fossil resources. This is attributed to the good condition of the transportation infrastructure of biomass in Mexico compared to the transportation of fossil resources, and the better distribution of biomass resource in the country. The model results suggest installing small processing facilities distributed through the supply chain. A gasification pathway to produce biohydrogen has been found to be meritorious. It is important to mention that in the production of aviation fuel, the required amount of hydrogen is small; therefore, the


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cost of transportation is lower than the capital costs for installation of the production plants. This can be observed in Table S5 of the supplementary material. The recommended raw materials have been found to be Jatropha Curcas and Higuerilla, whereas Camelina was the worst option. Finally, attractive solutions have been generated to balance the economic and environmental objectives. The production of aviation fuel from biomass and fossil resources simultaneously has been found to be more profitable than the production of aviation fuel from exclusively one of them. This proposal is very useful for countries or territories with suitable distribution of potential production of biomass, furthermore bioresources could be combined with solar or wind energy to produce any other liquid fuels thus broadening the horizon to combat climate change.

7. Nomenclature 7.1 Indexes a

Location of national market

i

Location of cultivation sites

i1

Location of biorefineries

i2

Location of biohydrogen production plants

j

Byproducts

k

Fossil resources extraction sites

k1

Location of refineries

k2

Location of hydrogen production plant

m

Type of biomass

n

Type of fossil mass

r

Processing routes to produce aviation biofuel

rt

Processing routes to produces aviation fuel

rbh

Processing routes to produces biohydrogen



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

rh

Processing routes to produce hydrogen

7.2 Variables Ai , m

Total used area for cultivation of any raw material

AiNew ,m

New area needed to produce and supply the biomass demand in each biorefinery

Bi1

Amount of aviation biofuel produced in each biorefinery

bi1, a

Aviation biofuel sent to national market

BHBi1

Flows of biohydrogen that arrives to each biorefinery

BTN a

Amount of aviation biofuel received in the national market

BHBi1

Total amount of biohydrogen sent to each biorefinery

BTCO2

CO2 released by aviation fuel consumption

BHRk1

Total amount of biohydrogen sent to each refinery

BTNItotal

Amount of aviation biofuel sent to international market

BTNItotal

Amount of aviation biofuel sent to international market

Cost feedstock

Total cost of feedstock

proces sin g Costavitionbiofuel Processing cost of biomass for aviation biofuel production proces sin g Costbiohydrogen Processing cost of biomass for biohydrogen production proces sin g Costaviation fuel

Processing cost of fossil mass for aviation fuel production

proces sin g Costhydrogen Processing cost of biomass for hydrogen production

proces sin g Costtotal Total cost of feedstock processing

CTM B

Cost of transportation of biomass to biorefineries

CTM H

Cost of transportation of biomass to biohydrogen production plants

CTNR

Cost of transportation of biomass to refineries

CTNH

Cost of transportation of fossil mass to hydrogen production plants


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CTB

Cost of transportation of aviation biofuel to national market

CTS

Cost of transportation of byproducts to national market

CTT

Cost of transportation of aviation fuel of fossil origin to national market

CTBI

Cost of transportation of aviation fuel from international market to national market

CTSI

Cost of transportation of byproducts from international market to national market

CTBE

Cost of transportation of aviation biofuel sent to international market

CTTE

Cost of transportation of aviation fuel of fossil origin sent to international market

CTSE

Cost of transportation of byproducts sent to international market

CTBHB

Cost of transportation of biohydrogen to biorefineries

CTBHR

Cost of transportation of biohydrogen to refineries

CTHB

Cost of transportation of hydrogen of fossil origin to biorefineries

transport Costtotal

Total cost of transportation

CCB

Capital cost by installation of biorefineries

CCBH

Capital cost by installation of biohydrogen production plants

CCR

Capital cost by installation of refineries

CCH

Capital cost by installation of hydrogen production plants

CapCost

Total capital cost

CIM

Coefficient of mass integration in coupled supply chains

CREM

Amount of CO2 that was captured in the biomass crops but was not used to produce aviation biofuel or biohydrogen

