Design, Integration, and Control of Organic Rankine Cycles with Thermal Energy Storage and Two-Phase Expansion System Utilizing Intermittent and Fluctuating Heat Sources—A Review
Abstract
:1. Introduction
2. Design and Integrated System of Organic Rankine Cycle for Medium- and Low-Temperature Heat Sources
- Design-point conditions, in which the operation is in a good or the best performance of the cycle,
- Off-design conditions, in which the operation is with significantly reduced performance, and
- Not working conditions, in which the operation is not recommended due to some financial constraints, lack of profitability, safety issues, reliability of components, regulation, and so on. For example, the generated power of the system is lower than its power consumption, resulting in an undesired overall efficiency. As a consequence, the business case for this system is unprofitable due to the inefficiency and associated cost of operation. For these reasons, it must shut down.
- Indirect TES system, in which TES is separately installed in the ORC system (see Figure 2). Various solar thermal power plants use this kind of design [19,28]. In addition, there are a number of configurations in these solar thermal power plants (e.g., concentrated solar power/CSP) where the hot TES is used to store and accumulate the hot source after the working fluid or thermal oil has been heated using a solar collector and the cold TES is used to store and accumulate the working fluid or thermal oil after it has been used in the heat exchanger. In addition to storing hot sources, it is also possible to store cooling sources, such as those derived from liquefied natural gas (LNG) [29], hydrogen, dimethyl ether (DME), or other cryogenic substances. It is also feasible to store and accumulate both hot and cold substances before using them if they are intermittent and fluctuating, as shown in Figure 2. Additionally, it is feasible to develop the Carnot battery, a future storage system that uses a high-temperature heat pump to raise the temperature of a storage medium utilizing excess electrical energy and waste heat using the indirect TES technology [30,31,32]. The use of cooling sources as a storage medium is another option [33]. For this Carnot battery, additional heat pump technology is required to raise the temperature of the hot sources or to decrease the temperature of the cooling sources so that it may be used later.
- Direct TES system, in which the TES is installed in the cycle; it could be combined with heat exchangers, evaporators, liquid heaters, condensers, and so on (see, Figure 3). Some studies [20,34] have described the possible configuration of this TES-evaporator, TES-liquid heater, and TES-condenser. However, only a few experimental studies have been conducted using this design. Since the TES is included in the heat exchangers, there are several challenges in designing the TES-heat exchangers, as follows:
- -
- Selection of proper heat exchangers,
- -
- Selection of suitable TES materials (sensible, phase change materials/PCM, thermochemical),
- -
- Proper placement and distribution of TES within heat exchangers,
- -
- Potential flow blockage issues or solidification,
- -
- Geometry and spacing consideration in the heat exchangers,
- -
- Effectiveness and efficiency,
- -
- Sizing materials of the storage system,
- -
- Thermal properties,
- -
- Economic and life cycle analysis, and
- -
- Hazard and safety issues.
3. Operation and Control of Organic Rankine Cycle System with Thermal Energy Storage and Two-Phase Expansion System and Discussion
- Fluctuating/intermittent heat sources based on variables, which refers to changes or variations in some parameters within a system. In this case of thermodynamic power cycles, it refers to variations in mass flow rate, temperature, or both simultaneously. Understanding and managing these variables may help to maintain steady and effective operations. In addition, this could assist in forecasting and govern the behavior of the system under varying conditions. Then, the model is used to improve performance, safety reasons, and so on.
- Fluctuating/intermittent heat sources based on frequency, which refers to variation in the frequency of occurrence over time. These fluctuations could occur on different time scales, such as days, hours, minutes, or seconds in some heat sources. Understanding and analyzing the pattern of this frequency might be crucial in planning resources, optimizing systems, assessing performance, and making decisions when the thermodynamic cycles operate in the desired and good performance. Sometimes, it uses statistical analysis, time series modelling, and real-time monitoring approaches to collect and explain the fluctuation.
Application | Heat Sources | Temperature (°C) | Mass Flow Rate (kg/s) | Fluctuating Variables | Fluctuating Frequency | Refs |
---|---|---|---|---|---|---|
Diesel engine | Exhaust | 120–500 | 0–0.4 | Temperature and mass flow rate | Seconds | [26] |
Gasoline engine | Exhaust | 120–700 | 0–0.1 | Temperature and mass flow rate | Seconds | [26] |
Cement clinker cooler | Exhaust air | 150–350 | 53.75 | Temperature | Minutes | [26] |
Steel billet reheating furnace | Off-gas | 800–950 | 1.5–8 | Mass flow rate | Minutes | [26] |
Steel electric arc furnace | Off-gas | 100–750 | 5–32 | Temperature and mass flow rate | Minutes | [26] |
Solar | Thermal oil | 230–280 | 0–12 | Temperature and mass flow rate | Hours-days | [26] |
Ocean thermal | Warm surface water or deep cooling water | 5–30 * ΔT = ~5–15 ** | 8.3–2020 | Temperature and mass flow rate | Hours-days | [39,40,41] |
Geothermal | Geothermal water | 80–350 * ΔT = ~40 ** | 0.001–229 | Temperature and mass flow rate | Hours, days, months, years | [42,43,44] |
Several applications | Air (for example, air cooling system of a geothermal power plant and air for evaporating the substance in the power plant utilizing the cooling from regasification system) | ΔT = ~40 ** | >0.01 | Temperature and mass flow rate | Hours, days, months, years | [45,46] |
Several applications | Water (for example, the air-cooling system of a geothermal power plant, and water for evaporating the substance in the power plant utilizing the cooling from regasification system) | ΔT = ~20 ** | >0.01 | Temperature and mass flow rate | Days, months, years | [45,47] |
3.1. System Identification
- Non-parametric model, in which the main objective of the model is to characterize the dynamic behavior of the system without explicitly setting the values of certain parameters. Frequency response and step response models are two examples of non-parametric models. Step response models look at how the system responds to a quick shift or step input, whereas frequency response models investigate how the system responds to various frequencies. An article has described this non-parametric model, for example [51].
