الفهرس 
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Abstract
Air quality control is considered to be essential to improve the environmental status of the ship and its Indoor Air Quality (IAQ). In marine applications, the International Maritime Organization (IMO) has adopted multiple standards to set the acceptable gas emission limits and specify the air circulation conditions in IAQ. Lately, the IMO has adopted several energy-efficiency regulations to reduce ship’s emissions to discard the harmful effect of emitted gases on the marine environment and the health of passengers and seafarers efficiently and economically. As a result, the design of the ship’s ventilation systems for enclosed spaces shall comply with the IMO fire-fighting and other related IAQ standards. Therefore, a more comprehensive design approach should be employed to comply with these standards and to maximize the ship energy efficiency.
In this study, an RO-RO ship measured data has been used to study, analyze, design and control the CO concentration in ship’s garage in an energy-efficient way. The obtained data includes the CO concentration during both loading and unloading conditions of the ship at different ventilation scenarios. Then, an Artificial Neural Network (ANN) model has been designed to estimate the CO concentration in the confined space at different loading condition. The designed ANN-model is then validated against the CO concentrations actual data at different ventilation scenarios.
Next, a Fuzzy controller has been designed that aims to control the CO concentration by collaborating the developed ANN-model. To develop the proposed Neuro-Fuzzy system to process on a real-time, the location and number of feedback sensors should also be determined. Therefore, a sensitivity analysis has been carried out on the observed data. Accordingly, two sensors proved to be required and their locations have been accurately specified.
However, and due to data limitation and the high uncertainty level of some system’s parameters (such as car numbers, their condition and their locations), an adaptive-fuzzy controller has been employed. The adaptive-fuzzy controller has been designed by engaging two different techniques; supervisory fuzzy and particle swarm optimization. The results have revealed that the controller operates robustly with a quick response to the change of CO concentrations to maintain the permissible allowable range.
Finally, a cost-benefit analysis has been carried out to demonstrate the effectiveness of the proposed system on the ship fuel consumption and its related emissions (CO2, SOx and NOx). The analysis has shown a reduction percentage of 80% at full garage operational loading condition (25%). This power saving percentage is due to the use of the proposed intelligent controller instead the on/off classical controller. By studying the pay-back period, it was found that the expenses will be refund after six months. Accordingly, the results have proven the significance of the proposed system in controlling the CO concentration within the safe limits in an energy-efficient mean.