CMPCO2

CO2 captured in the fields of cultivation

DS

Economic-environmental density

DomesticSales

Domestic sales

FBHRi 2,k1

Hydrogen flow from any hydrogen plants

ftn,route rt ,k1

Flows of fossil mass transformed into aviation fuel



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

FMBTtotali1,m

Total amount of raw material delivered to each biorefinery

FMBTi ,i1, m

Amount of biomass sent to the installation site of biorefinery

FMBTtotali1,m

Total amount of raw material processed in each biorefinery

FBNIi1

Aviation biofuel sent to international market

FfossilTtotalk1,n

Total amount of raw material delivered to each refinery

FSNIi1, j

Flows of byproducts sent to international market

FTINa

Aviation fuel of fossil sent to international market

FTk1,a

Aviation fuel of fossil origin sent to national market

FTNIk1

Aviation fuel of fossil origin sent to international market

FfossilTk,k1,n

Fossil mass sent to the installation site of any refinery

fm iroute 2 ,m ,rbh

Biomass processed in each biohydrogen production plant

FBHBi 2,i1

Biohydrogen sent to biorefineries

FBHRi 2,k1

Biohydrogen sent to refineries

Fk ,n

The amount of fossil mass extracted in each extraction site

FfossilTk , k1,n

Raw material sent to the refineries

FfossilH k ,k 2,n

Raw material sent to the hydrogen plants

FfossilHtotalk 2,n

Raw material delivered to each plant of hydrogen production

FfossilH k,k 2,n

Amount of biomass sent to the site of installation of the plant

fhkroute 2,n, rh

Fossil mass processed in each route (rh)

fhkroute 2, n , rh

Fossil mass processed in each hydrogen production plant

FHBk 2,i1

Hydrogen sent to biorefineries


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FHRk 2,k1

Hydrogen sent to refineries

FBHBi 2,i1

Hydrogen flow from any biohydrogen plant

FHBk 2,i1

Hydrogen flow from any hydrogen plant

f mroute , r ,i1

Flows of biomass transformed into aviation biofuel

FBNIi1

Aviation biofuel sent to international market from each biorefineries

FTNI k1

Aviation fuel of fossil origin sent to international market from each refineries

FSIN j ,a

Byproducts from any biorefinery

FSNI j ,i1

Byproducts sent to international market from any biorefinery

FMBTi ,i1, m

Raw material sent to the biorefineries

FMBHi ,i2,m

Raw material sent to the biohydrogen plants

FMBHtotali 2, m

The raw material delivered to each plant of production of biohydrogen

FMBHi ,i2,m

Biomass sent to the installation site of the plants

fm iroute 2 ,m ,rbh

Biomass processed in each route (rbh)

