Environmental Control System Research Articles

A Contrast Experiment on the Ventilation Direction towards Human Head in Personalized Environmental Control System (PECS)

A heating, ventilation, and air-conditioning system is designed for the entire space but falls short in meeting diverse individual needs. Therefore, the personalized environmental control system is proposed to address individual thermal requirements and it has been widely accepted and produced to have significant practical value for humans. In order to enhance the comfort level based on the PECS, the impact of the airflow direction towards the head in terms of physiological and psychological sensations was investigated. Different airflow directions were considered as follows: front blowing (FB-PV), side blowing (SB-PV), back blowing (BB-PV), top blowing (TB-PV), and a control group No-PV. A total of 56 participants were invited to assess the thermal environment, and their skin temperature was measured. The results revealed that the airflow towards the head improved thermal comfort and reduced the mean skin temperature by 0.4–0.6 °C, with FB-PV showing particularly promising results. TB-PV demonstrated the highest level of comfort with a score of +1.05 (slightly higher than “Just comfortable (+1)”) and scored significantly higher in overall willingness at +1.21 (higher than “Willing (+1)”). Conversely, FB-PV resulted in a discomfort level due to restricted breathing under the continuous airflow condition.

Research on Predictive Control Method of Pigsty Environment Based on Fuzzy Control

At present, most of the environmental control systems of pigsties use direct control methods; when factors, such as temperature and humidity, exceed the set threshold value, the corresponding actuator is turned on for regulation. However, such methods have problems such as low control accuracy, high energy consumption, and untimeliness. In order to save on energy consumption and improve control accuracy, this paper takes the predicted value, set value, and current detection value of the internal environment of a pigsty as input, and combines fuzzy control and direct control methods to realize the predictive control of the pigsty environment. The test results show that, compared with the direct control method, the fuzzy predictive control method can make fluctuations in the internal temperature and humidity of the pigsty less close to the set threshold value, while the ammonia concentration hardly exceeds the set threshold value. The results show that predictive control can more accurately control the internal environment of the pigsty and reduce energy costs by about 20%. Therefore, this method can provide scientific and effective environmental control methods for agricultural production processes, such as livestock breeding and greenhouse cultivation, in semi-confined spaces.

Thermal performance of three-wheel and split-wheel air cycle systems for a civil aircraft environmental control system (ECS)

The conventional approach of regulating cabin temperature and pressure on aircraft is to utilize engine-bleed air through an environmental control system (ECS). The extraction of bleed air from the engine leads to a decrease in thrust and an increase in drag on the ECS ram air duct, resulting in higher fuel consumption. Consequently, the ECS transitioned from being engine-powered to being electrically powered. In this study, the thermodynamic characteristics of three-wheel and split-wheel air cycle systems (ACSs) with a high-pressure water separation system (HPWS) were investigated by developing a parameter decomposition model and an iterative algorithm using MATLAB for a state-of-the-art electrically driven ECS (EECS) on the Boeing 787 Dreamliner. The efficiency of the ACS was assessed by establishing analytical correlations for the coefficient of performance (COP) using relevant literature on endo-reversible thermodynamic model (ETM). By employing these analytical correlations, the thermal performance of both ACSs can be accurately predicted without the need for system modeling and simulation, considering variations in the input variables and operating conditions, such as the temperatures of fresh air and ram air, the ratio of the mass flow rates of ram air and fresh air, and component parameters, including the efficiencies of the primary and secondary heat exchangers and the pressure ratios of the fan and compressor. The implementation of a split-wheel ACS instead of three-wheel ACS in the Boeing 787 EECS led to an improvement in the COP from 0.31 to 0.43, and also resulted in a reduction of 14.35 % in the input power.

