Operation Of Air Conditioning Systems Research Articles

Microcontroller Integration in an Automated Power Window Circuit System for Automobile Air Conditioning

Research depicts that vehicular accident is inevitable, especially during long-distance driving journeys due to occurrence such as dehydration and discomfort owing to the inadequate or poor operating performance of car airconditioning systems which can result in loss of the driver’s attention on the road. However, the aforementioned research gap or problem needs to be addressed to ensure the efficient operation of the car air-conditioning system to avert vehicular accidents. Hence, the goal of this research is to utilize a microcontroller to automatically control the car airconditioning system to ensure enhanced operating performance. Moreover, the key merit of this research is to assist drivers by preventing them from losing attention on the road due to a phenomenon such as dehydration and discomfort, especially, during long-distance driving journeys, which can cause vehicular accidents. Besides, another imperative component of this research is to automatically control, thus closing the vehicle’s window louvre once the air conditioner is in operation or turned on to ensure efficient utilization of energy (fuel) of the vehicle and ensure that the ambience in the vehicle is pretty comfortable for the passengers on board. The automated power window system is comprehensively verified by the proteus software. Nonetheless, the results via the simulation depict that the automated power window circuit considered in this paper can effectively and automatically close the vehicle’s window louvre once the air conditioner is turned on.

Optimized Energy-Flexible Operation Strategies of Air Conditioning Systems in Production Environments

Rising energy prices and regulatory requirements are forcing both large manufacturing companies and small and medium-sized enterprises to minimize costs and emissions associated with energy supply. To operate the considered systems more efficiently overall, numerous energy efficiency measures have already been successfully implemented in recent years. This is particularly the case for the cross-industry air conditioning systems that are the focus of this paper. However, due to the growing share of renewable energies and the resulting fluctuating electricity prices and CO2 emissions factors, energy flexibility measures are becoming increasingly relevant. In this context, flexibility measures allow industrial system operators to benefit from these fluctuations, primarily using energy storages and optimized operation strategies. However, the implementation of energy flexibility measures requires high data availability, a good understanding of the system, and flexibility in production planning. To be able to effectively use the existing energy flexibility potentials, air conditioning systems are examined, that contain widely used cross-section technologies. In this paper, a method to enable optimized energy-flexible operation of industrial air conditioning systems is presented. A common feature of manufacturing sites is a similar topology of the thermal supply infrastructure across industries. The complex overall system is divided into several subsystems, e.g., cooling supply system, ventilation system and the production hall. Production processes typically release waste heat into the indoor air of the production hall, which then must be cooled. By accounting for thermal and electrical output and optimizing the operation strategy using mathematical optimization, both operating costs and CO2 emissions associated with energy provision can be significantly reduced. The presented use case reveals that operational cost savings of up to 18 % and a reduction of CO2 emission associated with energy supply of up to 16 % can be realized by optimizing the cooling supply system on its own.

Research on Thermal and Humidity Environment of Cultural Relics Exhibition Room

In order to meet the needs of humanistic education, museum buildings have developed vigorously in recent years. The protection and display of cultural relics is an effective way to continue historical information, which has important social and cultural significance. During the design and operation of the building air conditioning system, both the thermal and humidity environment of the cultural relics exhibition room should be responsible for the safety of the displayed cultural relics and the thermal comfort of visitors should be considered. In this paper, the indoor thermal and humidity environment of a cultural relic exhibition room in Beijing was measured, and the indoor air velocity distribution and temperature and humidity distribution were obtained. The results show that the thermal and humidity environment of the cultural relics exhibition room is better, but the indoor temperature of the exhibition room is lower, which still has great energy saving potential. It needs to be improved in the future engineering practice to achieve double optimization of comfort and energy saving effect. In order to achieve accurate control of the indoor thermal and humidity environment of the cultural relics exhibition room, we built a data collection for thermal environment parameters and air quality, which is used for monitoring and data collection of the cultural relics exhibition room.