FHRk 2,k1

Hydrogen flow coming from any hydrogen plants

HBi1

Total amount of hydrogen sent to each biorefinery

HRk1

Total amount of hydrogen sent to each refinery

HBtotali1

Total amount of hydrogen that arrives to each biorefinery

HBi1

Flows of hydrogen of fossil origin that arrives to each biorefinery

HRtotalk1

Total amount of hydrogen that arrives to each refinery

InternationalCost

Import charge for aviation fuel and byproducts

InternationalSales

International sales



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PBH i 2

The amount of biohydrogen produced in each plant

Pr ofit

Total profit

Pi , m

Biomass produced

PHk 2

Amount of hydrogen produced in each hydrogen production plant

PMPCO2

CO2 released by the raw material processing

Si1, j

Amount of byproduct ( j ) produced in each biorefinery

si1, j ,a

Flows of byproducts sent to national market

SI j

Amount of byproduct ( j ) sent to international market

SPCO2

CO2 released by byproduct consumption

TMPCO2

CO2 released by the raw material transportation

TBTCO2

CO2 released by the aviation fuel transportation

TSPCO2

CO2 released by the byproducts transportation

TotalSales

Total sales

TI

Total amount of aviation fuel sent to international market

TS j ,a

Amount of byproduct ( j ) received in the national market

TOTALCO2

Total CO2 released

Tfossilk1

Amount of aviation fuel produced in each refinery

TNa

Amount of aviation fuel of fossil origin received in the national market

TNItotal

Amount of aviation fuel of fossil origin sent to international market

TBa

Amount of aviation fuel received in the national market

7.3 Binary variables

dk 2

Binary variable to associate the capital cost with the dimension of hydrogen of fossil origin production plant


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ek 2,n,rh

Binary variable to associate the capital cost with the dimension of processing route (rh)

uk 1

Binary variable to associate the capital cost with the dimension of refinery

vk1,n,rt

Binary variable to associate the capital cost with the dimension of processing route (rt )

wi 2

Binary variable to associate the capital cost with the dimension of biorefinery

xi 2,m, rh

Binary variable to associate the capital cost with the dimension of processing route (rbh)

yi1

Binary variable to associate the capital cost with the dimension of biorefinery

zi1, m ,r

Binary variable to associate the capital cost with the dimension of processing route ( r )

7.4 Parameters

AiExist ,m

Existing area used for cultivation

Aimax ,m

Available area to produce biomass

Bimin 1

Minimum capacity of aviation biofuel production

Bimax 1

Maximum capacity of aviation biofuel production

BTT

Unitary parameter of CO2 emissions by aviation fuel consumed in the international market

BTa

Unitary parameter of CO2 emissions by aviation fuel consumed in the national market

CMPi , m

Capture of carbon dioxide parameter

CDkH2,n,rh

Sizing parameter of each route (rh)



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CRkH2,n,rh

Base cost of each route (rh) installation

CBkH2

Base cost of hydrogen production plant installation

CDkR1,n,rt

Sizing parameter of each route (rt )

CRkR1,n , rt

Base cost of each route (rt ) installation

CBkR1

Base cost of refinery installation

CDiBH 2, m . rbh

Sizing parameter of each route (rbh)

CRiBH 2, m , rbh

Base cost of each route (rbh) installation

CBiBH 2

Base cost of biohydrogen production plant installation

CDiB1,m ,r

Sizing parameter of each route ( r )

CRiB1, m ,r

Base cost of each route ( r ) installation

CBiB1

Base cost of biorefinery installation

CCMPi 2, m,rbh Unitary parameter of emission by biomass transformation to biorefinery DBamax

Total demand of aviation fuel in the national market

DS max j ,a

Total demand of byproducts in the national market

Fkmax ,n

Available reserves of fossil mass

min fi1,route m,r

Minimum capacity of processing of raw material through route ( r )

max fi1,route m, r

Maximum capacity of processing of raw material through route ( r )

route min ftk1,n, rt

Minimum capacity of processing of raw material through route (rt )

route max ftk1,n, rt

Maximum capacity of processing of raw material through route (rt )

min fhkroute 2,n, rh

Minimum capacity of processing of raw material through route (rh)

max fhkroute 2,n, rh

Maximum capacity of processing of raw material through route (rh)


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PH kmin 2

Minimum capacity of hydrogen of fossil origin production

PH kmax 2

Maximum capacity of hydrogen of fossil origin production

PBHimin 2

Minimum capacity of biohydrogen production

PBHimax 2

Maximum capacity of biohydrogen production

min fmiroute 2, m , rbh

Minimum capacity of processing of raw material through route (rbh)

fmiroutemax 2, m ,rbh

Maximum capacity of processing of raw material through route (rbh)

PMPH k 2, n, rh Unitary parameter of CO2 emissions by fossil mass processed to produce hydrogen of fossil origin

PMPTk1,n,rt

Unitary parameter of CO2 emissions by fossil mass processed to produce aviation fuel

PMPBH i 2,m,rbh Unitary parameter of CO2 emissions by biomass processed to produce biohydrogen