Overall and local environmental collaborative control based on personal comfort model and personal comfort system

Most methods for creating an indoor thermal environment are based on controlling heating, ventilation, and air conditioning (HVAC) systems and do not consider the various needs of individuals in a multiperson space. Personal comfort systems (PCS) and personal comfort models (PCM) are popular technologies for achieving personal thermal comfort. This paper presents a thermal environmental collaborative control system (TECCS) that regulates environments at different spatial scales by leveraging the advantages of the HVAC system, PCS, PCM, and PCM-based automatic control to address the issue of individual differences in thermal demand in multiperson environments. The TECCS predicts thermal sensation votes (TSV) by combining facial skin temperature data obtained by an infrared sensor with environmental parameters. Subsequently, it performs the corresponding PCS control and adjusts the air conditioner according to the operating state of the PCS. This study proposes a collaborative control strategy with PCS at the core, enabling communication between thermal state recognition, HVAC system, and PCS. Twenty-eight adult males participated in the experiments testing the TECCS's performance. The results indicate that the TECCS can automatically regulate environments at different spatial scales based on thermal sensation prediction and that the operating state of the PCS can effectively guide air conditioning operations. Compared with constant setpoint control, the TECCS offers the advantage of improving thermal comfort. This paper also proposes future optimization directions based on the research results, focusing on recognition, equipment, and control.

Energy demand and carbon emission analyses of a solar-driven domestic regional environment mobile robot as household auxiliary heating and cooling method

Under the trend of energy saving and carbon reduction, reducing household energy consumption, which is an important part of social energy consumption, has become a hot topic. In order to solve the problem of mismatch between supply and demand of household environment control systems as well as to better meet the individual needs of users, we innovatively designed a solar-driven domestic regional environment mobile robot as auxiliary heating and cooling method. In this paper, a simplified thermal network analysis of the thermal conditions of a dwelling is utilized to calculate the energy saving and carbon reduction potential on summer and winter reference days in Beijing and Guangzhou, China. The simulation results show that the system has good energy saving potential and carbon reduction effect. In particular, the single-day energy saving potential of the system on winter days in Beijing reaches to 583.6 kWh compared to the traditional method. Under the power supply mode, the single-day carbon reduction can reach 462 kg CO2 on winter days in Guangzhou, and it can reach 333.2 kg CO2 on summer days in Beijing. We also discuss the effect of regional space size and clothing thermal resistance. As the local space size and the clothing thermal resistance increases, the energy saving potential and carbon reduction benefits of the system decrease, but the overall impact is small. The proposed mobile robot following in the vicinity of home inhabitants to help maintain their thermal comfort, can improve the efficiency of dwellings heating and cooling systems and utilize the limited solar energy resources in family area.

Environmental Protection Control System Based on IoT and Deep Learning Intelligent Monitoring Sensors

In order to improve the multi-sensor intelligent control performance of environmental pollution detection, this paper proposes a design model of environmental pollution detection intelligent system based on Internet of Things technology. Firstly, this paper uses ZigBee’s Internet of Things networking technology to design the network of the pollution detection system. Secondly, in order to verify the effectiveness of the environmental pollution detection method, this paper carries out system debugging analysis and simulation test, and uses visual dsp++simulation system to debug the system. The network of ZigBee Internet of Things networking technology is adopted to realize the network design of pollution detection system, and VIX bus technology is adopted to carry out environmental pollution detection data communication and information processing, so as to realize the optimal design of intelligent system of environmental pollution detection system. Finally, the experimental test and analysis were performed. The collection period of environmental pollution data is 10 s and the monitoring time is 22 h. Finally, according to the environmental model and parameters, we determine that the system can accurately detect environmental pollution information, and the results are accurate and reliable.

