Cycle Waste Heat Recovery System Research Articles

Methodological Solutions for Predicting Energy Efficiency of Organic Rankine Cycle Waste Heat Recovery Systems Considering Technological Constraints

Solving strategic IMO tasks for the decarbonization of maritime transport and the dynamics of its controlling indicators (EEDI, EEXI, CII) involves the comprehensive use of renewable and low-carbon fuels (LNG, biodiesel, methanol in the mid-term perspective of 2030, ammonia, and hydrogen to achieve zero emissions by 2050) and energy-saving technologies. The technology of regenerating secondary heat sources of the ship’s power plant WHR in the form of an Organic Rankine Cycle (ORC) is considered one of the most promising solutions. The attractiveness of the ORC is justified by the share of the energy potential of WHR at 45–50%, almost half of which are low-temperature WHR (80–90 °C and below). However, according to DNV GL, the widespread adoption of WHR-ORC technologies, especially on operating ships, is hindered by the statistical lack of system prototypes combined with the high cost of implementation. Developing methodological tools for justifying the energy efficiency indicators of WHR–ORC cycle implementation is relevant at all stages of design. The methodological solutions proposed in this article are focused on the initial stages of comparative evaluation of alternative structural solutions (without the need to use detailed technical data of the ship’s systems, power plant, and ORC nodes), expected indicators of energy efficiency, and cycle performance. The development is based on generalized results of variation studies of the ORC in the structure of the widely used main marine medium-speed diesel engine Wärtsilä 12V46F (14,400 kW, 500 min−1) in the operational load cycle range of 25–100% of nominal power. The algorithm of the proposed solutions is based on the established interrelationship of the components of the ORC energy balance in the P-h diagram field of thermodynamic indicators of the cycle working fluid (R134a was used). The implemented strategy does allow, in graphical form, for justifying the choice of working fluid and evaluating the energy performance and efficiency of alternative WHR sources for the main engine, taking into account the design solutions of the power turbine and the technological constraints of the ORC condensation system. The verification of the developed methodological solutions is served by the results of comprehensive variation studies of the ORC performed by the authors using the professionally oriented thermoengineering tool “Thermoflow” and the specification data of Wärtsilä 12V46F with an achieved increase in energy efficiency indicators by 21.4–7%.

Transcritical dual-loop Rankine cycle waste heat recovery system for China VI emission standards natural gas engine

This paper presents the design of a waste heat recovery system for a China VI emission standards (China VI) natural gas engine to address cooling challenges. A heat balance bench experiment was conducted to characterize the waste heat properties of the engine's exhaust, exhaust gas recirculation exhaust (EGR), and coolant. A transcritical dual-loop Rankine cycle system was modeled and analyzed using MATLAB to recover this waste heat. The system utilizes two loops: high-temperature loop to recover exhaust waste heat and EGR waste heat and low-temperature loop to recover waste heat from the coolant and high-temperature loop, improving overall recovery. The low-temperature loop is based on a transcritical cycle, making the system more compact than a subcritical alternative and better suited for vehicle applications. Analysis identified water as the working fluid for high-temperature loop and R32 as the optimal working fluid for low-temperature loop. The Rankine cycle waste heat recovery system was found to increase the engine's power output by approximately 71.29 kW at rated operating conditions, representing a 21.03% gain over the original power.

Alleviating the thermal aging of three-way catalyst applied to natural gas engine based on organic Rankine cycle

Natural gas (NG) is a possible pathway for developing countries to reduce transportation carbon footprint. However, its high exhaust temperature decreases the thermal efficiency of NG engines and accelerates the thermal aging of three-way catalyst (TWC). This study proposes to place an organic Rankine cycle waste heat recovery (ORC-WHR) system between an NG engine and its TWC to recover exhaust energy and extend the useful life of TWC. The feasibility of this design concept is confirmed by the simulation results of an experimentally validated 1D NG engine model combined with an ORC-WHR model at three TWC working temperatures representing field-average, modern, and future TWC technologies. Results show that the decrease in the outlet temperature of the ORC-WHR system benefits the availability of exhaust energy, improving the overall efficiency of the engine plus ORC-WHR system and extending the useful life of the TWC. Hence, more advanced catalyst technologies, which can achieve high conversion efficiency at lower temperatures, are deemed more favorable for ORC-WHR coupling. The cost-effectiveness of such combinations is better due to more exhaust energy recovered and less noble metal catalyst needed to be added.

