Design and Operation of a Future Hydrogen Supply Chain

0263–8762/06/$30.00+0.00 # 2006 Institution of Chemical Engineers Trans IChemE, Part A, June 2006 Chemical Engineering Research and Design, 84(A6): 423– 438

www.icheme.org/cherd doi: 10.1205/cherd.05193

DESIGN AND OPERATION OF A FUTURE HYDROGEN SUPPLY CHAIN Snapshot Model A. ALMANSOORI and N. SHAH
Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, UK

M

uch of the early research in the hydrogen supply chain area was focused on individual technologies of the supply chain, such as production, storage, or distribution, rather than dealing with the supply chain as a whole. The motivation behind this paper is the need to: (1) design a hydrogen supply chain that integrates the previously mentioned components within a single framework, (2) understand the important trade-offs in such a supply chain, and (3) have a full understanding of the data requirements and uncertainties in such an exercise. Optimization techniques were implemented to develop the hydrogen supply chain for the transport sector, therefore, determining the optimum infrastructural and operational costs. Of course, cost is not likely to be the sole determinant of performance in practice. The network of interest was formulated as a mixed-integer linear programming (MILP) problem. Also, the network is presented as a steady state ‘snapshot’ problem using Great Britain as a backdrop. The model and assumptions presented in this paper reveal that the optimum future hydrogen supply chain might consist of medium-to-large, centralized methane steam reforming plants. The hydrogen produced from these plants will then be delivered as a liquid via tanker trucks and stored in centralized storage facilities. Keywords: hydrogen; supply chain network design; MILP; Great Britain.

INTRODUCTION

fuels have been suggested to address energy-related issues, including methanol, ethanol, methane, liquefied petroleum gas and hydrogen. Among these sources, hydrogen may offer the greatest potential benefits in terms of reducing CO2 and harmful pollutant emissions. Reducing emissions can be achieved through medium or large scale hydrogen production facilities coupled with carbon capture and storage technologies. However, the latter capability is difficult to realise at the vehicle level. The use of hydrogen can also improve the security of primary energy supplies. For instance, statistics show that the world’s consumption of oil is growing fast with the steady decline of the recoverable reserves. Energy analysts are expecting oil and natural gas to last for approximately another 70 years (British Petroleum, 2004). Consequently, additional transition pathways need to be established from conventional fossil-based sources to renewable energy sources. Moreover, hydrogen can play an important role in enhancing the economic growth of developing countries like China, India or Indonesia. Unlike the current energy supply chain for electricity, natural gas or petrol, there is at present no integrated hydrogen supply chain to deliver hydrogen to consumers. The existing hydrogen distribution network is relatively small and incapable of serving the tremendous potential

Combustible petroleum products such as petrol and diesel are the primary energy sources for transport in today’s energy market. In the UK, for example, transport accounted for 74% of the total oil consumption in the year 2000, with road transport relying on petroleum fuels to power 99.8% of its vehicles (Pridmore and Bristow, 2002). Combustion of such fuels has increased the concentration of carbon dioxide (CO2) in the atmosphere from 280 to 370 parts per million over the past 150 years (Service, 2004). This has been deemed by some researchers to have been responsible for the 0.68C rise in the average global surface temperature. It is expected that the level of CO2 emissions will increase by 50% by 2020 (Service, 2004). Indeed, continuous dependency on current fuels poses significant challenges in air pollution, global warming and energy supply security. For many decades, people have been calling for the use of fuels generated from renewable sources as an alternative to the current non-renewable ones. A variety of alternative
Correspondence to: Professor N. Shah, Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, SW7 2AZ, UK. E-mail: [email protected]

423

424

ALMANSOORI and SHAH

demand required for future energy markets. According to Ogden (1999), the current ‘merchant’ hydrogen infrastructure delivers fuel at approximately 1% of the scale needed to serve a major energy market. The absence of such an infrastructure will obviously prevent the widespread adoption of hydrogen fuel cell vehicles. For hydrogen to become the dominant fuel, an appropriate supply chain must be developed in order to model different industrial aspects. These include: production and storage facilities, methods of transporting hydrogen, fuelling stations for hydrogen-powered applications and technologies that convert the fuel into energy through end-use systems. The design of this supply chain needs to be physically and economically feasible in terms of handling hydrogen demand fluctuations. The objective of this paper is to develop a model that gives indications of the optimal design of the future hydrogen supply chain for vehicle use in Great Britain and to examine the applicability of supply chain network design models for this type of problem. Although significant uncertainty remains in both hydrogen fuel processing and automotive propulsion technologies, it is interesting to think about the supply chain now to obtain indications of efficient structures. This will require us to pull together and validate as far as possible the relevant data from disparate sources. The proposed mathematical model will outline all possible architectures of the future hydrogen supply chain as well as the optimal cost of the network before they develop in practice. The models of interest will determine the number, location, capacity of production plants and storage facilities to be set up in each grid, the transportation links that need to be established in the network, and the production rates and flows of hydrogen. The objective is the minimization of the total cost of the network, both in terms of capital and operating costs. LITERATURE REVIEW The potential of hydrogen as a future fuel has been discussed as part of the energy agenda for decades. Many research projects led by academics and engineers have been steered towards the use of hydrogen in future energy supply chains for road transport, distributed heat and power generation, as well as for energy storage. Many governments such as the US, the EU, Japan and others have invested billions of dollars into hydrogen initiatives aimed to improve the current hydrogen technology and propel it to the market. Automobile and energy companies are spending even more to build demonstration fleets and fuelling stations. Policymakers are also studying the applicability and challenges towards shifting from an unsustainable carbon based economy to a sustainable hydrogen economy. Of these hydrogen activities, most of the demonstration projects and schemes examine one particular component of the hydrogen supply chain, such as technologies for production, storage or distribution of hydrogen, rather than focusing on the systems approach of designing and operating such an infrastructure. Much of the early work in hydrogen research has been driven by US legislative pressures, particularly in California to improve air quality. A well-known example is the Southern Californian case study to develop a hydrogen vehicle refuelling infrastructure. This led Ogden (Ogden,

1999; Ogden et al., 1999) to examine a number of near-term possibilities for producing and delivering compressedgaseous hydrogen. The proposed energy supply systems use commercially available technologies for hydrogen production, storage, and distribution, combined in some cases with technologies under development. For the longer term, Ogden (Ogden, 1999; Ogden et al., 1999) studied other centralized methods of hydrogen production, including gasification of biomass, coal or municipal solid waste, or electrolysis powered by wind, solar energy or nuclear power. Further discussions include the possibility of coupling thermochemical hydrogen production systems with CO2 separation and sequestration units. The design of a hydrogen supply chain has also received increasing attention in the UK in recent years. Guy (2001) examined the development of a transport infrastructure in London and the Southeast. The challenges in establishing such an infrastructure during the market development phase were also discussed from both environmental and economical perspectives. He concluded that the development of a hydrogen infrastructure is both feasible and economically attractive, despite the number of technical challenges that will be overcome in the near future with advancements in technology. In a more recent paper, Joffe et al. (2004) studied the feasibility of developing an initial hydrogen infrastructure for refuelling hydrogen buses in London, and whether this infrastructure might provide a sufficient and suitable platform for private vehicles. Their paper presented a methodology for modelling a hydrogen infrastructure and an analysis of the technical issues for installing a hydrogen facility. The proposed infrastructure focused only on installing different scales of on-site hydrogen production technologies, namely steam methane reforming and electrolysis of water, and assessing the operational decisions of such plants to satisfy the buses demand. However, it can be argued that the model fails to consider the economics associated with the hydrogen infrastructure, which are an important aspect for determining the optimal configuration of the hydrogen supply chain. The authors concluded that the choice of hydrogen production technology may have a significant impact on when the infrastructure would be installed, and on the flexibility of the timing of hydrogen production and bus refuelling. Up to now, there has been a lack of mathematical models that describe and integrate all components of hydrogen supply chain within a single framework. Van Den Heever and Grossmann (2003) discussed the integration of production planning and reactive scheduling for the optimization of a hydrogen supply network. The network described in their paper consists of five hydrogen production plants, four inter-connected pipelines and 20 customers. They proposed a methodology to solve the multiperiod mixed-integer nonlinear programming models for both the planning and scheduling levels. They claimed that their research provides a unique integration approach that has never been considered before. However, their work addresses the operational level of an existing, small hydrogen supply chain rather than discussing the design aspects of the network model itself. To the best of our knowledge, no one has investigated the role of optimization techniques, i.e., a mathematical

