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Although hydrogen refueling stations (HRSs) are becoming widespread across Japan and are essential for the operation of fuel cell vehicles, they present potential hazards. A large number of accidents such as explosions or fires have been reported, rendering it necessary to conduct a number of qualitative and quantitative risk assessments for HRSs. Current safety codes and technical standards related to Japanese HRSs have been established based on the results of a qualitative risk assessment and quantitative effectiveness validation of safety measures over ten years ago. In the last decade, there has been much development in the technologies of the components or facilities used in domestic HRSs and much operational experience as well as knowledge to use hydrogen in HRSs safely have been gained through years of commercial operation. The purpose of the present study is to conduct a quantitative risk assessment (QRA) of the latest HRS model representing Japanese HRSs with the most current information and to identify the most significant scenarios that pose the greatest risks to the physical surroundings in the HRS model. The results of the QRA show that the risk contours of 10−3 and 10−4 per year were confined within the HRS boundaries, whereas the risk contours of 10−5 and 10−6 per year are still present outside the HRS. Comparing the breakdown of the individual risks (IRs) at the risk ranking points, we conclude that the risk of jet fire demonstrates the highest contribution to the risks at all of the risk ranking points and outside the station. To reduce these risks and confine the risk contour of 10−6 per year within the HRS boundaries, it is necessary to consider risk mitigation measures for jet fires.

In the last couple of decades, there has been a growing concern in what effects fossil fuels are having on the environment, resulting in governments and governing organizations issuing stringent emission standards in an effort to curve their environmental damage. To meet these new standards, the transportation industry has been conducting research into alternative fuels, such as hydrogen, but one critical problem utilizing hydrogen is that there is almost no infrastructure. A network of hydrogen refueling stations similar to modern gasoline stations will be required to be constructed to meet future demand. The hydrogen refueling station model was created to aid in designing hydrogen facilities, thus accelerating their development while reducing design cost. A model was created using Simulink consisting of an electrolyzer that generates hydrogen, a compressor, numerous storage tanks, a dispensing unit that transfers hydrogen, and a vehicle component that consumes hydrogen fuel. The model was validated using data from existing hydrogen refueling stations, and the data obtained from testing the previous version of the hydrogen refueling station model to determine model accuracy and if the model has improved. The model has demonstrated that it can produce reasonable results for a station's performance and has improved compared to the previous version.

The future success of fuel cell electric vehicles requires a corresponding infrastructure. In this study, two different refueling station concepts for fuel cell passenger cars with 70 MPa technology were evaluated energetically. In the first option, the input of the refueling station is gaseous hydrogen which is compressed to final pressure, remaining in gaseous state. In the second option, the input is liquid hydrogen which is cryo-compressed directly from the liquid phase to the target pressure. In the first case, the target temperature of −33 °C to −40 °C [1] is achieved by cooling down. In the second option, gaseous deep-cold hydrogen coming from the pump is heated up to target temperature. A dynamic simulation model considering real gas behavior to evaluate both types of fueling stations from an energetic perspective was created. The dynamic model allows the simulation of boil-off losses (liquid stations) and standby energy losses caused by the precooling system (gaseous station) dependent on fueling profiles. The functionality of the model was demonstrated with a sequence of three refueling processes within a short time period (high station utilization). The liquid station consumed 0.37 kWh/kg compared to 2.43 kWh/kg of the gaseous station. Rough estimations indicated that the energy consumption of the entire pathway is higher for liquid hydrogen. The analysis showed the high influence of the high-pressure storage system design on the energy consumption of the station. For future research work the refueling station model can be applied to analyze the energy consumption dependent on factors like utilization, component sizing and ambient temperature.

Evacuation is a critical issue at metro stations, where damage, or even death, could be caused due to unexpected accidents if without proper evacuation. Multi-objectives are often desired in the evacuation management, and evacuation strategies should be tailored due to the dynamic features of overcrowded passengers and high uncertainty at the metro stations. A simulation-based approach integrating Light Gradient Boosting Machine (LightGBM) and Non-dominated Sorting Genetic Algorithm III (NSGA-III) is proposed to realize the automatic evacuation evaluation and adaptive optimization at metro stations. A framework consisting of 3 objectives and 9 influential factors is developed for the evacuation evaluation and adaptive optimization. A LightGBM based meta-model is used to construct the relationship between influential factors and objectives. A LightGBM and NSGA-III integrated optimization algorithm is employed to automatically evaluate the evacuation events and seek the adaptive strategies for the station renovation in order to achieve a safe evacuation under specific conditions. A station model simulating a metro station in Singapore is constructed to test the effectiveness and applicability of the proposed approach. It is found that (1) Among 50 target station based cases, 33 failing cases (at least one of the objectives fails) are identified, and the automatic evaluation results indicate the studied metro station could successfully evacuate the passengers when the passenger volume is no more than 1000, but it is very likely to fail when a higher volume exists; (2) Adaptive optimization strategies can be found for every different scenario, and an average 24.8% of the improvement degree can be achieved for all scenarios; (3) The adaptive multi-objective optimization is more cost-effective, presenting an average cost efficiency 2.04, which is significantly higher than the average of the non-adaptive optimization cost efficiency, 0.57. The novelty of this research lies in that (a) A LightGBM-based meta-model is built to construct the relationship between desired multi-objectives and the influential factors, which lays the foundation for the evacuation management from multi-objective optimization perspective; (b) The LightGBM and NSGA-III integrated model can realize the automatic evacuation evaluation and accordingly provide the adaptive strategies under specific conditions. Ministry of Education (MOE) Nanyang Technological University The Ministry of Education Tier 1 Grants, Singapore (No. 04MNP000279C120, No. 04MNP002126C120) and the Start-Up Grant at Nanyang Technological University, Singapore (No. 04INS000423C120) are acknowledged for their financial support of this research.

