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Abstract How to improve marine oil spill response efficiency to minimize environmental and socioeconomic impacts has been recognized as a growing, critical need worldwide in both scientific and practical fields. The efficiency much depends on how sound the response decisions can be made simultaneously at both systematic (or holistic) (e.g., best use of resources for the entire response system) and individual levels (e.g., optimal operation of skimmers on a spill site). This study proposed a new simulation-based multi-agent particle swarm optimization (SA-PSO) approach for supporting marine spill decision-making through the integrated simulation and optimization of response device allocation and process control. Agent-based modeling as an emerging simulation method was first applied for simulating oil spill fate and response. Particle swarm optimization method was further adopted to optimize response device/vessel allocation and performance with a minimal cost and time. Multi-agent system finally controlled and transmitted the results from agent-based modeling and particle swarm optimization as a dynamic and interactive system. The proposed method was tested by a hypothetical case study in the North Atlantic Ocean with consideration of oil weathering and non-weathering scenarios based on simplified conditions. Through the developed approach, the response time was reduced by 11.7% and 5.9% respectively under the two scenarios for vessel allocation and recovery operations with about 90% decrement of fuel consumption. The results showed the strong capability of the approach for decision makings in oil spill responses by recommending optimal management of resources and efficient response operation in a dynamic manner. Highlights • A novel approach was developed to support the decision-makings of offshore oil spill responses. • Dynamic optimization was coupled with the simulation of oil weathering processes. • The approach was examined by an offshore oil spill case under scenarios. • Sound decisions were provided in a dynamic and time-efficient manner. [ABSTRACT FROM AUTHOR]

This study developed a novel probabilistic agent-based approach for modeling of marine oily wastewater treatment processes. It begins first by constructing a probability-based agent simulation model, followed by a global sensitivity analysis and a genetic algorithm-based calibration. The proposed modeling approach was tested through a case study of the removal of naphthalene from marine oily wastewater using UV irradiation. The removal of naphthalene was described by an agent-based simulation model using 8 types of agents and 11 reactions. Each reaction was governed by a probability parameter to determine its occurrence. The modeling results showed that the root mean square errors between modeled and observed removal rates were 8.73 and 11.03% for calibration and validation runs, respectively. Reaction competition was analyzed by comparing agent-based reaction probabilities, while agents' heterogeneity was visualized by plotting their real-time spatial distribution, showing a strong potential for reactor design and process optimization. [ABSTRACT FROM AUTHOR]

• Proposed the simplified method to consider the hydrodynamic effect of vessel-bridge collisions. • Compared the collision-induced loads and responses obtained from three analysis models. • Proved the rationality of the proposed method for considering the hydrodynamic effect. • Demonstrated the high efficiency of the proposed model compared with the FSI simulations. The hydrodynamic effect in vessel-bridge collisions is either directly omitted or considered by the constant added mass (CAM) method in current studies. However, the high-resolution fluid-structure interaction (FSI) simulations indicate that the hydrodynamic effect plays an important role and cannot be adequately captured by the CAM method, particularly for oblique vessel-bridge collisions. On the other hand, the FSI simulations are time-consuming and have a high requirement for computational resources. For these reasons, a simplified analysis method was developed in this study to consider the hydrodynamic effect of vessel collision with bridge structures. In the developed method, mechanics of full 6-DoF ship maneuvering were established using several matrices to replace the explicit fluid domain of the FSI models, while the added mass method was adopted for the impacted pier to account for the hydrodynamic effect. Several collision scenarios were performed to compare the developed method with the FSI simulation and the CAM analysis. It was found that the proposed method is superior to the CAM method in terms of considering the hydrodynamic effect of vessel-bridge collisions, and its required computational cost can be significantly saved in comparison with the FSI model. From the perspective of calculation efficiency and accuracy, the proposed method is more suitable for the analysis and design of vessel-bridge collision in engineering practice than the FSI simulation and the CAM analysis. [ABSTRACT FROM AUTHOR]