Environmental management control systems and environmental innovation: Unintended consequences of the EU non-financial reporting directive

We examine the effect of regulatory-driven environmental management control systems (EMCS) on environmental innovation by exploiting the non-financial reporting Directive 2014/95/EU as a quasi-natural experiment. One explicit aim of this directive is to improve the measuring, monitoring, and managing of undertakings’ performance and their impact on society. A regulated context may compel managers to prioritize external regulatory requirements over their specific organizational contingencies. This shift could lead to a decrease in flexibility, which is needed to effectively accelerate environmental innovation, arguably one of the corporate flagships for enhancing environmental performance. We perform a difference-in-differences design using the EU directive as a plausible exogenous shock to EMCS and environmental innovation. Our results suggest that: (i) the directive under study increases the adoption of EMCS, (ii) non-regulated EMCS foster environmental innovation and, even more importantly, (iii) regulatory-driven EMCS have a short-term (temporary) lessening effect on environmental innovation. In further analysis we find that this lessening effect reverses from t+1 onwards, once internal frictions are overcome, with the effect being positive in the long run.

The Role of Greenhouse Technology in Streamlining Crop Production

Greenhouse technology has revolutionized modern agriculture by enabling year-round crop production, optimizing resource utilization, and enhancing crop yields and quality. This comprehensive review explores the multifaceted role of greenhouse technology in streamlining crop production processes. It delves into the principles of greenhouse design, covering essential components such as structural materials, covering materials, and environmental control systems. The article discusses the advantages of greenhouse cultivation, including extended growing seasons, protection from adverse weather conditions, and reduced pest and disease pressure. It examines the use of advanced technologies like hydroponics, aeroponics, and aquaponics in greenhouse systems, highlighting their potential for maximizing resource efficiency and minimizing environmental impact. The review also addresses the integration of precision agriculture techniques, such as sensors, automation, and data analytics, for optimizing greenhouse operations. Furthermore, it explores the economic aspects of greenhouse crop production, including initial investment costs, operational expenses, and market opportunities. The article emphasizes the importance of sustainable practices in greenhouse agriculture, focusing on energy conservation, water management, and waste reduction strategies. It also discusses the challenges associated with greenhouse technology adoption, such as high initial costs, technical complexity, and the need for skilled labor. Finally, the review concludes by outlining future research directions and the potential for greenhouse technology to contribute to global food security and sustainable agricultural practices.

Framework for Seismic Vulnerability Assessment of Nonstructural Elements Inside an Inflatable Lunar Habitat

The pursuit of knowledge and curiosity are once again driving humanity to seek a human presence on extraterrestrial bodies. While numerous challenges must be overcome on the moon to achieve this goal, the moonquake hazard has often been overlooked. Using NASA’s Moon to Mars Architecture requirements for surface habitation and accounting for the midterm/long-term mission duration, a framework for vulnerability assessment is developed for essential nonstructural elements (NSEs) inside an inflatable habitat subjected to moonquakes. The aim of this study is to develop an approach to assess the vulnerability of typical NSEs that support essential equipment, such as the environmental control and life support system, under a paucity of information related to the seismic hazard. We also emphasize that the launch dynamic environment and lunar seismic environment exhibit notable differences in their characteristics, which can lead to higher lateral acceleration levels than initially expected. This acceleration may be linked to high-occurrence seismic events. However, due to the lack of information regarding the frequency of these events, the risk of NSEs being subjected to unforeseen loads increases. It is imperative to equip future lunar habitats with seismic mitigation measures until additional seismometers are deployed near upcoming surface human outposts.

