Demonstration Case Study

The Regional data for demonstration case study in Table 4.2 is repeated here. Once the clusters are obtained, a biomass supply chain can be synthesised inside each cluster using the P-graph framework tools. The data for Cluster 1, given in Table 4.4 is used for demonstrating P-graph procedure that described in the previous section.

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The biomass types from Zones 1, 2, 3, and 4 are wood, sweet sorghum, grass silage and MSW respectively. The synthesis accounts for the locations of the energy carrier conversion operations. Figure 5.2 shows the brief schematic structure for the feasible process combinations that may form the supply chain. The symbol “●” represents a material such as raw biomass, intermediate energy carriers, or products. The labeled boxes represent the operation units and the symbol “T” with a frame represents the transportation activities.

The heat to power ratio of the customer demands is assumed 2:1, which is the average value for Europe during 2003 (ECOHEATCOOL, 2006). As a result, customer energy demands by zones become 1.13 TJ/y heating and 0.57 TJ/y power – for Zone 1, 3 TJ/y heating and 1.5 TJ/y power for Zone 2, 7.87 TJ/y heating and 3.93 TJ/y power – for Zone 3, and for Zone 4 22.01 TJ/y heating with 10.99 TJ/y power.

Forestry wood, energy crops (grass and sweet sorghum) and Municipal Solid Waste (MSW) are the input of raw materials. They are converted into other energy carriers, having higher energy densities and suitable for use in power generation facilities. CHP systems combining Fuel Cells, biofuel boilers, steam turbines and gas turbines are defined as options to be used for the regional energy conversion system. The electricity produced is supplied to the cluster customers. The generated heat is used for domestic, commercial and industrial applications, mainly for space heating and as hot utility.

Step 1. Identification of materials and streams. The synthesis method requires a comprehensive list and information of materials, as well as another list for the candidate operating units, as described in Section 5.2. The materials and streams for Cluster 1 biomass supply chain have been identified (Step 1 of the procedure from Section 5.2) and are shown in Table 5.1.

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Step2. Identification of the candidate operating units. The candidate operating units have been indentified using a qualified assessment using the general workflow from Figure 5.2 as a guide. They are listed in Table 5.2. For each candidate operating unit the table provides the streams/materials accepted as inputs, the outputs, the estimated performance and capital cost coefficients. The unit performance and economic data have been estimated to provide the basis for appropriate economic evaluation of the designs.

Step 3. Maximal superstructure and solution structures generation. Combining the information from Tables 5.1 and 5.2, the P-graph MSG algorithm has built the problem superstructure (referred to as the Maximal Structure), which is shown in Figure 5.2 Step 4. Optimisation of the superstructure. The software tool Combinatorial Process Network synthesis Editor (PNS Editor, 2009) was used to obtain the optimum solution for minimum production cost. The optimum solution provides the selected pathways which include:

 Input biomass quantities

 Type of energy carriers (input and intermediate materials)

 Operating units

 Final products for customers

The result from the P-graph ABB optimisation algorithm is illustrated in Figure 5.3.

Firstly, 1529.47 t/y of wood from Zone 1 (biomass A) are pelletised, transported, and used as fuel feed for the gasifier in Zone 1 as well as for the boilers in Zones 1, 2 and 3.

Pellets A (wood origin) of 22.11 t/y are used to generate heat in Zone 2 by the home bioenergy conversion unit. Another product stream, 1170.53 t/y of wood is sent to Zone 4 for direct use by steam boilers. Biomass B (sorghum) from Zone 2 is converted to 60.59 t/y bio-ethanol for the energy market.

