Design, improvements and applications of dual-pressure evaporation organic Rankine cycles: A review

Li, Jian; Peng, Xiayao; Yang, Zhen; Hu, Shuozhuo; Duan, Yuanyuan

doi:10.1016/j.apenergy.2022.118609

Cited by 33 publications

(13 citation statements)

References 127 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the addition of MPN to an ionic liquid can increase the ionic conductivity and thermopower. [10] The authors found that 75 % MPN is the optimum ratio, and the addition of excess DMSO decreases the S e value of Co(bpy) 3 2 + /Co(bpy) 3 3 +…”

Section: Effect Of Solvent Mixing and The Supporting Electrolytementioning

confidence: 99%

“…[38] Thus, the flow thermocell should be operated at the optimum flow rate for the best cooling and thermoelectric performance. Recently, Murakami and co-workers reported a promising result in which a flow thermocell generated a power density of 10 W m À 2 with the Co(bpy) 3 2 + / Co(bpy) 3 3 + redox pair in the electrolyte γ-butyrolactone. [37] The study of flow thermocells is less prevalent compared to the static type, partly because of the increased complexity of the system.…”

Section: àmentioning

confidence: 99%

“…This spin crossover changes the length of the metal-ligand bonds and is believed to disrupt the solvation structure and increase ΔS rc . As a result, the Co(bpy) 3 2 + / Co(bpy) 3 3 + redox couple shows a remarkably high S e value of 2.19 mV K À 1 in the solvent 3-methoxypropionitrile (MPN; Figure 5b). [11] The spin crossover of transition-metal complexes can be predicted by calculation of the relative energies of the high-spin and low-spin states in the reduced and oxidized forms, respectively (Figure 5c).…”

Section: Spin Entropymentioning

confidence: 99%

“…Most applications of phase separation and transition have been realized in aqueous thermocells, and the maximum ΔT is limited to around 60 K. If a greater ΔT value is available from the heat source, thermocells based on redox entropy are more suitable because the S e value is constant over the temperature range. Indeed, a very high output voltage of 250 mV was produced at a single-cell level from a ΔT of 120 K by using Co(bpy) 3 2 + /Co(bpy) 3 3 + in a nonvolatile organic electrolyte. [11]…”

Section: The Status Quo Of Thermocellsmentioning

confidence: 99%

“…The organic Rankine cycle (ORC) uses volatile organic solvents and allows a turbine generator to run at temperatures below 100 °C. [3] However, the high investment cost and safety concerns related to the vaporization of toxic or combustible organic solvents hinder its practical use. A solid thermoelectric generator (TEG) consists of an array of thermoelectric materials (generally made from ceramic or polymeric semiconductors) and produces electricity when a temperature difference is applied.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Advancement of Electrochemical Thermoelectric Conversion with Molecular Technology

et al. 2022

View full text Add to dashboard Cite

Thermocells are a thermoelectric conversion technology that utilizes the shift in an electrochemical equilibrium arising from a temperature difference. This technology has a long history; however, its low conversion efficiency impedes its practical usage. Recently, an increasing number of reports have shown drastic improvements in thermoelectric conversion efficiency, and thermocells could arguably represent an alternative to solid thermoelectric devices. In this Minireview, we regard thermocells as molecular systems consisting of successive molecular processes responding to a temperature change to achieve energy generation. Various molecular technologies have been applied to thermocells in recent years, and could stimulate diverse research fields, including supramolecular chemistry, physical chemistry, electrochemistry, and solid-state ionics. These research approaches will also provide novel methods for achieving a sustainable society in the future.

show abstract

Section: Effect Of Solvent Mixing and The Supporting Electrolytementioning

confidence: 99%

Section: àmentioning

confidence: 99%

Section: Spin Entropymentioning

confidence: 99%

Section: The Status Quo Of Thermocellsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Advancement of Electrochemical Thermoelectric Conversion with Molecular Technology

et al. 2022

View full text Add to dashboard Cite

show abstract

Advancement of Electrochemical Thermoelectric Conversion with Molecular Technology

et al. 2022

View full text Add to dashboard Cite

show abstract

Machine learning for design and optimization of organic Rankine cycle plants: A review of current status and future perspectives

Oyekale

Oreko

2023

WIREs Energy & Environment

View full text Add to dashboard Cite

The organic Rankine cycle (ORC) is widely acknowledged as a sustainable power cycle. However, the traditional approach commonly adopted for its optimal design involves sequential consideration of working fluid selection, plant configuration, and component types, before the optimization of state parameters. This way, the design process fails to achieve an optimal design in most cases, since the process relies heavily on empirical judgments. To improve the design process, researchers have been exploring lately the suitability of machine learning techniques. It is however not clear yet if data‐driven designs of ORC plants are practically viable and accurate. To bridge this gap, this article reviews literature studies in the field. Overviews were first presented on the ORC technology and machine learning modeling approaches. Next, studies that applied machine‐learning methods for the design and performance prediction of ORC plants were discussed. Furthermore, studies that focused on ORC machine learning optimizations were discussed. The artificial neural network (ANN) approach was observed as the technique most frequently applied for ORC design and optimization. Additionally, researchers agree in general that machine‐learning methods can achieve accurate results, with significant reductions of computational time and cost. However, there is the risk of using inadequate data size in the machine learning design approach, or insufficient data set training time, all of which can affect accuracy. It is hoped that this effort would spur the practical implementation of machine learning techniques in the future design and optimization of ORC plants, toward the achievement of more sustainable energy technology. This article is categorized under: Sustainable Energy > Energy Efficiency Sustainable Energy > Other Renewables

show abstract

Design, improvements and applications of dual-pressure evaporation organic Rankine cycles: A review

Cited by 33 publications

References 127 publications

Advancement of Electrochemical Thermoelectric Conversion with Molecular Technology

Advancement of Electrochemical Thermoelectric Conversion with Molecular Technology

Advancement of Electrochemical Thermoelectric Conversion with Molecular Technology

Machine learning for design and optimization of organic Rankine cycle plants: A review of current status and future perspectives

Contact Info

Product

Resources

About