- Parametric model, which the model entails choosing precise parameter values that correctly depict the dynamics of the system. Differential equations define the relationship between variables in the time domain, and transfer functions, which relate input and output variables in the frequency domain, are two examples of parametric models. While a more precise estimate of the model parameters is necessary for parametric models to offer a complete and accurate picture of the behavior of the system, methods like maximum likelihood estimation or dynamic programming can be used. For example, an article has described this parametric model [52].
- Acquisition of input/output data following an experimental protocol involves applying input signals to the system and recording the resulting output data. The experimentation methodology must be properly planned in order to capture a variety of operating situations and dynamics.
- Selection or estimation of the “model” structure (complexity): the next phase entails choosing or estimating the right model structure that accurately represents the dynamics of the system. This involves determining the level of the complex model, including the number of parameters and the mathematical equations employed to predict the behavior of the system.
- Estimation of the model parameters: after the model structure is established, the values of model parameters need to be estimated. This often entails using estimate techniques to fit the model to the obtained input/output data, such as least squares regression or maximum likelihood estimation.
- Validation of the identified model (structure and parameter values): the validation of the identified model is the last stage. By contrasting the predictions of models with new experimental data or established system behavior, one may evaluate the structure of the model and predicted parameter values. Through validation, it may be made sure that the chosen model accurately describes the dynamics of the system and can be used to create control systems or conduct future research.
3.2. Control System of Organic Rankine Cycle Integrated with Thermal Energy Storage and Two-Phase Expansion System
3.2.1. Proportional Integral Derivative (PID)
3.2.2. Fuzzy Logic
3.2.3. Model Predictive Control
3.2.4. Comparison of the Present Control System and Outlook for the Future
4. Future Directions of Research
5. Conclusions
- Intermittent and fluctuating heat sources, which may result in an incomplete evaporation process, are analyzed and described in order to explain the performance of the cycle that could be below design points (e.g., off-design conditions or even non-working conditions). In such cases, there are several configurations of power cycle based on the ORC system that are feasible, including ORC with a two-phase expansion system, ORC with TES (direct or indirect system), and ORC with a combination of a two-phase expansion system and TES (e.g., OFC).
- In such a situation, intermittent and fluctuating heat sources might be classified according to variables and frequency depending on the evaluation of performance and the operating circumstances. While intermittent and fluctuating heat sources based on frequency might help in statistical analysis in time series modelling for resource planning and optimization, intermittent and fluctuating heat sources based on variables could assist in regulating operating conditions in the steady state and optimization.
- The fluctuating variables are temperature and mass flow rate. Some potential process control systems are categorized and described to regulate these parameters and optimize the power cycle while utilizing intermittent and fluctuating heat sources. The most popular control systems used in ORC, conventional ones (like PID) and advanced ones (such as fuzzy logic and MPC), are outlined. In this control system, the pump acts as the actuator to control the mass flow through the evaporator and the closed-loop cycle, while the evaporator is a critical component, since it absorbs intermittent and fluctuating heat sources. Based on the literature study, it shows that the pump control system might offer higher output power (which likely closes to the design point) and the turbine/volumetric expander control system tends to provide better performance of thermodynamic system. Moreover, the comparison of the presented control system has been described. In addition, the possible future control system has been discussed in order to provide insight into the integrated advanced control system for ORC that involves ML, AI, and RL.
- Furthermore, the literature study shows that the configurations and design of the thermodynamic cycle and the process control system also play a key role in utilizing intermittent and fluctuating heat sources. It assists in increasing steady-state performance, leading to greater output power and an efficient operating system.
- At the end of this study, the review offers insight into the future direction of research in the integration, design, and control system of ORC utilizing intermittent and fluctuating heat sources. Moreover, energy symbiosis using ORC via a circular energy transition and decentralized network might play a key role, making it very extensive for application and influencing the market-driven industrialization of ORC technologies.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
T | Temperature (°C) |
AI | Artificial intelligence |
CFC | Chlorofluorocarbon |
CSP | Concentrated solar power |
DMC | Dynamic matric control |
DRL | Deep reinforcement learning |
FDMC | Fast dynamic matric control |
FV | Finite volume |
GWP | Global warming potential |
HCFC | Hydrochlorofluorocarbon |
HCFO | Hydrochlorofluoroolefin |
HFO | Hydrofluoroolefin |
HVAC | Heating, ventilation, and air conditioning |
LNG | Liquefied natural gas |
MAE | Mean absolute error |
ML | Machine learning |
MPC | Model predictive control |
ODP | Ozone depletion potential |
OFC | Organic flash cycle |
ORC | Organic Rankine cycle |
PCM | Phase change material |
PE-ORC | Partially evaporated ORC |
PID | Proportional integral derivative |
PI | Proportional integral |
PV | Photovoltaic |
RL | Reinforcement learning |
RMSE | Root mean square error |
SDG | Sustainable development goal |
TES | Thermal energy storage |
TFC | Trilateral flash cycle |
WHR | Waste heat recovery |
References
- Bao, J.; Zhao, L. A Review of Working Fluid and Expander Selections for Organic Rankine Cycle. Renew. Sustain. Energy Rev. 2013, 24, 325–342. [Google Scholar] [CrossRef]
- Quoilin, S.; Van Den Broek, M.; Declaye, S.; Dewallef, P.; Lemort, V. Techno-Economic Survey of Organic Rankine Cycle (ORC) Systems. Renew. Sustain. Energy Rev. 2013, 22, 168–186. [Google Scholar] [CrossRef] [Green Version]
- Mondejar, M.E.; Andreasen, J.G.; Pierobon, L.; Larsen, U.; Thern, M.; Haglind, F. A Review of the Use of Organic Rankine Cycle Power Systems for Maritime Applications. Renew. Sustain. Energy Rev. 2018, 91, 126–151. [Google Scholar] [CrossRef]
- Rahbar, K.; Mahmoud, S.; Al-Dadah, R.K.; Moazami, N.; Mirhadizadeh, S.A. Review of Organic Rankine Cycle for Small-Scale Applications. Energy Convers. Manag. 2017, 134, 135–155. [Google Scholar] [CrossRef]
- Mahmoudi, A.; Fazli, M.; Morad, M.R. A Recent Review of Waste Heat Recovery by Organic Rankine Cycle. Appl. Therm. Eng. 2018, 143, 660–675. [Google Scholar] [CrossRef]
- Loni, R.; Najafi, G.; Bellos, E.; Rajaee, F.; Said, Z.; Mazlan, M. A Review of Industrial Waste Heat Recovery System for Power Generation with Organic Rankine Cycle: Recent Challenges and Future Outlook. J. Clean. Prod. 2021, 287, 125070. [Google Scholar] [CrossRef]
- Wieland, C.; Schifflechner, C.; Dawo, F.; Astolfi, M. The Organic Rankine Cycle Power Systems Market: Recent Developments and Future Perspectives. Appl. Therm. Eng. 2023, 224, 119980. [Google Scholar] [CrossRef]
- Tartière, T.; Astolfi, M. ORC World Map. Available online: https://orc-world-map.org/ (accessed on 7 August 2023).