Workload Measurement Method for Manned Vehicles in Multitasking Environments

Workload (WL) measurement is a crucial foundation for human–machine collaboration, particularly in high-stress multitasking environments such as manned vehicle operations during emergencies, where operators often experience High Workload (HWL) levels, increasing the risk of human error. To address this challenge, this study introduces a novel WL measurement method that combines Task Demand Load (TDL) and Subject Load Capacity (SLC) to quantitatively assess operator workload. This method was validated through experiments with 45 subjects using the Environmental Control and Atmospheric Regeneration (ECAR) system. The statistical results showed that as the designed WL levels increased, the Average Workload (AWL), the NASA-TLX score, and the work time percentage increased significantly, while the task accuracy and the fixation duration decreased significantly. These results also revealed the impact of WL levels on human responses (such as subjective feeling, work performance, and eye movement). In addition, very strong correlations were found between AWL measurements and NASA-TLX scores (r = 0.75, p < 0.01), task accuracy (r = −0.73, p < 0.01), and work time percentage (r = 0.97, p < 0.01). Overall, these results proved the effectiveness of the proposed method for measuring WL. On this basis, this study defined WL thresholds by integrating task accuracy with AWL calculations, providing a framework for the dynamic management of task allocation between humans and machines to maintain operators within optimal WL ranges.

Occupant-centric dynamic heating demand in residential buildings based on a temporal-spatial combined quantification method

The occupant-centric Part-Time-Local-Space heating mode holds paramount significance for the carbon neutralization of buildings in China. It ensures a harmonious match between heat supply and personal demand in both temporal and spatial dimensions, effectively reducing space heating consumption. However, current studies predominantly focus on the temporal-spatial heating demands of occupants at the room scale, but the zonal scale heating demands remain inadequately explored. Thus, we developed a temporal-spatial combined quantification method to analyze residents’ indoor movement characteristics. It aims to identify the localized subzones in a room where residents have prolonged stays, with spatial boundaries of subzones entirely defined based on high-resolution positioning data. Using this approach, we analyzed the temporal-spatial heating demands of four households under single- and multiple-resident scenarios in typical rooms. Furthermore, we discussed the differences in demand between different heating modes. Results revealed distinctive “local space” demand features in living room and “part time” demand features in dining room. The areas of sofa subzones generally occupied less than 15 % of a room (within 4 m2), and stay durations in dining-table subzones ranged from 0.3 h to 1.2 h. Considering the entire monitored rooms, residents occupied only 4.1–12.1 % in space with 10.7–47.9 % in time per day, highlighting the significant energy-saving potential of the occupant-centric Part-Time-Local-Space heating mode. This evaluation provides a reference for the design and implementation of personalized environmental control system.

On the validation of numerical simulation with experimental results on compressible turbulent flow in an inertial particle separator device

To remove particles from entering the engine or Environmental Control System (ECS), an Inertial Particle Separator (IPS) can be situated in the intake. In IPS, a rapid change in flow direction is induced, and particles separate from the core outlet because of their inertia. Small particles with a low Stokes number (St) follow the flow and often enter the core outlet, while the large particles with a high St are thrown into the scavenge by their high inertia. Accurately capturing the unsteadiness of the fluid is crucial, especially with low scavenge mass flow fraction for the particles with low St. The numerical simulation of the compressible flow is validated here, implementing the 3D Improved Delayed Detached Eddy Simulation (IDDES) and the Reynolds-averaged Navier–Stokes (RANS) methods against experimental data. It is shown that the IDDES method can capture correctly the unsteadiness of the flow. After validation of the fluid phase, the particle separation efficiency was studied for different particle diameters using these turbulence numerical methods along with three different ambient temperatures: standard, hot, and cold. The separation efficiency values were in agreement with the experimental data.

Study on setting method of temperature sensor in aircraft environmental control system

This paper analyzes the working principle of each component of the aircraft environmental control system (ECS), takes the Boeing 737 aircraft as an example, and analyzes the distribution and role of temperature sensors in the aircraft ECS. Then, this paper presents a guideline for sensor setup suitable for each subsystem. The accuracy of the temperature sensor is measured, which verifies that the temperature sensor selection and set is reasonable and can ensure the good operation of the aircraft ECS.