Reliability optimization design of intelligent mechanical structure for waste heat recovery

In order to solve the problems of high energy consumption and serious waste of heat energy in the cooling of traditional oil cooler in hydraulic system, the author proposed the reliability optimization design of intelligent mechanical structure for waste heat recovery. The author has built a waste heat recovery test platform for hydraulic system, the influence of electrical load, oil flow rate and working medium flow rate on system operation and energy characteristics is studied. The experimental results show that: under the same working condition, compared with the oil cooler of the same specification, the maximum thermal efficiency of the proposed organic Rankine cycle waste heat recovery system is increased to 2.56%. The expander pressure ratio and system thermal efficiency increase with the increase of electric load and oil flow rate. With the increase of the flow rate of the working medium, the superheat of the working medium at the inlet of the expander decreases significantly, while the heat exchange of the evaporator and the output power of the expander increase. Under the test condition, the maximum heat exchange of evaporator is 4.18 kW, and the maximum output power of expander is 356 W. The energy saving effect of waste heat recovery system of hydraulic system is obtained, and the influence law of operation parameters on system performance is obtained.

Waste heat recovery and reuse for ship hydraulic oil temperature control system

In order to solve the problems of high energy consumption and serious waste of heat energy in the traditional oil cooler of Marine hydraulic system, the waste heat recovery and reuse of Marine hydraulic oil temperature control system is proposed. The hydraulic system waste heat recovery test platform has been established, the influence of electrical load, oil flow rate and working medium flow rate on system operation and energy characteristics is studied. The experimental results show that under the same working condition, compared with the oil cooler of the same specification, the maximum thermal efficiency of the proposed organic Rankine cycle waste heat recovery system is increased to 2.56%. The expander pressure ratio and system thermal efficiency increase with the increase of electric load and oil flow. The experimental results analyzed the energy saving effect of waste heat recovery system on hydraulic system, and obtained the rule of system operation efficiency.

Evaluating the thermodynamic potential for carbon capture from internal combustion engines

This work explores the potential for use of carbon dioxide separation processes, colloquially known as carbon capture, to accelerate decarbonization of the transport sector. Thermal swing absorption processes using an aqueous, CO2-selective solvent, provide an efficient means of separating CO2 from other exhaust gas constituents. Amine-based solvents–well known from acid gas treating and other post-combustion carbon capture processes–are highly selective, have a fast rate of reaction with CO2, and can use waste heat to drive the separation process. This work demonstrates how amine-based processes can be deployed for carbon removal from a generic exhaust gas source produced by an internal combustion engine (ICE) using waste heat in the exhaust. It includes an estimation of the exhaust enthalpy-limited capture rate for various sources considering the capture system performance, the CO2 concentration, and the exhaust temperature.CO2 capture rates of around 25–80% are shown to be thermodynamically feasible for various ICE applications using waste exhaust heat for solvent regeneration. Rates of around 15–50% are attainable without providing additional energy, if exhaust heat is also used to power the capture system pump and compressor. Although a higher exhaust CO2 concentration reduces the specific energy requirements of the separation process, the total energy required for a given capture rate increases due to the greater amount of CO2 removed. Thus, maximum capture rates are highest for applications with high exhaust temperature and low CO2 concentration (such as diesel generators), and lowest for applications with very low exhaust temperature such as ships. This analysis also shows that the potential for CO2 avoidance using carbon capture systems far exceeds that of organic rankine cycle waste heat recovery systems. Overall, expanding the use of carbon capture to ICE applications can provide an efficient means of utilizing waste exhaust heat while providing significant reductions in the energy industry's scope 3 greenhouse gas emissions.