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Figure 5.2 Combinatorially feasible process structures (Lam et al., 2010b)

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Table 5.1 Materials and streams used in the case study

Symbol Description Price

A Forestry Wood 25 €/t

B Sweet Sorghum 20 €/t

C Grass Silage 15 €/t

D Biomass MSW -10 €/t

AZ1, 2, 3, 4 Biomass A Transported to Zone 1, 2, 3 and 4 30 €/t

Ptr Petrol 1€/l

CO₂ CO₂ emission -

PA Pellet from Biomass A -

PAZ1, 2, 3 and 4 Pellet A transported to Zone 1, 2, 3 and 4 -

PC Pellet from Biomass C -

PCZ1, 2, 3 and 4 Pellet C transported to Zone 1, 2, 3 and 4 - SG 1,2,3 and 4 Syngas generated from Zone 1,2,3 and 4 - BG 1,2,3 and 4 Biogas generated from Zone 1,2,3 and 4 - Steam 1,2,3,4 Steam generated from Zone 1,2,3 and 4 -

Ba Sorghum Bagasse after juice extraction -

Juice Sorghum Juice after juice extraction -

ETN Ethanol -

EEM Ethanol for Energy Market 350€/t

PAEM Pellet A for energy market 70 €/t

PCEM Pellet C for energy market 60 €/t

Q 1,2,3 and 4 Heat generated for Zone 1,2,3 and 4 - P 1,2,3 and 4 Power generated for Zone 1,2,3 and 4 -

92 Table 1.2 Candidate operating units’ specification Symbo

l

Description Input Capital Cost Performance A_CC (€) B_CC (€/MW)

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The bagasse after the juice extraction from the sorghum (originating from Zone 2) is sent to anaerobic digestion in Zone 3, which produces biogas to be used as the fuel for a combined fuel cell and gas turbine unit. All of the grass feedstock (biomass C) collected in Zone 3 is pelletised. Grass pellets (1708.69 t/y) are sent to Zone 4 as the fuel for its pellets boiler and the rest are used as direct burning fuel for the house bioenergy conversion units in Zone 4. The waste-based resource, 940 t/y of MSW fraction (biomass D) is sent to a landfill in Zone 4 to produce biogas.

These intermediate energy carriers (pellets, steam, biogas and syngas) are converted to heat and power for meeting the energy demands described in the beginning of this section. For example, steam turbine and combined fuel cell and gas turbine in Zone 3 produce 7.87 TJ/y of heat and 3.93 TJ/y of power to fulfil the local demands. The surplus of biomass is converted into ethanol (60.59 t/y) and wood pellet (435.32 t/y), which can be transported to another cluster or traded in the market.

The total annualised cost for the system is 1852723€/y (lifespan: 15 y) which is equivalent to 0.036 €/MJ. Assuming the retail price for district heating is 0.03 €/MJ, the cost for power generation is 0.048€/MJ. This compares favourably with the recent average electricity wholesale price. The recent wholesale price include: USA, 2009:

0.013 – 0.045 €/MJ (EIA, 2010) and EU, 2008: 0.020 – 0.044 €/MJ (EUROSTAT, 2008)

The demonstration case study results show that the supply chain using biomass can achieve reasonable production cost levels, remaining within the profit margins for retail prices for most of Europe.

CO2 emission for the energy generation is 0.123 kg CO2/MJ, which is lower than the CO2 emission for coal-fired, oil-fired and LNG-fired power generation 0.27, 0.21 and 0.17 kg CO2/MJ (Hiroki, 2005)

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Figure 5.3 Optimum solution for minimum production cost with P-graph representation (Lam et al., 2010b)

95 5.4 Chapter Summary

A new methodology for the synthesis of regional-scope biomass energy supply chain networks has been formulated. It consists of two levels: clustering (presented in Chapter 4) and detailed synthesis using P-graph. It has been tested and the results confirmed the applicability at regional scale.

The applied two-level strategy has been proven to successfully manage the complexity of the biomass energy supply network problem, by simultaneously simplifying the corresponding infrastructure links and their eventual design and implementation tasks.

In this regard, applying the clustering technique at the upper design level plays a significant role.

96 6.1 Original Contributions

6.1.1 Theses

Based on the novel approaches and scientific contributions presented and illustrated by comprehensive case studies in the previous chapters, the following theses, representing three basic discoveries are summarised:

1. A two-level hierarchical methodology for regional resources and biomass