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Record W2889416057 · doi:10.1002/bbb.1922

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

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

Bibliographic record

VenueBiofuels Bioproducts and Biorefining · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioeconomy and Sustainability Development
Canadian institutionsnot available
Fundersnot available
KeywordsBiorefineryTriticaleBioproductsBiotechnologyMiscanthusRaw materialBiomass (ecology)AgronomyBiofuelMarginal landAgricultural engineeringBusinessBioenergyProduction (economics)EngineeringEconomicsBiology

Abstract

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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]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.205
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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