Energy-efficient time-delay compensated ventilation control system for sustainable subway air quality management under various outdoor conditions

Tariq, Shahzeb; Loy-Benitez, Jorge; Nam, Ki Jeon; Heo, SungKu

doi:10.1016/j.buildenv.2020.106775

Cited by 13 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, the ripening room has a certain area, and the distribution of environmental parameters in different areas is not uniform, so it is not possible to simply analyze a certain area when carrying out control. Due to the area of the storage room and the sensor itself, the system will experience a certain time delay and need to wait for a certain period of time to determine the real environmental state (Loy‐Benitez et al, 2021; Tariq et al, 2020). Therefore, it is crucial to establish a control model with fast, accurate and stable.…”

Section: Introductionmentioning

confidence: 99%

Consumer‐oriented intelligent ripening control system for post‐ripening fruit

Li,

Huang,

et al. 2023

J Food Process Engineering

View full text Add to dashboard Cite

To address the issues commonly encountered in traditional fruit ripening processes, which include reliance on manual experience, low efficiency, single control parameter, and significant fluctuations, a new intelligent fruit ripening control system has been designed. This system is based on a mathematical modeling algorithm, which allows for more effective and precise control over the ripening process. To ensure that the ripe fruits are transported to the fruit processing center in a low ripeness state, the proposed system adopts a strategy of precise control of temperature and ethylene, as well as range control of other environmental parameters. The RSSA‐PID model is used for ethylene and temperature control, which has high smoothness, fast response time, and high stability. Compared with the traditional PID model, the RSSA‐PID model has better dynamic performance and stability. Finally, the proposed system is applied to the ripening process of post‐ripening fruits, and the results show that the system achieves intelligent ripening, precise control, and low power consumption. The temperature and ethylene concentration are controlled within a variation of no more than 0.5%, and the humidity, oxygen, and carbon dioxide are maintained within reasonable thresholds. Overall, this paper provides a promising solution for the ripening process of post‐ripening fruits, which can benefit the fruit industry by improving the efficiency and quality of the supply chain.Practical ApplicationThe ripening chamber environment is a dynamic equilibrium environment with non‐linear characteristics because the fruits in the ripening chamber are constantly exchanging substances and energy with the environment through respiration and transpiration. An intelligent ripening control system (IRCS) for post‐ripening fruits is proposed in this paper (Figure 1). The whole ripening process runs automatically, which significantly reduces the ripening time and saves a lot of post‐processing time. The shorter ripening time combined with the latest intelligent software and hardware means that only as much energy as is actually needed is consumed. If the sales market changes, the fruit can be stored under air conditioning (CA) until the scheduled delivery date without significant further ripening and losses and complaints can be minimized.

show abstract

Section: Introductionmentioning

confidence: 99%

Consumer‐oriented intelligent ripening control system for post‐ripening fruit

Li,

Huang,

et al. 2023

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…A traditional mechanical ventilation system is commonly observed in subway stations for regulating interior pollutants. It plays an important role in reducing the particulate matter and the energy demand of the subway station [ 23 ]. However, its operating mechanism fails to account for the real-time fluctuation in the parameters that may cause energy waste or deficiency.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms

Bakht

Sharma

Park

et al. 2022

Toxics

View full text Add to dashboard Cite

Particulate matter (PM) of sizes less than 10 µm () and 2.5 µm () found in the environment is a major health concern. As PM is more prevalent in an enclosed environment, such as a subway station, this can have a negative impact on the health of commuters and staff. Therefore, it is essential to continuously monitor PM on underground subway platforms and control it using a subway ventilation control system. In order to operate the ventilation system in a predictive way, a credible prediction model for indoor air quality (IAQ) is proposed. While the existing deterministic methods require extensive calculations and domain knowledge, deep learning-based approaches showed good performance in recent studies. In this study, we develop an effective hybrid deep learning framework to forecast future and on a subway platform using past air quality data. This hybrid framework is an integration of several deep learning frameworks, namely, convolution neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN), and is called hybrid CNN-LSTM-DNN; it has the characteristics to capture temporal patterns and informative characteristics from the indoor and outdoor air quality parameters compared with the standalone deep learning models. The effectiveness of the proposed and forecasting framework is demonstrated using comparisons with the different existing deep learning models.

show abstract

Imputing missing indoor air quality data via variational convolutional autoencoders: Implications for ventilation management of subway metro systems

Loy-Benitez

Heo

2020

Building and Environment

View full text Add to dashboard Cite

Energy-efficient time-delay compensated ventilation control system for sustainable subway air quality management under various outdoor conditions

Cited by 13 publications

References 26 publications

Consumer‐oriented intelligent ripening control system for post‐ripening fruit

Consumer‐oriented intelligent ripening control system for post‐ripening fruit

Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms

Imputing missing indoor air quality data via variational convolutional autoencoders: Implications for ventilation management of subway metro systems

Contact Info

Product

Resources

About