Abstract
Capital investment, next to the product demand, sales and production costs, is one of the key metrics commonly used for project evaluation and feasibility assessment. Estimating the investment costs of a new product/process alternative during early stage design is a challenging task. This is especially important in biorefinery research, where available information and experiences with new technologies is limited. A systematic methodology for uncertainty analysis of cost data is proposed that employs (a) Bootstrapping as a regression method when cost data is available and (b) the Monte Carlo technique as an error propagation method based on expert input when cost data is not available. Four well-known models for early stage cost estimation are reviewed an analyzed using the methodology. The significance of uncertainties of cost data for early stage process design is highlighted using the synthesis and design of a biorefinery as a case study. The impact of uncertainties in cost estimation on the identification of optimal processing paths is found to be profound. To tackle this challenge, a comprehensive techno-economic risk analysis framework is presented to enable robust decision making under uncertainties. One of the results using an order-of-magnitude estimate shows that the production of diethyl ether and 1,3-butadiene are the most promising with economic risks of 0.24 MM$/a and 4.6 MM$/a due to uncertainties in cost estimations, respectively.
Highlights
Economic growth leads to the development of processes and activities that are highly energy dependent
A detailed evaluation among process alternatives is required for a robust decisionmaking and it demands a substantial amount of information, which is both time and resource intensive
We perform an in-depth analysis of the issues and challenges related to performing cost data estimation, and we develop methods and tools to properly address these issues in order to provide a robust decision-making platform for process synthesis and design
Summary
Economic growth leads to the development of processes and activities that are highly energy dependent. The use of fossil fuels as the main energy resource has many issues including long-term availability, supply security, and price volatility as well as environmental impact and climate change effects. Biorefineries arise potentially as a promising, clean, and renewable alternative to partly replace fossil fuels for both production of energy and chemicals. Several different types of biomass feedstock, and many conversion technologies, can be selected to match a range of products, and a large number of potential processing paths are available. Being based on biomass (natural feedstock), the economic and environmental viability of these processes is deeply dependent on local factors such as land use and availability, weather conditions, national or regional subsidies, and regulations. Designing a biorefinery requires a detailed screening among a set of potential configurations to identify the most suited option that satisfies a wide set of conditions. An important aspect of the methodology is data collection and uncertainty assessment of the cost data used for cost estimation in particular
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