• High-resolution FE models are developed and verified to reveal the fluid-structure interaction of ship-bridge collisions. • The influences of impact angles, impact positions, ship drafts and impact velocities on the hydrodynamic effect are explored. • Limitations in the current codes are identified for ship-bridge collisions considering the hydrodynamic effect. • An improved formula considering the hydrodynamic effect and impact position is proposed to estimate absorbed energy in oblique collisions. The hydrodynamic effect in ship-bridge collisions is usually considered by the constant added mass (CAM) method in current studies. However, no research to date has examined its applicability, particularly for oblique collisions. To this end, high-fidelity physics-based finite-element (FE) models are developed in this study to investigate the fluid-structure interaction (FSI) behavior during ship-bridge collisions with different impact angles. The FSI modeling method based on the LS-DYNA code is examined and verified by the potential flow solver WADAM. The hydrodynamic effect predicted by the developed FSI model is consistent with the WADAM result. The verified FSI models are used to assess the influences of several key parameters (e.g., impact angles, impact positions, ship drafts and impact velocities) on the hydrodynamic effect for different ship-bridge collision scenarios. It is found that the ship-bridge collision process obtained from the FSI simulation is different from that of the CAM method for oblique collision scenarios. The CAM method is unsuitable for oblique collisions because it is unable to capture the hydrodynamic effect properly. Also, the FSI simulations indicate that the current design specifications underestimate the impact force and the dissipated energy in oblique collisions. An analytical formula is improved to estimate the energy absorbed by either the ship or the bridge in oblique collisions. Good agreements are achieved between the FSI analysis results and the improved formula. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider the adaptive stabilization problem for feedforward nonlinear systems with time delays. An adaptive stabilizer is proposed. Our stabilizer takes a nested saturation feedback, and a set of switching logics is designed to tune online the saturation levels in a piecewise constant or switching manner. It has been shown that under our proposed control, all closed-loop states are bounded and asymptotic regulation is achieved. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a pseudo-decentralized adaptive control scheme for a class of large-scale feedforward nonlinear systems with unknown nonlinear effects within subsystems and unknown nonlinear interactions among subsystems. The local controller of each subsystem takes a nested saturation feedback, using the state of its own subsystem, and the saturation levels are tuned online in a switching manner via a set of switching logics, which requires some binary flag communication among subsystems. Global asymptotic regulation of the closed-loop states is achieved. [Copyright &y& Elsevier]

Abstract: In this paper, we consider global adaptive output-feedback control of nonlinear systems in output-feedback form, with unknown parameters entering nonlinearly. Such unknown parameters are not required to lie in a known compact set. Our proposed adaptive output-feedback controller is a switching-type controller, in which the controller parameter is tuned in a switching manner via a switching logic. Global stability results of the closed-loop system have been proved. [Copyright &y& Elsevier]

An emergency response system is important to protect the public and the environment and mitigate the negative effects from major accidents or natural disasters. It helps minimize time and losses by optimizing response operations and use of resources. Marine oil spills, as a typical accident, can cause significant, long-term and adverse impacts on ecological, social and economic systems. An efficient response system can significantly reduce both overall response time and cost especially when dealing with a large-scale spill. This paper proposed an emergency response system based on dynamic process simulation and system optimization modeling. This was achieved by the development of an enhanced particle swarm optimization (ME-PSO) algorithm with outstanding convergence performance and low computation cost characteristics which integrated multi-agent theory and evolutionary population dynamics. The performance was evaluated using various PSO algorithms with 13 benchmark functions under 48 parameter combinations. A case study on a representative marine oil spill was conducted to demonstrate the proposed methodology and its value in supporting emergency response decision-making. Allocation and deployment of responses from multiple response centers were optimized to save time and increase recovery efficiency with the process simulations of resource dispatch, oil weathering and oil removal. The research not only contributed to emergency decision making through an integrated, dynamic and simulation-optimization coupling approach but also provided emergency responders a powerful tool to improve their response capabilities in dealing with emergencies such as marine oil spills. [Display omitted] • An emergency response system (ERS) was developed and evaluated. • The ERS integrated dynamics simulation and system optimization modules. • An ME-PSO algorithm was developed to enhance the performance of system optimization. • Better performance of ME-PSO was evidenced by evaluating with benchmark functions and PSO variants. • A marine oil spill case applied the approach to save time and reduce damages by optimizing resource use and operations. [ABSTRACT FROM AUTHOR]

Human factors/errors (such as inappropriate actions by operators and unsafe supervision by organizations) are a primary cause of oil spill incidents. To investigate the influences of active operational failures and unsafe latent factors in offshore oil spill accidents, an integrated human factor analysis and decision support process has been developed. The system is comprised of a Human Factors Analysis and Classification System (HFACS) framework to qualitatively evaluate the influence of various factors and errors associated with the multiple operational stages considered for oil spill preparedness and response (e.g., oil spill occurrence, spill monitoring, decision making/contingency planning, and spill response); coupled with quantitative data analysis by Fuzzy Set Theory and the Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) to enhance decision making during response operations. The efficiency of the integrated human factor analysis and decision support system is tested with data from a case study to generate a comprehensive priority rank, a robust sensitivity analysis, and other theoretical/practical insights. The proposed approach improves our knowledge on the significance of human factors/errors on oil spill accidents and response operations; and provides an improved support tool for decision making. Image 1 • A qualitative-quantitative human and causal factors evaluation system in offshore oil spill accidents was proposed. • An enhanced HFACS for offshore oil spills with multi-stage analysis was developed. • The multi-criteria decision making (Fuzzy TOPSIS) was employed for analysis. • The significance of human factors/errors on oil spill accidents and response operations was improved. • The proposed system can be a decision making support tool. [ABSTRACT FROM AUTHOR]

