Cost Sensitivity Analysis

Fig. 7.8 Hydrogen cost sensitivity analyses for the four reactor types evaluated in the DTI techno-economics report on Photoelectro-chemical Hydrogen Production. Projected hydrogen costs as a function of efficiency, lifetime, and materials cost are shown...

This chapter attempts to explain some of the economic and commercial inflnences that affect the development of biocatalysts into snccessful mannfacturing processes and products. In this respect, biocatalysts are not special, and are subject to just the same effects as any other developing technology. In this chapter, 1 attempt to discuss many of these important influences, such as process costing, sensitivity analysis and product profitability appraisal. 1 would especially like to emphasise the following points ... 

H2Sim compares the end-use cost of using hydrogen in either FCV or hybridized, direct hydrogen combustion vehicles in 2020 with today s internal combustion engine vehicles, hybrid, and electric vehicles. It also considers a 2020 FCV with onboard production of hydrogen. The default costs associated with each of the vehicles included in H2Sim were summarized in Table 8.1. This chapter focuses on the fuel and the total end-use costs associated with each vehicle based on fuel and vehicle cost sensitivity analysis. 

The capital cost sensitivity analysis can not be done with Elsevier Edition 2.2 of HjSim. A full version of HjSim is required. 

Figure AlO. Nuclear capital cost sensitivity analysis.

Sensitivity Analysis An economic study should pinpoint the areas most susceptible to change. It is easier to predict expenses than either sales or profits. Fairly accurate estimates of capital costs and processing costs can be made. However, for the most part, errors in these estimates have a correspondingly smaller effecl than changes in sales price, sales volume, and the costs of raw materials and distribution. 

It is worthwhile to make tables or plot cuives that show the effect of variations in costs and prices on profitabihty. This procedure is called sensitivity analysis. Its purpose is to determine to which factors the profitabihty of a project is most sensitive. Sensitivity analysis should always be carried out to obseive the effect of departures from expec ted values. 

Example 3 Sensitivity Analysis The following data describe a project. Revenue from annual sales and total annual expense over a 10-year period are given in the first three columns of Table 9-5. The fixed-capital investment Cfc is 1 million. Plant items have a zero salvage value. Working capital C c is 90,000, and the cost of land Ci is 10,000. There are no tax allowances other than depreciation i.e., is zero. The fractional tax rate t is 0.50. For this project, the net present value for a 10 percent discount factor and straight-line depreciation was shown to be 276,210 and the discoiinted-cash-flow rate of return to be 16.4 percent per year. 

A further detail of this study included sensitivity analysis for the area of heat exchangers, discount rate, and fuel cost. The results are listed in Table 3-8. Option 2, the turboexpander scheme, was selected in terms of energy and maintenance savings, as well as enhanced reliability, availability, and safety. 

Life cycle cost (LCC) calculations are made to make sure that both the purchase price and the operating costs for life cycle are considered in investment decisions. In the chapter the basic calculation methods and sensitivity analysis are introduced. Examples of calculation results and references to LCC information sources are given. 

Different filter areas and types are compared in Fig. 16.6. Due to the short annual operating time in this case, the optimum area was rather small. In the sensitivity analysis the variation of the interest rate did not significantly influence the ranking of the alternatives. An increase in operating time results in larger filter areas becoming more cost-effective. 

Perform sensitivity and uncertainty analysis. Calculation of life-cycle costs and net benefits assumes that cash-flow profiles and the value of MARR are reasonably accurate. In most cases, uncertain assumptions and estimates are made in developing cash flow profile forecasts. Sensitivity analysis can be performed by testing how the outcome changes as the assumptions and input values change. 

Sion to our assumptions about the initial purchase price and the cost of gasoline. Figure 1 shows the LCC of the hybrid and the conventional car over the ten-year period as a function of the cost of gasoline. When gas prices are approximately 3 per gallon, the two cars cost about the same. This value is referred to as the break-even point. If gas prices reach 3.75 per gallon, the approximate cost in Japan, the hybrid car is more economical. Sensitivity analysis can also be conducted for other input variables, such as initial purchase price, miles driven per year and actual fuel economy. 

