Simulation of waste heat recovery system with fuzzy based evaporator model

Chowdhury, Jahedul Islam; Soulatiantork, Payam; Nguyen, Bao Kha

doi:10.1109/ascc.2017.8287506

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…The pressure at the expander inlet after considering the loss is shown in Figure 16. It can be seen from this figure that the expander inlet pressure for both cases of the overall model are similar except for some small deviations around time 200 s, 400 s, and 800 s. These deviations of pressure are caused by the predicting error of the evaporator outlet temperature in the fuzzy domain, which is one of the functions of the pressure loss correlation used in Equation (27). However, the error in the fuzzy-based evaporator model predicting the expander inlet pressure was only 0.001%, which is shown in Table 7.…”

Section: Results and Analysismentioning

confidence: 85%

“…A similar investigation carried out by Chen et al [18] indicated that up to 30% increase in the cycle efficiency is achievable if a WHR system is run at a supercritical pressure rather than a traditional subcritical pressure. The analysis and performance evaluation of supercritical ORC cycles mainly focused on fluid selections [6,15,19,20], design and optimization [15,[21][22][23][24][25][26][27]. Although steady-state models of ORC-WHR systems are necessary for the preliminary analyses as discussed above, they cannot be used either for performance evaluation in transient conditions or control system simulations.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Nonlinear Dynamic Evaporator Model in Supercritical Organic Rankine Cycle Waste Heat Recovery Systems

et al. 2018

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The organic Rankine cycle (ORC)-based waste heat recovery (WHR) system operating under a supercritical condition has a higher potential of thermal efficiency and work output than a traditional subcritical cycle. However, the operation of supercritical cycles is more challenging due to the high pressure in the system and transient behavior of waste heat sources from industrial and automotive engines that affect the performance of the system and the evaporator, which is the most crucial component of the ORC. To take the transient behavior into account, the dynamic model of the evaporator using renowned finite volume (FV) technique is developed in this paper. Although the FV model can capture the transient effects accurately, the model has a limitation for real-time control applications due to its time-intensive computation. To capture the transient effects and reduce the simulation time, a novel fuzzy-based nonlinear dynamic evaporator model is also developed and presented in this paper. The results show that the fuzzy-based model was able to capture the transient effects at a data fitness of over 90%, while it has potential to complete the simulation 700 times faster than the FV model. By integrating with other subcomponent models of the system, such as pump, expander, and condenser, the predicted system output and pressure have a mean average percentage error of 3.11% and 0.001%, respectively. These results suggest that the developed fuzzy-based evaporator and the overall ORC-WHR system can be used for transient simulations and to develop control strategies for real-time applications.

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Section: Results and Analysismentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Fuzzy Nonlinear Dynamic Evaporator Model in Supercritical Organic Rankine Cycle Waste Heat Recovery Systems

et al. 2018

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“…J.I. Chowdhury et al [10] recognize the evaporator and superheater of the WHRS to be the most important component of the cement manufacturing process as it defines the efficiency of the whole process. To reduce the computational time at the evaporator in a Finite Volume model, a fuzzy based evaporator is considered.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Simulation of a Waste Heat Recovery System for a Cement Manufacturing Process

Sivakumar¹

2021

JUSST

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As the ever-changing world continues to desperately look for alternative energy sources in the midst of an energy crisis, new technologies to recover power are revealing themselves and being implemented all across the globe. Most power plants are looking for more sustainable sources of energy over the long term. One such technology being adopted now by a lot of enterprises are Energy Recovery Systems. These systems work to retain and reuse energy that would otherwise be lost to the atmosphere after a certain process. They are sustainable and require comparatively lower capital. The objectives of this project revolve around the modelling of a Waste Heat Recovery System (WHRS) for a heat-intensive manufacturing process. The heat, which would otherwise be lost to the atmosphere, is trapped and converted by a heat recovery unit into reusable energy. The main principle on which such a system would operate is The Rankine Cycle, an idealized thermodynamic cycle. Successful implementation of such an energy recovery system would not just boost energy efficiency but also reduce operational costs. The modeling and simulation of the heat recovery system are done on an open-source chemical process flow software known as DWSIM. An analysis of this heat recovery model shows an increase of 19.66% in the energy efficiency of the manufacturing process. Heat recovery systems also have great benefits for the environment, as they reduce the emissions of greenhouse gases by such manufacturing plants and help reduce global warming.

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“…This heating condition gives a good thermal match between the heat source and the working fluids, thereby improving heat recovery [4]. In recent years, the supercritical ORC has attracted a lot of attention as many publications can be found on working fluid selection [5], circuit configuration and design [6] and performance analysis and optimisation [3,[7][8][9][10][11]. However, most analyses were based on steady-state conditions only, with a limited number of publications on dynamic conditions.…”

Section: Introductionmentioning

confidence: 99%

Control of Supercritical Organic Rankine Cycle based Waste Heat Recovery System Using Conventional and Fuzzy Self-tuned PID Controllers

Chowdhury

Thornhill

Soulatiantork

et al. 2019

Int. J. Control Autom. Syst.

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This research develops a supercritical organic Rankine cycle (ORC) based waste heat recovery (WHR) system for control system simulation. In supercritical ORC-WHR systems, the evaporator is a main contributor to the thermal inertia of the system, which is greatly affected by transient heat sources during operation. In order to capture the thermal inertia of the system and reduce the computation time in the simulation process, a fuzzy-based dynamic evaporator model was developed and integrated with other component models to provide a complete dynamic ORC-WHR model. This paper presents two control strategies for the ORC-WHR system: evaporator temperature control and expander output control, and two control algorithms: a conventional PID controller and a fuzzy-based self-tuning PID controller. The performances of the proposed controllers are tested for set point tracking and disturbance rejection ability in the presence of steady and transient thermal input conditions. The robustness of the proposed controllers is investigated with respect to various operating conditions. The results show that the fuzzy self-tuning PID controller outperformed the conventional PID controller in terms of set point tracking and disturbance rejection ability at all conditions encountered in the paper.

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Simulation of waste heat recovery system with fuzzy based evaporator model

Cited by 5 publications

References 10 publications

Fuzzy Nonlinear Dynamic Evaporator Model in Supercritical Organic Rankine Cycle Waste Heat Recovery Systems

Fuzzy Nonlinear Dynamic Evaporator Model in Supercritical Organic Rankine Cycle Waste Heat Recovery Systems

Design and Simulation of a Waste Heat Recovery System for a Cement Manufacturing Process

Control of Supercritical Organic Rankine Cycle based Waste Heat Recovery System Using Conventional and Fuzzy Self-tuned PID Controllers

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