Parameter extraction of solar photovoltaic (PV) models is a typical complex nonlinear multivariable strongly coupled optimization problem. In this paper, an improved whale optimization algorithm (WOA), referred to as IWOA, is proposed to accurately extract the parameters of different PV models. The original WOA has good local exploitation ability, but it is likely to stagnate and suffer from premature convergence when dealing with complex multimodal problems. To conquer this concerning shortcoming, IWOA develops two prey searching strategies to effectively balance the local exploitation and global exploration, and thereby enhance the performance of WOA. Three benchmark test PV models including single diode, double diode and PV module models, and two practical PV power station models with more modules in the Guizhou Power Grid of China are employed to verify the performance of IWOA. The experimental and comparison results comprehensively demonstrate that IWOA is significantly better than the original WOA and three advanced variants of WOA, and is also highly competitive with the reported results of some recently-developed parameter extraction methods.

Evacuation is critical for safety management due to the highly overcrowded passengers in the metro stations. A simulation-based approach integrating Random Forest (RF) and Non-dominated Sorting Genetic Algorithm III (NSGA-III) is proposed to perform the evacuation evaluation and optimization at metro stations. A 3D model of the metro station is built to simulate the dynamic process of passenger evacuation in metro stations. A framework consisting of 9 influential factors and 3 objectives is developed to model the input-output relationship in passenger evacuation. An RF-based meta-model is used to construct the relationship between influential factors and objectives. At last, NSGA-III is applied to seeking the optimal solutions for the station renovation in order to achieve a safe evacuation. A station model simulating a real metro station in Singapore is constructed to test the effectiveness and applicability of the proposed approach. It is found that (1) A safe evacuation could be achieved for the station, but along with the increasing passenger volume and panic level, the requirement of evacuation objectives, the evacuation time and density, may not be met. Especially under the high passenger volume conditions, the passenger density could reach up to 6.2 unit/m2 (extremely dangerous); (2) An average improvement degree, 7.5%, can be achieved for the optimization of 20 test cases, and a maximum improvement degree, 22.5%, can be achieved for the evacuation optimization at metro stations; (3) It could be difficult to keep both of the evacuation time and density within the standards if one major exit is closed, even after the optimization. But a larger average improvement degree, 10.8%, can be achieved by the proposed optimization approach, which indicates the optimal solutions still could reduce the risk to a great extent. The novelty of this research lies in that (a) An RF algorithm is incorporated to build the meta-model that can properly represent the relationship between influential factors and objectives, despite the complexity and even conflicting between them; (b) Optimal measures for the evacuation improvement are provided from the MOO perspective by integrating NSGA-III. This hybrid approach can be used as a decision tool to assist regulatory authorities in developing effective emergency evacuation evaluation and optimization plans with adequate consideration of the complexity and multi-objective nature under evacuation events. Ministry of Education (MOE) Nanyang Technological University The authors declare no conflict of interest. The Ministry of Education Tier 1 Grant, Singapore (No. 04MNP002126C120, No. 04MNP000279C120) and the Start-Up Grant at Nanyang Technological University, Singapore (No. 04INS000423C120) are acknowledged for their financial support of this research. The 1st author is grateful to Nanyang Technological University, Singapore for his Ph.D. research scholarship.