PMPi1, m , r

Unitary parameter of CO2 emissions by biomass processed to produced aviation biofuel

SSPj

Unitary parameter of CO2 emissions by byproducts consumed in the international market

SPj ,a

Unitary parameter of CO2 emissions by byproducts consumed in the national market

TSNI j

Unitary parameter of CO2 emissions by byproducts received in the national market form the international market

TSIN j , a

Unitary parameter of CO2 emissions by byproducts transported to consume sites in the national market from the international market

TSPi1, j , a

Unitary parameter of CO2 emissions by byproducts transported to consume sites in the national market from biorefineries

TTNI k1

Unitary parameter of CO2 emissions by aviation fuel transported to international market from national market



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

TBNIi1

Unitary parameter of CO2 emissions by aviation biofuel transported to international market from national market

TTINa

Unitary parameter of CO2 emissions by aviation fuel transported to national market from international market

TTk1,a

Unitary parameter of CO2 emissions by aviation fuel transported to consume sites in the national market from refineries

TBTi1,a

Unitary parameter of CO2 emissions by aviation biofuel transported to consume sites in the national market from biorefineries

TNPH k ,k 2,n

Unitary parameter of CO2 emissions by fossil mass transported to hydrogen production plants from extraction sites

TNPRk , k1,n

Unitary parameter of CO2 emissions by fossil mass transported to refineries from extraction sites

TMPBHi ,i2,m

Unitary parameter of CO2 emissions by biomass transported to biohydrogen production plants from cultivation sites

Tfossilkmin 1

Minimum capacity of aviation fuel production

Tfossilkmax 1

Maximum capacity of aviation fuel production

TMPBi ,i1,m

Unitary parameter of CO2 emissions by biomass transported to biorefineries from cultivation sites

UCSI j

International price of the byproducts

UCTI

International price of aviation fuel

UCaaviationbiofuel

National price of aviation fuel

UC subproducto j ,a

National price of the byproducts

UCTHRk 2,k1

Unitary cost of transportation between the installation site of hydrogen plants and installation site of refineries

UCTBHRi 2,k1

Unitary cost of transportation between the installation site of biohydrogen plants and installation site of refineries

UCTBHBi 2,i1

Unitary cost of transportation between the installation site of biohydrogen plants and installation site of biorefineries


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UCTSE j ,i1

Unitary cost of transportation between installation sites of biorefineries and international market

UCTTEk1

Unitary cost of transportation between installation sites of refineries and international market

UCTBEi1

Unitary cost of transportation between installation sites of biorefineries and international market

UCTSI j ,a

Unitary cost of transportation between international market and the final consumption

UCTTI a

Unitary cost of transportation between international market and the final consumption

UCTTk 1, a

Unitary cost of transportation between the installation site of refineries and the final consumption

UCTSi1, j ,a

Unitary cost of transportation between the installation site of bio refineries and the final site of consumption

UCTBi1,a

Unitary cost of transportation between the installation site of bio refineries and the final site of consumption

UCTNH k ,k 2,n

Unitary cost of transportation between the extraction site and installation site of hydrogen production plant

UCTNRk , k1, n

Unitary cost of transportation between the extraction site and installation site of refineries

UCTMHi,i 2,m

Unitary cost of transportation between the cultivation site and installation site of hydrogen production plant

UCTMBi ,i1, m

Unitary cost of transportation between the cultivation site and installation site of biorefineries

sin g UChkproces 1.n.rh

Unitary cost of biomass processing to produce hydrogen

sin g UCtkproces 1.n.rt

Unitary cost of biomass processing to produce aviation fuel

sin g UCbhiproces 2.m.rbh

Unitary cost of biomass processing to produce biohydrogen

sin g UCbtiproces 1.m.r

Unitary cost of biomass processing to produce aviation biofuel



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

UCfossilkfeedstoks ,n

Unitary price of fossil mass

UCi ,feedstock m

Unitary price of biomass

α m, r , j

Biomass to byproducts conversion factor

βi ,m

Efficient factor of biomass production

δn,rt ,k1

Fossil mass to aviation fuel conversion factor

γ i 2,m,rbh

Biomass to biohydrogen conversion factor

λk 2,n,rh

Fossil mass to hydrogen conversion factor

φm,r ,i1

Biomass to aviation biofuel conversion factor

τ n,rt ,k1

Stoichiometric coefficient of hydrogen consumption to produce aviation fuel of fossil origin