Enhancing sustainability and performance of automated air conditioners through optimizing product recovery in closed-loop supply chains

This article investigates the use of closed-loop supply chains (CLSC) to im?prove the sustainability and performance of automatic air conditioning systems. With the growing use of automated air conditioning systems in automobiles, it is necessary to analyze and optimize their efficiency even after their life span. To reliably anticipate the performance of automatic air conditioning systems, the suggested method employs a unique soft computing technology based on support vector machines (SVM). Furthermore, the research focuses on the deployment of CLSC, which allows for optimal product recovery and resource utilization. To optimize the multi-product, multi-time, multi-echelon network, a generalized CLSC model is built, considering costs, product recovery possibilities, unknown parameters, and environmental performance. The study sheds light on reverse logistics decision-making, such as centre placement and allocation, as well as cultivation of supplier relationships. Overall, this study?s incorporation of CLSC and the SVM-based performance prediction approach supports sustainable manufacturing practices. It emphasizes the significance of resource efficiency, waste minimization, and environmental effect mitigation in air conditioning system design, manufacture, and operation. Manufacturers may increase not just their environmental sustainability but also the performance and durability of their air conditioning systems by optimizing product recovery and utilizing CLSC. This study provides industry stakeholders with practical information, supporting the adoption of sustainable practices and contributing to a more sustainable and efficient manufacturing ecosystem.

Room Temperature Dynamic Control and Energy-saving System

According to statistics, the energy consumption of building air conditioning system operation accounts for about 50% of the energy consumption in the building operation phase. Therefore, reducing the energy consumption of building air conditioning systems is of great significance for building energy conservation. The traditional air conditioning system sets a constant temperature during operation and then adopts the PID control method to gradually approach this target value. This method does not take into account dynamic factors such as the building environment, personnel activities, and startup equipment rates, resulting in a large waste of energy consumption and poor comfort. This paper proposes a solution combining the Internet of Things and a heuristic optimization algorithm, in which the Internet of Things is responsible for collecting real-time parameters, and the optimization algorithm is responsible for solving the optimal control strategy, to realize intelligent real-time dynamic adjustment of indoor temperature. By comparing the actual operation results, it can be seen that the control system can reduce power consumption by about 10% and increase user satisfaction by about 8%.

STATUS AND PROSPECTS OF IMPROVING ENERGY EFFICIENCY CLEAN ROOMS AIR CONDITIONING SYSTEMS

The article is devoted to increasing the efficiency of the air conditioning systems of clean rooms, which maintain the microclimate parameters in a given range according to several indicators - the number and size per 1 m³ of dust particles, aerosols, microorganisms and pressure, humidity, and temperature. Clean rooms are used in microelectronics, instrumentation, medicine and medical industry, pharmacology, laboratories, optics production, food industry, biotechnology, aviation, and space industry. Recently, abroad and in Ukraine, with the aim of saving energy resources, fundamental research is being conducted in a number of technologies from the perspective of exergetic methodology. This contributes to an objective assessment of the degree of energy perfection of devices and processes related to energy conversion in modern technologies. For this purpose, the authors developed an exergetic method of analyzing the operation of the direct-flow central air conditioning system of clean rooms.

Water adsorption kinetics of silica based porous desiccants by volumetric method

The adsorption kinetics of solid desiccants is critical for the design and optimal operation of air-conditioning systems. However, owing to the complex combination of diffusion processes in the interparticle layer and intraparticle mesopores/micropores, adequately illustrating the adsorption kinetics of solid desiccants is difficult. In this study, the adsorption kinetics of a microsphere silica gel bead was investigated using the volumetric method in a limited batch adsorption system. An analytical solution was adopted for the biporous diffusion model to obtain the effective mesopore diffusivity. A comprehensive diffusion model was developed based on the initial estimation by the analytical solution. The model, i.e., the triple diffusion resistance model that includes three transport resistances, viz., interparticle, mesopore, and micropore resistance was subsequently validated through an experiment. The results show that the rate-limiting step in the entire diffusion and adsorption process changes from intraparticle to interparticle diffusion with increasing sample mass. The proposed model considering the effect of interparticle diffusion successfully determines the effective mesopore diffusivity for each sample mass.

