Publications
8 results found
Cooper N, Panteli A, Shah N, 2019, Linear estimators of biomass yield maps for improved biomass supply chain optimisation, APPLIED ENERGY, Vol: 253, ISSN: 0306-2619
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- Citations: 2
Panteli A, Giarola S, Shah N, 2018, Supply chain mixed integer linear program model integrating a biorefining technology superstructure, Industrial & Engineering Chemistry Research, Vol: 57, Pages: 9849-9865, ISSN: 0888-5885
A crucial element of the quest of curbing carbon dioxide emissions is deemed to rely on a biobased economy, which will rely on the development of financially sustainable biorefining systems enabling a full exploitation of lignocellulosic biomass (and its macrocomponents such as cellulose, hemicellulose, and lignin) for the coproduction of biofuels and bioderived platform chemicals. In this work, a general modeling framework conceived to steer decision-making regarding the strategic design and systematic planning of advanced biorefining supply networks is presented. The design task is formulated as a mixed integer linear program which accounts for the maximization of the supply chain profit, considering multiechelon, multiperiod, multifeedstock, and multiproduct aspects as well as spatially explicit features. The applicability of the proposed model, along with the use of a bilevel decomposition approach, are demonstrated with a case study of lignocellulose-based biorefining production systems in the South-West of Hungary. Results show the effectiveness of the tool in the decision-making regarding the systematic design of advanced biorefining SC networks. An economic analysis of different design configurations (i.e., centralized and distributed scenarios) through a holistic evaluation of the entire biobased SC, integrating technology superstructure, shows that both instances generate profitable investment decisions that could be equally trusted by the decision-maker unless regional restrictions are applied.
Panteli A, Giarola S, Shah N, 2017, Biobased Supply Chain Optimisation Model under Uncertainties, 27th European Symposium on Computer-Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 961-966, ISSN: 1570-7946
Koo L, Trokanas N, Panteli A, et al., 2017, Integration of CAPE Models and Data for the Domain of Biorefining: InterCAPEmodel Ontology Design, Editors: Espuna, Graells, Puigjaner, Publisher: ELSEVIER SCIENCE BV, Pages: 2341-2346
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- Citations: 1
Mountraki A, Tsakalova M, Panteli A, et al., 2016, Integrated Waste Management in Multiproduct Biorefineries: Systems Optimization and Analysis of a Real-Life Industrial Plant, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 55, Pages: 3478-3492, ISSN: 0888-5885
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- Citations: 9
Panteli A, Giarola S, Shah N, 2016, Lignocellulosic supply chain MILP model: a Hungarian case study, Editors: Kravanja, Bogataj, Publisher: ELSEVIER SCIENCE BV, Pages: 253-258
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- Citations: 3
Panteli A, Giarola S, Shah N, 2016, A generic MILP modelling framework for the systematic design of lignocellulosic biorefining supply chains, Pages: 197-199
The development of sustainable biobased economies could help overcome the high societal dependency on fossil resources. Therefore, research has focused on the study of advanced biorefining networks. The complexity of such production systems requires the use of efficient decision-making tools, enabling a full exploitation of biomass (and its macrocomponents, i.e. cellulose, hemicellulose and lignin) for the production of biobased products and platform chemicals (Kokossis and Yang, 2010). Therefore, it is also essential to identify the most promising pretreatment process that fractionates biomass into cellulose, hemicellulose and lignin and usually represents the highest cost part of the entire biorefining system. In addition, the deployment of second-generation technologies is still hindered by high capital costs as well as the existence of uncertainties (e.g. demand and price of biobased products) in the so far immature biobased market. Consequently, one of the most important and challenging aspects in the quest of producing a set of sustainable biobased products, is the design of an integrated and economically viable biorefinery supply network (Akgul et al., 2011; Martín and Grossmann, 2010; Čuček et al., 2014). Optimisation tools could play a powerful role supporting decision in such novel production systems, through the identification of the major cost drivers, the performance of sensitivity analysis as well as the assessment of economic and technical uncertainties (Kim et al., 2013). The aim of this work is the modelling and optimization of biorefining chain systems using an integrated approach to the modelling of all the entities involved across the technology chain, with the purpose of achieving a long-term, decision-making regarding the systematic design and planning of advanced biorefining networks.
Panteli A, Giarola S, Shah N, 2015, Supply chain optimization tools for the strategic planning of biorefining systems, Pages: 150-152
A crucial element of biobased economies is the development of economically sustainable biorefining systems enabling a full exploitation of biomass (and its macrocomponents, i.e. cellulose, hemicellulose and lignin) for the production of chemicals, materials and energy. The huge variety of biomass processing paths in second generation biorefineries leads to complex product portfolios indicating the necessity of maximizing the value derived from biomass feedstock when treated in a biorefinery with the aim to create economically feasible and environmentally sustainable biobased production systems (Kokossis and Yang, 2010). One of the most important and challenging aspects in the quest of producing a set of more sustainable biobased products, is the design of a more profitable, better integrated and more sustainable biorefinery supply chain network (Akgul et al., 2011; Martín and Grossmann, 2010; Čuček et al., 2014). The biomass pretreatment, which fractionates biomass into its three macrocomponents, usually represents the highest costing part of the entire biorefining system (CIMV, 2015). Optimization tools could play a decisive role supporting the decision making process in new biorefinery systems, through the performance of sensitivity analysis, the assessment of technical or economic uncertainties as well as the identification of the major cost drivers (Kim et al., 2013).
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