HVAC System Modeling and Control: Vapor Compression System Modeling and Control

Rasmussen, Bryan P.; Price, Christopher R.; Koeln, Justin P.; Keating, Bryan; Alleyne, Andrew G.

doi:10.1007/978-3-319-68462-8_4

Cited by 8 publications

(3 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model includes all important components in the refrigeration system, such as the compressor, condenser, evaporator, and control devices. Next, various novel engineering techniques are virtually evaluated through simulation to determine their impact on the efficiency and performance of the refrigeration system (Wolfs et al, 2018;Lin et al, 2020;Wang et al, 2020;Rasmussen et al, 2018;Franco et al, 2022;Rodríguez et al, 2020;Khosravi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Implementation of Refrigeration System Optimization Strategy with Latest Mechanical Engineering

Mujayyin,

Widikda,

Mukhsinin

et al. 2023

ijaemr

View full text Add to dashboard Cite

The cooling system plays a very important role in maintaining the optimum temperature and performance of the engine. In recent years, the latest mechanical techniques have been developed to optimize the cooling system in terms of energy efficiency, performance, and reliability. The objective of this study is to analyze and optimize the refrigeration system using the latest mechanical techniques. by using the latest machine techniques. The method used in this study is a combination of computer simulation and practical testing. and practical testing. First, a mathematical model of the existing refrigeration system was developed using leading simulation software. This model includes all important components in the refrigeration system, such as the compressor, condenser, evaporator, and control devices. control devices. Furthermore, various latest mechanical techniques are virtually evaluated through simulation to determine their impact on efficiency and performance. simulation to determine their impact on the efficiency and performance of the refrigeration system. After the virtual evaluation, the best design of the latest mechanical engineering is implemented on a prototype refrigeration system. on the prototype of the refrigeration system. Practical tests were conducted to validate the simulation results and measure the actual performance of the optimized refrigeration system. The data obtained from

show abstract

Section: Introductionmentioning

confidence: 99%

Implementation of Refrigeration System Optimization Strategy with Latest Mechanical Engineering

Mujayyin,

Widikda,

Mukhsinin

et al. 2023

ijaemr

View full text Add to dashboard Cite

show abstract

“…In recent years, many researchers have paid attention to the modeling and control of VCRS [6], [7]. Many modelbased control (MBC) methods can be found in the literature, such as adaptive control [8], decoupling multivariable control [9], linear-quadratic-Gaussian (LQG) control [10], optimal control [11] and model predictive control [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Design of Self-Tuning SISO Partial-Form Model-Free Adaptive Controller for Vapor-Compression Refrigeration System

Chen

2019

IEEE Access

View full text Add to dashboard Cite

Vapor-compression refrigeration systems (VCRS) are applied extensively in domestic, commercial and industrial refrigeration and are responsible for a high percentage of worldwide energy consumption. To achieve high energy efficiency, the application of advanced control methods in VCRS has increasingly attracted the attention of academia and industry. The model-free adaptive control (MFAC) strategy, as an important branch of advanced control research, encounters the problem of parameter tuning when applied to VCRS with strong nonlinearities. In this work, a parameter self-tuning methodology based on the self-learning and self-adapting properties of back propagation neural networks with the System Error set and/or Gradient Vector set as inputs is proposed to adjust the parameters utilized in SISO Partial-Form Model-Free Adaptive Control (SISO-PFMFAC). To test the performance of this novel methodology named SISO-PFMFAC-NNSEGV, qualitative and quantitative comparisons are carried out between the proposed method and the decentralized single PIDs given in the simulation platform provided by the benchmark PID 2018. The integral absolute error (IAE), the integral time-weighted absolute error (ITAE), the integral absolute variation of control signal (IAVU) and a combined index J c are used to evaluate the performance. As a result, the proposed method shows the best performance with a higher tracking accuracy and less variation of the control signal with a combined index J c = 0.7088, which represents a 29.1% improvement over the benchmark PID controller and a 9.6% improvement over the SISO-PFMFAC controller, making it a promising control method for vapor-compression refrigeration systems.INDEX TERMS Partial-form model-free adaptive control, parameter self-tuning, back propagation neural networks, vapor-compression refrigeration system.

show abstract

“…The design for such systems is usually for the steady state operation which is well understood. However, for systems with significant transients, e.g., short duration load demands, say during a heat wave, there may need to be active control of these subsystems (Rasmussen et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Building Comfort and Environmental Control

Wen¹

2019

Encyclopedia of Systems and Control

View full text Add to dashboard Cite

Heating, ventilation, and air condition (HVAC) systems regulate building temperature and humidity distributions. Sound HVAC system operation is critical for maintaining the comfort level of building occupants, providing the proper operating condition for building equipments and contents, and in general achieving a healthy and safe building environment. HVAC is also a major energy consumer. Balancing comfort and environmental control versus energy consumption is a major theme in building HVAC control. This entry describes the modeling of the HVAC system dynamics in buildings, the HVAC control problem addressing human comfort and energy consumption, and the adaptation to variations in the ambient operating condition.

show abstract

HVAC System Modeling and Control: Vapor Compression System Modeling and Control

Cited by 8 publications

References 76 publications

Implementation of Refrigeration System Optimization Strategy with Latest Mechanical Engineering

Implementation of Refrigeration System Optimization Strategy with Latest Mechanical Engineering

Design of Self-Tuning SISO Partial-Form Model-Free Adaptive Controller for Vapor-Compression Refrigeration System

Building Comfort and Environmental Control

Contact Info

Product

Resources

About