In all analyses, there is uncertainty about the accuracy of the results that may be dealt with via sensitivity analyses [1, 2]. In these analyses, one essentially asks the question What if These allow one to vary key values over clinically feasible ranges to determine whether the decision remains the same, that is, if the strategy initially found to be cost-effective remains the dominant strategy. By performing sensitivity analyses, one can increase the level of confidence in the conclusions. Sensitivity analyses also allow one to determine threshold values for these key parameters at which the decision would change. For example, in the previous example of a Bayesian evaluation embedded in a decision-analytic model of pancreatic cancer, a sensitivity analysis (Fig. 24.6) was conducted to evaluate the relationship... 

Several methods are available for the analysis of trichloroethylene in biological media. The method of choice depends on the nature of the sample matrix cost of analysis required precision, accuracy, and detection limit and turnaround time of the method. The main analytical method used to analyze for the presence of trichloroethylene and its metabolites, trichloroethanol and TCA, in biological samples is separation by gas chromatography (GC) combined with detection by mass spectrometry (MS) or electron capture detection (ECD). Trichloroethylene and/or its metabolites have been detected in exhaled air, blood, urine, breast milk, and tissues. Details on sample preparation, analytical method, and sensitivity and accuracy of selected methods are provided in Table 6-1. 

Calculations of economic profitability can only be predictive in the phase of process development, before a plant is on stream for a long time. Therefore, individual components of costs and market evaluations will bear some uncertainty. This uncertainty is relatively high for pharmaceuticals and agrochemicals. The impact of these uncertainties on the profitability of a process may be quantified by a sensitivity analysis. This analysis provides information about the sensitivity of the process economics to changes in parameters relevant for the profitability (investment costs, price and consumption of raw materials, utility unit costs, product value and demand, etc.), and therefore on the reliability of the result of the economic evaluation. In the early stages of process development, a high sensitivity indicates the areas requiring attention for continued R D work. 

Industrial analytical laboratories search for methodologies that allow high quality analysis with enhanced sensitivity, short overall analysis times through significant reductions in sample preparation, reduced cost per analysis through fewer man-hours per sample, reduced solvent usage and disposal costs, and minimisation of errors due to analyte loss and contamination during evaporation. The experience and criticism of analysts influence the economical aspects of analysis methods very substantially. 

Major advantages of LVI methods are higher sensitivity (compare the 100-1000 iL volume in LVI to the maximum injection volume of about 1 iL in conventional splitless or on-column injection), elimination of sample preparation steps (such as solvent evaporation) and use in hyphenated techniques (e.g. SPE-GC, LC-GC, GC-MS), which gives opportunities for greater automation, faster sample throughput, better data quality, improved quantitation, lower cost per analysis and fewer samples re-analysed. At-column is a very good reference technique for rapid LVI. Characteristics of LVI methods are summarised in Tables 4.19 and 4.20. Han-kemeier [100] has discussed automated sample preparation and LVI for GC with spectrometric detection. 

Finally, we should mention that in addition to solving an optimization problem with the aid of a process simulator, you frequently need to find the sensitivity of the variables and functions at the optimal solution to changes in fixed parameters, such as thermodynamic, transport and kinetic coefficients, and changes in variables such as feed rates, and in costs and prices used in the objective function. Fiacco in 1976 showed how to develop the sensitivity relations based on the Kuhn-Tucker conditions (refer to Chapter 8). For optimization using equation-based simulators, the sensitivity coefficients such as (dhi/dxi) and (dxi/dxj) can be obtained directly from the equations in the process model. For optimization based on modular process simulators, refer to Section 15.3. In general, sensitivity analysis relies on linearization of functions, and the sensitivity coefficients may not be valid for large changes in parameters or variables from the optimal solution. 

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