Effect factors of part-load performance for various Organic Rankine cycles using in engine waste heat recovery

The Organic Rankine Cycle (ORC) is regarded as one of the most promising waste heat recovery technologies for electricity generation engines. Since the engine usually operates under different working conditions, it is important to research the part-load performance of the ORC. In order to reveal the effect factors of part-load performance, four different forms of ORCs are compared in the study with dynamic math models established in SIMULINK. They are the ORC applying low temperature working fluid R245fa with a medium heat transfer cycle, the ORCs with high temperature working fluid toluene heated directly by exhaust condensing at low pressure and high pressure, and the double-stage ORC. It is regarded that the more slowly the system output power decreases, the better part-load performance it has. Based on a comparison among the four systems, the effects of evaporating pressure, condensing condition, working fluid, and system structure on part-load performance are revealed in the work. Further, it is found that the system which best matches with the heat source not only performs well under the design conditions, but also has excellent part-load performance

Nonlinear model predictive control of an Organic Rankine Cycle for exhaust waste heat recovery in automotive engines

Energy recovery from exhaust gas waste heat can be regarded as an effective way to improve the energy efficiency of automotive powertrains, thus reducing CO2 emissions. The application of Organic Rankine Cycles (ORCs) to waste heat recovery is a solution that couples effectiveness and reasonably low technological risks. On the other hand, ORC plants are rather complex to design, integrate and control, due to the presence of heat exchangers operating with phase changing fluid, and several control devices to regulate the thermodynamic states of the systems. Furthermore, the power output and efficiency of ORC systems are extremely sensitive to the operating conditions, requiring precise control of the evaporator pressure and superheat temperature. This paper presents an optimization and control design study for an Organic Rankine Cycle plant for automotive engine waste heat recovery. The analysis has been developed using a detailed Moving Boundary Model that predicts mass and energy flows through the heat exchangers, valves, pumps and expander, as well as the system performance. Starting from the model results, a nonlinear model predictive controller is designed to optimize the transient response of the ORC system. Simulation results for an acceleration-deceleration test illustrate the benefits of the proposed control strategy

Recent developments of control strategies for organic Rankine cycle (ORC) systems

Organic Rankine cycle (ORC) is one of the most rapidly growing approaches to utilizing low grade thermal energy. This paper deals with the main control problems existed in ORC systems and overviews the main approaches presented in literature. The main ORC operating modes are introduced, the control strategies of ORC systems are then surveyed. Thus, this paper presents a comprehensive review of overall control strategies for ORC energy conversion systems and points out research trend on ORC control systems

Systematic Methods for Working Fluid Selection and the Design, Integration and Control of Organic Rankine Cycles—A Review

Efficient power generation from low to medium grade heat is an important challenge to be addressed to ensure a sustainable energy future. Organic Rankine Cycles (ORCs) constitute an important enabling technology and their research and development has emerged as a very active research field over the past decade. Particular focus areas include working fluid selection and cycle design to achieve efficient heat to power conversions for diverse hot fluid streams associated with geothermal, solar or waste heat sources. Recently, a number of approaches have been developed that address the systematic selection of efficient working fluids as well as the design, integration and control of ORCs. This paper presents a review of emerging approaches with a particular emphasis on computer-aided design methods

Dynamic modeling and control strategies of organic Rankine cycle systems: Methods and challenges

Organic Rankine cycle systems are suitable technologies for utilization of low/medium-temperature heat sources, especially for small-scale systems. Waste heat from engines in the transportation sector, solar energy, and intermittent industrial waste heat are by nature transient heat sources, making it a challenging task to design and operate the organic Rankine cycle system safely and efficiently for these heat sources. Therefore, it is of crucial importance to investigate the dynamic behavior of the organic Rankine cycle system and develop suitable control strategies. This paper provides a comprehensive review of the previous studies in the area of dynamic modeling and control of the organic Rankine cycle system. The most common dynamic modeling approaches, typical issues during dynamic simulations, and different control strategies are discussed in detail. The most suitable dynamic modeling approaches of each component, solutions to common problems, and optimal control approaches are identified. Directions for future research are provided. The review indicates that the dynamics of the organic Rankine cycle system is mainly governed by the heat exchangers. Depending on the level of accuracy and computational effort, a moving boundary approach, a finite volume method or a two-volume simplification can be used for the modeling of the heat exchangers. From the control perspective, the model predictive controllers, especially improved model predictive controllers (e.g. the multiple model predictive control, switching model predictive control, and non-linear model predictive control approach), provide excellent control performance compared to conventional control strategies (e.g. proportional–integral controller, proportional–derivative controller, and proportional–integral–derivative controllers). We recommend that future research focuses on the integrated design and optimization, especially considering the design of the heat exchangers, the dynamic response of the system and its controllability

Plant Modeling, Model Reduction and Power Optimization for an Organic Rankine Cycle Waste Heat Recovery System in Heavy Duty Diesel Engine Applications