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423– 438

A FUTURE HYDROGEN SUPPLY CHAIN modelling approach, in designing and operating a future national hydrogen supply chain. The use of an optimization approach would provide a technological springboard for the early phases of developing a hydrogen supply chain. It will predict the required investment in new hydrogen production plants and distribution networks. It will also enable governments to choose, in advance, the optimum configurations for hydrogen production, storage and delivery systems. This may be of a vital importance for national and international policy makers before making strategic decisions. PROBLEM DESCRIPTION The hydrogen supply chain of interest consists of: medium-to-large centralized hydrogen production facilities, transportation modes and large-scale storage facilities. We assume that hydrogen may be produced from three different energy sources: natural gas (methane), coal and biomass via two distinct types of commercially proven technologies, namely steam methane reforming and gasification. The purified hydrogen generated from the central facility has to either be liquefied or compressed before being stored or distributed. Liquid hydrogen is stored in super-insulated spherical tanks to minimize heat loss and boil-off rate, then delivered via tanker trucks or railway tank cars. In contrast, compressed-gaseous hydrogen is stored in pressurized cylindrical vessels to increase the energy density, and distributed by tube trailers or railway tube cars. The different types of storage facilities would be located either next to the production facilities or away from the production source serving as distribution terminals. The model is used to establish and investigate a number of strategic decisions required to fulfil the customers’ needs. These decisions include: the number, location, type and capacity of hydrogen production plants and storage facilities, the total production rate of hydrogen in each grid, the determination of the total average inventory in each grid, and the size and type of transportation flow. Taking these decisions into account, the model also minimizes both capital and operating costs of the hydrogen supply chain. The hydrogen network is assumed to operate at steadystate conditions according to which demand is timeinvariant. It is important to note that this is usually a simplified assumption since demand varies with time. The network described by the model is demand-driven, which means that the establishment of production plants, storage facilities and transportation links mainly depend on demand. Moreover, the network design problem is formulated as an MILP problem. It is worth mentioning that in this paper only a ‘snapshot’ of the supply chain is developed without deriving a migration pathway from the existing infrastructure. Migration pathways will be considered in future work. This paper examines the design and operation of three different configurations for the future hydrogen supply chain based on the physical form of the hydrogen. The first configuration considers the distribution of liquid hydrogen via tanker trucks and railway tank cars to various storage facilities. In the second configuration, hydrogen will be distributed as a compressed gas via tube trailers

425

and railway tube cars. The last configuration addresses the delivery of liquid hydrogen only by tanker trucks. MODEL COMPONENTS Demand Estimation The first step in designing a system to deliver hydrogen transportation fuel is characterizing the hydrogen demand. We have estimated the total hydrogen demand in Great Britain as a function of total number of vehicles, average total distance travelled and vehicle fuel economy. The estimated demand is assumed to supply private-and-light goods (PLG) vehicles and buses at 2002 levels. This is based on the ‘asymptotic’ assumption that 100% of the abovementioned vehicles would be powered by proton exchange membrane fuel cells. Table 1 shows the parameters that were used in calculating the total equivalent hydrogen demand. Another important issue in designing a hydrogen supply chain is identifying the geographical distribution of the demand, i.e., the number of vehicles per square kilometre. This approach is accomplished by dividing Great Britain into 34 grid squares of equal size. Next, the population in each grid is calculated and divided by the total population of Great Britain. The ratio obtained is then multiplied by the total equivalent demand in order to determine the demand of hydrogen in each grid. Of course, this assumes people in different parts of Great Britain consume the same amount of fuel. A more detailed model might also ‘zoom in’ and use a denser grid in regions with high consumption. Production Plants Since hydrogen can be produced from a wide variety of feedstocks including natural gas, coal and biomass, a set of plant types with different production technologies can be used. The production capacity of each plant is bound between minimum and maximum limits. The establishment of plant types will be determined by the demand of the grid, the failure of the grid to fulfil its needs from neighbouring grids, and the cost of transportation. Each plant type incurs fixed capital and unit production costs while producing at a steady-state. The production decisions comprise a definition of the number, location and capacity of plant types. The total production rates of hydrogen in each grid are also contained within the production decisions. Transportation Modes Delivery of hydrogen from the production plants to the storage facilities is undertaken by trucks or rail—assuming

Table 1. Parameters used for estimating total hydrogen demand in Great Britain. Parameter

PLG vehicles

Number of vehicles (Df T, 2004) Average distance travelled (km y21) (Df T, 2004) Fuel economy (kg H2 km21) (NRC, 2004; Larson et al., 1996)

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423 –438

6

27.17  10 16.47  103 0.0096

Buses 92.00  103 56.52  103 0.1177

426

ALMANSOORI and SHAH

that the railway networks required already exist. The establishment of transportation links between various grids is determined by the cost of the transportation mode versus the cost of establishing a new production facility. Each transportation link has a specific capacity, deterministic delivery distance, and minimum/maximum allowable flow rates. Transportation decisions include whether to establish a link between different grids and what the flow rate should be. Storage Facilities Each hydrogen form generated—liquid or gas—can be stored in different types of storage facilities according to its physical form. The capacity of storage facilities is subject to upper and lower limits. The establishment of storage types is essential and may be independent of the plant locations. Each storage facility type has fixed capital and unit storage costs. Hydrogen could be held in storage facilities for a specified period to serve demand and supply fluctuations as well as plant interruptions. The stored hydrogen will be supplied to customers according to the daily demand. Storage decisions include the number, location and capacity of storage types, as well as the total average inventory of hydrogen in each grid. MODEL FORMULATION The mathematical model proposed to solve this problem is formulated as an MILP. In the following sections, the model notation, constraints, and objective function terms are described in more detail. Notation Indices g g0 i l p s

grid squares grid squares such that g0 = g product physical form type of transportation modes plant type with different production technologies storage facility type with different storage technologies

DTig DWl FEl FPl GEl LUTl L lgg0 MEl PCapmin pi PCapmax pi PCCpi Qmin il Qmax il SCapmin si

SCCsi SPl TCapil TMAl TMCil UPCpi USCsi

a b

capital charge factor—payback period of capital investment, y total demand for product form i in grid g, kg d21 driver wage of transportation mode l, $ h21 fuel economy of transportation mode l, km L21 fuel price of transportation mode l, $ L21 general expenses of transportation mode l, $ d21 load/unload time of product for transportation mode l, h trip21 average delivery distance between grids g and g0 by transportation mode l, km trip21 maintenance expenses of transportation mode l, $ km21 minimum production capacity of plant type p for product form i, kg d21 maximum production capacity of plant type p for product form i, kg d21 capital cost of establishing plant type p producing product form i, $ minimum flow rate of product form i by transportation mode l, kg d21 maximum flow rate of product form i by transportation mode l, kg d21 minimum storage capacity of storage type s for product form i, kg

maximum storage capacity of storage type s for product form i, kg capital cost of establishing storage type s storing product form i, $ average speed of transportation mode l, km h21 capacity of transportation mode l transporting product form i, kg trip21 availability of transportation mode l, h d21 cost of establishing transportation mode l transporting product form i, $ unit production cost for product form i produced by plant type p, $ kg21 unit storage cost for product form i at storage type s, $ kg21 d21 network operating period, d y21 storage holding period—average number of days’ worth of stock, d