Multi-objective optimization and algorithm improvement on thermal coupling of SOFC-GT-ORC integrated system

Optimizing thermal coupling of an integrated system and improving its energy cascade utilization (ECU) level are important ways to improve system performance. An integrated power generation system consisting of an anode recirculation internal reforming solid oxide fuel cell (ARIR-SOFC), a gas turbine (GT), an organic Rankine cycle (ORC) waste heat recovery system and a liquefied natural gas (LNG) supply system is established. Additionally, heat exchange network (HEN) subsystem formed by heat exchange streams is specially considered to further improve ECU level. Optimization model is proposed where HEN scheme is optimized simultaneously with operating parameters and working fluids of ORC. Because of the complexity of model, it is key to focus on the solution strategy. An adaptive improved multi-objective particle swarm optimization (IMOPSO) algorithm is established to improve diversity and accuracy of solutions. Finally, a case is solved and results show that HEN scheme plays an important role in system performance.

A novel analysis framework for the organic Rankine cycle waste heat recovery system: From the viewpoint of turbine design

The turbine is crucial equipment in the ORC system, not only has a significant influence on system efficiency but also dominates the equipment investment costs. In this paper, a novel design framework for the radial inflow turbine is proposed, aiming to promote the standardization or serialization design of the ORC turbine, and the innovativeness of combining it into the system analysis is illustrated. The reliability of the design framework is examined by comparing with CFD simulation, nearly all of the deviations within ±5%. Better prediction accuracy can be achieved when R is close to 0.6, and the suitable value of α4 is 65° to realize a better turbine performance. Some other results reveal that the Ẇout has a positive proportional to r4 and Ma4, but ηts is not sensitive to them. Besides from the viewpoint of thermodynamics, the ηts of the turbine is hardly affected by the type of working fluid. For the case study, the turbine with R236ea has achieved the best matching degree with the heat source, which improves near 10% of the total output power for the MGT-ORC system. This paper gives new ideas for ORC turbine design and system analysis which contributes to promoting its application.

A Q-learning based transient power optimization method for organic Rankine cycle waste heat recovery system in heavy duty diesel engine applications

In recent years, the organic Rankine cycle waste heat recovery (ORC-WHR) technology gains popularity in heavy-duty diesel engine applications. Drastic fluctuations of the waste heat caused by variable daily operation of mobile heavy-duty trucks bring an extreme transient power optimization challenge to ORC-WHR systems. Existing power optimization methods either neglect transient behavior of the Rankine cycle system or compromise model accuracy for computation efficiency. Different from literature, this study first time proposes a model-free reinforcement learning method to achieve online transient power optimization for the ORC-WHR system and explains the benefits of learning method in this application. A tabular Q-learning is formulated to optimize the net power on an experimentally validated ORC-WHR system. Q-learning is explained in detail using states, action, and policy information. To quantify the power optimization of the proposed method, Proper-Integral-Derivative method, state-of-art offline and online Dynamic Programming methods are implemented. The results showed that Q-learning generated 22% more cumulative energy than the energy Proper-Integral-Derivative method generated. Furthermore, Q-learning produces 96.6% of cumulative energy that the offline Dynamic Programming generates over a transient engine condition, while it requires less computation cost and is executed online. Additionally, the Q-learning produces 0.5% more cumulative energy than the machine learning-based online Dynamic Programming results and exhibits better vapor temperature robustness than the online Dynamic Programming method (4 °C-28 °C superheat by Q-learning vs. 5 °C-94 °C superheat by online Dynamic Programming). Given the excellent power production performance, low computation cost requirement and high robustness, the proposed Q-learning method has the potential to improve the power production of the ORC-WHR system with different configurations.

Control of Organic Rankine Cycle, a neuro-fuzzy approach

Undesirable emissions in internal combustion engines are of major concern due to their negative effect on the human health and global warming. Implementing an organic Rankine cycle (ORC) in mobile applications can help to mitigate their negative effect by transferring some of their wasted heat into useful electrical or mechanical energy. Considering the stringent regulations in automobile industry ensuring the safety of equipment before implementing them on the vehicles is vital. In this study, a novel control approach is proposed to ensure the safe operation of Organic Rankine Cycle (ORC) waste heat recovery (WHR) system and stabilize its work output when subjected to transient heat sources in a range of waste heat from heavy-duty diesel engines. The control strategy comprises a neuro-fuzzy controller based on the inverse dynamics of the ORC system to control the superheating at the evaporator outlet by adjusting the pump speed and a PI controller to maintain the expander work output by regulating the mass flow rate at the expander inlet. The performance of the control strategy is investigated with respect to set-point tracking and its robustness is tested in the presence of noises. The simulation results indicate an enhancement in the controller performance by combination of feedforward and feedback controllers based on neuro-fuzzy technologies. The proposed control scheme not only can obtain satisfactory transient response under various loading conditions, but also can achieve desirable disturbance rejection performance.