- Anastasovski, A.; Rasković, P.; Guzović, Z. A Review of Heat Integration Approaches for Organic Rankine Cycle with Waste Heat in Production Processes. Energy Convers. Manag. 2020, 221, 113175. [Google Scholar] [CrossRef]
- Daniarta, S.; Kolasiński, P. A Preliminary Study of Two-Phase Volumetric Expanders and Their Application in ORC Systems. In Proceedings of the 6th International Seminar on ORC Power Systems, Munich, Germany, 11–13 October 2021; Wieland, C., Karellas, S., Quoilin, S., Schifflechner, C., Dawo, F., Spliethoff, H., Eds.; Technical University of Munich: Munich, Germany, 2021. [Google Scholar]
- Francesconi, M.; Briola, S.; Antonelli, M. A Review on Two-Phase Volumetric Expanders and Their Applications. Appl. Sci. 2022, 12, 10328. [Google Scholar] [CrossRef]
- Van Heule, X.; De Paepe, M.; Lecompte, S. Two-Phase Volumetric Expanders: A Review of the State-of-the-Art. Energies 2022, 15, 4991. [Google Scholar] [CrossRef]
- Ottaviano, S.; Poletto, C.; Ancona, M.A.; Melino, F. Experimental Investigation on Micro-ORC System Operating with Partial Evaporation and Two–Phase Expansion. Energy Convers. Manag. 2022, 274, 116415. [Google Scholar] [CrossRef]
- Dawo, F.; Buhr, J.; Schifflechner, C.; Wieland, C.; Spliethoff, H. Experimental Assessment of an Organic Rankine Cycle with a Partially Evaporated Working Fluid. Appl. Therm. Eng. 2023, 221, 119858. [Google Scholar] [CrossRef]
- White, M.T. Cycle and Turbine Optimisation for an ORC Operating with Two-Phase Expansion. Appl. Therm. Eng. 2021, 192, 116852. [Google Scholar] [CrossRef]
- Daniarta, S.; Kolasiński, P.; Imre, A.R. Thermodynamic Efficiency of Trilateral Flash Cycle, Organic Rankine Cycle and Partially Evaporated Organic Rankine Cycle. Energy Convers. Manag. 2021, 249, 114731. [Google Scholar] [CrossRef]
- Daniarta, S.; Imre, A.R.; Kolasiński, P. Thermodynamic Efficiency of Subcritical and Transcritical Power Cycles Utilizing Selected ACZ Working Fluids. Energy 2022, 254, 124432. [Google Scholar] [CrossRef]
- Alva, G.; Liu, L.; Huang, X.; Fang, G. Thermal Energy Storage Materials and Systems for Solar Energy Applications. Renew. Sustain. Energy Rev. 2017, 68, 693–706. [Google Scholar] [CrossRef]
- Tian, Y.; Zhao, C.Y. A Review of Solar Collectors and Thermal Energy Storage in Solar Thermal Applications. Appl. Energy 2013, 104, 538–553. [Google Scholar] [CrossRef] [Green Version]
- Daniarta, S.; Nemś, M.; Kolasiński, P. A Review on Thermal Energy Storage Applicable for Low- and Medium-Temperature Organic Rankine Cycle. Energy 2023, 278, 127931. [Google Scholar] [CrossRef]
- Cao, J.; Zheng, L.; Zheng, Z.; Peng, J.; Hu, M.; Wang, Q.; Leung, M.K.H. Recent Progress in Organic Rankine Cycle Targeting Utilisation of Ultra-Low-Temperature Heat towards Carbon Neutrality. Appl. Therm. Eng. 2023, 231, 120903. [Google Scholar] [CrossRef]
- Malwe, P.; Gawali, B.; Shaikh, J.; Deshpande, M.; Dhalait, R.; Kulkarni, S.; Shindagi, V.; Panchal, H.; Sadasivuni, K.K. Exergy Assessment of an Organic Rankine Cycle for Waste Heat Recovery from a Refrigeration System: A Review. Chem. Eng. Commun. 2023, 210, 837–865. [Google Scholar] [CrossRef]
- Lecompte, S.; Huisseune, H.; van den Broek, M.; Vanslambrouck, B.; De Paepe, M. Review of Organic Rankine Cycle (ORC) Architectures for Waste Heat Recovery. Renew. Sustain. Energy Rev. 2015, 47, 448–461. [Google Scholar] [CrossRef]
- Wieland, C.; Schifflechner, C.; Braimakis, K.; Kaufmann, F.; Dawo, F.; Karellas, S.; Besagni, G.; Markides, C.N. Innovations for Organic Rankine Cycle Power Systems: Current Trends and Future Perspectives. Appl. Therm. Eng. 2023, 225, 120201. [Google Scholar] [CrossRef]
- Xie, H.; Yang, C. Dynamic Behavior of Rankine Cycle System for Waste Heat Recovery of Heavy Duty Diesel Engines under Driving Cycle. Appl. Energy 2013, 112, 130–141. [Google Scholar] [CrossRef]
- Li, X.; Xu, B.; Tian, H.; Shu, G. Towards a Novel Holistic Design of Organic Rankine Cycle (ORC) Systems Operating under Heat Source Fluctuations and Intermittency. Renew. Sustain. Energy Rev. 2021, 147, 111207. [Google Scholar] [CrossRef]
- Ahmed, A.M.; Kondor, L.; Imre, A.R. Thermodynamic Efficiency Maximum of Simple Organic Rankine Cycles. Energies 2021, 14, 307. [Google Scholar] [CrossRef]
- Pelay, U.; Luo, L.; Fan, Y.; Stitou, D.; Rood, M. Thermal Energy Storage Systems for Concentrated Solar Power Plants. Renew. Sustain. Energy Rev. 2017, 79, 82–100. [Google Scholar] [CrossRef]
- Yang, J.; Li, Y.; Tan, H.; Bian, J.; Cao, X. Optimization and Analysis of a Hydrogen Liquefaction Process Integrated with the Liquefied Natural Gas Gasification and Organic Rankine Cycle. J. Energy Storage 2023, 59, 106490. [Google Scholar] [CrossRef]
