A conceptual framework for siting biorefineries in the Canadian Prairies

Abstract

Ethanol is increasingly used as a means to reduce gasoline consumption. As a result, it has also attracted analysis of its economic, social, and environmental merit. In order for the ethanol production industry to continue to expand, these issues must be confronted in future development. Although technological development is often relied upon, carefully considered ethanol refi nery siting also mitigates some of these concerns. Five alternative siting locations were selected in the western Canadian Prairies. These were evaluated using 12 criteria which represent regional resources, economic conditions, government support, or social indicators. The criteria were weighted to represent the perspectives of two stakeholders. The Preference Ranking Organization Method for Enrichment and Evaluations (PROMETHEE) method was applied to this data, ranking the alternative sites. Several future scenarios were created to analyze the sensitivity of the results to both statistical data and subjective inputs. The rankings proved to be robust, and varied little in the different scenarios. Southern Alberta had an advantage with a high ethanol byproduct demand, education level, and ethanol demand. Southern Manitoba benefi tted from the lowest labor and miscellaneous costs, due to higher unemployment. Saskatchewan suffers from low byproduct demand and a decrease in water availability while having a heated economy which increases costs. In addition, Saskatchewan as a whole is currently the leader in ethanol production, resulting in less net demand, reduced access to government incentives, and more local competition. Southern Alberta and Southern Manitoba are the optimal regions for future ethanol biorefi nery, where as the Saskatchewan locations are the least attractive.

Multi‐criteria assessment of yellow, green, and woody biomasses: pre‐screening of potential biomasses as feedstocks for biorefineries

“…The result of the partial ranking (Φ + and Φ − ) and full ranking Φ net (Eqns and in Appendix 1(b)) for the selected biomass alternatives (a i ) with respect to criterion j is shown in Table . The results of partial ranking (Φ + ) and (Φ − ) are sometimes contradicting (Table ), thus it is relevant to undertake a net outranking analysis. Based on the net ranking (Φ net ), the preference on biomass types are in the order of grass‐clover, pure grass (from grassland), alfalfa, switchgrass, wheat straw, willow, maize, ryegrass, Miscanthus (autumn harvest), barley straw, Miscanthus (spring harvest), oil seed rape (straw), and poplar (Table ).…”

“…Furthermore, each of the methods has advantages and drawbacks; however, we cannot conclude that one method generally is better suited than the others for analyzing the suitability of alternatives, in our case biomass types and related farming systems. In the current study, when the prioritization order of the selected biomass types are compared between the weighted average method and the PROMETHEE, variations on the preferences are found (Table ), which could be because in the pair‐wise comparison each biomass type is checked for the preferences for each selected criterion, whether there exist indifference between ‘a’ and ‘b’ (see steps 3–4 in Appendix 1(b)). Løken further argued that when aggregating the preference information for all relevant criteria, the method also determine the extent of an alternative to outrank other alternatives, which make the comparison less static.…”

“…PROMETHEE‐II stands for the complete ranking, involving the net outranking flows; the higher the net flow, the better the alternative. The use of outgoing flows (PROMETHEE I) cannot rank two alternatives if their two flows are contradicting, and the net flows (in PROMETHEE II) are used when a large number of alternatives are used in the evaluation process . The detail mathematical representations of the method are shown in Appendix 1(b).…”

Biorefining in the prevailing energy and materials crisis: a review of sustainable pathways for biorefinery value chains and sustainability assessment methodologies

“…This method has been applied to determine a framework for integrating environmental, economic and technical factors for establishing an ethanol refinery in Canada, as discussed in Ref. [142]. The correlation coefficient of the criteria can also be determined to prioritize the possible best parameters [141].…”

Selecting the best statistical distribution with PROMETHEE and GAIA

“…Behzadian et al (2010) enumerated 195 papers, from its conception until 2008, where PROMETHEE is applied in environment management (47 papers), business and financial management (25), hydrology and water management (28), chemistry (24), logistics and transportation (19), manufacturing and assembly (19), energy management (17), social (7), design (2), agriculture (2), education (2), sports (1), information technology (1) and medicine (1) . Recently, PROMETHEE has been used in water management (Kodikara et al, 2010;Silva et al, 2010), banking (Doumpos & Zopounidis, 2010), energy management (Ghafghazi et al, 2010;Oberschmidt et al, 2010), manufacturing and assembly (Kwak & Kim, 2009;Saidi Mehrabad & Anvari, 2010;Tuzkaya et al, 2010;Venkata Rao & Patel, 2010;Zhu et al, 2010), logistics and transportation (Lanza & Ude, 2010;Safaei Mohamadabadi et al, 2009;Semaan & Zayed, 2010), quality (Nikolic et al), chemistry (Cornelissen et al, 2009;Ni et al, 2009), maritime commerce (Castillo-Manzano et al, 2009), strategy (Ghazinoory et al, 2009), project management (Halouani et al, 2009), construction (Castillo-Manzano et al, 2009;Frenette et al, 2010), urban development (Juan et al, 2010), location analysis (Luk et al, 2010), environment (Nikolić et al, 2010;Soltanmohammadi et al, 2009;Zhang et al, 2010;Zhang et al, 2009), safety (Ramzan et al, 2009) and e-commerce (Andreopoulou et al, 2009). PROMETHEE method is on the basis of two sorting methods: Promsort (Araz & Ozkarahan, 2007) and FlowSort (Nemery & Lamboray, 2008).…”