υm,r ,i1

Stoichiometric coefficient of hydrogen consumption to produce aviation biofuel

8. Acknowledgements Financial support provided by CONACyT, through grants 239765 and 253660, for the development of this project is gratefully acknowledged.

9. Supporting Information Unit prices and costs used in the mathematical model are presented in Table S1. Yields of different processing routes to produce aviation fuel and yields of different processing routes to produce hydrogen are presented in Table S2. Table S3 shows the produced hydrogen through different processing routes. Table S4 shows the unit information for the CO2 emissions associated to different involved activities. All the points


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that conform the Pareto curve and their values can be consulted in Table S5. Sustainability indicators for the Pareto curve, activities carried out by each state for all analyzed scenarios are shown in Table S6. This information is available free of charge via the Internet at http://pubs.acs.org/.

10. Cited Literature [1] PEMEX (2010) Memory of work 2010. Annual report for the Mexican Ministry of Oil. Mexico City. Mexico.www.pemex.com/files/content/Version_completa_memoria_de_labores_2010.pdf. (Accessed, 30 January, 2016). [2] SAGARPA-SIAP (2011) Mexican System of Information about Agriculture and Fishing. Advance of planting and harvesting for Mexico. Mexico City, Mexico.www.siap.gob.mx/index.php?option=com_wrapper&view=wrapper&Itemid =347. (Accessed, February, 2016). [3] ICAO (2016). ICAO Environmental report 2013. http://cfapp.icao.int/EnvironmentalReport-2013 (Accessed, January, 2017). [4] IMO (2016). Third IMO GHG Study 2014. www.imo.org/OurWork/Environment/PollutionPrevention/AirPollution/Pages/Green house-Gas-Studies-2014.aspx (Accessed, January, 2017). [5] Al-Nuaimi, I.; Bohra, M.; Selam, M.; Chaudhary, H. A.; El-Halwagi, M. M.; Elbashir, N. O. Optimization of synthetic jet fuels aromatic/paraffinic composition via experimental and property integration methods. Chem. Eng. Technol. 2016, 39(12), 2217-2228. [6] Hong, T. D.; Soerawidjaja, T. H.; Reksowardojo, I. K.; Fujita, O.; Duniani, Z.; Pham, M. X. A study on developing aviation biofuel for the Tropics: Production processExperimental and theoretical evaluation of their blends with fossil kerosene. Chem. Eng. Process. 2013, 74, 124– 130.



