On the suitable superstructure thermoeconomic optimization of a waste heat recovery system for a Brazilian diesel engine power plant

Morawski, Alexandre Persuhn; Araújo, Leonardo Rodrigues de; Schiaffino, Manuel; Oliveira, Renan Cristofori de; Chun, Andre; Ribeiro, Letícia Costa; Santos, José Joaquim Conceição Soares; Donatelli, João Luiz Marcon; Cunha, Carla César Martins

doi:10.1016/j.enconman.2021.113947

Cited by 16 publications

(3 citation statements)

References 45 publications

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“…This streamlined approach boasts straightforward programming and consistently delivers favorable outcomes, even in scenarios involving complex, multimodal functions. Moreover, the scientific community has exhibited significant interest in the application of this optimization technique, as evidenced by references [20,[24][25][26][27]36,37]. The genetic algorithm method integrated into EES is an adaptation of the publicly available Pikaia optimization program (version 1.2, April 2002), which was originally developed by Paul Charbonneau and Barry Knapp at The National Center for Atmospheric Research (NCAR).…”

Section: Genetic Algorithmmentioning

confidence: 99%

Gray Wolf Optimizer for the Cogeneration Power Plant Design Within a Framework of Commercial Software and Mathematical Platform

Araújo,

Gasperazzo,

Rocha

2023

Preprint

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In a realm where finite natural fossil fuel reservoirs coexist with escalating energy requisites and critical ecological contamination thresholds, matters pertaining to the configuration of thermal systems, encompassing energy efficacy, financial assessment, project intricacy, ecological consciousness, and fine-tuned optimization, have progressively piqued the scientific community's curiosity. Hence, thermoeconomic optimization emerges as a promising avenue for enhancing the efficiency of thermal system designs. Nevertheless, the intricacies of thermoeconomic optimization in thermal system design typically involve a multitude of components, interconnected processes, and flows, which collectively give rise to a complex system of nonlinear equations stemming from both thermodynamic and economic modeling. Moreover, the inherent objective functions in these optimization challenges are analytically daunting, characterized by traits like discontinuity, multimodality, and non-differentiability, further compounded by a multitude of decision variables. In this context, metaheuristic methods present themselves as promising and appealing tools for optimizing such intricate systems. In this study, we employ two metaheuristic methods, namely the Genetic Algorithm (GA) and the Gray Wolf Optimizer (GWO), to optimize the regenerative gas turbine cogeneration system, recognized in the literature as the CGAM problem. The thermoeconomic optimization challenge is tackled and resolved through the computational integration of a commercial software package (EES) and a mathematical platform (Matlab). Within this framework, the thermodynamic and economic modeling, as well as the thermoeconomic optimization components, are seamlessly integrated into the Engineering Equation Solver (EES). EES, in turn, calculates the thermodynamic properties for all streams within the cogeneration system while concurrently solving mass and energy balances as necessitated by the evaluation of the objective function. It is worth noting that the GA operates as an optimization tool within EES, whereas the GWO is implemented in Matlab and effectively integrated with EES. This study reveals that, despite GWO's relatively longer computational time attributable to the integration between Matlab and EES, it stands out as notably efficient in addressing the given problem, primarily owing to its reduced demand for objective function evaluations during the optimization process. Moreover, both the decision variables and the objective function tend to converge towards values closely aligned with those found in the reference literature.

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Section: Genetic Algorithmmentioning

confidence: 99%

Gray Wolf Optimizer for the Cogeneration Power Plant Design Within a Framework of Commercial Software and Mathematical Platform

Araújo,

Gasperazzo,

Rocha

2023

Preprint

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“…Another study used structural parametric thermo‐economic optimization to choose the best technology for waste heat recovery for a Brazilian diesel engine power plant. The compared technologies were a conventional Rankine cycle, an ORC, and a Kalina cycle 37 . The articles, which were close to the subject of this study, are reviewed above.…”

Section: Introductionmentioning

confidence: 99%

“…The compared technologies were a conventional Rankine cycle, an ORC, and a Kalina cycle. 37 The articles, which were close to the subject of this study, are reviewed above. The purpose of the present work is described in the following.…”

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confidence: 99%

A comparison of using organic Rankine and Kalina cycles as bottom cycles in a solar‐powered steam Rankine cycle

Mirjavadi

Pourfayaz

Pourmoghadam

et al. 2022

Energy Science & Engineering

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This study has attempted to compare two thermodynamic cascade cycles, which in this paper are presented as Systems A and B. System A was consisted of a steam Rankine cycle (SRC) as a top cycle and an organic Rankine cycle (ORC) as a bottom cycle. System B was consisted of the same SRC as the top cycle and a Kalina cycle as the bottom cycle. The comparison of both systems has been made by changing a number of parameters. The chosen parameters for this comparison were bottom cycle mass flow rate, aperture area of the collector, top cycle condensing pressure, and bottom cycle turbine inlet and outlet pressures. Also, a short economic evaluation has been made between two proposed cascade cycles (Systems A and B). The result indicated that the Kalina cycle shows superiority over the ORC as the bottom cycle in a solardriven SRC. Furthermore, it was determined that the most effective parameters on the overall efficiency of the systems are the condensing pressure of the top cycle and the outlet pressure of the bottom cycle turbine. Also, among the chosen parameters, the outlet pressure of the bottom cycle turbine had the highest effect on the efficiency of the bottom cycles.Considering the economic aspect, the results showed that the levelized costs of energy for both systems are quite equal at 0.011 ($/kWh).

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Optimum operations and performance comparison of CO2-propane and CO2-R152a mixture-based transcritical power cycles recovering diesel power plant waste heat

Mondal

Saha

2023

Clean Techn Environ Policy

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On the suitable superstructure thermoeconomic optimization of a waste heat recovery system for a Brazilian diesel engine power plant

Cited by 16 publications

References 45 publications

Gray Wolf Optimizer for the Cogeneration Power Plant Design Within a Framework of Commercial Software and Mathematical Platform

Gray Wolf Optimizer for the Cogeneration Power Plant Design Within a Framework of Commercial Software and Mathematical Platform

A comparison of using organic Rankine and Kalina cycles as bottom cycles in a solar‐powered steam Rankine cycle

Optimum operations and performance comparison of CO2-propane and CO2-R152a mixture-based transcritical power cycles recovering diesel power plant waste heat

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