Continuous variables DLig DigI FC FCC FOC GC LC MC Ppig PTig Qilgg0 STig TCC TDC TOC

demand for product form i in grid g satisfied by local production, kg d21 imported demand of product form i to grid g, kg d21 fuel cost, $ d21 facility capital cost, $ facility operating cost, $ d21 general cost, $ d21 labour cost, $ d21 maintenance cost, $ d21 production rate of product form i produced by plant type p in grid g, kg d21 total production rate of product form i in grid g, kg d21 flow rate of product form i by transportation mode l between grids g and g0 , kg d21 total average inventory of product form i in grid g, kg transportation capital cost, $ total daily cost of the network, $ d21 transportation operating cost, $ d21

Integer variables NPpig NSsig NTU

number of plants of type p producing product form i in grid g number of storage facilities of type s for product form i in grid g number of transport units

Binary variables Xilgg0 Yig

Parameters CCF

SCapmax si

Zig

1 if product form i is to be transported from grids g to g0 by transportation mode l, 0 otherwise 1 if product form i is to be exported from grid g, 0 otherwise 1 if product form i is to be imported into grid g, 0 otherwise

Constraints Demand constraints As previously mentioned, each grid has its own deterministic demand. This demand must be fulfilled eventually by production facilities established within a particular grid, i.e., local production, or by importing products from other neighbouring grids. Therefore, the demand satisfied by local production of a product form i in grid g (DLig ) is expressed by the following constraint: DLig  PTig

8i, g

(1)

On the other hand, the demand for a product form i in grid g satisfied by neighbouring grids (DIig ) is equal to the

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423– 438

A FUTURE HYDROGEN SUPPLY CHAIN total flow imported to that grid by all types of transportation modes (Qilg0 g): X DIig ¼ Qilg0 g 8i, g (2) l,g0

427

the establishment of a transportation mode between two grids in the network: max 0 0 0 Qmin il Xilgg  Qilgg  Qil Xilgg

8i, l, g, g0 ; g = g0 (8)

The total grid demand (DTig ) must equal the demand satisfied by the local production plus the demand imported from other grids: DTig ¼ DLig þ DIig

8i, g

(3)

Production facilities constraints A total mass balance on a grid must be written to determine the total daily production rate of a particular grid. Since we assume a steady-state operation, the sum of the total flow rate of each product entering grid g (Qilg0 g) plus the total production rate of the same grid (PTig ) must equal the total flow rate leaving this grid (Qilgg0 ) plus the total demand required by grid g itself (DTig ): X PTig ¼ (Qilgg0  Qilg0 g ) þ DTig 8i, g (4) l,g0

The total production rate of a product form i in grid g is equal to the production rate of all plants of type p established in that same grid: X Ppig 8i, g (5) PTig ¼ p

The production rate of a product form i produced by any plant of type p in grid g (Ppig) cannot exceed certain limits. Thus, there is always a maximum production capacity for any product (PCapmax pi ). Moreover, there is often a minimum production rate (PCapmin pi ) that must be maintained while the plant is operating: max PCapmin pi NPpig  Ppig  PCappi NPpig

8p, i, g

(6)

Constraint (6) means that the maximum daily production rate of product form i produced by plant type p is constrained by the number of production facilities NPpig. Likewise, the total production rate of each product form i in grid g (PTig ) cannot exceed certain limits. Therefore, PTig is bound between the minimum and maximum production capacities of all plants that are established in this particular grid: X X T PCapmin PCapmax pi NPpig  Pig  pi NPpig 8i, g p

p

(7)

Flow of a product form i between different grids can only occur in one direction. This is because if a grid can only satisfy its needs by importing from other grids it would not make sense for that grid to export to other grids: Xilgg0 þ Xilg0 g  1 8i, l, g, g0 ; g = g0

A particular grid can only import product from neighbouring grids or export product to other grids, or neither but not both for the same reason stated earlier: 8i, l, g, g0 ; g = g0

(10)

Zig  Xilg0 g 8i, l, g, g0 ; g = g0 Yig þ Zig  1 8i, g

(11) (12)

Yig  Xilgg0

Storage facilities constraints An important issue in the operation of this network is the ability of the storage facilities to hold the product for a certain period of time in order to accommodate for any demand and supply fluctuations. Therefore, storage facilities could be built either locally within a specific grid next to the production facility—if established—or outside the grid boundary away from the production source. During steady-state operation, the total inventory of a product form i in grid g (STig ) is equal to a function of the corresponding demand (DTig ) multiplied by the storage period (b), days of cover: STig ¼ bDTig

8i, g

(13)

The parameter b is introduced to cover fluctuations in both supply and demand as well as plant interruptions. The capacity of each storage facility of type s storing product form i (SCapsi) cannot exceed certain limits. This consideration will guarantee that the total inventory of each product in each grid will be bound within certain limits: X

Transportation constraints There must be a continuous flow of product between different grids in order to satisfy the required demand. The flow of a product form i from grid g to a different grid g0 will only exist if the transportation mode is established. Thus, there is always a minimum and a maximum and Qmax flow rate of products (Qmin il il ) needed to justify

(9)

s

T SCapmin si NSsig  Sig 

X

SCapmax si NSsig

8i, g

s

(14) This constraint also implies that the total inventory of product form i stored in a grid g is constrained by the number of storage facilities NSsig.

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423 –438

428

ALMANSOORI and SHAH

Non-negativity constraints All continuous and integer non-negative:

variables

must

be

DLig  0

8i, g

(15)

DIig

8i, g

(16)

0

NPpig  0 8i, p, g NSsig  0 8i, s, g

(17) (18)

PTig  0

(19)

8i, g

Ppig  0

8i, p, g

Qilgg0  0

(20) 0

8i, l, g, g , g = g

0

(21)

STig  0 8i, g

(22) Objective Function

The aim of the proposed model is to minimise both capital and operating costs of the hydrogen supply chain. The former are one-time costs associated with the establishment of production plants, storage facilities, and transportation links. On the other hand, operating costs are incurred on a daily basis and are associated with the cost of production of hydrogen at the plants, the cost of their storage, and the cost of their transportation through the network. Although a variety of metrics could be investigated in a more detailed study, such as well-to-wheel analysis of CO2 emissions, we focus on cost here. In the subsequent sections, the cost terms that are used to estimate the overall cost of the hydrogen supply chain are discussed in detail. Facility capital cost The facility capital cost is related to the establishment of production plants and storage facilities at candidate locations. It is calculated by multiplying the number of plants and storage facilities by their capital cost as follows: ! X X X PCCpi NPpig þ SCCsi NSsig (23) FCC ¼ i,g

p

affect the capital cost of transporting hydrogen. On the other hand, the cost of the transport unit (TMCil) includes the cost of the transport container, the cost of the undercarriage and the cost of the cab. In the case of rail transport, there will be no cab cost. The number of trucks and/or railcars required to satisfy a certain flow between different grids are given by the following relationship:
 X Qilgg0 2Llgg0 þ LUTl (24) NTU ¼ TMAl TCapil SPl i,l,g,g0

s

Transportation capital cost The capital cost of different types of transportation modes takes into account the number of the transport units, i.e., trucks or railcars, required to satisfy the demand and the cost of each unit. The number of transport units (NTU ) depend significantly on the average distance travelled between different grids (Llgg0 ). Long delivery distances mean more trucks or railcars are required to deliver a given quantity of hydrogen, which can result in a higher transportation capital cost. The capacity of a transport container (TCapil) is also an important factor, especially for long distances, since it determines the number of trips that must be made between the production plant and the storage facility. It is clear that large storage containers will reduce the cost of transportation as fewer trucks or railcars are required. In addition, the flow rate of products between various grids (Qilgg0 ) and the transportation mode availability (TMAl), average speed (SPl), and loading/unloading time (LUTl) are other main factors that

It can be noted from equation (24) that Llgg0 is multiplied by two to account for the return journey. Therefore, the transportation capital cost is given by the following equation: (25)