- Eppinger, B.; Steger, D.; Regensburger, C.; Karl, J.; Schlücker, E.; Will, S. Carnot Battery: Simulation and Design of a Reversible Heat Pump-Organic Rankine Cycle Pilot Plant. Appl. Energy 2021, 288, 116650. [Google Scholar] [CrossRef]
- Dumont, O.; Frate, G.F.; Pillai, A.; Lecompte, S.; De Paepe, M.; Lemort, V. Carnot Battery Technology: A State-of-the-Art Review. J. Energy Storage 2020, 32, 101756. [Google Scholar] [CrossRef]
- Liang, T.; Vecchi, A.; Knobloch, K.; Sciacovelli, A.; Engelbrecht, K.; Li, Y.; Ding, Y. Key Components for Carnot Battery: Technology Review, Technical Barriers and Selection Criteria. Renew. Sustain. Energy Rev. 2022, 163, 112478. [Google Scholar] [CrossRef]
- Novotny, V.; Basta, V.; Smola, P.; Spale, J. Review of Carnot Battery Technology Commercial Development. Energies 2022, 15, 647. [Google Scholar] [CrossRef]
- Kolasiński, P. Experimental and Modelling Studies on the Possible Application of Heat Storage Devices for Powering the ORC (Organic Rankine Cycle) Systems. Therm. Sci. Eng. Prog. 2020, 19, 100586. [Google Scholar] [CrossRef]
- Ho, T.; Mao, S.S.; Greif, R. Increased Power Production through Enhancements to the Organic Flash Cycle (OFC). Energy 2012, 45, 686–695. [Google Scholar] [CrossRef]
- Bonolo de Campos, G.; Bringhenti, C.; Traverso, A.; Takachi Tomita, J. Thermoeconomic Comparison between the Organic Flash Cycle and the Novel Organic Rankine Flash Cycle (ORFC). Energy Convers. Manag. 2020, 215, 112926. [Google Scholar] [CrossRef]
- He, T.; Chong, Z.R.; Zheng, J.; Ju, Y.; Linga, P. LNG Cold Energy Utilization: Prospects and Challenges. Energy 2019, 170, 557–568. [Google Scholar] [CrossRef]
- Daniarta, S.; Kolasiński, P. Features and Characteristics of Low-Grade Heat Storage for Organic Rankine Cycle. In Proceedings of the 6th International Seminar on ORC Power Systems, Munich, Germany, 11–13 October 2021. [Google Scholar]
- Faizal, M.; Rafiuddin Ahmed, M. On the Ocean Heat Budget and Ocean Thermal Energy Conversion. Int. J. Energy Res. 2011, 35, 1119–1144. [Google Scholar] [CrossRef] [Green Version]
- Vera, D.; Baccioli, A.; Jurado, F.; Desideri, U. Modeling and Optimization of an Ocean Thermal Energy Conversion System for Remote Islands Electrification. Renew. Energy 2020, 162, 1399–1414. [Google Scholar] [CrossRef]
- Li, D.; Yue, J.; Zhang, L.; Duan, X. Numerical Study on Ocean Thermal Energy Conversion System. J. Renew. Sustain. Energy 2018, 10, 44501. [Google Scholar] [CrossRef]
- Han, C.; Yu, X. (Bill) Sensitivity Analysis of a Vertical Geothermal Heat Pump System. Appl. Energy 2016, 170, 148–160. [Google Scholar] [CrossRef] [Green Version]
- Bu, X.; Jiang, K.; Li, H. Performance of Geothermal Single Well for Intermittent Heating. Energy 2019, 186, 115858. [Google Scholar] [CrossRef]
- Duggal, R.; Rayudu, R.; Hinkley, J.; Burnell, J.; Wieland, C.; Keim, M. A Comprehensive Review of Energy Extraction from Low-Temperature Geothermal Resources in Hydrocarbon Fields. Renew. Sustain. Energy Rev. 2022, 154, 111865. [Google Scholar] [CrossRef]
- Kanbur, B.B.; Xiang, L.; Dubey, S.; Choo, F.H.; Duan, F. Cold Utilization Systems of LNG: A Review. Renew. Sustain. Energy Rev. 2017, 79, 1171–1188. [Google Scholar] [CrossRef]
- Kahraman, M.; Olcay, A.B. Techno-Economic Analysis of Evaporative Cooling Enhancement Methods of a 21 MW Air-Cooled Geothermal Power Plant. Geothermics 2023, 107, 102598. [Google Scholar] [CrossRef]
- Lee, S. Multi-Parameter Optimization of Cold Energy Recovery in Cascade Rankine Cycle for LNG Regasification Using Genetic Algorithm. Energy 2017, 118, 776–782. [Google Scholar] [CrossRef]
- Imran, M.; Pili, R.; Usman, M.; Haglind, F. Dynamic Modeling and Control Strategies of Organic Rankine Cycle Systems: Methods and Challenges. Appl. Energy 2020, 276, 115537. [Google Scholar] [CrossRef]
- Casella, F.; Mathijssen, T.; Colonna, P.; van Buijtenen, J. Dynamic Modeling of Organic Rankine Cycle Power Systems. J. Eng. Gas Turbines Power 2013, 135, 42310. [Google Scholar] [CrossRef]
- Quoilin, S.; Aumann, R.; Grill, A.; Schuster, A.; Lemort, V.; Spliethoff, H. Dynamic Modeling and Optimal Control Strategy of Waste Heat Recovery Organic Rankine Cycles. Appl. Energy 2011, 88, 2183–2190. [Google Scholar] [CrossRef]
- Zhang, J.; Zhang, W.; Hou, G.; Fang, F. Dynamic Modeling and Multivariable Control of Organic Rankine Cycles in Waste Heat Utilizing Processes. Comput. Math. Appl. 2012, 64, 908–921. [Google Scholar] [CrossRef] [Green Version]
- Cai, J.; Shu, G.; Tian, H.; Wang, X.; Wang, R.; Shi, X. Validation and Analysis of Organic Rankine Cycle Dynamic Model Using Zeotropic Mixture. Energy 2020, 197, 117003. [Google Scholar] [CrossRef]
