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Special Issue "Modelling and Control of Heating, Ventilation, Air Conditioning and Refrigeration (HVAC&R) Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Energy and Buildings".

Deadline for manuscript submissions: closed (10 March 2021) | Viewed by 8025

Special Issue Editors

Prof. Dr. Alessandro Beghi
E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, 35122 Padova, Italy
Interests: modelling, identification, simulation, and control applications; energy systems; mechatronic systems; manufacturing processes and systems; automotive systems; HVAC&R systems.
Dr. Mirco Rampazzo
E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, 35122 Padova, Italy
Interests: modelling, identification, and control applications; HVAC&R systems; Nature-Inspired Optimization Algorithms; Machine Learning applications.
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Special Issue Information

Dear Colleagues,

The Guest Editors are inviting submissions to a Special Issue of Energies on the subject area of “Modelling and Control of Heating, Ventilation, Air Conditioning, and Refrigeration (HVAC&R) Systems”. HVAC&R systems consume large amounts of energy and, not infrequently, their inefficient operation translates into poor performance and brings to an increasing environmental impact in terms of Greenhouse Gas (GHG) emissions. Indeed, HVAC&R systems inherent complexity together with non-stationary plant operating conditions and complex patterns of user demand, and their intrinsically non-linear characteristics, make the efficient operation of this kind of systems a difficult task. Besides the traditional approach, which improves the energy efficiency of HVAC&R systems through advanced hardware design, nowadays advanced control systems can improve both system efficiency and performance by mostly acting on software components, taking advantage of the recent developments of digital technologies.

The aim of this Special Issue is to collect contributions on recent advances in the field of modelling and control of HVAC&R systems. Topics of interest for publication include, but are not limited to:

  • Modelling and simulation of HVAC&R systems;
  • Advanced control of HVAC&R systems;
  • Building Energy Management System (BEMS);
  • Commercial and Transport Refrigeration control applications;
  • Optimization and management of HVAC&R systems;
  • Fault detection, diagnosis, and predictive maintenance of HVAC&R systems;
  • Machine Learning in HVAC&R control applications;
  • Intelligent control of HVAC&R systems;
  • IoT for Smart HVAC Control;
  • Control of HVAC&R systems for Industry 4.0 applications.

Prof. Dr. Alessandro Beghi
Dr. Mirco Rampazzo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • HVAC&R systems
  • Modelling
  • Simulation
  • Control Methods
  • Intelligent Control
  • Machine Learning
  • Optimization Techniques
  • Fault Detection
  • Predictive Maintenance
  • IoT
  • Industry 4.0

Published Papers (7 papers)