Application of CPMV* for improving the indoor thermal environment under global solar radiation

The impacts of solar radiation on indoor thermal comfort are growing with the popularity of curtain walls in modern constructions. In order to accurately assess indoor thermal comfort under global solar radiation, a previous study proposed a corrected predicted mean vote with global solar radiation (CPMV*) and validated the model in summer. To further investigate the year-round applicability of the model, field experiments were conducted in transparent envelope buildings in winter, and 1098 valid thermal responses were obtained. The results indicate that CPMV* fits well with the thermal sensation vote, and it has important applications in guiding the design of indoor thermal environments and the operation of ventilation and air conditioning systems throughout the year. Besides, the predicted percentage dissatisfied with global solar radiation and the percentage unacceptable are proven to be feasible in evaluating the thermal acceptability of buildings with solar radiation. Based on CPMV*, we can calculate the indoor thermal comfort zone matching various solar radiation intensities and reasonably regulate the actual operation of the heating, ventilation and air conditioning systems. The indoor thermal environment exposed to solar radiation would be significantly improved by these findings.

A market study of early adopters of fault detection and diagnosis tools for rooftop HVAC systems

Retrofit fault detection and diagnosis (FDD) products are undergoing major advances in their ability to optimize the operation and maintenance of building heating, ventilation, and air conditioning (HVAC) systems as a result of advances in artificial intelligence, cloud computing, and low-cost sensor networks. However, many market barriers still exist to their widespread adoption. This paper is the first to study the market and barriers for FDD products from the perspective of early adopters of HVAC rooftop retrofit FDD products as part of a field study of early adopters. Researchers engaged multiple sites and installed different FDD products at these sites for the purposes of (1) evaluating market readiness, market barriers, and user experience, (2) evaluating energy and demand savings, (3) evaluating the purchase, installation, training, and use process, and (4) determining overall FDD product effectiveness. This paper presents and focuses on the market study goals, methods, results, and findings determined based on survey data collected from key stakeholders participating in the study. These stakeholders included FDD providers, energy efficiency program managers and engineers, HVAC contractors, building owners, facility managers, energy managers, and consultants. The study provides results and analyses concerning (1) current knowledge and awareness levels for FDD technologies, (2) attitudes towards FDD products, (3) market barriers and resources available for FDD, and (4) insights on how stakeholders perceive and determine the value and cost for purchasing, using, and adopting FDD technologies and products. The study produced twelve (12) key findings, which provide valuable input for energy efficiency programs in their development of more effective FDD marketing programs that can increase adoption of FDD technologies and reduce barriers to widespread adoption.

Experimental study of condensation characteristics and operation strategies of induction radiant air-conditioning system

To study the operation and condensation characteristics of an induction radiant air-conditioning system as a novel system that combines induction ventilation and radiant air conditioning, the characteristics of convection and condensation of an indoor terminal device radiant induction unit were studied. The mass transfer coefficient, condensation temperature, and condensation rate were analyzed experimentally. The time required to achieve a steady state indoor thermal environment was collected. The results indicated that the primary airflow rate and temperature mainly affected the mass transfer coefficient, condensation temperature, and condensation rate. When the indoor thermal environment reached a steady state, the heat transfer performance of the radiant induction unit was mainly affected by the primary air temperature. When condensation occurred, the primary airflow rate had a greater influence on the condensation rate. The start-up and response performances of the system were better than those of a traditional radiant air-conditioning system. In the condensation condition, reasonable control strategies maintain the primary air temperature and adjust the primary air flow rate, which can ensure sufficient heat transfer and stabilize the indoor thermal environment faster.