- Landau, I.D.; Zito, G. Digital Control Systems: Design, Identification and Implementation; Springer: Berlin/Heidelberg, Germany, 2006; Volume 130. [Google Scholar]
- Shi, Y.; Lin, R.; Wu, X.; Zhang, Z.; Sun, P.; Xie, L.; Su, H. Dual-Mode Fast DMC Algorithm for the Control of ORC Based Waste Heat Recovery System. Energy 2022, 244, 122664. [Google Scholar] [CrossRef]
- Feng, Y.-Q.; Zhang, Q.; Xu, K.-J.; Wang, C.-M.; He, Z.-X.; Hung, T.-C. Operation Characteristics and Performance Prediction of a 3 KW Organic Rankine Cycle (ORC) with Automatic Control System Based on Machine Learning Methodology. Energy 2023, 263, 125857. [Google Scholar] [CrossRef]
- Yang, F.; Cho, H.; Zhang, H.; Zhang, J.; Wu, Y. Artificial Neural Network (ANN) Based Prediction and Optimization of an Organic Rankine Cycle (ORC) for Diesel Engine Waste Heat Recovery. Energy Convers. Manag. 2018, 164, 15–26. [Google Scholar] [CrossRef]
- Pili, R.; Wieland, C.; Spliethoff, H.; Haglind, F. Numerical Analysis of Feedforward Concepts for Advanced Control of Organic Rankine Cycle Systems on Heavy-Duty Vehicles. J. Clean. Prod. 2022, 351, 131470. [Google Scholar] [CrossRef]
- Peralez, J.; Tona, P.; Lepreux, O.; Sciarretta, A.; Voise, L.; Dufour, P.; Nadri, M. Improving the Control Performance of an Organic Rankine Cycle System for Waste Heat Recovery from a Heavy-Duty Diesel Engine Using a Model-Based Approach. In Proceedings of the 52nd IEEE Conference on Decision and Control, Firenze, Italy, 10–13 December 2013; pp. 6830–6836. [Google Scholar]
- Peralez, J.; Nadri, M.; Dufour, P.; Tona, P.; Sciarretta, A. Control Design for an Automotive Turbine Rankine Cycle System Based on Nonlinear State Estimation. In Proceedings of the 53rd IEEE Conference on Decision and Control, Los Angeles, CA, USA, 14 December 2014; pp. 3316–3321. [Google Scholar]
- Peralez, J.; Nadri, M.; Dufour, P.; Tona, P.; Sciarretta, A. Organic Rankine Cycle for Vehicles: Control Design and Experimental Results. IEEE Trans. Control Syst. Technol. 2017, 25, 952–965. [Google Scholar] [CrossRef]
- Wang, X.; Shu, G.; Tian, H.; Liu, P.; Jing, D.; Li, X. Dynamic Analysis of the Dual-Loop Organic Rankine Cycle for Waste Heat Recovery of a Natural Gas Engine. Energy Convers. Manag. 2017, 148, 724–736. [Google Scholar] [CrossRef]
- Ni, J.; Zhao, L.; Zhang, Z.; Zhang, Y.; Zhang, J.; Deng, S.; Ma, M. Dynamic Performance Investigation of Organic Rankine Cycle Driven by Solar Energy under Cloudy Condition. Energy 2018, 147, 122–141. [Google Scholar] [CrossRef]
- Marchionni, M.; Bianchi, G.; Karvountzis-Kontakiotis, A.; Pesyridis, A.; Tassou, S.A. An Appraisal of Proportional Integral Control Strategies for Small Scale Waste Heat to Power Conversion Units Based on Organic Rankine Cycles. Energy 2018, 163, 1062–1076. [Google Scholar] [CrossRef]
- Wang, X.; Shu, G.; Tian, H.; Liu, P.; Jing, D.; Li, X. The Effects of Design Parameters on the Dynamic Behavior of Organic Ranking Cycle for the Engine Waste Heat Recovery. Energy 2018, 147, 440–450. [Google Scholar] [CrossRef]
- Chowdhury, J.I.; Thornhill, D.; Soulatiantork, P.; Hu, Y.; Balta-Ozkan, N.; Varga, L.; Nguyen, B.K. Control of Supercritical Organic Rankine Cycle Based Waste Heat Recovery System Using Conventional and Fuzzy Self-Tuned PID Controllers. Int. J. Control. Autom. Syst. 2019, 17, 2969–2981. [Google Scholar] [CrossRef]
- Lin, S.; Zhao, L.; Deng, S.; Ni, J.; Zhang, Y.; Ma, M. Dynamic Performance Investigation for Two Types of ORC System Driven by Waste Heat of Automotive Internal Combustion Engine. Energy 2019, 169, 958–971. [Google Scholar] [CrossRef]
- Wang, X.; Wang, R.; Jin, M.; Shu, G.; Tian, H.; Pan, J. Control of Superheat of Organic Rankine Cycle under Transient Heat Source Based on Deep Reinforcement Learning. Appl. Energy 2020, 278, 115637. [Google Scholar] [CrossRef]
- Wang, X.; Cai, J.; Wang, R.; Shu, G.; Tian, H.; Wang, M.; Yan, B. Deep Reinforcement Learning-PID Based Supervisor Control Method for Indirect-Contact Heat Transfer Processes in Energy Systems. Eng. Appl. Artif. Intell. 2023, 117, 105551. [Google Scholar] [CrossRef]
- Chowdhury, J.I.; Nguyen, B.K.; Thornhill, D.; Hu, Y.; Soulatiantork, P.; Balta-Ozkan, N.; Varga, L. Fuzzy Nonlinear Dynamic Evaporator Model in Supercritical Organic Rankine Cycle Waste Heat Recovery Systems. Energies 2018, 11, 901. [Google Scholar] [CrossRef] [Green Version]
- Wang, Z.; Yu, Z.; Guo, S.; Li, X. Fuzzy PID Control Applied in Evaporator of Organic Rankine Cycle System. In Proceedings of the 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China, 4–7 August 2019; pp. 605–609. [Google Scholar]
