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  • Conference paper
    Giarola S, Shah N, 2013,

    Techno-economic and environmental assessment of ionic liquid-based biorefineries

    , Pages: 301-302
  • Conference paper
    Budinis S, Fabozzi D, Thornhill NF, 2013,

    A control technique based on compressor characteristics with applications to carbon capture and storage systems

    Introduction: Compressors are vital pieces of equipment within the process industry and they are going to be important in the next few years for dealing with carbon dioxide from carbon capture and storage (CCS) systems. Compressor characteristics (also called compressor maps) represent the operation of the machine in a graphical form. They are provided by the manufacturer of the compressor together with the machine and they are generated via experiments at reference conditions. The most common compressor characteristics represent the pressure ratio of the machine (i.e. the ratio between the output pressure and the input pressure) as a function of inlet flowrate and rotational shaft speed. For a single speed machine there is a single characteristic curve rather than a map (where instead the same function is plotted more times for different shaft speeds). While the inlet flowrate of the machine is generally a boundary condition of the compression system, the rotational shaft speed is very often the manipulated variable of the control system for a variable speed compressor. State of the art and open questions: Steady state and dynamic simulations are routinely used by academics and practitioners to represent and analyse the behaviour of a compressor during different activities such as design, control and optimization of the machine. In the literature there are many examples of compressor dynamic models (Botros et al., 1991, Venturini, 2005, Camporeale et al., 2006). Different techniques have been proposed for simulation and control applications. However the models found in the literature do not rely much on the compressor characteristics. The reason for that is that they usually represent simple compressors i.e. single stage lab-size machine that can be tested in a lab to provide the parameters needed for the model calibration. This type of machine is closer to an ideal compressor than an industrial compressor. For this reason a simplified model cannot capture accurate

  • Journal article
    Bernardi A, Giarola S, Bezzo F, 2012,

    Optimizing the economics and the carbon and water footprints of bioethanol supply chains

    , BIOFUELS BIOPRODUCTS & BIOREFINING-BIOFPR, Vol: 6, Pages: 656-672, ISSN: 1932-104X
  • Journal article
    Giarola S, Shah N, Bezzo F, 2012,

    A comprehensive approach to the design of ethanol supply chains including carbon trading effects

    , BIORESOURCE TECHNOLOGY, Vol: 107, Pages: 175-185, ISSN: 0960-8524
  • Journal article
    Bernardi A, Giarola S, Bezzo F, 2012,

    A framework for water footprint optimisation in the bioethanol supply chain

  • Journal article
    Giarola S, Zamboni A, Bezzo F, 2011,

    Spatially explicit multi-objective optimisation for design and planning of hybrid first and second generation biorefineries

    , Computers and Chemical Engineering, Vol: 35, Pages: 1782-1797, ISSN: 0098-1354

    Climate change mitigation has become a binding driver in biofuels production. First generation bioethanol, initially indicated as the most competitive option, is now incurring in ever increasing discredits forcing the transition towards more sustainable productions (i.e. second and third generation technologies). This paper addresses the strategic design and planning of corn grain- and stover-based bioethanol supply chains through first and second generation technologies. A Mixed Integer Linear Programming framework is proposed to optimise the environmental and financial performances simultaneously. Multi-period, multi-echelon and spatially explicit features are embodied within the formulation to steer decisions and investments through a global approach. A demonstrative case study is proposed involving the future Italian biomass-based ethanol production. Results show the effectiveness of the optimisation tool at providing decision makers with a quantitative analysis assessing the economic and environmental performance of different design configuration and their effect in terms of technologies, plant sizes and location, and raw materials. © 2011 Elsevier Ltd.

  • Book
    Zamboni A, Giarola S, Bezzo F, 2011,

    Towards Second Generation Bioethanol. Supply Chain Design and Capacity Planning

    This work proposes a multi-period and spatially explicit framework conceived to drive strategic policies on biofuels. A Mixed Integer Linear Programming (MILP) model is proposed as quantitative tool to optimise the oncoming transition towards more sustainable infrastructures. This paper addresses the design of bioethanol supply chains where both corn grain and stover are considered as suitable biomass. A Mixed Integer Linear Program is proposed to optimise the system financial performance and to comply with EU environmental regulation by taking into account a wide number of technological options. Bioethanol production in Northern Italy is chosen as a demonstrative case study. © 2011 Elsevier B.V.

  • Journal article
    Dal-Mas M, Giarola S, Zamboni A, Bezzo Fet al., 2011,

    Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty

    , Biomass and Bioenergy, Vol: 35, Pages: 2059-2071, ISSN: 0961-9534

    Fossil fuel depletion and the increase of greenhouse gases emissions has been pushing the search for alternative fuels for automotive transport. The European Union has identified biofuel technology as one option for reducing its dependence on imported energy. Ethanol is a promising biofuel, but great uncertainty on the business profitability has recently determined a slowdown in the industry expansion. In particular, geographical plant location, biomass price fluctuation and fuel demand variability severely constrain the economic viability of new ethanol facilities. In this work a dynamic, spatially explicit and multi-echelon Mixed Integer Linear Program (MILP) modeling framework is presented to help decision-makers and potential investors assessing economic performances and risk on investment of the entire biomass-based ethanol supply chain. A case study concerning the corn-to-ethanol production supply chain in Northern Italy is used to demonstrate the effectiveness of the proposed modeling approach. The mathematical pattern addresses the issue of optimizing the ethanol supply network over a ten years' time period under uncertainty on biomass production cost and product selling price. The model allows optimizing economic performances and minimize financial risk on investment by identifying the best network topology in terms of biomass cultivation site locations, ethanol production plant capacities, location and transport logistics. © 2011 Elsevier Ltd.

  • Conference paper
    Pastorino R, Budinis S, Curro F, Palazzi E, Fabiano Bet al., 2011,

    Syngas Fuel Cells: from Process Development to Risk Assessment

    , 10th International Conference on Chemical and Process Engineering, Publisher: AIDIC SERVIZI SRL, Pages: 1081-1086, ISSN: 1974-9791
  • Conference paper
    Giarola S, Zamboni A, Bezzo F, 2011,

    Supply chain design and capacity planning: From first to second generation biofuel systems

    , Pages: 253-258, ISSN: 2283-9216

    Supply Chain optimisation tools may help managing the oncoming transition from first to second generation bioethanol productions. A multi-objective Mixed Integer Linear Programming is developed to design bioethanol supply chains where both corn grain and stover are considered as suitable biomass. Environmental and financial performance are simultaneously taken as design drivers and alternative process design options are considered, too. Northern Italy is taken as geographical benchmark to assess a real-world case study. Results show how the design transition from first to second generation bioethanol systems substantially depends on the specific trade-off between environmental and financial objectives. © 2011, AIDIC Servizi S.r.l.

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