2009
DOI: 10.1016/j.jclepro.2009.02.007
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Pinch analysis approach to carbon-constrained planningfor sustainable power generation

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Cited by 109 publications
(73 citation statements)
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References 35 publications
(68 reference statements)
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“…Pinch analysis is highlighted as one of the key methodologies for optimisation, integration and reduction of pollution [67][68][69]. Pinch analysis is typically based on material and energy flows; however, similar methodologies for minimising pollution-related costs are also reported [70,71].…”
Section: Additional Considerationsmentioning
confidence: 99%
“…Pinch analysis is highlighted as one of the key methodologies for optimisation, integration and reduction of pollution [67][68][69]. Pinch analysis is typically based on material and energy flows; however, similar methodologies for minimising pollution-related costs are also reported [70,71].…”
Section: Additional Considerationsmentioning
confidence: 99%
“…At the macroscale level, it also becomes necessary to install additional or compensatory electricity generation to make up for the inevitable energy losses incurred by the CC retrofits in the power sector. Previous approaches to the analysis of CCS systems have been demonstrated using the MARKAL optimization model (van den Broek et al, 2008) and using life cycle assessment techniques (Koornneef et al, 2008). Although there are still significant uncertainties with respect to the economics of CCS systems, technical uncertainties lie mainly in life cycle CO 2 emissions as well as the permanence and capacity of carbon storage (Hansson and Bryngelsson, 2009).…”
Section: Introductionmentioning
confidence: 99%
“…This paper thus deals with problem of planning optimal retrofit of power plants at the sectoral, regional or national level. A similar static problem has been addressed in our previous work (Tan et al, 2009) which uses a graphical pinch analysis approach, but an improved discrete mathematical programming formulation is presented in this paper. Furthermore, fuzzy variants of the crisp (non-fuzzy) optimization model are introduced to account for conflicting goals as well as parametric uncertainties.…”
Section: Introductionmentioning
confidence: 99%
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