This paper studies an order acceptance and scheduling problem in distributed manufacturing to minimize the total costs consisting of rejection cost, production cost, transportation cost and tardiness cost. Two mixed-integer programming models are formulated, which are further improved by the proposed enhancement techniques. A logic-based Benders decomposition (LBBD) method and a branch and check search framework are developed in an attempt to realize the optimal and intelligent decisions of order acceptance, order assignment and scheduling. The proposed LBBD method following the principle of "divide and conquer" divides the original problem into determining the master problem of order acceptance and assignment, and determining the subproblem of order sequencing in each factory. A dynamic programming algorithm is also proposed to efficiently solve the subproblems. Extensive computational experiments are conducted, and the results demonstrate the effectiveness and efficiency of the enhancement techniques for the formulations, as well as the LBBD method. Besides, the value of distributed manufacturing is verified and the sensitivity of key cost factors is presented. [ABSTRACT FROM AUTHOR]

• Physics-model-derived causal priors can be embedded into deep neural networks. • Metformin sandy column transport experiment results were used as seed data. • Impact from underexplored parameters (F, α and L) was estimated for the first time. • Metformin has long-range transport potential in groundwater. • Presented an AI-for-Science paradigm using both domain priors and data-driven methods. This study introduces a novel approach to transport modelling by integrating experimentally derived causal priors into neural networks. We illustrate this paradigm using a case study of metformin, a ubiquitous pharmaceutical emerging pollutant, and its transport behaviour in sandy media. Specifically, data from metformin's sandy column transport experiment was used to estimate unobservable parameters through a physics-based model Hydrus-1D, followed by a data augmentation to produce a more comprehensive dataset. A causal graph incorporating key variables was constructed, aiding in identifying impactful variables and estimating their causal dynamics or "causal prior." The causal priors extracted from the augmented dataset included underexplored system parameters such as the type-1 sorption fraction F , first-order reaction rate coefficient α , and transport system scale. Their moderate impact on the transport process has been quantitatively evaluated (normalized causal effect 0.0423, -0.1447 and -0.0351, respectively) with adequate confounders considered for the first time. The prior was later embedded into multilayer neural networks via two methods: causal weight initialization and causal prior regularization. Based on the results from AutoML hyperparameter tuning experiments, using two embedding methods simultaneously emerged as a more advantageous practice since our proposed causal weight initialization technique can enhance model stability, particularly when used in conjunction with causal prior regularization. amongst those experiments utilizing both techniques, the R-squared values peaked at 0.881. This study demonstrates a balanced approach between expert knowledge and data-driven methods, providing enhanced interpretability in black-box models such as neural networks for environmental modelling. [Display omitted] [ABSTRACT FROM AUTHOR]

Numerical models have been widely applied in simulating subsurface Non-aqueous Phase Liquid (NAPL) contamination processes. In order to examine modeling uncertainties and improve simulation performance, a new hybrid stochastic - design of experiment (DOE) aided parameterization method was developed by using a coupled experimental and modeling approach. In a case study, an existing commercial groundwater modeling tool BioF&T 3D was applied to conduct numerical simulations of subsurface contamination processes based on flow cell experiments. Parameterization results indicated that porosity, distribution coefficient, and Henry's constant were the most significant parameters. The result also revealed their interactions. The DOE predicted responses were found reasonably close to the actual ones from the models' simulations. Monte Carlo simulation was applied to conduct uncertainty analysis within the narrowed parameters ranges, which were generated by centralizing the DOE optimized values, and the combinations of parameters were further updated when better responses were found. After parameterization, R 2 valued 0.80, 0.91, 0.89, and 0.90 for benzene, toluene, ethylbenzene, and xylene (BTEX), respectively. A good consistency (R 2  = 0.76 to 0.90 for BTEX) was also achieved during the model verification, which confirmed that after the parameterization processes, the simulation model can potentially be used for predictions under similar conditions. [ABSTRACT FROM AUTHOR]

