Strategic Design of Bioethanol Supply Chains Including Commodity Market Dynamics

Abstract

The blending between biobased and conventional fuels is mandatory according to European directives. Bioethanol has had beneficial effects for energy security issues in some countries of the world (e.g., U.S., Brazil), but it is still not widespread in Italy. This paper analyzes the establishment of a corn-based bioethanol supply chain in northern Italy through a mixed-integer linear programming modeling framework, which allows its financial performance to be evaluated by optimizing the spatially explicit layout in terms of production technologies, biomass production sites, and the transport network. The paper relaxes the too stiff and limiting assumption of keeping constant, for long-term horizons, the price/cost of raw materials, products, and utilities. The economic assessment is based on the forecasted price dynamics of the commodities related to ethanol production. Specifically, three price-forecast models are introduced and tuned according to the Italian context. The robustness of the supply chain with respect to changes in price evolution is assessed to mitigate the risk for investors. Finally, different strategies to keep the supply chain operative are discussed, and their impact on final customers and Italian taxpayers is detailed.

Planning strategies to address operational and price uncertainty in biodiesel production

“…The model maximizes profit and uses a parameter that represents the decision-makers' attitude towards financial risk and also a weighing factor to model the trade-off between the expected profit and GHG emissions. Mazzeto et al [38] used scenario modelling to capture the uncertainty of the corn and ethanol prices using forecasted prices. The model optimizes the NPV of the whole supply chain during its entire operative life.…”

Optimal Design of a Carbon Dioxide Separation Process with Market Uncertainty and Waste Reduction

“…σ and P are the standard deviation of the prices and the average of relative errors, respectively. rand is a stochastic function normally distributed, and A, B, and C are adaptive parameters calculated with linear regression, minimizing the square error between real quotations and those predicted by the model [51].…”

Economic Sustainability of Products and Processes

“…This is a straightforward assumption for both Oil and Gas and Petrochemical sectors. Nonetheless, Mazzetto et al (2013Mazzetto et al ( , 2015 showed that CO can be taken as the RC also in the strategic design of bioethanol SC where biomaterials are used as raw inlet streams. The candidate EM assumes the following general structure:…”

“…Rasello and Manca (2014) discuss an approach to DSCM of oil refineries. Equally, Mazzetto et al (2013Mazzetto et al ( , 2015 use suitable EM for the strategic design of bioethanol DSCM.…”