Integrating biomass quality variability in stochastic supply chain modeling and optimization for large-scale biofuel production

Castillo-Villar, Krystel K.; Ekşioğlu, Sandra D.; Taherkhorsandi, Milad

doi:10.1016/j.jclepro.2017.02.123

Cited by 52 publications

(32 citation statements)

References 41 publications

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“…Also in the projection it is applied the results of research (Castillo et al 2017) that the costs of raw material can increase by 27-31%, if the biomass has lower quality, depending on the higher share of moisture and ash.…”

Section: Data Sources and Methodologymentioning

confidence: 99%

Life cycle cost of biomass power plant: Monte Carlo simulation of investment

Odavić¹,

Zekić

Milić

2017

Ekon Polj

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Section: Data Sources and Methodologymentioning

confidence: 99%

Life cycle cost of biomass power plant: Monte Carlo simulation of investment

Odavić¹,

Zekić

Milić

2017

Ekon Polj

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“…The assumed EoS of the BtC biorefinery concept are based on scale-up factors from the literature up to a maximum of 2 million t FM of straw supply per year. However, financial scale-up risks occur with larger capacities, which hamper the implementation of large-scale BtC concepts significantly (Castillo-Villar, Eksioglu, & Taherkhorsandi, 2017). Integration of such risk factors promotes small-scale concepts because of lower risk investments, but on the contrary reduces EoS of large-scale BtC concepts (Bruins & Sanders, 2012).…”

Section: Discussionmentioning

confidence: 99%

Linking a farm model and a location optimization model for evaluating energetic and material straw valorization pathways—A case study in Baden‐Wuerttemberg

et al. 2018

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Diminishing fossil carbon resources, global warming, and increasing material and energy needs urge for the rapid development of a bioeconomy. Biomass feedstock from agro‐industrial value chains provides opportunities for energy and material production, potentially leading to competition with traditional food and feed production. Simulation and optimization models can support the evaluation of biomass value chains and identify bioeconomy development paths, potentials, opportunities, and risks. This study presents the linkage of a farm model (EFEM) and a techno‐economic location optimization model (BIOLOCATE) for evaluating the straw‐to‐energy and the innovative straw‐to‐chemical value chains in the German federal state of Baden‐Wuerttemberg taking into account the spatially distributed and price‐sensitive nature of straw supply. The general results reveal the basic trade‐off between economies of scale of the energy production plants and the biorefineries on the one hand and the feedstock supply costs on the other hand. The results of the farm model highlight the competition for land between traditional agricultural biomass utilization such as food and feed and innovative biomass‐to‐energy and biomass‐to‐chemical value chains. Additionally, farm‐modeling scenarios illustrate the effect of farm specialization and regional differences on straw supply for biomass value chains as well as the effect of high straw prices on crop choices. The technological modeling results show that straw combustion could cover approximately 2% of Baden‐Wuerttemberg's gross electricity consumption and approximately 35% of the district heating consumption. The lignocellulose biorefinery location and size are affected by the price sensitivity of the straw supply and are only profitable for high output prices of organosolv lignin. The location optimization results illustrate that economic and political framework conditions affect the regional distribution of biomass straw conversion plants, thus favoring decentralized value chain structures in contrast to technological economies of scale.

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“…Firstly, regulations have a detrimental role in supporting the diffusion of renewable feedstocks to industrial applications in geographically diversified markets. For example, the demand and production of biofuels from biomass and agricultural residues was increased in the United States of America mainly due to the legislation enacted by the Energy Independence and Security Act of 2007 (Castillo-Villar et al 2017). In the pharmaceutical industry, governmental regulations should contemporarily ensure the triplet Bqualitysafety -efficacy^of renewable feedstock-based medications (Desai et al 2018) and alleviate sectoral barriers to growth (Festel 2011).…”

Section: Supply Chain (Re)configuration Driversmentioning

confidence: 99%

Mapping supply dynamics in renewable feedstock enabled industries: A systems theory perspective on ‘green’ pharmaceuticals

Tsolakis

Srai

2018

Oper Manag Res

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This paper provides a multi-stage multi-layer mapping methodology for capturing the macro-level supply chain dynamics that govern industrial systems using renewable feedstocks. The mapping approach combines the Industrial Systems Mapping and System Dynamics principles to systematically capture the interrelations across: (i) institutional players, (ii) sector specialists, (iii) products and intermediates, (iv) production operations, and (v) firms within the supply chain. The interfaces are further explored at four interconnected and mutually interacting theme areas of analysis, namely: (i) renewable chemical feedstocks, (ii) production technologies, (iii) target markets, and (iv) value and economic viability. We demonstrate the applicability of our approach by mapping the dynamics in industrial systems for the production of 'green' pharmaceuticals, particularly via the illustrative case of paracetamol. Through the use of the proposed integrated mapping process the case study demonstrates the principal interrelationships and inter-firm dynamics between the different layers of analysis. Three main drivers are identified that could enhance supply network transformations for improved viability of these developing industrial systems, namely: (i) regulatory conformance with market requirements, (ii) system level feasibility assessment of given renewable feedstocks, and (iii) target market volume demand. The causal feedback elements of the provided mapping technique indicate that it could support the analysis of industrial systems' transformation dynamics enabled by renewable feedstocks. The standardisation of the methodology and its elements provides for an effective visualisation technique with cross-industry relevance.

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Integrating biomass quality variability in stochastic supply chain modeling and optimization for large-scale biofuel production

Cited by 52 publications

References 41 publications

Life cycle cost of biomass power plant: Monte Carlo simulation of investment

Life cycle cost of biomass power plant: Monte Carlo simulation of investment

Linking a farm model and a location optimization model for evaluating energetic and material straw valorization pathways—A case study in Baden‐Wuerttemberg

Mapping supply dynamics in renewable feedstock enabled industries: A systems theory perspective on ‘green’ pharmaceuticals

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