Numerical Simulation of Airflow Velocity and Temperature Distribution in an Aircraft Cabin

In this work, a computational fluid dynamics (CFD) model is established to calculate the airflow distribution in the cabin of a single-channel Boeing 737-800 airplane. The RNG k-ε model, considering the calculation of turbulence kinetic energy and its rate of dissipation, is adopted to solve turbulence problems in physical field simulation. The proposed three-dimensional model is able to consider the particular physical factors and spatial structures, which can simulate the detailed distributions of complex physical fields, compared with the one-dimensional lumped parameter model. The velocity and temperature fields under different air supply velocities are then numerically investigated. The calculation results show that when the air velocity at the top air supply inlet is 1.4 m/s, the velocity and temperature distribution inside the cabin can both meet the comfort requirements of humans in the airworthiness standards. The results from this work can help to design an aircraft environmental control system.

Environmental Monitoring and Control System of Green House with Microcontroller and GSM Using IoT Devices

This paper presents a comprehensive solution for monitoring and controlling greenhouse environments using microcontroller technology and GSM connectivity through IoT devices. The system integrates various sensors to collect real-time data on environmental parameters such as temperature, humidity, soil moisture, and light intensity within the greenhouse. A microcontroller unit processes the sensor data and executes control actions to optimize environmental conditions for plant growth. Through GSM connectivity, the system enables remote monitoring and control, allowing users to access greenhouse data and adjust settings from anywhere using a mobile device or computer. The proposed system offers efficient management of greenhouse conditions, leading to improved crop yield, reduced resource consumption, and enhanced sustainability in agriculture. Continuing from the abstract, the system architecture employs a microcontroller, such as Arduino or Raspberry Pi, as the central processing unit. The microcontroller interfaces with a variety of sensors including temperature sensors, humidity sensors, soil moisture sensors, and light sensors to continuously monitor the greenhouse environment. Data collected from these sensors are processed in real-time by the microcontroller to make informed decisions regarding environmental control parameters such as irrigation, ventilation, and lighting. Control actions are executed autonomously based on predefined thresholds and algorithms to maintain optimal growing conditions for the plants. The inclusion of GSM connectivity enables the system to communicate data and receive commands remotely. Users can access the greenhouse monitoring system through a web or mobile application, providing real-time updates on environmental conditions and allowing for remote control of the greenhouse parameters. Furthermore, the integration of IoT devices allows for scalability and flexibility in the system architecture, facilitating the addition of new sensors or control mechanisms as needed. Additionally, data analytics and machine learning techniques can be implemented to further optimize the system's performance and predictive capabilities. Overall, the proposed monitoring and control system offers a cost-effective, efficient, and sustainable solution for greenhouse management, with potential applications in commercial agriculture as well as small-scale or hobbyist setups

Importance and challenges of integrating BLSS into ECLSS

Bioregenerative Life Support Systems (BLSS) are a key facet of plans for long term habitats on other celestial bodies. With the Artemis program headed to the moon and SpaceX pushing towards Mars the roadmap is about 10–20 years. Therefore it is vital to consider how BLSS will work with the traditional physicochemical Environmental Control and Life Support Systems (ECLSS). Biological systems are complicated. Their inputs and outputs cannot be turned on and off the way physicochemical systems can. Therefore the accurate monitoring and prediction of these systems is fundamental to integrating the BLSS into the life support system as a whole. This paper will summarize the history of BLSS research, the system requirements for integrating a BLSS into the ECLSS, and the research needed to meet these requirements. As the world looks towards the future of humans living on other celestial bodies, there is a lot of work to do to support keeping them safe and healthy. Bioregenerative Life Support Systems have potential to provide massive support, if they are effectively integrated with the physicochemical systems.