Optimization of Air Conditioning Performance with Al2O3-SiO2/PAG Composite Nanolubricants Using the Response Surface Method

A variety of operational parameters can influence the operation of an automobile air-conditioning (AAC) system. This issue is solved by using optimization techniques that can recommend the ideal parameters for the best results. To improve the performance of AAC system usings Al2O3-SiO2/PAG composite nanolubricants, the response surface method (RSM) was employed. RSM was used to design the experimental work, which was based on a face composite design (FCD). The RSM quadratic models were helpful in determining the links between the input parameters and the responses. The addition of composite nanolubricants improved the overall performance of AAC systems. The parameters were optimized using the RSM’s desirability approach, with the goal of increasing cooling capacity and the coefficient of performance (COP), while reducing compressor work and power consumption. The ideal parameters for the AAC system were found to be 900 rpm compressor speed, 155 g refrigerant charge, and 0.019% volume concentration, with a high desirability of 81.60%. Test runs based on the optimum circumstances level were used to estimate and validate cooling capacity, compressor work, COP, and power consumption. Both predicted and measured values were in good agreement with each other. A new RSM model was successfully developed to predict the optimal conditions for AAC system performance.

The Optimal Operation and Dispatch of Commerce Air-Conditioning System by Considering Demand Response Strategies

The purpose of this paper is to discuss an optimal operation and schedule of commerce air-conditioning system by considering the demand response in order to obtain the maximal benefit; this paper first collects the operating data of the chiller units in commercial users, calculates the cooling load of each unit, and derives the relationship between the cooling loads and power consumption of each unit. The weather information, such as temperature and humidity of inside/outside, are collected in the EXECL database, and the cooling load of the mall’s space is simulated by using the Least Square Support Vector Machine (LSSVM). Under the selected plan of power reduction, the requirement of space cooling loads, and the various operation constraints, the dispatch model of the commerce air-conditioning system with demand response strategies is formulated to minimize the total cost. A Modify Particle Swarm Optimization with Time-Varying Acceleration Coefficients (MPSO-TVAC) is proposed to solve the daily economic dispatch of the air-conditioning system. In the MPSO-TVAC procedure, the dynamic control parameters are embedded in the particle swarm of the PSO-TVAC in order to improve the behavior patterns of each particle swarm and increase its search efficiency in high dimensions. Different modifications in moving patterns of MPSO-TVAC are proposed to search the feasible space more effectively. By using MPSO-TVAC, it provides an optimal mechanism for variables regulated to increase the efficiency of the performing search and look for the probability of an optimal solution. Simulation results also provide an efficient method for commercial users to reduce their electricity bills and raise the ability of the market’s competition.

Dynamic Calibration Method of Sensor Drift Fault in HVAC System Based on Bayesian Inference.

Sensor drift fault calibration is essential to maintain the operation of heating, ventilation and air conditioning systems (HVAC) in buildings. Bayesian inference (BI) is becoming more and more popular as a commonly used sensor fault calibration method. However, this method focused mainly on sensor bias fault, and it could be difficult to calibrate drift fault that changes with time. Therefore, a dynamic calibration method for sensor drift fault of HVAC systems based on BI is developed. Taking the drift fault calibration of the chilled water supply temperature sensor of the chiller as an example, the performance of the proposed dynamic calibration method is evaluated. Results show that the combination of the Exponentially Weighted Moving-Average (EWMA) method with high detection accuracy and the proposed BI dynamic calibration method can effectively improve the calibration accuracy of drift fault, and the Mean Absolute Percentage Error (MAPE) value between the calibrated and normal data is less than 5%.

Enhancing the detection performance of a vision-based occupancy detector for buildings

Occupant behaviour is one of the key parameters that significantly impact the operation of heating, ventilation and air-conditioning (HVAC) systems and the energy performance of buildings. Detailed occupancy information can improve HVAC operation and utilisation of building spaces. Strategies such as vision-based occupancy detection and recognition have recently garnered much interest. This study investigated the performance of a vision-based deep learning detection technique for enhancing building system operations and energy performances. The model used was the Faster region-based convolutional neural network with Inception V2. Two occupancy detection model configurations were developed, tested and evaluated. Both models were analysed based on the application of the detector within a selected case study building, along with an evaluation based on different evaluation metrics. The results suggest the occupancy detector using model 1 provided an overall accuracy of 95.23%, while model 2 (the occupancy activity detector) provided an accuracy of 89.37%. In addition, the F 1 score, which represents the harmonic mean between the precision and recall values, was 0.9756 for model 1 and 0.8298 for model 2. Building energy simulation was used to evaluate and compare the impact of such an approach on indoor occupancy heat gains. The study highlighted the potential of the detection approaches, but further development is necessary, including optimisation of the model, full integration with HVAC controls and further model training and field testing.