Modeling of an Organic Rankine Cycle Waste Heat Recovery system for automotive engine applications

The remarkable investments made by manufacturers over the last few decades have contributed to improving the performance of internal combustion engines in every aspect: lower polluting emissions, greater specific power and thermal efficiency. Despite this, on an average, about 40% of the thermal power theoretically available from the combustion of the fuel is still stored in the exhaust gases and therefore dispersed in the environment. In this work the modeling and validation of a waste heat recovery (WHR) plant will be described, combining the engine with a low temperature Organic Rankine Cycle (ORC) system, in order to investigate the feasibility of this system on board of a vehicle, analyzing the quantity of thermal power recovered and made available in the form of electrical power. The ORC plant is modeled using a 0D/1D thermo-fluid dynamic approach. Starting from experimental tests, a map-based model for the piston pump and the scroll expander has been developed. The model has been validated through the use of a vector optimization technique, exploiting a genetic algorithm (MOGA). Subsequently, this system has been coupled to a spark ignition engine for automotive applications, adapting its speed range to comply with the ORC experimental tests. To have an accurate control over the expander inlet temperature, a bypass circuit and two throttles actuated by a PI controller have been implemented. The simulations were performed by considering 18 engine points at maximum load and different rpm. An average thermal efficiency increase of the system of 2.6% was obtained by introducing the recovery plant, and wide improvement chance can be foreseen in the case of ORC full-power use.

Bioethanol produced from regenerative wooden resource to operate an organic Rankine cycle-waste heat recovery system in case of a tractor propulsion Diesel engine: preliminary simulation results

Bioethanol has become by nowadays a common-used organic agent by thermal installations in direct modes, like dual or single alternative fuel for classic engines, or in indirect modes, as primary source in fuel cells, or operating thermal energy recovery systems, which is the subject of the present work. Among all the other well-known industrial obtaining methods, avoiding higher costs and improving process efficiency, the enzymatic fermentation of the saccharide components coming from wooden biomass ensures a more profitable way to produce bioethanol, with the requested properties in order to operate as a thermal agent in an Organic Rankine Cycle (ORC) installation. The aim of using this system is to increase the efficiency of a tractor-turbocharged Diesel engine by its waste heat recovery (WHR).

Prediction and optimization of isentropic efficiency of vortex pump under full operating conditions in Organic Rankine Cycle waste heat recovery system based on deep learning and intelligent algorithm

The isentropic efficiency of vortex pump in Organic Rankine Cycle (ORC) system has an important influence on the performance of the system. In this paper, vortex pump experimental data and deep learning are combined to construct an experimental data-driven isentropic efficiency prediction model of vortex pump under full operating conditions. The activation function and the number of hidden layer nodes in the model are filtered by nested screening technique. Through bilinear interpolation algorithm, the influence of vortex pump operation parameters on the isentropic efficiency is analyzed. In addition, the optimization boundary is selected in the four-dimensional space. Finally, the deep learning prediction model is combined with Linear Decreasing Inertia Weight Particle Swarm Optimization (LDIWPSO) to predict and optimize the maximum isentropic efficiency of vortex pump under full operating conditions. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Coefficient of Determination (R-square) are combined to evaluate the prediction accuracy of the model. The prediction and optimization results show that the maximum isentropic efficiency of vortex pump can reach 22.89%. The combination of deep learning and LDIWPSO can predict and optimize the maximum isentropic efficiency for vortex pump with high precision. It also provides a reference for the maximum value of vortex pump isentropic efficiency in theoretical analysis and numerical simulation.

Thermo-environmental analysis and performance optimisation of transcritical organic Rankine cycle system for waste heat recovery of a marine diesel engine

ABSTRACT Energy efficient and environmentally friendly shipping have been primary concerns for the maritime industry. One of the alternatives to overcome these issues onboard is organic Rankine cycle (ORC) waste heat recovery system (WHRS). In this study, a transcritical ORC WHRS for a marine diesel engine is investigated at different engine operating loads by thermodynamic and environmental analysis. The engine exhaust gas is used as the waste heat source and R152a is selected as the working fluid. The energetic, exergetic and environmental parameters are analysed and the performance optimisation is conducted by using genetic algorithm. The results indicate that by employing the ORC system onboard, it is possible to increase the overall thermal efficiency of the ship power generation system by more than 2.5% and the system can save up to 678.1 tonnes CO2 per year when the system is operated at the optimal conditions.

