Neural Fuzzy Control of the Indoor Air Quality Onboard a RO–RO Ship Garage

Agamy, Hossam Eldine Nabil; Abdel-Geliel, M.; Mosleh, Mosaad; El-Serafy, K.A.; Mohamed, Nasr Abdelrahman Nasr

doi:10.1007/s40815-020-00804-1

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…In addition, the authors of reference [34,35] predicted air quality and emissions in 4 ports in Turkey (Ambarli, Izmir, Mersin, and Kocaeli ports) and Busan Port in Korea, respectively. Moreover, the authors of reference [36] simulated the indoor air quality of roll-on/ roll-of (RORO) ships and predicted pollution emitted from cars in maritime ports. Furthermore, the authors of reference [37] developed a container terminal logistics generalized computing architecture (CTL-GCA) for planning, scheduling and decision making to establish a better connection among liners and rubber-tired gantry cranes (RTGCs) and block community to reduce carbon emissions.…”

Section: Overall Port Areasmentioning

confidence: 99%

“…Te authors indicated that besides meteorological data and air quality data, ship activities, as one of the main sources of emissions in port areas, should be considered in the prediction model to enhance the performance. Using neural networks, the authors of reference [36] developed a predictive model for controlling the CO concentration in RORO ships indoors. Tey considered CO concentration, load (number of cars), and the reference fow rate of the ventilation system as the input variables of their model.…”

Section: Techniques To Promote Green Port Operations (Rq3)mentioning

confidence: 99%

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Machine Learning for Promoting Environmental Sustainability in Ports

Mansoursamaei

Moradi

González-Ramírez

et al. 2023

Journal of Advanced Transportation

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Maritime transportation is one of the essential drivers of the global economy as it enables both lower transportation costs and intermodal operations across multiple forms of transportation. Maritime ports are essential interfaces that support cargo handling between sea and hinterland transportation. Besides, in this area, environmental protection is becoming extremely important. Global warming, air pollution, and greenhouse gas emissions are all having a detrimental influence on the environment and will most likely continue to do so for future generations. Hence, there is a growing need to promote environmental sustainability in maritime-based transportation. The application of machine learning (ML), as one of the main subdomains of artificial intelligence (AI), can be considered a component within the process of digital transformation to advance green activities in maritime port logistics. Thus, this article presents the results of a systematic literature review of the recent literature on machine learning for promoting environmentally sustainable maritime ports. It collects and analyses the articles whose contributions lie in the interplay between three main dimensions, i.e., machine learning, port-related operations, and environmental sustainability. Throughout a review protocol, this research is constituted on the major focuses of impact, problems, and techniques to discern the current state of the art as well as research directions. The research findings indicate that the articles using polynomial regression models are dominant in the literature, and the recurrent neural network (RNN) and long short-term memory (LSTM) are the most recent approaches. Moreover, in terms of environmental sustainability, emissions and energy consumption are the most studied problems. mAccording to the research gaps observed in the review, two broad directions for future research are identified: (i) altering attention on a greater diversity of machine learning approaches for promoting environmental sustainability in ports and (ii) leveraging new outlooks to perform more green practical works on port-related operations.

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Section: Overall Port Areasmentioning

confidence: 99%

Section: Techniques To Promote Green Port Operations (Rq3)mentioning

confidence: 99%

Machine Learning for Promoting Environmental Sustainability in Ports

Mansoursamaei

Moradi

González-Ramírez

et al. 2023

Journal of Advanced Transportation

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“…The configuration of FLC is suitable for different PV system applications either power generation as in [2,21] or power quality improvement as in [32]. Therefore, normalized FLC membership functions are preferred [33], as illustrated in Figure 4, to generalize the FLC structure and parameters for different PV system power rating and applications. The general rule base structure of FLC is shown in Table 1 [2].…”

Section: Fuzzy Logic Controller (Flc)mentioning

confidence: 99%

PV Systems Control Using Fuzzy Logic Controller Employing Dynamic Safety Margin under Normal and Partial Shading Conditions

et al. 2021

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Because of the unpredictable activity of solar energy sources, photovoltaic (PV) maximum power point tracking (MPPT) is essential to guarantee the continuous operation of electrical energy generation at optimal power levels. Several works have extensively examined the generation of the maximum power from the PV systems under normal and shading conditions. The fuzzy logic control (FLC) method is one of the effective MPPT techniques, but it needs to be adapted to work in partial shading conditions. The current paper presents the FLC-based on dynamic safety margin (DSM) as an MPPT technique for a PV system to overcome the limitations of FLC in shading conditions. The DSM is a performance index that measures the system state deviation from the normal situation. As a performance index, DSM is used to adapt the FLC controller output to rapidly reach the global maxima of the PV system. The ability of the proposed algorithm and its performance are evaluated using simulation and practical implementation results for single phase grid-connected PV system under normal and partial shading operating conditions.

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