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Enregistrement W2889416057 · doi:10.1002/bbb.1922

The Potential of Triticale Evaluated by the Power of Engineering and Business Analytics

2018· article· en· W2889416057 sur OpenAlexaboutno aff
Paul Stuart

Notice bibliographique

RevueBiofuels Bioproducts and Biorefining · 2018
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueBioeconomy and Sustainability Development
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBiorefineryTriticaleBioproductsBiotechnologyMiscanthusRaw materialBiomass (ecology)AgronomyBiofuelMarginal landAgricultural engineeringBusinessBioenergyProduction (economics)EngineeringEconomicsBiology

Résumé

récupéré en direct d'OpenAlex

Triticale is a human-developed crop resulting from the breeding of wheat and rye, and having the potential to become a major industrial crop platform. Those involved with biorefinery development who may only know about triticale through the Star Trek episode The Trouble With Tribbles should look more closely. The wheat-rye hybrid is a versatile crop with potential for grain, forage and bioproducts manufacture. Lovell (2016) reports that Alberta and Saskatchewan farmers grow most of Canada's triticale, and seeded acres have increased more than 75% over 2012-2015 to reach over 80,000 acres. Triticale's numerous competitive advantages against other crops have been demonstrated, including a potential of 20% higher yield than CPS wheat, higher biomass and starch content than other crops, good agronomics on marginal soil, lack of competition for food and feed applications, and a good prospect for genetic modifications for improved trait expression. Triticale thus offers great potential for use as a biorefinery feedstock. Since triticale has a higher yield and starch content than wheat, it is also attracting interest for other uses such as ethanol production, chemicals, biomaterials and biocomposites. Making the right biorefinery investment choice depends on a broad range of issues such as feedstock access and price, emerging technologies, bio-product functionality, and new product markets. For certain investors, a sustainable competitive position must be ensured over the long term, and evaluating the likelihood of this is far from obvious. Increased competition for access to feedstock, optimization of the existing business model, and value creation and maximization are critical biorefinery investment drivers. At the same time, business nuances arise from different biorefinery investment contexts, e.g., greenfield versus retrofit implementation, forest versus agricultural feedstock, and commodity-driven versus value-added-oriented product portfolio. In this study, the sustainability and competitiveness of different investment options from triticale have been assessed in a series of multi-criteria decision-making (MCDM) panels to identify the most important assessment criteria and “preferred” investment options. In contrast to conventional investment analyses which often emphasize short-term profitability metrics for decision-making, this work took into account a wide breadth of complementary decision-making criteria representing (a) business-oriented performance such as return on investment, (b) potential environmental impacts calculated using LCA, and (c) market risk and competitiveness. The most important of the criteria in these categories were identified in preliminary MCDM's, and then combined in a single final MCDM panel. More specifically, the papers in this issue follow the order shown below. The first paper outlines how investment decisions were considered in this overall program, and overviews the design of an ambitious program centered on using product and process design tools for the evaluation of triticale-based biorefinery concepts. The second paper deals with the model considered for the gathering of triticale biomass, and the triticale cost model assumed for this work. Papers 3, 4 and 5 summarize the results from the intermediate MCDM panels dealing with identification of the most pertinent market-based, techno-economic, and environmental evaluation criteria respectively. Finally, the sixth paper pulls the results together – evaluating different technology pathways for polylactic acid (PLA) production from triticale, in an MCDM that takes a sustainability perspective based on the initial MCDM's. The systematic application of business and engineering analytics used in these studies illustrates (a) the potential for triticale as a feedstock for biorefineries, and (b) perhaps of equal importance, the opportunity from applying these analytics, based on primary data on technology and markets at different levels of maturity, across the value chain, for identifying relative strengths and weaknesses even at the early stages of development. We hope that the results from this body of knowledge serves readers well in their own research and technology evaluation context. The overall methodology used in this series of studies illustrated the strength of the trade-off MCDM method for distinguishing between product-process options using a sustainability perspective in a number of ways such as (a) the transfer of knowledge on a range of sustainability criteria to decision-makers having diverse backgrounds, (b) the systematic approach raises awareness and respect of the panel members for the interpretation of the sustainability criteria, (c) the diverse criteria are compared and weighed on a comparable basis, taking into account the outcomes of each alternative, and (d) the results of preferred and less-preferred production alternatives can be systematically explained, and alignment is achieved between decision-makers. The authors of the work would like to thank the Canadian Triticale Biorefinery Initiative (CTBI), Natural Sciences and Engineering Research Council of Canada (NSERC), Agriculture Canada, the Department of Chemical Engineering at Polytechnique-Montréal – and our many collaborators for their financial and technical support that made this work possible. Author: Paul R Stuart Contact: [email protected]

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,677
Score d'incertitude au seuil0,192

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,205
Écart entre enseignants0,193 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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