Examinando por Autor "Gusev, Sergei"
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- PublicaciónSólo datosDesign and experimental validation of an adaptive control law to maximize the power generation of a small-scale waste heat recovery system(Applied Energy, 2017-10-27) Hernandez, Andres; Desideri, Adriano; Gusev, Sergei; Ionescu, Clara M.; Den Broek, Martijn VanIncreasing the energy efficiency of industrial processes is a challenge that involves, not only improving the methodologies for design and manufacturing, but optimizing performance during part-load operation and transient conditions. A well-adopted solution consists of developing waste heat recovery (WHR) systems based on Organic Rankine Cycle (ORC) power units. The highest efficiency for such cycle is obtained at low superheating values, corresponding to the situation where the system exhibits time-varying nonlinear dynamics, triggered by the fluctuating nature of the waste heat source. In this paper, an adaptive control law using the Model Predictive Control (MPC) framework is proposed. This work goes a step beyond most of the existing scientific works in the field of ORC power systems, since the MPC controller is implemented in a lab-scale prototype, and its performance compared against a gain-scheduled PID strategy. The experimental results show that the adaptive MPC outperforms the gain-scheduled PID based strategy, as it allows to accurately regulate the evaporating temperature, while keeping vapor condition at the inlet of the expander i.e., the superheating, in a safe operating range, thus increasing the net power generation.
- PublicaciónSólo datosDesign and experimental validation of an adaptive control law to maximize the power generation of a small-scale waste heat recovery system(Applied Energy, 2017-10-17) Hernandez, Andres; Desideri, Adriano; Gusev, Sergei; Ionescu, Clara M.; Van Der Broek, Martijn; Quoilin, Sylvain; Lemort, Vincent; De Keyser, RobinIncreasing the energy efficiency of industrial processes is a challenge that involves, not only improving the methodologies for design and manufacturing, but optimizing performance during part-load operation and transient conditions. A well-adopted solution consists of developing waste heat recovery (WHR) systems based on Organic Rankine Cycle (ORC) power units. The highest efficiency for such cycle is obtained at low superheating values, corresponding to the situation where the system exhibits time-varying nonlinear dynamics, triggered by the fluctuating nature of the waste heat source. In this paper, an adaptive control law using the Model Predictive Control (MPC) framework is proposed. This work goes a step beyond most of the existing scientific works in the field of ORC power systems, since the MPC controller is implemented in a lab-scale prototype, and its performance compared against a gain-scheduled PID strategy. The experimental results show that the adaptive MPC outperforms the gain-scheduled PID based strategy, as it allows to accurately regulate the evaporating temperature, while keeping vapor condition at the inlet of the expander i.e., the superheating, in a safe operating range, thus increasing the net power generation.
- PublicaciónSólo datosExperimental validation of a multiple model predictive control for waste heat recovery organic Rankine cycle systems(Applied Thermal Engineering, 2021-07-05) Hernández, Andrés; Ruiz, Fredy; Gusev, Sergei; De Keyser, Robin; Sylvain, Quoilin; Vincent, LemortWaste heat recovery systems are today considered as a valuable solution to increase energy efficiency of industrial applications and heavy-duty vehicles, as it uses a thermodynamic organic Rankine cycle system to recover the heat losses to produce electrical or mechanical power. Optimal performance of such machines is often achieved at conditions where complex time-varying nonlinear dynamics are encountered, making the automatic control strategy a fundamental element to maximise the energy efficiency. In this paper the development of a multiple model predictive controller suitable for industrial implementation is presented, and its effectiveness is experimentally validated for the task of maximising output power of a small-scale ORC power unit used in a waste heat recovery application. The main advantage of the proposed controller is the possibility to use different model structures to describe local dynamics without increasing complexity of the optimisation problem. Additionally, experimental results illustrate that the entire operating range of the system might be classified in two regions, a quasi-linear and a highly nonlinear region for ‘high’ and ‘low’ superheating degrees respectively. Closed-loops tests lead to the conclusion that a single linear model predictive controller might only be used under suboptimal operation of low power production (on the quasi-linear region for ‘high’ superheating), otherwise leading to poor performance or even instability. Alternatively, the proposed strategy keeps the cycle stable over the entire range of conditions and allows to increase the net electrical energy produced by at least , even under drastic waste heat source variations, when operating closer to the minimum allowed superheating degree.