Neural network modeling (e.g., deep neural networks) has been growingly applied in environmental data and imaging analysis but is significantly limited by its large dataset size and quality demand. To help address the challenge, this study developed an integrated framework by combining the convolutional neural networks (CNNs) and the improved deep convolutional generative adversarial networks (i-DCGANs). Optimization algorithms introduced to DCGANs for enhancing data augmentation. The employment of local interpretable model-agnostic explanations benefited the modeling prediction and reliability. The framework was tested by an environmental case study on characterizing the scanning electron microscopy (SEM) images of the weathered microplastics and oil-dispersants agglomerates (WMODAs) under various weathering conditions. The optimized model achieved a high score on the F-test (0.9192) with an accuracy of 0.9986, indicating a robust prediction. The classified results effectively differentiated WMODAs at different weathering degrees to facilitate a better understanding of the impact of microplastics on oil fate and transport in the oceans where plastic and oil pollution coexist in a growing trend. The proposed approach could be useful for image-related classification in other fields along with the findings to help fill some knowledge gaps of oil-plastic co-contamination. [Display omitted] • Enhanced environmental imaging analysis by using deep neural networks. • Improved deep convolutional generative adversarial networks for data augmentation. • The transfer learning ResNet model categorized WMODAs the best. • Increased model reliability and accuracy by using model interpretation algorithm. • Acquired high accuracy in classifying weathered MP-oil-dispersant agglomerates. [ABSTRACT FROM AUTHOR]

• Light and heavy crude oils and three O 3 -based processes were examined/compared. • UV/O 3 /PS process removed more than 90% of crude oils in 30 min. • 100% of the n -alkanes and PAHs and over 90% of TOC were removed in 1 h in UV/O 3 /PS process. • Acute toxicity of effluent to Vibrio fischeri and Artemia franciscana was significantly reduced. • Flocs characterization revealed the fate of insoluble components during oxidation. Efficient on-site treatment technology is crucial for mitigating marine oily wastewater pollution. This work investigates the ozone (O 3), ultraviolet (UV)/O 3 , UV/O 3 /persulfate (PS) processes for the treatment of marine oily wastewater, including degradation performance, acute toxicity evaluation, and oil flocs analysis in a benchtop circulating flow photoozonation reactor. Degradation performances have been studied by measuring the degradation rate of total oil concentrations, specific oil components (n -alkanes and polycyclic aromatic hydrocarbons (PAHs)), and total organic carbon (TOC). The results show that UV/O 3 /PS could significantly enhance the removal efficiency than the other two processes, with above 90% of removal efficiency in 30 min. Acute toxicity analysis further shows that the wastewater quality is significantly improved by four-fold of the EC 50 of Vibrio fischeri , and the mortality of Artemia franciscana decreases from 100% to 0% after 48 h exposure. Further, the morphology and functional groups of flocs have been further characterized, showing that the floating flocs could be further degraded especially in UV/O 3 /PS process. Our study further raised discussions regarding the future on-site application of O 3 -based systems, based on the results generated from the treatment efficiency, toxicity, and flocs characterization. The regulation of the oxidation strength and optimization of the reaction systems could be a practical strategy for on-site marine oily wastewater treatment. [Display omitted] [ABSTRACT FROM AUTHOR]

Salinity variability strongly affects the behaviors of oil degrading bacteria for spilled oil biodegradation in the marine environment. However, limited studies explored the strategies of microbes on salinity-mediated crude oil biodegradation. In this study, a halotolerant bio-emulsifier producer, Exiguobacterium sp. N4 1P, was examined as a model strain for Alaska North Slope (ANS) crude oil (0.5%, v /v) biodegradation. Results indicated that Exiguobacterium sp. N4 1P could tolerant a wide range of salinity (0–120 g/L NaCl) and achieve the highest degradation efficiency under the salinity of 15 g/L NaCl due to the highest biofilm formation ability. Moreover, increased salinity induced decreased cell surface hydrophobicity and a migration of microbial growth from oil phase to aqueous phase, leading to limited bio-emulsifier productivity and depressed degradation of insoluble long-chain n-alkanes while enhancing the degradation of relative soluble naphthalene. Research findings illustrated the microbial eco-physiological mechanism for spilled oil biodegradation under diverse salinities and advanced the understanding of sophisticated marine crude oil biodegradation process. Unlabelled Image • Microbial eco-physiological strategies for salinity mediated oil biodegradation • The halotolerant Exiguobacterium sp. N4 1P examined as a model strain • Salinity levels led to a shift in microbial growth, CHS and biofilm formation. • Salinity levels affected the bio-emulsifiers productivity and then oil degradation. [ABSTRACT FROM AUTHOR]