- Cioccolanti, L.; De Grandis, S.; Tascioni, R.; Pirro, M.; Freddi, A. Development of a Fuzzy Logic Controller for Small-Scale Solar Organic Rankine Cycle Cogeneration Plants. Appl. Sci. 2021, 11, 5491. [Google Scholar] [CrossRef]
- Enayatollahi, H.; Fussey, P.; Nguyen, B.K. Control of Organic Rankine Cycle, a Neuro-Fuzzy Approach. Control Eng. Pract. 2021, 109, 104728. [Google Scholar] [CrossRef]
- Enayatollahi, H.; Sapin, P.; Unamba, C.K.; Fussey, P.; Markides, C.N.; Nguyen, B.K. A Control-Oriented ANFIS Model of Evaporator in a 1-KWe Organic Rankine Cycle Prototype. Electronics 2021, 10, 1535. [Google Scholar] [CrossRef]
- Toub, M.; Reddy, C.R.; Razmara, M.; Shahbakhti, M.; Robinett, R.D.; Aniba, G. Model-Based Predictive Control for Optimal MicroCSP Operation Integrated with Building HVAC Systems. Energy Convers. Manag. 2019, 199, 111924. [Google Scholar] [CrossRef]
- López-Bautista, A.O.; Flores-Tlacuahuac, A.; Gutiérrez-Limón, M.A. Robust Model Predictive Control for a Nanofluid Based Solar Thermal Power Plant. J. Process Control 2020, 94, 97–109. [Google Scholar] [CrossRef]
- Keller, M.; Neumann, M.; Eichler, K.; Pischinger, S.; Abel, D.; Albin, T. Model Predictive Control for an Organic Rankine Cycle System Applied to a Heavy-Duty Diesel Engine. In Proceedings of the 2020 IEEE Conference on Control Technology and Applications (CCTA), Montreal, QC, Canada, 24–26 August 2020; pp. 442–449. [Google Scholar]
- Shi, Y.; Zhang, Z.; Chen, X.; Xie, L.; Liu, X.; Su, H. Data-Driven Model Identification and Efficient MPC via Quasi-Linear Parameter Varying Representation for ORC Waste Heat Recovery System. Energy 2023, 271, 126959. [Google Scholar] [CrossRef]
- Xu, B.; Rathod, D.; Yebi, A.; Filipi, Z. A Comparative Analysis of Real-Time Power Optimization for Organic Rankine Cycle Waste Heat Recovery Systems. Appl. Therm. Eng. 2020, 164, 114442. [Google Scholar] [CrossRef]
- Zhao, D.; Deng, S.; Zhao, L.; Xu, W.; Wang, W.; Nie, X.; Chen, M. Overview on Artificial Intelligence in Design of Organic Rankine Cycle. Energy AI 2020, 1, 100011. [Google Scholar] [CrossRef]
- Oyekale, J.; Oreko, B. Machine Learning for Design and Optimization of Organic Rankine Cycle Plants: A Review of Current Status and Future Perspectives. WIREs Energy Environ. 2023, 12, e474. [Google Scholar] [CrossRef]
- Eyerer, S. Contribution to Improve the Organic Rankine Cycle: Experimental Analysis of Working Fluids and Plant Architectures; Verlag Dr. Hut: München, Germany, 2021; ISBN 3843948542. [Google Scholar]
- Györke, G.; Deiters, U.K.; Groniewsky, A.; Lassu, I.; Imre, A.R. Novel Classification of Pure Working Fluids for Organic Rankine Cycle. Energy 2018, 145, 288–300. [Google Scholar] [CrossRef]
- Daniarta, S.; Nemś, M.; Kolasiński, P.; Pomorski, M. Sizing the Thermal Energy Storage Device Utilizing Phase Change Material (PCM) for Low-Temperature Organic Rankine Cycle Systems Employing Selected Hydrocarbons. Energies 2022, 15, 956. [Google Scholar] [CrossRef]
- Urbanucci, L.; D’Ettorre, F.; Testi, D. A Comprehensive Methodology for the Integrated Optimal Sizing and Operation of Cogeneration Systems with Thermal Energy Storage. Energies 2019, 12, 875. [Google Scholar] [CrossRef] [Green Version]
- Bird, T.J.; Jain, N. Dynamic Modeling and Validation of a Micro-Combined Heat and Power System with Integrated Thermal Energy Storage. Appl. Energy 2020, 271, 114955. [Google Scholar] [CrossRef]
- Tarragona, J.; Pisello, A.L.; Fernández, C.; de Gracia, A.; Cabeza, L.F. Systematic Review on Model Predictive Control Strategies Applied to Active Thermal Energy Storage Systems. Renew. Sustain. Energy Rev. 2021, 149, 111385. [Google Scholar] [CrossRef]
- Jiang, R.; Yu, X.; Chang, J.; Yu, X.; Wang, B.; Huang, R.; Li, Z. Effects Evaluation of Fin Layouts on Charging Performance of Shell-and-Tube LTES under Fluctuating Heat Sources. J. Energy Storage 2021, 44, 103428. [Google Scholar] [CrossRef]
- Daniarta, S.; Kolasiński, P. An Integration of Geothermal Energy, Waste, and Cold Energy System Employing the Technology of Organic Rankine Cycle. In Proceedings of the 6th International Seminar on ORC Power Systems, Munich, Germany, 11–13 October 2021; Wieland, C., Karellas, S., Quoilin, S., Schifflechner, C., Dawo, F., Spliethoff, H., Eds.; Technical University of Munich: Munich, Germany, 2021. [Google Scholar]
- Asghari, M.; Afshari, H.; Jaber, M.Y.; Searcy, C. Dynamic Deployment of Energy Symbiosis Networks Integrated with Organic Rankine Cycle Systems. Renew. Sustain. Energy Rev. 2023, 183, 113513. [Google Scholar] [CrossRef]
Authors | Type of Controller and Type of Studies | Main Component | Manipulated/Controlled Parameters | Sensors | Actuators | Performances |
---|---|---|---|---|---|---|