A closed-loop data-fusion framework for air conditioning load prediction based on LBF

Accurate air conditioning load prediction is a key component of intelligent building management system for ensuring energy saving and safe operation of air conditioning system. In order to improve the prediction accuracy, a particle filter (PF) load prediction fusion estimation method based on long short-term memory (LSTM) and back propagation neural network (BP) is proposed. Firstly, spearman correlation analysis is used to select the influencing factors with high correlation as feature input. Aiming at the problem that the original signal is easy to be disturbed by noise and the data features are not obvious, locally weighted scatterplot smoothing (LOWESS) method is used to denoise the data to improve the data quality for further accurate prediction. Secondly, the data-driven air conditioning load state-space representation is established, which takes air conditioning load as the state variable and takes the load features collected by the sensor in real-time as the input variables. Thirdly, combined with the space representation method of air conditioning load based on LSTM-BP, PF is introduced to estimate the air conditioning load by using the fusion model. Meanwhile, the output load value of BP is fed back to the fusion model as the observation value to update the state-space representation of air conditioning load. Finally, two practical cases are used to verify the effectiveness of the method. The results indicate that the proposed method can provide more accurate and robust air conditioning load prediction.

Energy, economic, and environmental impacts of enhanced ventilation strategies on railway coaches to reduce Covid-19 contagion risks

In the last years, the Covid-19 outbreak raised great awareness about ventilation system performance in confined spaces. Specifically, the heating, ventilation, and air conditioning system design and operating parameters, such as air change per hour, air recirculation ratio, filtration device performance, and vents location, play a crucial role in reducing the spread of viruses, moulds, bacteria, and general pollutants. Concerning the transport sector, due to the impracticability of social distancing, and the relatively loose requirements of ventilation standards, the SARS-COV-19 outbreak brought a reduction of payload (up to 50%) for different carriers. Specifically, this has been particularly severe for the railway sector, where train coaches are typically characterized by relatively elevated occupancy and high recirculation ratios. In this framework, to improve the Indoor Air Quality and reduce the Covid-19 contagion risk in railway carriages, the present paper investigates the energy, economic and environmental feasibility of diverse ventilation strategies. To do so, a novel dynamic simulation tool for the complete dynamic performance investigation of trains was developed in an OpenStudio environment. To assess the Covid-19 contagion risk connected to the investigated scenarios, the Wells-Riley model has been adopted. To prove the proposed approach's capabilities and show the Covid-19 infection risk reduction potentially achievable by varying the adopted ventilation strategies, a suitable case study related to an existing medium-distance train operating in South/Central Italy is presented. The conducted numerical simulations return interesting results providing also useful design criteria.

Study on the impacts of occupant distribution on the thermal environment of tall and large public spaces

In tall and large (T&L) public spaces, occupant distribution and its variation may impact the thermal environment and thus the operation of heating, ventilation, and air conditioning (HVAC) systems. Such studies are rare in previous literature, so in-situ tests were conducted to demonstrate dynamic occupant distribution characteristics and the potential impact on the thermal environment in the waiting room of a high-speed railway station. The building energy model (BEM) and the computational fluid dynamics (CFD) model were then co-simulated to analyze the impacts under different occupant distribution scenarios. To improve the indoor thermal environment affected by occupant distribution, the thermal coupling relationships between different subzones were considered and identified, and a supply air reallocation optimization based on occupant distribution was proposed for T&L-space public buildings. The results showed that both the temporal and spatial variations of occupants in different subzones easily led to the thermal non-uniformity of an entire large space with a difference over 1.5 °C. Meanwhile, the thermal coupling effect between different subzones should not be neglected in the distribution of supply air since the thermal uniformity of the case building was improved by 15.89%–20.03%, effectively reducing local overcooling and overheating.