With pressure from strict emission and fuel consumption regulations, researchers are searching for improved internal combustion engine performance. Especially for the heavy-duty vehicles, which takes up 7% of the total vehicle volume while consume around 30% of transportation energy in US. Around 40-60% of energy is wasted as heat in heavy-duty diesel (HDD) vehicles in different engine operating conditions, which mainly includes the waste heat in exhaust gas, exhaust gas recirculation (EGR) circuit, and engine coolant. Waste heat recovery (WHR) techniques are potential to achieve the fuel economy and emission reduction goals. Among the available WHR techniques, organic Rankine cycle (ORC) is preferred by many researchers for its mature technologies and high efficiency. The aim of this dissertation is to analyze the power of HDD vehicle by: (i) building a high fidelity, physics-based ORC-WHR dynamic system plant model, (ii) building a reduced order model framework, and (iii) conducting the power analysis based on the developed plant and reduced models. The dynamic system plant model is built, which includes heat exchangers, a turbine expander, pumps, control valves, compressible volumes, junctions and a reservoir. Components are modelled and calibrated individually. Subsequently, the component models are integrated into an entire ORC-WHR system model. The entire ORC-WHR system model is validated over transient engine conditions. Actuator sensitivity study is conducted for the ORC-WHR power generation analysis using the ORC-WHR plant model. Besides the ORC-WHR plant model, a reduced order model framework is developed utilizing Proper Orthogonal Decomposition (POD) and Galerkin projection approaches. The POD-Galerkin reduced order model framework inherits the system physics from the high fidelity, physics-based ORC-WHR plant model. POD Galerkin reduced order models are compared with three existing models (finite volume model, moving boundary model and 0D lumped model) and show their advantages over the existing models in terms of accuracy or computation cost. In addition, identification method is applied to the low order POD Galerkin reduced order model to increase the accuracy. Given the validated ORC-WHR plant model and POD Galerkin reduced order model framework, the ORC-WHR system power analysis is conducted. Steady state power analysis is conducted over two quasi-steady driving cycles using the ORC-WHR plant model. An engine model is developed to predict the exhaust conditions in transient engine operating conditions. Transient power analysis is conducted with ORC-WHR plant model and engine model co-simulation by optimizing three vapor temperature reference trajectories. Finally, dynamic programming (DP) is implemented with the POD-Galerkin reduced order model to generate ORC-WHR power benchmark in a driving cycle, which can give the guidance on the ORC power optimization and evaluate the controller performance

Modelling and control of waste heat recovery systems for heavy-duty applications

Internal combustion engines (ICEs) are likely to be used in heavy-duty applications for many years and it is important to continue improving their efficiency. Undesirable emissions in internal combustion engines are of major concern due to their negative effect on the human health and global warming. One approach is to recover waste heat from the exhaust of heavy-duty diesel engines (HDDEs) using waste heat recovery (WHR) technologies. WHR based on organic Rankine cycle (ORC) is a promising technology, which offers potential to reduce the fuel consumption of HDDEs by converting the wasted thermal energy to alternative useful electrical or mechanical energy. In the ORC, the evaporator is considered the most critical component of the system. Careful modelling of the evaporator unit is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. This study uses an Adaptive Network-based Fuzzy Inference System (ANFIS) modelling technique to provide efficient control-oriented evaporator models for prediction of heat source and refrigerant temperatures at the evaporator outlet. The ANFIS model benefits from feed-forward output calculation and backpropagation capability of neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using hybrid gradient-descent least-square estimate (GD-LSE) and particle swarm optimisation (PSO) techniques is investigated and the performance of both techniques are compared in terms of RMSE and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both techniques beyond capability of numerical models. However, a better accuracy is achieved for the models trained using the PSO algorithm. Experimentally-measured data is collected from a 1-kWe ORC prototype developed in Clean Energy Processes (CEP) laboratory at Imperial College London and the proposed ANFIS techniques is applied in order to investigate the application of the neuro-fuzzy technique for modelling the evaporator unit. Comparison of the experimental data and the neuro-fuzzy models predictions reveals an acceptable accuracy in predicting the evaporator outlet temperature and pressure. A novel control approach is also proposed to ensure the safe operation of ORC waste heat recovery 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 noise. The simulation results indicate an enhancement in the controller performance by combination of feedforward and feedback controllers based on neuro-fuzzy techniques. The proposed control scheme not only can obtain satisfactory transient response under various loading conditions, but also can achieve desirable disturbance rejection performance

Modeling and control of advanced powertrain systems and Waste Heat Recovery technologies for the reduction of CO2 emissions in light-duty vehicles

2014 - 2015Transportation is the major sector in the EU where greenhouse gas emissions are still rising. Therefore, in the recent years, the OEMs research and development has focused on the reduction of carbon dioxide (CO2) and pollutants emissions. On the other hand, the European Commission proposed targets for the further reduction of CO2 emissions from new cars by 2020. In this scenario, concepts such as the engines downsizing and other advanced technologies as well as more costly hybrid solutions and, more recently, waste heat recovery (WHR) systems have been proposed... [edited by author]XIV n.s