TCC ¼ NTU  TMCil

Facility operating cost The facility operating cost is related to the cost required to operate the production plants and storage facilities efficiently. It is obtained by multiplying the unit cost of production and storage by the corresponding amount of production and storage: ! X X X T UPCpi Ppig þ USCsi Sig (26) FOC ¼ i,g

p

s

Transportation operating cost The transportation operating cost consists of fuel, labour, maintenance, and general costs. The daily fuel cost contributes significantly to the total operating cost. It is a function of daily fuel usage and fuel price:
 X 2Llgg0 Qilgg0 FPl (27) FC ¼ FEl TCapil i,l,g,g0 where the first and second terms of the multiplication in equation (27) represent fuel price and daily fuel usage, respectively. The daily labour cost associated with transporting the hydrogen between different grids is given as a function of the total delivery time and driver wage:

 X Qilgg0 2Llgg0 DWl þ LUTl (28) LC ¼ TCapil SPl i,l,g,g0 Again, the first and second terms of the multiplication in equation (28) represent driver wage and total delivery time, respectively. The maintenance cost includes general maintenance of the transportation systems. It is a function of the total daily distance driven and the cost per unit distance travelled:
 X 2Llgg0 Qilgg0 MEl (29) MC ¼ TCapil i,l,g,g0

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423– 438

A FUTURE HYDROGEN SUPPLY CHAIN The last operating cost is the general cost. It consists of transportation insurance, license and registration, and outstanding finances. It depends on the number of transport units and the corresponding expenses:

 X Qilgg0 2Llgg0 GEl þ LUTl (30) GC ¼ TMAl TCapil SPl i,l,g,g0 Finally, the total transportation operating cost is equal to the sum of fuel, labour, maintenance and general costs: TOC ¼ FC þ LC þ MC þ GC

(31)

MODEL SUMMARY By combining the cost terms derived earlier in Objective Function section, we obtain the total daily cost of the hydrogen supply chain: TDC ¼

FCC þ TCC þ FOC þ TOC aCCF

min TDC

. hydrogen produced in medium-to-large, centralised plants and delivered to various storage facilities as a liquid via tanker trucks and railway tank cars; . hydrogen produced in medium-to-large, centralized plants and distributed to various storage facilities as a compressed gas via tube trailers and railway tube cars; and . the last configuration is similar to the first one but only tanker trucks are allowed for delivery. In building the model, data has been collected from a variety of sources. We have applied a number of engineering-oriented methods to validate the data as far as possible. These include comparison of similar data from alternative sources, comparison with petroleum supply chain data, first principles modelling, costing conventions and thermodynamic and dimensional analysis. We believe this helps to generate a definitive data set for other researchers. The data is also believed to be representative and valid for real cases.

(32)

The first term of the right-hand-side of equation (32) is divided by the network operating period (a) and the annual capital charge factor (CCF ) to find the cost per day in US dollars. Finally, the total daily cost of the network is minimized by the optimization:

Hydrogen Demand The daily demand of hydrogen required in each grid square is summarized in Table 3. The total demand of these grids is satisfied during the network operating period which is 365 days per year (d y21). The capital charge factor associated with the network investment is assumed to be three years.

(33)

The above minimization is subject to all the constraints given in Constraints section. GREAT BRITAIN-BASED CASE STUDY The mainland of Great Britain was chosen for several reasons. Great Britain has a fairly well established environmental culture with the potential to develop the infrastructure of a new source of energy such as hydrogen. Also, data that is required to design such an infrastructure is easily accessible and available from different authorities. In addition, Great Britain has a long-term strategic initiative to promote a widespread use of hydrogen in the transport sector. For example, it is one of the European countries that are currently involved in the EU-funded Clean Urban Transport for Europe (CUTE) project. The fruit of this project was the introduction of three fuel cell buses in London at the beginning of 2004 (TfL, 2004). Considering the above advantages, Great Britain represents an ideal case study to map out all possible hydrogen networks. The versatility of the examined model was tested using a number of production and storage technologies as well as a number of transportation modes. The types of production, storage, and transportation technologies are summarized in Table 2. These technologies were then combined together to form three different hydrogen supply chain configurations. All configurations have the same production technologies. However, the key difference in these configurations is the physical form of the hydrogen produced which determines the type of transport and storage. The three studied configurations are:

429

Production Facilities The minimum and maximum production capacity of each plant type with respect to each product form and PCapmax (PCapmin pi pi ) are assumed to be 10 000 and 480 000 kilograms per day (kg d21), respectively. These values are based on commercial and near commercial medium-to-large hydrogen plants (NRC, 2004). The capital costs and the unit production costs of these plants are listed in Table 4. A detailed economic analysis was performed to estimate these cost figures. This analysis appears in Appendix A. Transportation Modes The maximum flow rate of liquid and compressedgaseous hydrogen transported via trucks or railcars (Qmax il ) was assumed to be 960 000 kg d21, while the minimum flow rate (Qmin il ) was assumed to be equal to the capacity Table 2. Production, storage and distribution technologies for the examined case study. Production technology Storage technology Transportation method

† † † † † † † †

Steam methane reforming (SMR) Coal gasification Biomass gasification Liquid hydrogen storage Compressed gas storage Liquid hydrogen (LH2) tanker truck Liquid hydrogen railway tank car Compressed-gaseous hydrogen (CH2) tube trailer † Compressed-gaseous hydrogen railway tube car

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423 –438

430

ALMANSOORI and SHAH Table 3. Demand of hydrogen in each grid square. Grid, g

The average distance within each grid was also determined and assumed to be the same for all grids. The values of these delivery distances are shown in Table B.1 in Appendix B.

Hydrogen demand (t d21)

01 02 03 04 05 06 07 08 09 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

102 80 158 198 41 130 173 7 85 316 385 9 635 902 143 24 489 997 497 41 63 624 1047 861 356 63 394 879 2727 188 208 252 185 136

Storage Facilities The minimum and maximum storage capacities of each storage type with respect to each product form (SCapmin si and SCapmax si ) were assumed to be 10 000 and 540 000 kilograms, respectively. The maximum value was estimated by doubling the size of the largest tank available at NASA (Peschka, 1992), whereas the minimum value was based on tanks available in today’s gas industry. A comprehensive assessment was carried out to determine the capital cost for the establishment of different storage facility types and the unit storage costs. The results obtained are summarized in Table 6. The average number of days’ worth of stock (b) in these storage facilities was assumed to be 10 days. RESULTS AND DISCUSSION

of each transport mode (see Table 5). This means that the minimum allowable quantity of hydrogen flow between grids is equal to a fully-loaded transport unit. The maximum allowable quantity, however, is based on the assumption that individual modes cannot transport more than what is produced by a large production facility. The parameters used to calculate the capital and operating costs for the different types of transportation modes are listed in Table 5. It is worth mentioning that most of the values in Table 5 were obtained from a report by Amos (1998). Other values for fuel price, driver wage, and maintenance and general costs were obtained from different sources as indicated in the table. The average delivery distances between different grids were estimated based on the area of the grid. These distances may vary depending on the grid’s location and were measured from the centre of each grid, if possible.

