Multi-fidelity robust design optimization of an ORC turbine for high temperature waste heat recovery

Serafino, Aldo; Obert, Benoit; Cinnella, Paola

doi:10.1016/j.energy.2022.126538

Cited by 9 publications

(4 citation statements)

References 40 publications

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“…To address this issue, governments have implemented various initiatives and policies aimed at identifying eco-friendly energy alternatives to fossil fuels . The utilization of green energy sources can successfully mitigate carbon emissions and contribute to achieving the goals of ‘peak carbon’ and ‘carbon neutrality’ established by individual nations . Among the myriad green energy sources available, geothermal energy stands out as a clean and sustainable energy option with minimal environmental impact .…”

Section: Introductionmentioning

confidence: 99%

“… 2 The utilization of green energy sources can successfully mitigate carbon emissions and contribute to achieving the goals of ‘peak carbon’ and ‘carbon neutrality’ established by individual nations. 3 Among the myriad green energy sources available, geothermal energy stands out as a clean and sustainable energy option with minimal environmental impact. 4 Geothermal energy is abundant and boasts a substantial reservoir, estimated at approximately 3.6 × 1014 GWh within the Earth.…”

Section: Introductionmentioning

confidence: 99%

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“3E” Analysis and Working Fluid Selection for a Cogeneration System for Geothermal Large Temperature Difference Utilization

Yin,

Zhu,

Zhang

et al. 2024

ACS Omega

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Widespread utilization of geothermal energy as a clean energy source can reduce the use of fossil fuels and thereby protect the environment. Previous combined heat and power (CHP) systems using low-temperature geothermal heat as a heat source have limited utilization of geothermal heat. In this study, a cogeneration system based on organic Rankine cycle (ORC) and absorption heat exchanger (AHE) is designed. We analyzed the thermal efficiency, exergy efficiency, and economics of the proposed system utilizing 85 °C lowtemperature geothermal heat, and the optimal operating fluid for this system is discussed. The results show that the optimal working fluid for the proposed system is R245fa. Using R245fa as the working fluid, the proposed system can achieve an exergy efficiency of 61.39%, when the heat source outlet temperature can be as low as 23 °C, while the proposed system can achieve the lowest LCOE of 0.082 ($/kW •h) when using R22 as the working fluid.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“3E” Analysis and Working Fluid Selection for a Cogeneration System for Geothermal Large Temperature Difference Utilization

Yin,

Zhu,

Zhang

et al. 2024

ACS Omega

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“…This enables it to utilize low-grade heat sources, including renewable sources such as solar [4][5][6][7] and bio-thermal [8][9][10][11], to name only a few. Thus, ORC systems find applications in Waste Heat Recovery (WHR) [12][13][14] and Combined Heat and Power (CHP) [15][16][17] systems.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Artificial Neural Network Model of a Limaçon-to-Circular Gas Expander with an Inlet Valve

Hossain,

Sultan,

Phung

et al. 2024

Thermo

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In this work, an artificial neural network (ANN)-based model is proposed to describe the input–output relationships in a Limaçon-To-Circular (L2C) gas expander with an inlet valve. The L2C gas expander is a type of energy converter that has great potential to be used in organic Rankine cycle (ORC)-based small-scale power plants. The proposed model predicts the different performance indices of a limaçon gas expander for different input pressures, rotor velocities, and valve cutoff angles. A network model is constructed and optimized for different model parameters to achieve the best prediction performance compared to the classic mathematical model of the system. An overall normalized mean square error of 0.0014, coefficient of determination (R2) of 0.98, and mean average error of 0.0114 are reported. This implies that the surrogate model can effectively mimic the actual model with high precision. The model performance is also compared to a linear interpolation (LI) method. It is found that the proposed ANN model predictions are about 96.53% accurate for a given error threshold, compared to about 91.46% accuracy of the LI method. Thus the proposed model can effectively predict different output parameters of a limaçon gas expander such as energy, filling factor, isentropic efficiency, and mass flow for different operating conditions. Of note, the model is only trained by a set of input and target values; thus, the performance of the model is not affected by the internal complex mathematical models of the overall valved-expander system. This neural network-based approach is highly suitable for optimization, as the alternative iterative analysis of the complex analytical model is time-consuming and requires higher computational resources. A similar modeling approach with some modifications could also be utilized to design controllers for these types of systems that are difficult to model mathematically.

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“…First, a lowtemperature organic mass is pressurized and its physical properties, such as boiling point and flow, are modified using a mass pump. Then, under the influence of the pump, the circulating mass in the system absorbs heat at medium pressure in the evaporator, causing it to vaporize and form saturated steam [1][2][3][4].This steam then transforms into high-temperature and highpressure gases. Finally, these gases are further heated by the superheater.…”

Section: Introductionmentioning

confidence: 99%

Research on Organic Rankine Cycle-Based Industrial Circulating Water Cogeneration System

Yang

2023

JERR

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A fertilizer plant wastewater recycling as a research object, the establishment of ORC (Organic Rankine Cycle, referred to as ORC) cogeneration system, using MATLAB software to simulate the operation of the system was found to change the evaporation temperature, condensing temperature, superheat and subcooling degree of the four factors will have an impact on the performance of the system, in which the evaporation temperature and degree of superheat to improve the performance of the system, the condensing temperature and degree of subcooling have a negative effect on the system efficiency. The evaporation temperature and superheat are favorable factors for improving the system performance, while the condensation temperature and subcooling degree adversely affect the system efficiency. Through the verification calculation of specific working conditions, under the conditions of evaporating temperature 110℃, condensing temperature 27℃, superheating degree 8℃ and subcooling degree 3℃, the thermal efficiency of the system can reach about 25%, and the energy efficiency reaches about 45%. The experimental results show that this designed ORC cogeneration system is able to recycle industrial waste heat to a greater extent, which is of great significance to improve the efficiency of energy utilization.

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Multi-fidelity robust design optimization of an ORC turbine for high temperature waste heat recovery

Cited by 9 publications

References 40 publications

“3E” Analysis and Working Fluid Selection for a Cogeneration System for Geothermal Large Temperature Difference Utilization

“3E” Analysis and Working Fluid Selection for a Cogeneration System for Geothermal Large Temperature Difference Utilization

An Optimized Artificial Neural Network Model of a Limaçon-to-Circular Gas Expander with an Inlet Valve

Research on Organic Rankine Cycle-Based Industrial Circulating Water Cogeneration System

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