Among other flood forecasting tools, fuzzy logic is one of a simple and flexible approach which can be implemented in river basins where adequate hydrologic data is available and not good enough to use in a more sophisticated model. This paper assesses the potential of fuzzy logic approach for real time flood forecasting using the minimum implication function type Mamdani fuzzy inference system by applying the model to the Kelantan River basin in Malaysia. The developed models were tested for forecasting the downstream water levels of Guilemard and Kuala Krai stations using upstream hourly telemetric water levels of Dabong and Tualang stations. The membership functions (MFs) of triangular shapes with several fuzzy rule sets were utilized to check the efficiency of the fuzzy logic approach for the Kelantan River flood forecasting. For the Guilemard station, models of 8, 10 and 15 rules' sets were tested. In the calibration and validation events, the Guilemard station models' achieved MAE, ranges 0.35–0.45 m, N–S coefficient, ranges 0.87 – 0.89 and Coefficient of Determination ranges, R 2 ranges 0.91–0.95. The water level prediction model developed for the Kuala Krai station consisted of 19 rules with triangular shape MFs. It shows the ranges for MAE: 0.26–0.76 m, N–S coefficient: 0.78–0.93 and R 2 : 0.91–0.96 in calibration and validation periods. The efficiencies of the developed models show acceptable levels according to the tested performance indicators implying the potential of establishing a flood forecasting system by using the fuzzy logic approach in the Kelantan River basin, Malaysia.

Synchronization stations are commonly used to model kitting operations in manufacturing systems. At a kitting station, the components required for assembly are grouped together prior to release to the assembly line. When the supply process of components is stochastic, exact analysis is hard in a general setting. This paper presents an approximate analysis of a kitting station with multiple components. The stochastic supply process of each component is characterized by mean and variability parameters of the corresponding fabrication facility, and the system imposes a limit on total inventory for each component. The resulting synchronization station model is analyzed using an aggregation procedure that uses the analysis of a synchronization station with two inputs as a key building block. Numerical results indicate that the estimates of throughput and queue length obtained from the analysis validate well with those obtained from detailed simulations. The analysis also helps to quantify the effect of the number of components and variability on kitting delays.

A composite line source emission (CLSE) model was developed to specifically quantify exposure levels and describe the spatial variability of vehicle emissions in traffic interrupted microenvironments. This model took into account the complexity of vehicle movements in the queue, as well as different emission rates relevant to various driving conditions (cruise, decelerate, idle and accelerate), and it utilised multi-representative segments to capture the accurate emission distribution for real vehicle flow. Hence, this model was able to quickly quantify the time spent in each segment within the considered zone, as well as the composition and position of the requisite segments based on the vehicle fleet information, which not only helped to quantify the enhanced emissions at critical locations, but it also helped to define the emission source distribution of the disrupted steady flow for further dispersion modelling. The model then was applied to estimate particle number emissions at a bi-directional bus station used by diesel and compressed natural gas fuelled buses. It was found that the acceleration distance was of critical importance when estimating particle number emission, since the highest emissions occurred in sections where most of the buses were accelerating and no significant increases were observed at locations where they idled. It was also shown that emissions at the front end of the platform were 43 times greater than at the rear of the platform. Although the CLSE model is intended to be applied in traffic management and transport analysis systems for the evaluation of exposure, as well as the simulation of vehicle emissions in traffic interrupted microenvironments, the bus station model can also be used for the input of initial source definitions in future dispersion models.

Daily rainfalls are simulated through a two station model at a key station and satellite stations whilst maintaining the first three moments and relevant correlations. Wet and dry runs of the occurrence process of daily rainfall are assumed to have a geometric distribution. Disaggegration of daily rainfalls into hourly values is made through dimensionless accumulated hourly amounts generated by a beta distribution. It is postulated that the occurrence process of hourly rainfall has also a geometric distribution but it is conditioned on the total daily rainfall. Application is made to the Tevere (Tiber) basin in central Italy. The simple reduced parameter approach is seen to reproduce satisfactorily extremes and other statistical properties of daily and hourly rainfalls.

The backstepping technique was used in order to synthesise a non-linear control law for the non-linear model of the power plant station. The non-linearities in the model are related to steam flow through the super-heater and action of turbine valves. The time constant of the boiler is considered as an unknown parameter. Its value has to be adapted. The backstepping procedure presents a straightforward method to implement the law of adaptation, which ensures the stability. Symbolic computations were used to derive control law. The performed simulations have shown that the performance and quality of adaptive non-linear control system is the same across the entire range of operation and the adapted parameter converges to its nominal value.

The Fiering model for the simultaneous generation of synthetic flows at a key station and one statellite station is reconsidered. After pointing out some inadequacies in Fiering's analysis, the model is reformulated. It is then shown how its parameters may be chosen so as to preserve the historical monthly (say) means and variances at each station, and also the first-serial correlation at each station and the simultaneous cross-correlation. The complete correlation structure of the model is obtained. It is further shown how to preserve the historical skewness coefficients of each flow. Finally, a possible extension involving two satellite stations is briefly mentioned.

Lawrance and Fiering two-station models are reconsidered and a more general model for the generation of flow sequences at S satellite stations assuming that flows at M master stations are known is obtained and presented herein. There are no restrictions about the number of parameters of the time-series models which are adopted to describe the studied flow sequences.