Peralez et al., 2013 [58] | PID, simulation and experiment | Evaporator | The temperature and pressure of the evaporator outlet and the speed of the pump | Pressure, mass flow rate, and temperature | Pump, bypass of the evaporator, bypass of the expander, and turbine speed | The maximum error is 1.9 °C in feedforward action |
Peralez et al., 2014 [59] | PID, simulation | Evaporator | Superheat temperature of the evaporator outlet, evaporating pressure, and speed of the pump | Pressure, mass flow rate, and temperature | Pump, bypass of the evaporator, bypass of the expander, and turbine speed | The maximum error is 5 °C when the observer is used |
Peralez et al., 2017 [60] | PID, simulation and experiment | Evaporator | Superheat temperature of the evaporator outlet, evaporating pressure, and speed of the pump | Pressure, mass flow rate, and temperature | Pump, bypass of the evaporator, bypass of the expander, and turbine speed | The maximum error is 5 °C when the observer is used |
Wang et al., 2017 [61] | PID, simulation | Evaporator and cooling loop | Superheat temperature at evaporator outlet, evaporating pressure, speed of the pump, and cooling water mass flow rate | Mass flow rate and evaporating pressure, and temperature | Pump | The efficiency of the system increased from 9–10% up to almost 18% |
Marchionni et al., 2018 [63] | PI, simulation | Evaporator | Turbine inlet temperature, the pump revolution speed, the turbine revolution speed, and the opening position of recirculating valve | Temperature | Pump and valve | Energy recovered using several PI control systems on the following: Only pump: 15.2 kWh Only turbine: 11.6 kWh Pump and turbine: 12.0 kWh Pump and valve: 14.7 kWh |
Ni et al., 2018 [62] | PID, simulation | Evaporator | Superheat temperature of the evaporator outlet, evaporating pressure, output power, and speed of the pump | Mass flow rate and temperature | Pump | 24% increase in generated power |
Wang et al., 2018 [64] | PID, simulation | Evaporator | Evaporating pressure, condensing pressure, exhaust outlet temperature, and speed of the pump | Mass flow rate, temperature, and pressure | Pump | Satisfactory control of the system with the same PID and different design parameters and there was oscillation detection. |
Chowdhury et al., 2019 [65] | PID, simulation | Evaporator | Evaporator temperature, expander output power, speed of the pump, and valve opening | Mass flow rate and temperature | Pump and valve | Conventional PID is ±0.58 °C |
Lin et al., 2019 [66] | PID, simulation | Evaporator | Evaporator temperature, output power, evaporating pressure, and speed of the pump | Pressure and temperature | Pump and expander | Fluctuation of pressure is smoother. Using PID results in a 1.43 bar evaporation drop instead of an 8.69 bar |
Wang et al., 2020 [67] | DRL-PID, simulation | Evaporator | Superheat temperature under fluctuating heat input and speed of the pump | Temperature and other heat transfer parameters (for DRL) | Pump | Average absolute tracking error
|
Wang et al., 2023 [68] | DRL-PID, simulation | Evaporator | Superheat temperature under fluctuating heat input and speed of the pump | Temperature and other heat transfer parameters (for DRL) | Pump | Average absolute tracking error
|
Authors | Type of Controller and Type of Studies | Main Component | Manipulated/Controlled Parameters | Sensors | Actuators | Performances |
---|---|---|---|---|---|---|
Chowdhury et al., 2018 [69] | Fuzzy, simulation | Evaporator | Refrigerant mass flow rate, heat source mass flow rate, heat source temperature, the outlet temperature of refrigerant, outlet temperature of heat sources | Mass flow rate and temperature | Pump | The responses of the model to transient inputs are sufficiently steady, according to the results, and sufficient. It may be inferred from this that the model was solidly constructed to be applied to the WHR system’s dynamic situation. The validation results show that the evaporator outputs can be predicted using the fuzzy inference method in a dynamic environment. |
Wang et al., 2019 [70] | Fuzzy PID, simulation | Evaporator | The temperature of the evaporator outlet and mass flow rate | Mass flow rate and temperature | Pump | According to the simulation results, fuzzy PID control has increased steady-state performance, greater accuracy, and can react more quickly. To reach a new level, this new unified system makes effective use of each system. |
Chowdhury et al., 2019 [65] | Fuzzy logic, simulation | Evaporator and expander | Evaporator temperature, mass flow rate, and pump speed | Mass flow rate and temperature | Pump and valve | The results demonstrate that, under all circumstances, the fuzzy self-tuning PID controller outperformed the traditional PID controller in terms of set point tracking and disturbance rejection capabilities. |