The aim of the proposed model is to outline the structure of the future hydrogen supply chain by satisfying hydrogen demand for vehicular use in Great Britain. As mentioned, the model was constrained to supply liquid and compressed-gaseous hydrogen via trucks and rail to various storage facilities. It was also constrained to deliver liquid hydrogen via trucks only to compare the difference in cost between the single- and multi-mode hydrogen supply networks, as well as to study the effect of varying the type of transportation modes on the final network configuration. The optimal solution of the three configurations led to the network structures shown in Figures 1 –3. The number of production and storage facilities established in each grid of these figures is enclosed within a square or a circle, respectively. The number above each transportation link, i.e., arc, denotes the corresponding flow rate of liquid and compressed-gaseous hydrogen (Qilgg0 ) in metric tonnes per day (t d21). The exact values of these flow rates between different grids with respect to transportation modes are listed in Tables C.1 and C.2 of Appendix C. In addition, a summary of the results obtained for the different continuous variables is tabulated in C.3 and C.4. Figure 1 illustrates that tanker trucks are the main distribution mode used to deliver liquid hydrogen from the point of production to the point of use. This implies that liquid tanker trucks are a preferable means of delivery for short distances and moderate hydrogen demand. For long

Table 4. Capital and unit production costs of hydrogen production technologies. Plant type, p Steam reforming

Product form, i Plant capital cost, PCCpi (million $) Unit production cost, UPCpi ($ kg21)

Coal gasification

Biomass gasification

LH2

CH2

LH2

CH2

LH2

CH2

535 1.53

379 0.94

958 1.71

771 1.06

1412 3.08

907 1.71

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423– 438

A FUTURE HYDROGEN SUPPLY CHAIN

431

Table 5. Parameters used to estimate the capital and operating costs of transportation modes. Transportation mode, l Capacity, TCapil (kg trip21) (Amos, 1998) Container cost ($) (Amos, 1998) Undercarriage cost ($) (Amos, 1998) Cab cost ($) (Amos, 1998) Total cost, TMCil ($) (Amos, 1998) Fuel economy, FEl (km L21) (Amos, 1998) Average speed, SPl (km h21) (Amos, 1998) Mode availability, TMAl (h d21) (Amos, 1998) Load/unload time, LUTl (h trip21) (Amos, 1998) Driver wage, DWl ($ h21) (Sinnott, 2005) Fuel price, FPl ($ L21) (DTI, 2004) Maintenance expenses, MEl ($ km21) (Barnes and Langworthy, 2003) General expenses, GEl ($ d21) (Victoria Transport Policy Institute, 2004)

Table 6. Capital and unit storage costs of hydrogen storage facilities. Storage type, s Product form, i Storage capital cost, SCCsi (million $) Unit storage cost, USCsi ($ kg21 d21)

Cryogenic Pressurized spherical tank cylindrical vessel LH2 122 0.005

CH2 1894 0.076

distances and large quantities, railway tank cars become more attractive due to their low transportation operating cost and high storage capacity. However, in the case of the compressed-gaseous hydrogen network (see Figure 2) both railway tube cars and tube trailers are utilised since their storage capacity does not differ significantly. Also, there are fewer transportation links established in the compressed-gaseous hydrogen network than in the liquid hydrogen network. This is because of the high cost associated with distributing gaseous hydrogen to various locations. Therefore, more production plants are established to fulfil the required demand. It can be shown from Figures 1 –3 that grid 29, which covers London and the surrounding region, has the highest number of production plants and storage facilities. This could be explained by the high population density in this region. The fewest production plants and storage facilities are in grids covering the Scotland region. The reason for this is the low population density and thus a low hydrogen demand. Moreover, it can be observed that a number of grids, such as 1, 15 and 34, do not satisfy their local hydrogen needs. Therefore, the demand is fulfilled from neighbouring plants; this is a more economically feasible option than establishing a new production facility in those areas. The figures also show that grids 18 and 23, which include the Manchester and Birmingham areas, have the second highest number of storage facilities, and at least two production plants. This is due to the fact that this region is the second most populated area in Great Britain and is considered to be a hub of heavy industrial activities. In addition, it can be found from these figures that steam methane reforming plants are established in all grids, since it is the most widespread and currently the least expensive method to produce hydrogen. On the other hand, the establishment of different types of storage facilities is necessary in all grids in order to supply customers with their required hydrogen demand.

Tanker truck

Tube trailer

Tank railcar

Tube railcar

4082 350 000 60 000 90 000 500 000

181 100 000 60 000 90 000 250 000

9072 400 000 100 000 — 500 000

454 200 000 100 000 — 300 000

2.55 55 18 2 23 1.16 0.0976 8.22

4.25 45 12 12 23 0.28 0.0621 6.85

The cost for each of the examined network configurations is 64.56, 494.10 and 64.57 million dollars per day ($ d21), respectively. A breakdown of these costs is listed in Table 7. In order to contextualize the cost values, the network costs are expressed in dollars per kilogram of hydrogen delivered ($ kg21). In effect, the cost for the liquid and gaseous hydrogen based networks become 4.82 and 36.89 $ kg21, respectively. Also, the respective costs for the networks are 0.31 and 2.35 in dollars per kilometre ($ km21). Putting these values in perspective, the cost for a petrol network is 0.11 $ km21 assuming that the price of untaxed petrol is 0.5 pounds per litre and an exchange rate of 1.5. It can be seen from Table 7 that the cost of the liquid hydrogen network is nearly eight times lower than that of the compressed-gaseous hydrogen network. Hugo (2005) also demonstrated that if cost is used as an objective, the preferred form of hydrogen is liquid. One of the main contributors to the cost is the capacity of the transport container which is about 20 times higher for liquid hydrogen than compressed gas. Certainly, this would increase the number of trips required between production plants and storage facilities; therefore increasing the overall cost of the hydrogen network. Another main factor behind the high cost of the compressed-gaseous hydrogen network is the storage and distribution costs of hydrogen gas. This is due to the low energy density of gaseous hydrogen at atmospheric pressure—approximately 70 lower than that of liquid hydrogen (Hord, 1978). To overcome this issue highpressure cylindrical vessels are used, hence increasing the cost of the system and the safety requirements. Also, the cost of medium-to-long term storage of compressed hydrogen gas is prohibitive because such storage method is too bulky, i.e., requiring many storage vessels. Based on studies by Amos (1998) and Simbeck and Chang (2002), the unit storage cost for liquid hydrogen is 18 $ kg21 compared to 281 $ kg21 for a pressure vessel. In summary, despite the high energy cost of liquefaction, storage and delivery of liquid hydrogen prevail over the high storage and distribution costs of compressed hydrogen gas. Additionally, Table 7 shows that the difference in cost between the single- and multi-mode networks is insignificant. While this negligible difference would imply that neither mode has a particular advantage over the other, the decision to use truck distribution is still made. This is because it is more favourable to use tanker trucks instead

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423 –438

432

ALMANSOORI and SHAH

Figure 1. Network structure of liquid hydrogen produced via mediumto-large steam methane reforming plants, stored in medium-to-large storage facilities, and distributed via tanker trucks and railway tank cars.

of railway tank cars for transporting liquid hydrogen due to the flexibility in operations. Trucks are also easier to schedule, more efficient, and can be dispatched to users at any time, especially in emergencies. In addition, trucks use a road infrastructure which is well-established, whereas railways use a confined network that is shared by other users. Table 7 also demonstrates that the capital cost of production and storage facilities for the compressed-gaseous network is much higher than that of the liquid-based network. This finding indicates that the former network has more production plants and hence fewer transportation links. To verify the outcome, the overall demand satisfied by local production facilities for both networks was calculated. The result shows that 85% and 88% of the overall demand is fulfilled by local production in the case of liquid and compressed-gaseous networks, respectively. These figures are an indicator of the degree of centralisation. Higher distribution and storage costs of compressed hydrogen gas result in a more decentralized solution, i.e.,

Figure 2. Network structure of compressed-gaseous hydrogen produced via medium-to-large steam methane reforming plants, stored in mediumto-large storage facilities, and distributed via tube trailers and railway tube cars.

more plants established; thus more demand is being satisfied by local production. The MILP models used in this paper were solved by a Pentium 4, 1.8 GHz Dell machine running version 9.0 of the CPLEX solver accessed via the GAMS modelling tool (Brooke et al., 1998). The corresponding computational statistics are summarized in Table 8. As can be seen from the table, the short times required for solving the three different configurations and the low optimality gaps are satisfactory. CONCLUSIONS AND FUTURE RESEARCH The future hydrogen supply chain will be quite different from the current energy systems because of concerns about long-term viability as well as the existence of stringent environmental regulations. In the past, research efforts

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423– 438

A FUTURE HYDROGEN SUPPLY CHAIN

433

Table 8. Summary of computational results. Configuration Number of constraints Number of integer variables Number of continuous variables Optimality gap (%) CPU time (s)

1

2

3

11 807 2448 2491 0.01 2093

11 807 2448 2491 0.01 27781

6197 1326 1369 0.01 717

were directed towards analysing individual sub-processes within the supply chain such as: production, storage or distribution. Recently, however, attention has been increasingly drawn to focus on the design and operation of the supply chain as a whole due to the large economic benefits achieved from this process. The aims of this work were, therefore, to: . develop quantitative tools that can support strategic decisions in hydrogen supply chain design and operation; . identify the main data requirements for such an activity; and . assess the capability of supply chain design models to address such problems.

