Learning rates and their impacts on the optimal capacities and production costs of biorefineries

Abstract

Industry statistics indicate that technology‐learning rates can dramatically reduce both feedstock and biofuel production costs. Both the Brazilian sugarcane ethanol and the United States corn ethanol industries exhibit drastic historical cost reductions that can be attributed to learning factors. Thus, the purpose of this paper is to estimate the potential impact of industry learning rates on the emerging advanced biofuel industry in the United States. Results from this study indicate that increasing biorefinery capital and feedstock learning rates could significantly reduce the optimal size and production costs of biorefineries. This analysis compares predictions of learning‐based economies of scale, S‐Curve, and Stanford‐B models. The Stanford‐B model predicts biofuel cost reductions of 55 to 73% compared to base case estimates. For example, optimal costs for Fischer‐Tropsch diesel decrease from $4.42/gallon to $2.00/gallon. The optimal capacities range from small‐scale (grain ethanol and fast pyrolysis) producing 16 million gallons per year to large‐scale gasification facilities with 210 million gallons per year capacity. Sensitivity analysis shows that improving capital and feedstock delivery learning rates has a stronger impact on reducing costs than increasing industry experience suggesting that there is an economic incentive to invest in strategies that increase the learning rate for advanced biofuel production. © 2014 Society of Chemical Industry and John Wiley & Sons, Ltd

A critical analysis of thermochemical cellulosic biorefinery capital cost estimates

“…Th e TEAs published in the open literature have also served as the basis for subsequent analyses of additional biofuel pathways [4][5][6][7][8][9][10] and in other fi elds examining cellulosic biofuels in the contexts of biorefi nery learning rates, 11,12 energy policy, [13][14][15] future competitiveness with fossil fuels, [15][16][17] supply chains, 19-22 and sustainability. 23,24 Th ese subsequent analyses incorporate and build on the results of the original TEAs, causing the assumptions and methodological choices used to calculate pathway capital costs, which commonly undergo no changes other than to account for price infl ation, to be refl ected throughout the results of the subsequent analyses.…”

Modelling and Simulating the Dynamics of the European Demand for Bio-Based Plastics

“…This implies an increased market price for bio-based plastics due to higher production costs [12][13][14]. However, studies have also demonstrated that production scale size and technological progress can reduce the market price of bio-based plastics by lowering production costs [15][16]. We also know from the literature that policy incentives in the form of direct and indirect subsidies can reinforce this effect [2].…”

“…We then constructed a qualitative "causal loop" diagram [18] that represents the structure and dynamic behaviour of the bio-based plastics value chain and formulated our simulation model, the so-called "stock and flow" diagram [18]. We used the relevant market data for the mathematical models as well as scaling and learning rates taken from the current literature [3,5,9,[14][15][16][17]. We integrated only those segments into our model, where bio-based plastics compete directly as a drop-in for certain applications and can compete directly with fossil-based plastics.…”

Opportunities and Challenges of the European Green Deal for the Chemical Industry: An Approach Measuring Innovations in Bioeconomy

“…It becomes clear from this data that different numbers of employees are required for each plant concept, especially for the production labor and chargehand labor. The applied economic learning rates have been determined according to the presented methodology, depending on the plant concept [63][64][65][66][67][68] (Table 6). The learning rate is the relative cost reduction occurring for each doubling of the cumulative installed capacity.…”