Generalization of exergy analysis

Pavelka, Michal; Klika, Václav; Vágner, Petr; Maršík, František

doi:10.1016/j.apenergy.2014.09.071

Cited by 44 publications

(27 citation statements)

References 42 publications

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“…It was shown in [16] that maximum of electric power coincides with minimum of entropy production if ∆T = 0, which can be seen also from Eq. (51) easily.…”

Section: Gibbs Energy Flux Constraintsupporting

confidence: 49%

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Pitfalls of Exergy Analysis

Vágner

Pavelka

Maršík

2017

Journal of Non-Equilibrium Thermodynamics

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The well-known Gouy-Stodola theorem states that a device produces maximum useful power when working reversibly, that is with no entropy production inside the device. This statement then leads to a method of thermodynamic optimization based on entropy production minimization. Exergy destruction (difference between exergy of fuel and exhausts) is also given by entropy production inside the device. Therefore, assessing efficiency of a device by exergy analysis is also based on the Gouy-Stodola theorem. However, assumptions that had led to the Gouy-Stodola theorem are not satisfied in several optimization scenarios, e.g. non-isothermal steady-state fuel cells, where both entropy production minimization and exergy analysis should be used with caution. We demonstrate, using nonequilibrium thermodynamics, a few cases where entropy production minimization and exergy analysis should not be applied.

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“…It was shown in [16] that maximum of electric power coincides with minimum of entropy production if ∆T = 0, which can be seen also from Eq. (51) easily.…”

Section: Gibbs Energy Flux Constraintsupporting

confidence: 49%

“…Let us illustrate the latter approach on a fuel cell in a steady state, as in [16]. The objective function is the electrical power.…”

Section: Introductionmentioning

confidence: 99%

Pitfalls of Exergy Analysis

Vágner

Pavelka

Maršík

2017

Journal of Non-Equilibrium Thermodynamics

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“…In summary, EPM with the entropy production (58) leads to thermodynamic fluxes (reaction rates) (59), which are different from fluxes (57). The latter choice of fluxes was however shown to be compatible with the widely accepted law of mass action and Butler-Volmer equation, see Grmela [34] and Pavelka et al [35], and we thus prefer them to the former choice of fluxes in the case of nonlinear chemical kinetics.…”

Section: Chemical Kineticsmentioning

confidence: 99%

Gradient Dynamics and Entropy Production Maximization

Janečka

Pavelka

2018

Journal of Non-Equilibrium Thermodynamics

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Abstract:We compare two methods for modeling dissipative processes, namely gradient dynamics and entropy production maximization. Both methods require similar physical inputs-how energy (or entropy) is stored and how it is dissipated. Gradient dynamics describes irreversible evolution by means of dissipation potential and entropy, it automatically satisfies Onsager reciprocal relations as well as their nonlinear generalization (Maxwell-Onsager relations), and it has statistical interpretation. Entropy production maximization is based on knowledge of free energy (or another thermodynamic potential) and entropy production. It also leads to the linear Onsager reciprocal relations and it has proven successful in thermodynamics of complex materials. Both methods are thermodynamically sound as they ensure approach to equilibrium, and we compare them and discuss their advantages and shortcomings. In particular, conditions under which the two approaches coincide and are capable of providing the same constitutive relations are identified. Besides, a commonly used but not often mentioned step in the entropy production maximization is pinpointed and the condition of incompressibility is incorporated into gradient dynamics.

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“…One of the effective ways to calculate energy quality is by analyzing the exergy of the biomass. Pavelka [42] defines exergy as the maximal theoretical useful work acquired if the system is in thermodynamic equilibrium with the surroundings via reversible process. In context of biomass, exergy plays a vital role in evaluating the energy potentiality that is stored in the form of chemical bonds of the compounds.…”

Section: Torrefaction Index (Ti)mentioning

confidence: 99%

Utilizing Downdraft Fixed Bed Reactor for Thermal Upgrading of Sewage Sludge as Fuel by Torrefaction

Karki

Poudel

2017

Applied Sciences

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A lab-scale downdraft fixed bed reactor was used for the study of sewage sludge, a non-lignocellulosic biomass, torrefaction to enhance the thermochemical properties of sewage sludge. The torrefaction was carried out for a temperature range of 200-350 • C and a residence time of 0-50 min. Degree of torrefaction, torrefaction index, chemical exergy, gas analysis, and molar ratios were taken into account to analyze the torrefied product with respect to torrefaction temperature. The effect of torrefaction temperature was very pronounced and the temperature range of 250-300 • C was considered to be the optimum torrefaction temperature range for sewage sludge. Chemical exergy, calorific value and torrefaction index were significantly influenced by the change in the relative carbon content resulting in decrease of the O/C and H/C molar ratios.

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Generalization of exergy analysis

Cited by 44 publications

References 42 publications

Pitfalls of Exergy Analysis

Pitfalls of Exergy Analysis

Gradient Dynamics and Entropy Production Maximization

Utilizing Downdraft Fixed Bed Reactor for Thermal Upgrading of Sewage Sludge as Fuel by Torrefaction

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