Figure 3. Network structure of liquid hydrogen produced via mediumto-large steam methane reforming plants, stored in medium-to-large storage facilities, and distributed via tanker trucks.

Table 7. Breakdown of total hydrogen network costs. Configuration

1

Capital cost Plants and storage 47.31  109 facilities Transportation modes 84.24  106 Total capital cost ($) 131.55  109 Operating cost Plants and storage 21.16  106 facilities Transportation modes Fuel cost 42.38  103 Labour cost 66.38  103 Maintenance cost 9.40  103 General cost 1.35  103 119.50  103 Total operating costs ($ d21) Total network cost ($ d21) 64.56  106

2

3

513.28  109

47.31  109

1.14  109 514.42  109

80.22  106 19.00  109

22.77  106

21.16  106

212.08  103 47.63  103 1.19  106 66.42  103 71.84  103 10.22  103 27.96  103 1.32  103 1.50  106 125.59  103 494.10  106

64.57  106

In this work, we have studied three different configurations concerned with the establishment of the future hydrogen supply chain. The results obtained provide a good indication of the value of having a model that takes into account different existing components in these networks. Based on our data and assumptions, the optimal configuration of the future hydrogen supply chain at the stage where hydrogen is an entrenched fuel for transportation may be the production of hydrogen from natural gas in medium-to-large, centralized reforming plants. This network makes use of tanker trucks to deliver liquid hydrogen to storage facilities. The resulting architecture is compatible with the existing petroleum supply chain in which petrol is produced from centralized refineries and distributed to several storage terminals by mostly trucks. A further feasible configuration of the hydrogen supply chain is the production of hydrogen from natural gas in medium-to-large, centralized reforming plants and transported as compressed gas to users. Based on the results obtained, this option can be quite expensive at the current stage due to the high capital cost of the storage facilities. However, as storage technologies are likely to develop in the near future, delivery of hydrogen via tube trailers will become cheaper than liquid hydrogen transport. This will lead to a gaseous hydrogen based network that is competitive because of the low production cost of compressed hydrogen gas. At this stage, the preliminary computational results of the models are promising. However, there are major tasks that still need further investigation to improve our model. The following outstanding tasks are summarized below: (1) Consider the evolution of the network over time, rather than a snapshot of the network at one point in time. This will require building ‘pathway’ models within a robust optimization framework. Also, we will consider an unsteady-state form of the problem according to which demand is time-variant.

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423 –438

434

ALMANSOORI and SHAH

(2) At the present stage, the decision of plant establishment is governed by the cheapest hydrogen production technology without taking into account the availability of primary energy sources. The models will be improved to consider the distribution of energy sources as well as different scales of production. (3) Determining the number of fuelling stations required to replenish customers with hydrogen fuel and take into account secondary distribution of hydrogen within individual grid squares.

REFERENCES Amos, W.A., 1998, Costs of storing and transporting hydrogen, 1–216 (National Renewable Energy Laboratory, Golden). Barnes, G. and Langworthy, P., 2003, The Pre-mile Costs of Operating Automobiles and Trucks, 1– 46 (Minnesota Department of Transportation, Office of Research Services, St Paul). British Petroleum, 2004, BP Statistical Review of World Energy 2004, 1–44 (Surrey). Brooke, A. et al., 1998, GAMS: A User’s Guide, 1–276 (GAMS Development Corporation: Washington). Department of Trade and Industry (DTI), 2004, Information and Statistics: Energy Prices, 23 August 2004, from http://www.dti.gov.uk/energy/ inform/energy_prices/tables/table_531_mar04.xls. Department for Transport (DfT), Transport Trends: 2002 Edition, 28 July 2004, http://www.dft.gov.uk. Guy, K.W.A., 2001, The hydrogen economy—developing the infrastructure, 6th World Congress of Chemical Engineering, Melbourne. Hord, J., 1978, Is hydrogen a safe fuel? International Journal of Hydrogen Energy, 3(2): 157–176. Hugo, A., 2005, Environmentally conscious process selection, design and optimization, in Department of Chemical Engineering and Chemical Technology, 266 (Imperial College London, London). Joffe, D., Hart, D. and Bauen, A., 2004, Modelling of hydrogen infrastructure for vehicle refuelling in London, Journal of Power Sources, 131(1–2): 13 –22.

Larson, E.D., Worrell, E. and Chen, J.S., 1996, Clean fuels from municipal solid waste for fuel cell buses in metropolitan areas. Resources, Conservation and Recycling, 17(4): 273– 298. National Research Council (NRC), 2004, The Hydrogen Economy— Opportunities, Costs, Barriers, and R&D Needs, 240 (The National Academies Press, Washington). Ogden, J.M., 1999, Prospects for building a hydrogen energy infrastructure, Annual Review of Energy and the Environment, 24: 227 –279. Ogden, J.M., Steinbugler, M.M. and Kreutz, T.G., 1999, A comparison of hydrogen, methanol and gasoline as fuels for fuel cell vehicles: implications for vehicle design and infrastructure development. Journal of Power Sources, 79(2): 143– 168. Peschka, W., 1992, Thermal insulation, storage and transportation of liquid hydrogen, in Liquid Hydrogen: Fuel of the Future, 71–103 (SpringerVerlag, Wien, Germany). Peter, M.S. and Timmerhaus, K.D., 1991, Cost estimation, in Clark, J.B. and Morriss, J.M. (eds). Plant Design and Economics for Chemical Engineers, 150 –215 (McGraw-Hill, New York). Pridmore, A. and Bristow, A.L., 2002, The Role of Hydrogen in Powering Road Transport, 1–31 (Tyndall Centre for Climate Change Research, Leeds). Service, R.F., 2004, Toward a hydrogen economy: The carbon conundrum, Science, 305(5686): 962–963. Simbeck, D.R. and Chang, E., 2002, Hydrogen Supply: Cost Estimate for Hydrogen Pathways—Scoping Analysis, 1–69 (National Renewable Energy Laboratory, Golden). Sinnott, R.K., 2003, Costing and project evaluation, in Coulson & Richardson’s Chemical Engineering: Chemical Engineering Design, 242–282 (Butterworth-Heinemann, Oxford). Transport for London (TfL), 2004, London Buses: Fuel Cell Buses, 30 December 2004, from http://www.tfl.gov.uk/buses/downloads/ fuel-cell-buses.pdf. Van den Heever, S.A. and Grossmann, I.E., 2003, A strategy for the integration of production planning and reactive scheduling in the optimization of a hydrogen supply network, Comput Chem Eng, 27(12): 1813–1839. Victoria Transport Policy Institute, 2004, Transportation Cost and Benefits Analysis: Vehicle Costs, 2 July from http://www.vtpi.org/tdm/. The manuscript was received 9 August 2005 and accepted for publication after revision 16 March 2006.

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423– 438

A FUTURE HYDROGEN SUPPLY CHAIN

435

APPENDIX A: COST ANALYSIS A detailed cost analysis was performed to estimate the cost of the hydrogen supply chain components. However, for the sake of brevity, only the procedure used to calculate the capital and operating costs of hydrogen production via steam methane reforming plants will be provided.

Table A.1. Summary of results for liquid hydrogen produced via steam methane reforming plants.