Cioccolanti et al., 2021 [71] | Fuzzy logic, simulation and experiment | TES | The temperature of TES, the temperature of diathermic oil in the linear Fresnel reflectors (LFRs), and the collected thermal power | Temperature | n.a. | The suggested fuzzy logic control reduces the number of changes between the various operating modes while increasing the contribution of the TES unit to feeding the ORC unit. |
Enayatollahi, et al., 2021 [72] | Neuro-fuzzy, simulation | Evaporator | The outlet temperature of the evaporator and mass flow rate | Temperature, rotating speed of the pump, and mass flow rate | Pump | The inverse neuro-fuzzy controller was successful in lowering the steady state error for regulating the temperature at the evaporator outlet. A PID controller’s settling time is increased and the chattering effect on pump speed is decreased when combined with an inverse neuro-fuzzy controller. |
Enayatollahi et al., 2021 [73] | Neuro-fuzzy, simulation | Evaporator | Evaporator outlet temperature, and evaporator outlet pressure | Temperature, pressure, and mass flow rate | Pump | The neuro-fuzzy models provide minimal computing complexity, high accuracy, and accurate predictions of the evaporator output pressure and temperature. |
Authors | Type of Controller and Type of Studies | Main Component | Manipulated/Controlled Parameters | Sensors | Actuators | Performances |
---|---|---|---|---|---|---|
Toub et al., 2019 [74] | MPC, simulation and experiment | Building HVAC system | The mass flow rate of the heat pump and room air temperature | Temperature | ORC with indirect TES and heat pump | In comparison to a conventional system, MPC for micro-concentrated solar power (CSP) incorporated into HVAC system of building results in 37% energy savings and a 70% decrease in energy costs. |
López-Bautista et al., 2020 [75] | MPC, simulation and experiment | Storage tanks and boiler | Al2O3-water nanofluid flow rate and temperature of nanofluid at the outlet of solar collector | Temperature | Valve | Compared to conventional control theory, the temperature in the solar collector is successfully guided to surpass 63% of the required value. |
Keller et al., 2020 [76] | MPC, simulation and experiment | Evaporator | Pump rotation speed and superheating temperature at the inlet of the expander | Temperature | Pump | Mean absolute error (MAE) and root mean square error (RMSE) for MPC are 4.5 K and 6.2 K |
Shi et al., 2022 [54] | MPC (Fast DMC/FDMC and DMC), simulation and experiment | ORC dynamic model | Pump rotation speed, expander rotation speed, mass flow rate of cooling air, superheating, evaporating pressure, and condenser pressure | Temperature and mass flow rate | Pump | FDMC increases tracking efficiency and reduces processing load by pulling data from the unconstrained optimal solution rather than handling the constrained quadratic programming problem. DMC takes 0.21 s on average to compute online, but FDMC takes 0.0012 s (mode 1) and 0.013 s (mode 2). |
Shi et al., 2023 [77] | MPC via quasi-linear parameter varying (QLPV), simulation and experiment | ORC dynamic model | Pump rotation speed, expander rotation speed, superheating temperature, and evaporating pressure | Temperature and pressure | Pump | Under the suggested controller, the integral squared error in-dex could be reduced by approximately 98.1% and 97.5% for the tracking performance for superheating and pressure, respectively |
Control System | Pros | Cons |
---|---|---|
PID |
|
|
Fuzzy logic |
|
|
MPC |
|
|
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Imre, A.R.; Daniarta, S.; Błasiak, P.; Kolasiński, P. Design, Integration, and Control of Organic Rankine Cycles with Thermal Energy Storage and Two-Phase Expansion System Utilizing Intermittent and Fluctuating Heat Sources—A Review. Energies 2023, 16, 5948. https://doi.org/10.3390/en16165948
Imre AR, Daniarta S, Błasiak P, Kolasiński P. Design, Integration, and Control of Organic Rankine Cycles with Thermal Energy Storage and Two-Phase Expansion System Utilizing Intermittent and Fluctuating Heat Sources—A Review. Energies. 2023; 16(16):5948. https://doi.org/10.3390/en16165948
Chicago/Turabian StyleImre, Attila R., Sindu Daniarta, Przemysław Błasiak, and Piotr Kolasiński. 2023. "Design, Integration, and Control of Organic Rankine Cycles with Thermal Energy Storage and Two-Phase Expansion System Utilizing Intermittent and Fluctuating Heat Sources—A Review" Energies 16, no. 16: 5948. https://doi.org/10.3390/en16165948