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Research

Article
Controlling the Thermal Power of a Wall Heating Panel with Heat Pipes by Changing the Mass Flowrate and Temperature of Supplying Water—Experimental Investigations
Energies 2020, 13(24), 6547; https://doi.org/10.3390/en13246547 - 11 Dec 2020
Cited by 3 | Viewed by 645
Abstract
Renewable energy sources for the purpose of heating buildings cooperate perfectly with so-called low-temperature heating systems. Water loop surface heating systems had been thoroughly tested. In contrast, thermal performance of wall panels with heat pipes have not been fully recognized, yet. The determination [...] Read more.
Renewable energy sources for the purpose of heating buildings cooperate perfectly with so-called low-temperature heating systems. Water loop surface heating systems had been thoroughly tested. In contrast, thermal performance of wall panels with heat pipes have not been fully recognized, yet. The determination of the thermal power as well as the control of panels thermal performance cannot be performed with the methods developed for water loop systems. In this paper, the novel heating panels with heat pipes were tested to analyze the possibility of controlling their performance by changing the mass flowrate of heating water and its temperature. Specific heating power of the investigated panels varies from 16.9 W/m2 to 93.8 W/m2 when supplying a water temperature ranging from 35 °C to 65 °C and mass flowrate from 10 g/s to 47.5 g/s. Investigations revealed that the thermal performance of the panels is more sensitive to the changes of temperature than to the changes of mass flowrate of supplying water, and thus, should be controlled by changing the supply water temperature at low mass flowrates to obtain a low energy usage of pumps (diminished pressure losses) and good quality of controlling. Full article
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Article
An Exact Algorithm for the Optimal Chiller Loading Problem and Its Application to the Optimal Chiller Sequencing Problem
Energies 2020, 13(23), 6372; https://doi.org/10.3390/en13236372 - 02 Dec 2020
Cited by 4 | Viewed by 730
Abstract
The optimal management of multiple chiller systems calls for the solution of the so-called optimal chiller loading (OCL) problem. Due to the interplay of continuous and logical constraints, OCL is an NP-hard problem, so that a variety of heuristic algorithms have been proposed [...] Read more.
The optimal management of multiple chiller systems calls for the solution of the so-called optimal chiller loading (OCL) problem. Due to the interplay of continuous and logical constraints, OCL is an NP-hard problem, so that a variety of heuristic algorithms have been proposed in the literature. Herein, an algorithm for its exact solution, named X-OCL, is developed under the assumption that the chillers’ power consumption curves are quadratic. The proposed method hinges on a decomposition of the solution space so that the overall OCL problem is decomposed to a set of equality constrained quadratic programming problems that can be solved in closed form. By applying the new X-OCL solver to well known case studies, we assess and compare the performances of several literature algorithms, highlighting also some errors in the published results. Moreover, X-OCL is used to design a greedy optimal chiller sequencing (OCS) solver, called X-OCS. The X-OCS is tested on two literature benchmarks and on the model of the heating, ventilation and air-conditioning (HVAC) system of a semiconductor plant, over a two-year period. The performances of X-OCS are remarkably close to the theoretical optimal performance. Full article
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Article
Model-Based Condenser Fan Speed Optimization of Vapor Compression Systems
Energies 2020, 13(22), 6012; https://doi.org/10.3390/en13226012 - 17 Nov 2020
Viewed by 957
Abstract
Vapor compression systems (VCS) cover a wide range of applications and consume large amounts of energy. In this context, previous research identified the optimization of the condenser fans speed as a promising measure to improve the energy efficiency of VCS. The present paper [...] Read more.
Vapor compression systems (VCS) cover a wide range of applications and consume large amounts of energy. In this context, previous research identified the optimization of the condenser fans speed as a promising measure to improve the energy efficiency of VCS. The present paper introduces a steady-state modeling approach of an air-cooled VCS to predict the ideal condenser fan speed. The model consists of a hybrid characterization of the main components of a VCS and the optimization problem is formulated as minimizing the total energy consumption by respectively adjusting the condenser fan and compressor speed. In contrast to optimization strategies found in the literature, the proposed model does not relay on algorithms, but provides a single optimization term to predict the ideal fan speed. A detailed experimental validation demonstrates the feasibility of the model approach and further suggests that the ideal condenser fan speed can be calculated with sufficient precision, assuming constant evaporating pressure, compressor efficiency, subcooling, and superheating, respectively. In addition, a control strategy based on the developed model is presented, which is able to drive the VCS to its optimal operation. Therefore, the study provides a crucial input for set-point optimization and steady-state modeling of air-cooled vapor compression systems. Full article
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Article
An Air Terminal Device with a Changing Geometry to Improve Indoor Air Quality for VAV Ventilation Systems
Energies 2020, 13(18), 4947; https://doi.org/10.3390/en13184947 - 21 Sep 2020
Cited by 8 | Viewed by 733
Abstract
This study aimed to develop a new concept for an air terminal device for a VAV (variable air volume) ventilation system that would improve overall ventilation efficiency under a varying air supply volume. In VAV systems, air volume is modified according to the [...] Read more.
This study aimed to develop a new concept for an air terminal device for a VAV (variable air volume) ventilation system that would improve overall ventilation efficiency under a varying air supply volume. In VAV systems, air volume is modified according to the thermal load in each ventilated zone. However, lowering the airflow may cause a lack of proper air distribution and lead to the degradation of hygienic conditions. To combat this phenomenon, an air terminal device with an adapting geometry to stabilize the air throw, such that it remains constant despite the changing air volume supplied through the ventilation system, was designed and studied. Simulations that were performed using the RNG k–ε model in the ANSYS Fluent application were later validated on a laboratory stand. The results of the study show that, when using the newly proposed terminal device with an adaptive geometry, it is possible to stabilize the air throw. The thermal comfort parameters such as the PMV (predicted mean vote) and PPD (predicted percentage of dissatisfied) proved that thermal comfort was maintained in a person-occupied area regardless of changing airflow though the ventilation system. Full article
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Article
Steady-State Performance Prediction for a Variable Speed Direct Expansion Air Conditioning System Using a White-Box Based Modeling Approach
Energies 2020, 13(18), 4757; https://doi.org/10.3390/en13184757 - 11 Sep 2020
Cited by 1 | Viewed by 915
Abstract
When using a certain type of Heating, Ventilation & Air Conditioning (HVAC) systems, it is primary to obtain their steady-state operating behaviors for achieving a better indoor thermal environment. This paper reports a development of a white-box-based dynamic model for a direct expansion [...] Read more.
When using a certain type of Heating, Ventilation & Air Conditioning (HVAC) systems, it is primary to obtain their steady-state operating behaviors for achieving a better indoor thermal environment. This paper reports a development of a white-box-based dynamic model for a direct expansion (DX) air conditioning (A/C) system to predict its steady-state operating performance under variable speed operation. The established model consists of five sub-models, i.e., a compressor, an electronic expansion valve, an evaporator, a condenser and a conditioned space. Each sub-model was developed based on partial lumped parameter approach. Using the available data generated from an experimental DX A/C system, both transient and steady-state behaviors predictions agreed well with the experimental ones. With the help of the validated white-box model, the inherent steady-state operating performance expressed in terms of the relationship among total cooling capacity (TCC), equipment sensible heat ratio (E SHR) and coefficient of performance (COP) under various speed combinations of compressor and supply fan were further examined. The results show that a higher COP could be achieved when the DX A/C system was operated at a higher fan speed or a lower compressor speed for dealing with a larger required E SHR. This model could be helpful for A/C system design and controller development. Full article
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Article
Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control
Energies 2020, 13(17), 4339; https://doi.org/10.3390/en13174339 - 21 Aug 2020
Cited by 13 | Viewed by 2136
Abstract
District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution [...] Read more.
District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid. Full article
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Article
Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings
Energies 2020, 13(12), 3246; https://doi.org/10.3390/en13123246 - 23 Jun 2020
Cited by 4 | Viewed by 878
Abstract
Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have [...] Read more.
Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants’ comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems’ control. A building’s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34% while providing significant indoor air quality improvement. Full article
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