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

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

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

A Challenging Future for the IC Engine: New Technologies and the Control Role

[FR] Un challenge pour le futur du moteur a` combustion interne : nouvelles technologies et ro¿le du contro¿le moteur ¿ Les nouvelles normes sur les e¿missions, en particulier le CO2, pourraient re¿duire l¿utilisation du moteur a` combustion interne pour les ve¿hicules. Cet article pre¿sente une revue de diffe¿rentes technologies en cours de de¿veloppement afin de respecter ces normes, depuis de nouveaux concepts de combustion jusqu¿a` des syste`mes avance¿s de suralimentation ou de post-traitement. La plupart de ces technologies demande un contro¿le pre¿cis des conditions de fonctionnement et impose souvent de fortes contraintes lors de l¿inte¿gration des syste`mes. Dans ce contexte et en profitant des dernie`res avance¿es dans les mode`les, les me¿thodes et les capteurs, le contro¿le moteur jouera un ro¿le clef dans la mise en œuvre et le de¿veloppement de la prochaine ge¿ne¿ration de moteurs. De l¿avis des auteurs, le moteur a` combustion interne restera la technologie dominante pour les ve¿hicules des prochaines de¿cennies.[EN] New regulations on pollutants and, specially, on CO2 emissions could restrict the use of the internal combustion engine in automotive applications. This paper presents a review of different technologies under development for meeting such regulations, ranging from new combustion concepts to advanced boosting methods and after-treatment systems. Many of them need an accurate control of the operating conditions and, in many cases, they impose demanding requirements at a system integration level. In this framework, engine control disciplines will be key for the implementation and development of the next generation engines, taking profit of recent advancements in models, methods and sensors. According to authors¿ opinion, the internal combustion engine will still be the dominant technology in automotive applications for the next decades.F. Payri; Luján, JM.; Guardiola, C.; Pla Moreno, B. (2015). A Challenging Future for the IC Engine: New Technologies and the Control Role. Oil & Gas Science and Technology ¿ Revue d¿IFP Energies nouvelles. 70(1):15-30. doi:10.2516/ogst/2014002S153070

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

Real-Time Optimization for Estimation and Control: Application to Waste Heat Recovery for Heavy Duty Trucks

This thesis aims at the investigation and development of the control of waste heat recovery systems (WHR) for heavy duty trucks based on the organic Rankine cycle. It is desired to control these systems in real time so that they recover as much energy as possible, but this is no trivial task since their highly nonlinear dynamics are strongly affected by external inputs (disturbances). Additionally, nonlinear operational constraints must be satisfied. To deal with this problem, in this thesis a dynamic model of a WHR that is based on first principles and empirical relationships from thermodynamics and heat transfer is formulated. This model corresponds to a DAE of index 1. In view of the requirements of the employed numerical methods, it includes a spline-based evaluation method for the thermophysical properties needed to evaluate the model. Therewith, the continuous differentiability of the state trajectories with respect to controls and states on its domain of evaluation is achieved. Next, an optimal control problem (OCP) for a fixed time horizon is formulated. From the OCP, a nonlinear model-predictive control (NMPC) scheme is formulated as well. Since NMPC corresponds to a state feedback strategy, a state estimator is also formulated in the form of a moving horizon estimation (MHE) scheme. In this thesis, we make use of efficient numerical methods based on the direct multiple shooting (DMS) method for optimal control, backward differentiation formulae for the solution of initial value problems for DAE, and the corresponding versions of the real-time iteration (RTI) scheme in order to approximately solve the OCP and implement the MHE and NMPC schemes. The simultaneous implementation of NMPC and MHE schemes based on RTI has been already proven to be stable in the control literature. Several numerical instances of the DMS method for the proposed OCP, NMPC and MHE schemes are tested assuming a given real-world operation scenario consisting of truck exhaust gas data recorded during a real trip. These data have been kindly provided by our industry cooperation partner Daimler AG. Additionally, the PI and LQGI control strategies, of wide-spread use in the literature of control of WHR, are also considered for comparison with the proposed scheme. An important result of this thesis is that, considering the highest energy recovery obtained from both strategies as a reference for the given operation scenario, the proposed NMPC scheme is able to reach an additional energy generation of around 3% when the full state vector is assumed to be known, and its computational speed allows it to update the control function in times shorter than the considered sampling time of 100 [ms], which makes it a suitable candidate for real-time implementation. In a more realistic scenario in which the state has to be estimated from noisy measurements, a combination of both aforementioned NMPC and MHE schemes yields an additional energy generation of around 2%. Concretely, this thesis presents novel results and advances in the following areas: • A first principles DAE model of the WHR is presented. The model is derived from the energy and mass conservation considerations and empirical heat transfer relationships; and features a tailored evaluation method of thermophysical properties with which it possesses the property of being at least continuously differentiable with respect to its controls and states on its whole domain of evaluation. • A new real-time optimization control strategy for the WHR is developed. It consists of an NMPC strategy based on efficient simulation, optimization and control tools developed in previous works. The scheme is able to explicitly handle nonlinear constraints on controls and states. In contrast to other NMPC instances for the WHR found in the literature, our scheme's efficient numerical treatment make it real-time feasible even if the full nonlinear WHR dynamics are considered. • To the author's knowledge, this is the first implementation that considers both the NMPC and the MHE approaches used simultaneously in the control of the WHR. The combination of NMPC and MHE produces a closed-loop, model-based implementation that can treat realistic measurements as inputs and calculates the corresponding control functions as outputs