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[7] Martinez-Gomez, J.; Nápoles-Rivera, F.; Ponce-Ortega, J. M.; El-Halwagi, M. M. Optimization of the production of syngas from shale gas with economic and safety considerations. Appl. Therm. Eng. 2017, 110, 678-685. [8] Challiwala, M.; Ghouri, M.; Linke, P.; El-Halwagi, M. M.; Elbashir, N. A combined thermo-kinetic analysis of various methane reforming technologies: Comparison with dry reforming. J. CO2 Util. 2017, 17, 99-111. [9] Noureldin, M. M. B.; Elbashir, N. O.; Gabriel, K. J.; El-Halwagi, M. M. A process integration approach to the assessment of CO2 fixation through dry reforming, ACS Sustainable Chem. Eng. 2015, 3 (4), 625–636. [10] Pan, C.; Fan, Y.; Hou, H. Fermentative production of hydrogen from wheat bran by mixed anaerobic cultures. Ind. Eng. Chem. Res. 2008, 47, 5812–5818. [11] Zhang, D.; Vassiliadis, V. S. Chlamydomonas reinhardtii metabolic pathway analysis for biohydrogen production under non-steady-state operation. Ind. Eng. Chem. Res. 2015, 54, 10593-10605. [12] Pan, C.; Fan, Y.; Hou, H. Fermentative production of hydrogen from wheat bran by mixed anaerobic cultures. Ind. Eng. Chem. Res. 2008, 47, 5812-5818. [13] Fan, Y.; Guo, Y. Bioconversion of aging corn to biohydrogen by dairy manure compost. Ind. Eng. Chem. Res. 2009, 48(5), 2493-2498. [14] Zhang, Y.; Wright, M. M. Product selection and supply chain optimization for fast pyrolysis and biorefinery system. Ind. Eng. Chem. Res. 2014, 53, 19987-19999. [15] Marvin, W. A.; Schmidt D. L.; Daoutidis, P. Biorefinery location and technology selection through supply chain optimization. Ind. Eng. Chem. Res. 2013, 52, 3192−3208. [16] Murillo-Alvarado, P. E.; Ponce-Ortega, J. M.; Serna-Gonzalez, M.; Castro-Montoya, A. J.; El-Halwagi, M. M. Optimization of pathways for biorefineries involving the selection of feedstocks, products, and processing steps. Ind. Eng. Chem. Res. 2013, 52, 5177−5190.


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[17] Sepulveda-González, I. Jet biofuel. Production of energy-related crops for commercial aircraft. Revista Mexicana de Ciencias Agrícolas. 2012, 3(3), 579-594. [18] Moraes, M. A. F. D.; Nassar, A. M.; Moura, P.; Leal, R. L. V.; Cortez, L. A. B. Jet biofuels in Brazil: Sustainability challenges. Renew. Sust. Energ. Rev. 2014, 40, 716– 726. [19] El-Halwagi, A. M.; Rosas, C.; Ponce-Ortega, J. M.; Jiménez-Gutiérrez, A.; Mannan, M. S.; El-Halwagi M. M. Multi-objective optimization of biorefineries with economic and safety objectives, AIChE J. 2013, 59(7), 2427–2434.} [20] El-Halwagi, M. M. A return on investment metric for incorporating sustainability in process integration and improvement projects. Clean Tech. Environ. Policy. 2017, 19, 611-617. [21] Chiaramonti, D.; Prussi, M.; Buffi, M.; Tacconi, D. Sustainable bio kerosene: Process routes and industrial demonstration activities in aviation biofuels. Appl. Energ. 2014, 136, 767–774. [22] Santibañez-Aguilar, J. E.; González-Campos, J. B.; Ponce-Ortega, J. M.; SernaGonzález M.; El-Halwagi M. M. Optimal planning and site selection for distributed multiproduct biorefineries involving economic, environmental and social objectives. J. Clean. Prod. 2014, 65, 270-294. [23] Santibañez-Aguilar, J. E.; Ponce-Ortega, J. M.; González-Campos, J. B.; SernaGonzález M.; El-Halwagi M. M. Optimal planning for the sustainable utilization of municipal solid waste. Waste Manage. 2013, 33, 2607–2622. [24] Santibañez-Aguilar, J. E.; Ponce-Ortega, J. M.; González-Campos, J. B.; SernaGonzález, M.; El-Halwagi, M. M. Synthesis of distributed biorefining networks for the value-added processing of water hyacinth. ACS Sust. Chem. Eng. 2013, 1, 284−305. [25] Dominguez-Garcia, S.; Gutierrez-Antonio, C.; De Lira-Flores, J. A.; Ponce-Ortega, J. M. Optimal planning for the supply chain of biofuels for aviation in Mexico. Clean Techn. Environ. Policy. 2017, 1-16.