Parameter

Value

Design production capacity Plant availability Annual actual production NG
required per H2 generated NG consumed Steam consumed CO2 produced Liquefaction energy Liquefaction power Storage pressure Storage period Storage capacity LH2 dispenser rate Number of dispensers required Unit cost of SMR unit Size factor of SMR unit Unit cost of liquefaction unit Size factor of liquefaction unit Unit cost of LH2 storage Size factor of LH2 storage Unit cost of LH2 dispenser Cost of SMR unit Cost of liquefaction unit Cost of LH2 storage Cost of LH2 dispenser Cost of site for plant General facilities cost Engineering permits & start-up Fixed capital investment (FCI) Contingency Working capital Total capital cost Unit capital cost NG Electricity Steam Manpower (operating labour þ general staff)

480 000 kg d21 329 d y21 157 680 t y21 381 Btu NG/scf H2 66 812 kg h21 150 053 kg h21 183 283 kg h21 11 kWh kg21 H2 220 000 kW 1–2 atm 1 day 480 000 kg 5000 kg h21 dispenser 4 $312 kg21 d21 H2 70% $700 kg21 d21 H2 75% $19 kg21 H2 70% $100 000/dispenser $93 000 000 $227 000 000 $6 000 000 $400 000 $16 000 000 $65 000 000 $39 000 000 $447 000 000 $45 000 000 $43 000 000 $535 000 000 $1115 kg d21 $64 000 000 y21 $90 000 000 y21 $14 000 000 y21 $5 000 000

Maintenance & repairs Operating supplies Interest Local taxes Insurance Total operating cost Unit production cost

$20 000 000 y21 $4 000 000 y21 $25 000 000 y21 $15 000 000 y21 $5 000 000 y21 $242 000 000 y21 $1.53 kg21




Remark 62 million scf d21 90% utilization rate Calculated from above 72% LHV efficiency Calculated from above Material balance calculation Material balance calculation

Source

(Simbeck and Chang, 2002)

(Amos, 1998) Calculated from above (Simbeck and Chang, 2002) Assumption Calculated from above (Simbeck and Chang, 2002) Calculated from above At 100 t d21 H2 basis 21

At 100 t d

H2 basis

At 100 t d21 H2 basis Calculated from above Calculated from above Calculated from above Calculated from above 5% of total process unit cost 20% of total process unit cost 10% of direct cost excluding land 10% of FCI 3 months payback period Calculated from above $2.58 million21 Btu LHV $0.05 kWh21 $12 t21 Labour wage ¼ $26 h21 Total personnel required ¼ 80 Personnel availability ¼ 2010 h y21 5% of direct cost excluding land 1% of direct cost excluding land 5% of FCI þ contingency 3% of FCI þ contingency 1% of FCI þ contingency

(Simbeck (Simbeck (Simbeck (Simbeck (Simbeck (Simbeck (Simbeck

and and and and and and and

Chang, Chang, Chang, Chang, Chang, Chang, Chang,

2002) 2002) 2002) 2002) 2002) 2002) 2002)

(Peter and Timmerhaus, 1991) (Peter and Timmerhaus, 1991) (Peter and Timmerhaus, 1991) (Peter and Timmerhaus, 1991)

(British Petroleum, 2004) (DTI, 2004) (Sinnott, 2003) (Sinnott, 2003) (Peter (Peter (Peter (Peter (Peter

Calculated from above

Natural gas.

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423 –438

and and and and and

Timmerhaus, Timmerhaus, Timmerhaus, Timmerhaus, Timmerhaus,

1991) 1991) 1991) 1991) 1991)

01

065 108 108 152 206 216 241 344 328 341 415 404 446 482 536 664 587 580 631 730 706 682 682 723 831 791 788 785 821 843 972 899 837 852

Grid no.

01 02 03 04 05 06 07 08 09 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

108 065 152 152 197 247 247 352 346 351 459 408 457 492 529 673 597 587 648 746 715 691 691 749 835 802 795 795 827 853 983 909 848 861

02

108 152 065 108 162 108 152 248 222 241 323 298 344 389 421 563 487 482 539 637 655 581 580 628 736 743 689 685 723 749 870 796 735 751

03

152 152 108 065 054 152 108 216 222 216 282 290 323 349 390 533 457 449 496 596 577 553 552 590 663 663 656 656 688 710 843 769 709 722

04

206 197 162 054 065 194 120 228 241 228 291 314 332 361 399 548 471 461 509 609 590 566 565 602 675 677 670 670 701 723 856 783 723 735

05

216 247 108 152 194 065 108 170 120 152 241 197 248 305 345 476 400 389 457 551 510 492 484 539 617 606 597 597 629 658 787 713 650 662

06

241 247 152 108 120 108 065 108 120 108 174 194 216 241 283 427 351 341 389 488 469 445 444 482 555 556 550 550 580 602 736 662 602 614

07 344 352 248 216 228 170 108 065 120 076 076 194 170 170 194 368 292 275 314 415 403 381 381 410 482 502 488 488 511 531 679 605 542 550

08 328 346 222 222 241 120 120 120 065 054 162 076 130 194 241 359 283 271 345 435 401 375 366 421 502 489 448 480 514 540 670 596 533 537

09 341 351 241 216 228 152 108 076 054 065 108 054 108 152 194 323 247 241 305 399 365 343 341 389 466 451 444 444 482 508 629 555 497 511

10 415 459 323 282 291 241 174 076 162 108 065 222 152 108 130 323 247 216 248 344 361 341 323 341 411 451 444 431 444 460 629 555 485 488

11 404 408 298 290 314 197 194 194 076 054 222 065 162 228 278 390 314 302 377 467 423 406 406 453 533 507 500 500 542 571 686 612 552 568

12 446 457 344 323 332 248 216 170 130 108 152 162 065 108 162 229 152 152 241 323 271 247 247 305 390 359 351 351 389 421 540 466 404 410

13 482 492 389 349 361 305 241 170 194 152 108 228 108 065 054 229 152 108 152 248 271 241 216 241 361 349 341 323 341 361 522 448 377 381

14 536 529 421 390 399 345 283 194 241 194 130 278 162 054 065 269 194 120 120 224 305 269 222 222 343 381 361 328 328 341 535 461 381 377

15 664 673 563 533 548 476 427 368 359 323 323 390 229 229 269 065 076 170 275 323 054 076 170 275 381 130 170 229 314 361 389 315 269 305

16 587 597 487 457 471 400 351 292 283 247 247 314 152 152 194 076 065 108 216 276 120 108 152 241 341 197 216 241 305 345 416 343 290 314

17 580 587 482 449 461 389 341 275 271 241 216 302 152 108 120 170 108 065 108 174 194 152 108 152 241 269 241 216 241 269 416 343 269 275

18 631 648 539 496 509 457 389 314 345 305 248 377 241 152 120 275 216 108 065 108 290 241 152 108 174 361 305 241 216 224 464 389 290 275

19 730 746 637 596 609 551 488 415 435 399 344 467 323 248 224 323 276 174 108 065 328 275 170 076 076 392 314 229 170 162 464 381 269 241

20 706 715 655 577 590 510 469 403 401 365 361 423 271 271 305 054 120 194 290 328 065 054 162 269 377 076 120 194 290 341 347 273 229 269

21 682 691 581 553 566 492 445 381 375 343 341 406 247 241 269 076 108 152 241 275 054 065 108 216 323 120 108 152 241 290 315 241 194 229

22

23 682 691 580 552 565 484 444 381 366 341 323 406 247 216 222 170 152 108 152 170 162 108 065 108 216 222 152 108 152 194 315 241 162 170

Table B.1. Delivery distances within and between different grids squares.

