Sustainable Supply Chain Planning for the Forest Biorefinery

Dansereau, Louis Patrick; El‐Halwagi, Mahmoud M.; Stuart, Paul

doi:10.1201/9781439809136-c50

Cited by 7 publications

(2 citation statements)

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“…The optimization of the supply chain has been presented as an MILP (Mixed Integer Linear Programming) problem considering several objectives . Some methodologies for obtaining optimal supply chains of biorefineries are based on a steady state operation. − Other research efforts have included the dynamic behavior. ,− Furthermore, recently some works on this topic have included environmental criteria for optimal selection of biorefinery configuration, raw materials, products, and processing paths, − as well as optimal site selection involving transportation costs. − Because bioresources are not constant in time, some methodologies have been developed to determine optimal supply chains under uncertainty. , …”

Section: Introductionmentioning

confidence: 99%

Synthesis of Distributed Biorefining Networks for the Value-Added Processing of Water Hyacinth

Santibáñez-Aguilar¹,

Ponce-Ortega²,

González-Campos³

et al. 2013

ACS Sustainable Chem. Eng.

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Water hyacinth causes severe ecological problems in the infested water bodies. Several strategies have been proposed to eliminate this plant. Nevertheless, most of them have not been economically attractive. This paper proposes a general superstructure and a mathematical programming model for the sustainable elimination of water hyacinth through a distributed biorefining network. The proposed model optimizes the selection of the products, the siting and sizing for the processing facilities, and the selection of the markets, while accounting for technical and economic constraints. A case study for the central part of Mexico, where water hyacinth is a serious problem, is used to show the applicability of the proposed holistic approach. The results show that an optimally synthesized distributed biorefining network is capable of sustainable and economic elimination of water hyacinth from contaminated water bodies while generating value. Additionally, the results shown through Pareto curves allow the identification of a set of optimal solutions featuring trade-offs between economic and environmental objectives.

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Section: Introductionmentioning

confidence: 99%

Synthesis of Distributed Biorefining Networks for the Value-Added Processing of Water Hyacinth

Santibáñez-Aguilar¹,

Ponce-Ortega²,

González-Campos³

et al. 2013

ACS Sustainable Chem. Eng.

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“…With respect to the supply chain optimization for biorefineries, van Dyken et al 17 developed a mixed integer linear programming (MILP) model for the optimization of biomass supply chains by considering the effect on biomass quality of each step in transport, storage, and processing where the primary biomass quality observed was the moisture and energy content. Dansereau et al 18 developed a margin-centric approach to the optimization of the forest-biorefinery supply chains. Gigler et al 19 proposed a dynamic modeling approach toward optimization of agricultural or biomass supply chains, where the appearance and biomass quality were the two key parameters optimized; the appearance states are affected by handling and biomass quality is affected by processing, storage, and transportation.…”

Section: Introductionmentioning

confidence: 99%

Facility Location and Supply Chain Optimization for a Biorefinery

Bowling

Ponce-Ortega

El‐Halwagi

2011

Ind. Eng. Chem. Res.

166

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This paper presents a systematic approach for the optimal production planning and facility placement of a biorefinery. A structural representation is first developed to include sources of biomass feedstock, distributed preprocessing hubs, and centralized processing facilities to produce desired products and byproducts. An optimization formulation is developed to determine the optimal supply chain, size, operational strategies, and location of the biorefinery and preprocessing hub facilities. The model considers simultaneously the optimal selection of different configurations considering the specific location configuration (centralized and/or distributed), selection of biomass, and processing facilities to determine the one with the maximum overall net profit. The objective function considers the overall sales and the costs for the feedstocks, transportation costs, capital costs for the facilities, and the operational costs for the facilities. The model also considers nonlinear economy-of-scale behavior of the capital-cost functions that are reformulated using disjunctive models to yield convex relationships to guarantee a global optimal solution. The proposed model was applied to two case studies.

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Multiobjective optimization of biorefineries with economic and safety objectives

et al. 2013

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A new approach for the incorporation of safety criteria into the selection, location, and sizing of a biorefinery is introduced. In addition to the techno‐economic factors, risk metrics are used in the decision‐making process by considering the cumulative risk associated with key stages of the life cycle of a biorefinery that includes biomass storage and transportation, process conversion into biofuels or bioproducts, and product storage. The fixed cost of the process along with the operating costs for transportation and processing as well as the value of the product are included. An optimization formulation is developed based on a superstructure that embeds potential supply chains of interest. The optimization program establishes the tradeoffs between cost and safety issues in the form of Pareto curves. A case study on bio‐hydrogen production is solved to illustrate the merits of the proposed approach. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2427–2434, 2013

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Sustainable Supply Chain Planning for the Forest Biorefinery

Cited by 7 publications

References 0 publications

Synthesis of Distributed Biorefining Networks for the Value-Added Processing of Water Hyacinth

Synthesis of Distributed Biorefining Networks for the Value-Added Processing of Water Hyacinth

Facility Location and Supply Chain Optimization for a Biorefinery

Multiobjective optimization of biorefineries with economic and safety objectives

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