Thermo-economic analysis and multi-objective optimization of S-CO2 Brayton cycle waste heat recovery system for an ocean-going 9000 TEU container ship

Supercritical CO2 recompression Brayton cycle (S-CO2 RBC) can be used as waste heat recovery system in a ship because of the advantages of high efficiency, compact size and lower cost. This paper proposes a modified S-CO2 RBC system, namely the dual turbine-alternator-compressor (TAC) S-CO2 RBC system, which to be used to recover the waste heat from the main engine exhaust gas of an ocean-going 9000 TEU container ship. Mathematical and simulation models for the modified system are established to investigate the improvement in the performance. Parametric study is conducted to analyze the effects of key thermodynamic parameters on the system performance. The Imperialist Competitive Algorithm - based multi-objective optimization is carried out to get the modified system’s optimal operating parameters, aiming to maximize the system net power output, energetic efficiency and exergetic efficiency as well as to minimize the heat exchanger area per unit power and the levelized cost of energy. The results show that the performance of the modified system is strengthened significantly, whose highest net power output, energetic efficiency and exergy efficiency are 452.2 kW, 24.53% and 41.47%, approximately 26.58%, 16.67% and 16.90% higher than that of the S-CO2 RBC system. Compared with the S-CO2 RBC system, the modified system is more compact because its heat exchanger area per unit power decreased by 44.08%. The modified system can contribute to decreasing the ship auxiliary engine fuel consumption and the Energy Efficiency Design Index by about 1.01% and 1.02%, and improving the thermal efficiency of the ship main engine system by 3.23%. The results of this study can provide theoretical support for the application of the dual TAC S-CO2 RBC waste heat recover system on ships.

A Rigorous Model Order Reduction Framework for Waste Heat Recovery Systems Based on Proper Orthogonal Decomposition and Galerkin Projection

A proper orthogonal decomposition (POD) and Galerkin projection-based model order reduction framework is developed for the evaporator heat exchanger in heavy-duty diesel engine organic Rankine cycle waste heat recovery system. The dynamics in the heat exchanger are first modeled by a finite-volume model, composed of highly nonlinear, coupled, partial differential equations, and then used to generate snapshots from which basis functions are defined. Reduced order models (ROMs) are then derived using the Galerkin projection approach. The accuracy and the execution time of different POD ROMs are evaluated against the high-fidelity finite-volume model. The results show that the POD ROM dimension can be selected based on the specific requirements of accuracy and computation time demanded by the intended model utilization. The proposed ROM framework can be utilized to generate ROMs with various dimensions for different purposes, such as estimator design, model-based control, and benchmark generation.

Experimental Validation of Nonlinear Model Predictive Control for a Heavy-Duty Diesel Engine Waste Heat Recovery System

Abstract This paper discusses an experimental validation of a real-time augmented control scheme for an organic Rankine cycle (ORC) waste heat recovery (WHR) system. A nonlinear model predictive control (NMPC) is designed to regulate the working fluid vapor temperature after the evaporator. The NMPC utilizes a six-state reduced order moving boundary (MB) evaporator model. The state estimator is constructed using an extended Kalman filter (EKF) given the working fluid outlet vapor temperature and exhaust gas outlet temperature as measurements. Working fluid evaporation pressure is controlled by an external proportional-integral-derivative (PID) control loop. The experimental validation first compares the performance of the augmented control scheme with that of a traditional multiple loop PID control with a feedforward term over an engine transient. The experimental study shows that the augmented control scheme outperforms the baseline multiloop PID control in both terms tracking error and settling time during transient engine operation. The performance of the augmented control scheme is further validated over three additional transient conditions with alterations to both the engine transient and the working fluid reference temperature. The NMPC validation shows that the working fluid vapor temperature can be controlled within 1% error margin relative to the targeted reference.