APPENDIX B: DELIVERY DISTANCES

723 749 628 590 602 539 482 410 421 389 341 453 305 241 222 275 241 152 108 076 269 216 108 065 108 328 241 152 108 120 389 305 194 170

24 831 835 736 663 675 617 555 482 502 466 411 533 390 361 343 381 341 241 174 076 377 323 216 108 065 434 341 241 152 130 484 415 269 229

25 791 802 743 663 677 606 556 502 489 451 451 507 359 349 381 130 197 269 361 392 076 120 222 328 434 065 120 222 328 381 365 291 247 290

26 788 795 689 656 670 597 550 488 448 444 444 500 351 341 361 170 216 241 305 314 120 108 152 241 341 120 065 108 216 269 248 174 130 174

27 785 795 685 656 670 597 550 488 480 444 431 500 351 323 328 229 241 216 241 229 194 152 108 152 241 222 108 065 108 162 241 174 054 076

28 821 827 723 688 701 629 580 511 514 482 444 542 389 341 328 314 305 241 216 170 290 241 152 108 152 328 216 108 065 054 341 276 120 076

29

843 853 749 710 723 658 602 531 540 508 460 571 421 361 341 361 345 269 224 162 341 290 194 120 130 381 269 162 054 065 392 329 170 120

30

972 983 870 843 856 787 736 679 670 629 629 686 540 522 535 389 416 416 464 464 347 315 315 389 484 365 248 241 341 392 065 108 222 275

31

899 909 796 769 783 713 662 605 596 555 555 612 466 448 461 315 343 343 389 381 273 241 241 305 415 291 174 174 276 329 108 065 162 216

32

837 848 735 709 723 650 602 542 533 497 485 552 404 377 381 269 290 269 290 269 229 194 162 194 269 247 130 054 120 170 222 162 065 054

33

852 861 751 722 735 662 614 550 537 511 488 568 410 381 377 305 314 275 275 241 269 229 170 170 229 290 174 076 076 120 275 216 054 065

34

436 ALMANSOORI and SHAH

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423– 438

A FUTURE HYDROGEN SUPPLY CHAIN

437

APPENDIX C: DETAILED RESULTS Table C.1. Flow rate of liquid hydrogen via transport modes for configurations 1 and 3. Configuration 1

Configuration 3

Mode

From

To

Flow rate, Qilgg0 (kg d21)

Mode

From

To

Flow rate, Qilgg0 (kg d21)

TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK RTC RTC RTC RTC

G03 G03 G03 G04 G04 G10 G10 G10 G11 G11 G14 G22 G22 G22 G22 G22 G22 G24 G24 G25 G25 G27 G27 G28 G28 G29 G29 G32 G04 G22 G22 G32

G01 G02 G06 G05 G07 G09 G12 G13 G08 G15 G15 G16 G17 G18 G21 G23 G26 G19 G30 G20 G30 G26 G33 G33 G34 G30 G34 G31 G13 G13 G33 G33

102 130 80 020 129 500 41 060 172 670 85 280 9480 69 300 7370 85 160 57 760 24 450 8520 36 640 63 170 87 490 57 018 17 430 81 540 40 610 82 890 5792 80 478 75 430 6030 23 530 129 900 207 720 36 040 49 690 9072 20 050

TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK TRK

G03 G03 G03 G03 G07 G07 G07 G07 G10 G10 G10 G11 G11 G11 G14 G22 G22 G22 G22 G22 G22 G22 G22 G24 G24 G25 G25 G27 G28 G28 G29 G29 G32 G32

G01 G02 G04 G06 G04 G05 G06 G09 G09 G12 G13 G08 G13 G15 G15 G15 G16 G17 G18 G21 G23 G26 G33 G19 G30 G20 G30 G33 G33 G34 G30 G34 G31 G33

102 130 80 020 135 838 4082 62 102 41 060 125 418 78 750 6530 9480 148 050 7370 6980 80 840 57 760 4320 24 450 8520 36 640 63 170 87 490 62 810 4082 17 430 81 540 40 610 82 890 86 270 75 430 6030 23 530 129 900 207 720 19 248

G: grid square; TRK: tanker truck; RTC: railway tank car.

Table C.2. Flow rate of hydrogen gas via transport modes for configuration 2. Configuration 2 Mode

From

To

Flow Rate, Qilgg0 (kg d21)

TUB TUB TUB TUB TUB TUB TUB TUB TUB TUB RTB RTB RTB RTB RTB RTB RTB RTB RTB RTB RTB RTB RTB RTB RTB RTB

G04 G10 G10 G11 G14 G22 G22 G25 G29 G33 G03 G03 G03 G04 G10 G11 G11 G22 G22 G22 G22 G24 G24 G24 G27 G28

G05 G09 G12 G08 G15 G16 G21 G20 G30 G34 G01 G02 G06 G07 G13 G13 G15 G13 G17 G18 G23 G15 G19 G30 G26 G23

41 060 85 280 9480 7370 57 760 24 450 63 170 40 610 153 430 135 930 102 130 80 020 129 500 172 670 69 300 49 670 38 150 36 060 8520 36 640 6030 47 010 17 430 34 530 62 810 81 460

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423 –438

438

ALMANSOORI and SHAH Table C.3. Summary of results for configurations 1 and 3.

Configuration Variable G01 G02 G03 G04 G05 G06 G07 G08 G09 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19 G20 G21 G22 G23 G24 G25 G26 G27 G28 G29 G30 G31 G32 G33 G34
T Sig

1

3

DLig (kg d21)

DIig (kg d21)

PTig (kg d21)

STig (t)


DLig (kg d21)

DIig (kg d21)

PTig (kg d21)

— — 157 930 197 940 — — — — — 315 940 384 810 — 480 000 902 240 — — 480 000 960 000 480 000 — — 623 950 960 000 861 030 356 500 — 393 730 878 540 2 726 570 — — 252 230 — —

102 130 80 020 — — 41 060 129 500 172 670 7370 85 280 — — 9480 155 030 — 142 920 24 450 8520 36 640 17 430 40 610 63 170 — 87 490 — — 62 810 — — — 187 960 207 720 — 185 030 135 930

— — 469 580 447 710 — — — — — 480 000 477 340 — 480 000 960 000 — — 480 000 960 000 480 000 — — 960 000 960 000 960 000 480 000 — 480 000 960 000 2 880 000 — — 480 000 — —

1021 800 1579 1979 411 1295 1727 74 853 3159 3848 95 6350 9022 1429 245 4885 9966 4974 406 632 6240 10 475 8610 3565 628 3937 8785 27 266 1880 2077 2522 1850 1359

— — 157 930 — — — 172 670 — — 315 940 384 810 — 480 000 902 240 — — 480 000 960 000 480 000 — — 623 950 960 000 861 030 356 500 — 393 730 878 540 2 726 570 — — 252 230 — —

102 130 80 020 — 197 940 41 060 129 500 — 7370 85 280 — — 9480 155 030 — 142 920 24 450 8520 36 640 17 430 40 610 63 170 — 87 490 — — 62 810 — — — 187 960 207 720 — 185 030 135 930

— — 480 000 — — — 480 000 — — 480 000 480 000 — 480 000 960 000 — — 480 000 960 000 480 000 — — 915 432 960 000 960 000 480 000 — 480 000 960 000 2 880 000 — — 479198 — —

is only given for network configuration 1 since networks 2 and 3 have the same values.

Table C.4. Summary of results for configuration 2. Configuration Variable G01 G02 G03 G04 G05 G06 G07 G08 G09 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19 G20 G21 G22 G23 G24 G25 G26 G27 G28 G29 G30 G31 G32 G33 G34

2 DigL (kg d21)

DigI (kg d21)

PTig (kg d21)

— — 157 930 197 940 — — — — — 315 940 384 810 — 480 000 902 240 — — 480 000 960 000 480 000 — — 623 950 960 000 861 030 356 500 — 393 730 878 540 2 72 6570 — 207 720 252 230 185 030 —

102 130 80 020 — — 41 060 129 500 172 670 7370 85 280 — — 9480 155 030 — 142 920 24 450 8520 36 640 17 430 40 610 63 170 — 87 490 — — 62 810 — — — 187 960 — — — 135 930

— — 469 580 411 670 — — — — — 480 000 480 000 — 480 000 960 000 — — 480 000 960 000 480 000 — — 798 820 960 000 960 000 397 110 — 456 540 960 000 2 880 000 — 207 720 252 230 320 960 —

Trans IChemE, Part A, Chemical Engineering Research and Design, 2006, 84(A6): 423– 438