Methodology

The following sections will outline the structure of the dissertation, and describe the underlying methodology of the methods used in the case study. In order to perform a comprehensive case study, an extensive amount of data must be gathered. These data include process parameters, economic data and knowledge about the technical system where the process operates. In this work, these data have been gathered from state-of-the-art reports and scientific state-of-the-articles regarding the production of algae biomass.

1.4.1. Literature review – building knowledge

In order to complete a comprehensive case study, an extensive amount of data must be collected. These data include process factors, economic data and knowledge about the technical system where the process operates. The first section centers on the description of microalgae technology potential and current status, covering algal biology, cultivation, harvest, extraction and conversion to liquid bio-fuels. The second section of the dissertation consists of a broad review of all the literature surrounding the most relevant economic assessment.

1.4.2. Theoretical and analytical framework

The second part outlines a cost analysis for the production of microalgae biomass from specific technology. It includes a comprehensive investigation based on a certain number of assumptions. A Microsoft Excel model of the process was developed in order to create a tool for analyzing the whole algae cultivation process based on the data collected from the literature. Capital and operating costs were estimated based on literature studies, and standard engineering calculations. During the thesis, scenario analyses were performed considering alternative possible outcomes. Chapter 4.2 details microalgae biomass production scenarios for productivity scale, volume scale and certain assumptions within the system (CO₂, water and nutrients).

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1.4.3. Investment cost estimation methods

The investment cost estimation methods can be divided into three main groups (Zugarramurdi et al., 1995):

1. Universal methods: determine investment costs as one value and therefore allow a very rough estimate (Kerdoncuff, 2008). Total fixed capital can be calculated from the current sale price of the product and annual capacity of the plant (Woods, 1975). This method is applied if data is available only for other comparable production facilities. The estimation error is at about 40 percent.

2. Lang factor: This factorial approach was first suggested by Lang in 1948. This method is used to estimate the order of magnitude of investment. It establishes that the costs of an industrial plant can be obtained by multiplying the costs of the basic equipment by a factor. These factors, known as ‘Lang factors” are characteristic of the industry sector considered, particularly the type of products manufactured, the average cost of equipment items used, plant capacity and location (Lang, 1948). The estimation error is between 10 and 15 percent (Jelen & Black, 1983; Kerdoncuff 2008)

3. A detailed estimation of investment costs: A detailed estimation of investment costs requires a direct estimation of each investment position. Therefore, a detailed plan on the materials used, spatial plant set- up and further specification are needed. This is the most time consuming and information demanding method. (Gerrard, 2000). The estimation error for this method is up to five percent (Emhjellen & Osmundsen, 2002).

The methods primarily differ in terms of the data requirements as well as the time and financial resources consumed. The option for the cost methodology model chosen was the Lang factor, because it is possible to identify the approximate cost of major equipment. An important topic is the accuracy of the data inputs. One important limitation in applying this particular case is the limited access to data. The accuracy of the estimate will vary depending on the level of detail known about the design of the microalgae biomass production plant.

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1.4.4. Capacitive adjustments

In our case study, cost data from the former study was used and adjustments were necessary. Following this routine, to determine the cost of generating microalgae biomass at any other scale, it is necessary to modify the cost of the major equipment according to the scale factor. Equation (1) characterizes the economies of scale because purchasing a piece of equipment with twice the capacity is less than twice as expensive (when the exponent is less than 1.0). If, for a specific piece of equipment, this exponent is larger than 1.0, the most cost-effective way of scaling up is to duplicate the equipment.

The cost of a process or equipment can be scaled up or down from a basic size by using an exponential law for which a value varies between 0.3 and a maximum scale-up factor of 1. For our study, an exponential scaling factor for the ratio of capacities of 0.85 is chosen. (Acién et al., 2012).

Equation 1: Scaling of equipment

The major equipment for the PBR system will be scalable based on the chosen size of the facility. For the model to be scalable, it will be based on a general facility design, which resulted from a mixture of the literature, research and own calculations. The scaling up of the model will allow it to automatically recalculate fixed costs. A certain number of specific costs aren’t easy to estimate. They are primarily estimated based on the origin of the purchased equipment cost or the fixed capital investment (FCI) since there is no correlation between them. It is possible to take as an example the raw materials and utilities. Those expenses are estimated from the mass and energy balances (e.g. raw materials demand, utilities) (Ereev & Patel, 2011).

If producers and consumers of a technology increase experience, the costs for manufacturing and usage drop. The relationship established is expressed in Equation 2 with the production costs of the first unit produced C₀, the cumulative production A, the costs per unit after producing A units of a product Ccum and the experience index b.

Equation 2: Experience curve

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The progress ratio indicates at which rate the costs per unit will decrease if production is doubled (Pienkos & Darzins, 2009).

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