Transfer function models for instantaneous internal cooling loads to describe time lag effect of conversion process

For the better operation of air-conditioning systems and the improvement of office building thermal environment, internal heat loads resulting from occupants, equipment, and lightings need to be accurately quantified because they are main components of space cooling loads. However, existing methods are hard to describe the complicated variation characteristics of internal cooling loads and time lag effect of conversion processes between electric energy and internal cooling loads, leading to limited application and unstable accuracy. To tackle this problem, in this study, transfer function models for instantaneous internal cooling loads were firstly established. Second, an experiment was conducted based on a chamber with good insulation. Third, experimental data and radiant time factors were employed in model discretization, parameter identification, and order decrease steps to acquire unknown parameters and develop models as second-order and third-order discrete transfer functions. Fourth, a simulation was conducted to validate the models, and a mean absolute percentage error of 8.08% was attained. Finally, a comparison between proposed and previously developed methods was carried out to evaluate model performance. The proposed models achieved a maximum internal cooling loads reduction of 75.57%, and the maximum time lag resulting from the heat storage characteristics of indoor thermal masses was approximately 30 min. With two easily accessible input parameters, the proposed models exhibited promising applicability and feasibility during the real-time operation of buildings. This paper provides a theoretical basis for accurately obtaining instantaneous internal cooling loads and thus is meaningful in improving building thermal environment and enhancing comfort levels.

Modeling and Coordination of Commercial Buildings in Distribution Systems

Commercial buildings spend vast amount of money every year on their electricity consumption. Though optimal scheduling and control of commercial buildings had been widely discussed in the literature, coordination of multiple buildings connected to power networks still need further consideration. Privacy issue is a major consideration to building managers, which makes the coordination problem very challenging. This article first provides a convex formulation for the optimal operation of heating, ventilation, and air conditioning systems in commercial buildings. Then, this article proposes the use of the alternating direction method of multiplier to enable privacy-based coordination of multiple commercial buildings in the electric grid. The formulated problem considers the thermal discomfort of the occupants and the building power consumption on the one hand, and the power losses of the power network on the other hand. The algorithm uses minimal amount of exchanged data to avoid any privacy invasion. A case study of multiple commercial buildings is done on an IEEE 15-bus distribution system to verify the proposed problem formulation. The results showed that the algorithm can achieve comparable performance to the centralized case in terms of minimizing the thermal discomfort, power consumption, and power losses.

Fault detection and diagnosis using tree-based ensemble learning methods and multivariate control charts for centrifugal chillers

Fault detection and diagnosis (FDD) of centrifugal chillers plays an essential role in reducing energy consumption and ensuring safe operation of heating, ventilation and air conditioning systems. In the existing chiller FDD strategy, Data-driven based system modelling methods and statistical analysis-based detection techniques have been widely discussed and applied in recent years. This paper studies the performance of tree-based ensemble learning methods in chiller modelling and analyses multivariate control charts' detection effect. Predictions from the reference model are used in tandem with multivariate control charts to isolate faulty behavior from normal behavior, and then specific failures are identified through the diagnosis model when anomalies are detected. Firstly, in order to establish the optimal chiller reference model and diagnosis model, three tree-based ensemble learning algorithms, namely (i) random forest (RF), (ii) extreme gradient boosting (XGBoost), and (iii) light gradient boosting machine (LightGBM), are evaluated and compared with commonly used support vector machine (SVM). Secondly, Hotelling's T2, multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) are introduced and their performance in chiller fault detection is tested. Finally, the proposed LightGBM-MEWMA method is validated by the experimental data of ASHRAE RP-1043. Results show that the proposed method effectively identifies seven common chiller faults. Especially at low severity levels, the average detection rate is 88.71%, and the diagnosis accuracy rate is 82.3%.