Estimating droplet size and count distributions over a prolonged period of time following a cough in indoor environments

An empirical correlation and a set of machine learning (ML) models were developed to estimate droplet size and count distributions over an extended duration after a cough at different relative humidities (RHs), air temperatures and locations within an indoor environment. Experiments covered RHs of 20%–80% and air temperatures of 21 °C–26 °C. Droplet count distributions for 4 size bins (0.3–0.5, 0.5–1, 1–3 and 3–5 μm) were recorded for 70 min within the distance of 2 m from the cough source. Different ML models, including decision tree, random forest and artificial neural network, were trained for each size bin to predict the associated count distribution. Amongst these models, random forest showed a slight superiority in performance. The coefficient of determination for the random forest models ranged from 0.912 to 0.989, indicating robust correlations between the features and the response variables. An empirical correlation was established linking the count distribution of 0.3–0.5 μm droplets to time, RH and distance along the cough direction. Both ML models and the correlation accurately predicted the trends and the distributions, providing valuable data for validating computational simulations and informing indoor environment control systems to reduce the risk of virus transmission.

Study on general industrial solid waste and carbon reduction in China: Coupling coordination model, life cycle assessment and environmental safety control

The production of general industrial solid waste (GISW) which has grown rapidly along with the economy, has become a bottleneck that is impeding the sustainable and healthy growth of society and the economy. At the same time, China, as the country with the largest carbon emissions at present, has set a double carbon target, so China is faced with the dual goal of promoting the synergy of GISW pollution and carbon reduction. This paper systematically reviews the actual production data, literature reports, standard specifications, etc. of GISW and carbon dioxide emission in China, and processes the data with synergistic effect model. Both GISW and carbon emission in China show an obvious spatial aggregation effect, and the coupling and coordination index of both grows from moderate maladjustment to senior coordination stage, and the synergistic effect of different regions gradually increases. Selecting typical pollutants from key provinces analyzed based on coupling coordination degree, and the life cycle assessment was performed using SimaPro 9.0 software, the results show that fly ash is more destructive to the environment and causes higher carbon emissions than the other two GISW, with carbon emission equivalent being about 1.8 times that of tailings and 1.6 times that of EMR. Compared with the carbon emission equivalent of 1.12 × 103 kg CO2 eq in the preparation of traditional cementitious materials, the preparation of coal-based cementitious materials generates less greenhouse gases with carbon emission equivalent of about 1.06 × 103 kg CO2 eq, which has a significant solid waste consumption effect as well as carbon reduction effect. Meanwhile, from the GISW storage process, a disposal environmental safety control system is established mainly for heavy metals, volatile organic compounds and atmospheric pollutants by comprehensively referring to literature and standard specifications.

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.

A Study on Buyer’s Behaviour Towards Next Gen Home Products with Special Reference to Erode District

This study delves into the intricacies of buyer behavior concerning Next Gen Home Products in Erode District, India, with the aim of assessing consumer awareness and understanding of these innovative products and identifying key factors influencing purchase decisions. Employing a descriptive research design, the study utilized a sample size of 119 respondents from diverse occupational backgrounds through stratified random sampling. Statistical tools such as frequency analysis, descriptive statistics, ANOVA analysis, and correlation analysis were employed for data analysis. The findings of the study reveal that consumer awareness of Next Gen Home Products is highest for factors such as ‘Comprehension of Home Systems Controlled Remotely’ and ‘Familiarity with Automated Environmental Control Systems,’ while ‘Familiarity with Intelligent Home Technology’ ranks lowest. Factors significantly influencing buyer decisions include ‘Endorsements from Personal Contacts’ and ‘Influence of Online Consumer Feedback,’ followed by ‘Product Design and Visual Appeal’ and ‘Customer Support Quality.’ Notably, a positive correlation exists between consumer awareness and purchasing factors. Age, educational qualification, occupation, and gender were also found to influence consumer behavior. Based on these findings, several suggestions are proposed, including educational initiatives to enhance consumer awareness, leveraging personal contacts and online feedback, focusing on product design, improving customer support quality, emphasizing long-term savings benefits, and effectively communicating cost-effectiveness. In conclusion, this study provides valuable insights for businesses and policymakers in Erode District to align their strategies with consumer preferences in the Next Gen Home Products market. By implementing these suggestions, companies can improve their market performance and enhance the adoption of innovative home solutions in the region.