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[26] Gutiérrez-Antonio, C.; Romero-Izquierdo, A. G.; Gómez-Castro, F. I.; Hernández, S. Energy integration of a hydrotreatment process for sustainable biojet fuel production. Ind. Eng. Chem. Res. 2016, 55, 8165−8175. [27] Hosseini S.E.; Wahid M. A. Hydrogen production from renewable and sustainable energy. Renew. Sust. Energ. Rev. 2016, 57, 850-866. [28] Martín M.; Grossmann I.E. Optimal use of hybrid feedstock, switchgrass and shale gas for the simultaneous production of hydrogen and liquid fuels. Energy. 2013, 55, 378391. [29] Sabio N.; Kosting A.; Guillén-Gosálbez G.; Jiménez L. Holistic minimization of the life cycle environmental impact of hydrogen infrastructures using multi-objective optimization and principal component analysis. Int. J. Hydrogen Energy. 2012, 37, 5385-5405. [30] Sabio N.; Gadalla M.; Guillén-Gosálbez G.; Jiménez L. Strategic planning with risk control of hydrogen supply chains for vehicle use under uncertainty in operating costs: A case study of Spain. Int. J. Hydrogen Energy. 2010, 35, 6836-6852. [31] Martín M.; Grossmann I.E. Energy optimization of hydrogen production from lignocellulosic biomass. Comput. Chem. Eng. 2011, 35, 1798-1806.


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Table 1. Production of hydrogen and aviation fuel for all scenarios Aviation

Study

Aviation fuel,

Hydrogen,

case

kton/year

kton/year

I

6,293.10

0.63

-

-

II

-

-

15,317.00

2.65

III

15,334.00

3.28

6,293.10

-

IV

11,341.00

3.69

1,774.10

7.42

biofuel, kton/year

Biohydrogen, kton/year

Table 2. Economic results for all scenarios Study

Transportation

Processing

case

cost, M$/year

I

225.45

246.91

II

205.32

III IV

Feedstock

Capital Cost

Import cost,

Export sales,

National sales,

Profit,

M$/year

M$/year

M$/year

M$/year

M$/year

2,490.50

0.00

-

2,388.00

3,087.00

2,512.10

2,652.30

21,530.00

2,432.48

-

27,460.00

6,629.80

7,269.70

330.10

2,894.90

24,030.00

2,415.80

-

32,320.00

7,259.50

9,908.70

196.45

1,421.30

22,190.00

2,522.95

-

11,310.00

22,320.00

7,299.30

cost, M$/year cost, M$/year

Table 3. Sustainability indicators for all scenarios Study

CO2 emissions,

CO2 captured,

CREM,

Profit,

case

kton/year

kton/year

kton/year

M$/year

I

22,397.00

-

-

II

67,467.00

103,840.00

III

88,931.00

IV

23,400.00

DS, $/ton

CIM

2,512.10

112.16

Ind.

41,081.00

7,269.70

107.75

0.60

103,840.00

40,465.00

9,908.70

111.42

0.61

103,510.00

-

7,299.30

311.76

1.00



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Figures

Figure 1. Superstructure of integrated supply chain to produce aviation fuel from fossil resources and biomass


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Figure 2. Schematic representation of distribution of aviation fuel for Scenario 1.



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Figure 3. Schematic representation of distribution of aviation biofuel for Scenario 2


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Figure 4. Schematic representation of distribution of aviation fuel for Scenario 3



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Figure 5. Pareto curve


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Figure 6. DS Behavior as a function of CO2 emissions



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Figure 7. DS Behavior as a function of economic benefit


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Figure 8. Schematic representation of distribution of aviation biofuel for Scenario 4 (a)



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Figure 9. Schematic representation of distribution of aviation biofuel for Scenario 4 (b)


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For Table of Content Use Only

Synopsis: This paper presents an optimization approach to design supply chains for the production of aviation fuels. The approach integrates the use of biomass and fossil resources to produce hydrogen and aviation fuel taking synergistic advantages of both types of feedstocks.


Strategic Planning for the Supply Chain of Aviation Biofuel with

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