Real-time realization of Dynamic Programming using machine learning methods for IC engine waste heat recovery system power optimization

This study aims to present a method for real-time realization of Dynamic Programming algorithm for power optimization in an organic Rankine Cycle waste heat recovery system. Different from existing studies, for the first time machine learning algorithms are utilized to extract the rules from offline Dynamic Programming results for optimal power generation. In addition, for the first time a single state Proper Orthogonal Decomposition and Galerkin Projection based reduced order model is combined with Dynamic Programming for its high accuracy and low computation cost. For a transient driving cycle, Dynamic Programming algorithm is utilized to generate the optimal working fluid pump speed. A total of eleven state-of-art machine learning algorithms are screened to predict this pump speed. Random Forest algorithm is then selected for its best pump speed prediction accuracy. A rule-based method is added to the Random Forest model to improve energy recovery. As one of the main discoveries in this study, in the rule extraction process, the Random Forest model reveals that the time delayed exhaust gas mass flow rate and exhaust temperature improve the rule extraction accuracy. This observation points out the difference between steady state and transient optimization and that the steady state optimization results do not necessarily hold true in transient conditions. Another key observation is that Random Forest – rule-based method retrieves 97.2% of the energy recovered by offline Dynamic Programming in a validation driving cycle. In addition, the inclusion of rule-based method significantly increases the Random Forest model’s energy recovery from 66.5% to 97.2%. This high accuracy means that the machine learning models can be used to extract Dynamic Programming rules for real-time application.

A comparative analysis of dynamic evaporator models for organic Rankine cycle waste heat recovery systems

As the Organic Rankine Cycle Waste Heat Recovery (ORC-WHR) gained research attention in recent years, the evaporator models are required in plant modeling and model-based controls. However, model comparison and selection works are lacking in ORC-WHR application. Different from the modeling work in literature, this paper aims to first time present a comparative study of three evaporator models for Organic Rankine Cycle Waste Heat Recovery (ORC-WHR) systems using the same set of identification parameters and experimental data. Finite volume model and moving boundary model are the most popular modeling methodologies in the field of ORC-WHR. Meanwhile, 0-D lumped models attract some research attention thanks to their low computational cost and least modeling effort. This paper first presents the three models, which are then validated with experiments data collected in a heavy-duty diesel engine ORC-WHR system. In the model comparison process, accuracy, computational cost and modeling effort are evaluated. All three models exhibit decent working fluid vapor temperature prediction accuracy with 6.6 K of mean error and 1.27% mean error percentage both in steady state and transient conditions. 0-D lumped model is found to be accurate enough for many application purposes, which is not found in literature. Based on the comparison results, model selection recommendation is given based on disparate application purposes and different phases of ORC-WHR system development.

An experimentally validated, energy focused, optimal control strategy for an Organic Rankine Cycle waste heat recovery system

This paper presents a Nonlinear Model Predictive Controller (NMPC) designed to provide optimal control input for maximum turbine power generation in an Organic Rankine Cycle (ORC) Waste Heat Recovery (WHR) system. While the literature is rich in ORC-WHR system modeling and control approaches in simulation environments, the fundamental dynamic analysis, system aging, thermal inertia, and experimental implementation of power optimization based optimal ORC-WHR control are still lacking. These factors are key to fully understanding and controlling the dynamic behavior of the system and are the main focus of this study. In contrast to prior literature, this work experimentally evaluates the nonlinear dynamics of the ORC system to comprehensively understand the controller design requirements. A power optimization-based Nonlinear Model Predictive Controller (NMPC) is derived utilizing an Extended Kalman Filter (EKF) as a state estimator. Simulation results indicate that optimal turbine power generation is obtained with minimal working fluid superheat for the system under study. Consequently, a superheat-tracking controller is designed, and the performance of the controller is simulated over step inputs. The designed controller is then experimentally validated on an ORC test rig with a 13L Heavy Duty Diesel Engine (HDDE).During experimental evaluation of the controller, it was discovered that the control-oriented model is susceptible to system aging effects and therefore, the model was calibrated online to match the behavior of the aged system. Moreover, evaporator thermal inertia was found to play a vital role attenuating the fluctuating frequency components of the exhaust conditions. The tuned controller provided satisfactory control response for transient engine conditions and maintained the working fluid temperature within acceptable limits.