Sensitivity analysis

The evaluations thus far have taken the results of the models at face value, therefore sensitivities and uncertainties in the models have not been considered yet. This section discusses the sensitivity analysis study which was conducted in order to investigate the effect of parameters variations on the production costs results.

In the previous section some parameters were identified as having an important effect on the production costs of liquid hydrocarbon fuels. These parameters were selected for the sensitivity analysis and were: biofuels production rate (GJ/h), capital costs (i.e. TPC) and biomass cost. Other parameters included: O&M costs, real interest rate, plant availability and plant life as these were identified by other studies for also significantly affecting production costs [90, 104, 167]. The sensitivity analysis was carried out by changing each parameter in turn by ±10% of its default value. It should be noted that the plant availability parameter (see section 6.2.2) was changed by -10% to +5% since the plant’s operating hours cannot exceed the maximum hours per year (+110% of 8000hrs/yr = 8800>8760 hrs). The sensitivity analysis results of the CFB-FT concept, which was the most economic BTL system, are presented here. The sensitivity analysis of the other BTL concepts gave similar results with the CFB-FT concept thus the sensitivity analysis results of the other BTL concepts are presented in Appendix C.

Figure 6-5 shows the sensitivity of the production costs of the CFB-FT concept to the variations of the selected model parameters. Figure 6-5 is a spider plot which is widely used for presenting sensitivity analysis results. Steeper curves indicate a higher degree of sensitivity to deviations from the original estimates/values of the model parameters. The point in the graph where all lines meet indicates the original estimated value of the cost model’s output (i.e. biofuel production cost).

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Figure 6-5: Sensitivity of biofuel production cost of the CFB-FT concept

Table 6-9 shows the variations on production costs of the CFB-FT concept which resulted from the variations of the selected model parameters from their default value. From Figure 6-5 and Table 6-9 it can be seen that production rate is the most sensitive model parameter which has the greatest effect on production costs. The product energy yield used as a default (47.6%) is a reasonable estimate of the IPSEpro model since the vast majority of published studies on techno-economics of large-scale BTL plants based on FT synthesis report energy yields of 39-50% (see Table 3-6 at section 3.3). The performance of FT synthesis reactors in general is established but there is limited experience of FT reactors operation using biomass derived syngas. This increases the uncertainty of the overall results. The sensitivity of the model to the production rate of biofuels suggests that improving process performance should be an early priority.

13 14 15 16 17

-10% -5% 0 5% 10%

Production Cost (£/GJ)

% change in parameter value

Biomass cost Total plant cost O&M cost Production rate Interest Plant availability Plant life Original estimate: 14.65 £/GJ

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Table 6-9: Production costs of the CFB-FT concept as a result of parameters’ variations

-10% -5% 0 5% 10%

The plant availability is the next most sensitive parameter. Therefore, it is imperative to the viability of large scale BTL plants that the plant operating hours are as high as possible to maximise annual production and reduce production costs. The 8000 hrs/yr default availability which was selected in the model is a reasonable estimate, however situations can occur, such as seasonal demand or feedstock availability, where this parameter must be carefully set to prevent serious errors in the calculated biofuel production costs. The experience gained in the first year of the plant’s operation and the optimisation carried out in the meantime will most likely result in an increase in the plant availability.

The plant availability is closely followed by the overall total plant cost with regards to the impact on production costs. Regarding the calculated value, there is some uncertainty inevitable in study estimates of this type. Errors of ±30% are typical, and increased accuracy can only be achieved through very detailed and expensive analysis of the specific case study. It should be noted that there are inherent uncertainties in projecting BTL plant costs given the pre-commercial status of some of the major pieces of equipment included in the concepts examined. The next section deals with the uncertainties on total plant costs and other input parameters and how these affect production cost which is the model’s output.

System sensitivity to the delivered feedstock cost is often investigated in BTL system studies. The biomass cost is the fourth most sensitive parameter, however this cost can vary enormously and certainly well outside the 10% limit tested here. As rising biomass prices can be expected in the short and medium term (see section 6.5.3.1), the conclusion of a long term biomass supply contract with fixed quantities and prices should be sought with a respectable agricultural or forest management company. If it is decided that several

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companies should supply biomass during a large scale BTL plant project, a joint delivery commitment should be agreed. The next section examines uncertainties associated with feed cost in more detail.

Finally, the model is less sensitive to the interest rate, O&M costs and plant life chosen.

Regarding the calculated value of O&M costs, there is some uncertainty since the estimate was based on published data (see section 6.4.2). Increased accuracy can only be achieved through very detailed analysis of the individual cost elements of O&M costs.

However, this is time consuming and there are limited reliable resources on detailed O&M costs of BTL plants. Based on the demonstrated low sensitivity of the model to this parameter, there is questionable gain in the accuracy of results for such a high level of detail.

The interest rate and plant life influence the annual repayments of borrowed capital. The life of the project is largely within the control of the project developer and can be planned for. Interest rates can also be controlled by agreeing fixed rates with the lenders throughout the project’s life. Thus the uncertainty associated with these parameters can be minimised.