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    Formalization and solution of an optimal control problem for air quality planning

    Access Status
    Fulltext not available
    Authors
    Carnevale, C.
    Finzi, G.
    Padula, Fabrizio
    Turrini, E.
    Volta, M.
    Date
    2014
    Type
    Conference Paper
    
    Metadata
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    Citation
    Carnevale, C. and Finzi, G. and Padula, F. and Turrini, E. and Volta, M. 2014. Formalization and solution of an optimal control problem for air quality planning, pp. 5296-5301.
    Source Title
    IFAC Proceedings Volumes (IFAC-PapersOnline)
    ISBN
    9783902823625
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/52802
    Collection
    • Curtin Research Publications
    Abstract

    © IFAC.In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. Due to difficulty to cope with the complexity of environmental models, decision support systems are essential tools to help Environmental Authorities to plan air quality policies that fulfill EU Directive 2008/50 requirements in a cost-efficient way. Thus, the main concern is to search for policies capable of taking into account both the environmental and the economical problems. This work presents a new formalization and the first results of an optimal control problem, addressing the selection of efficient control policies over a certain time horizon to reduce air quality pollution. Dynamic programming offers a powerful tool that allows an iterative formalization of the environmental problem as a constrained optimal control problem. An objective function has to be minimized along a given finite time horizon. A set of dynamic varying constraints on the applicability thresholds of emission reduction technologies (control variables) is considered. When minimizing the objective function, the nondecreasing property of each technology application level and the maximum feasible reduction levels have been constrained. This approach has been tested over the Lombardia region in northern Italy.

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