Development of emergency response systems by intelligent and integrated approaches for marine oil spill accidents

Oil products play a pervasive role in modern society as one of the dominant energy fuel sources. Marine activities related to oil extraction and transportation play a vital role in resource supply. However, marine oil spills occur due to such human activities or harsh environmental factors. The emergency accidents of spills cause negative impacts on the marine environment, human health, and economic loss. The responses to marine oil spills, especially large-scale spills, are relatively challenging and inefficient due to changing environmental conditions, limited response resources, various unknown or uncertain factors and complex resource allocation processes. The development of previous research mainly focused on single process simulation, prediction, or optimization (e.g., oil trajectory, weathering, or cleanup optimization). There is still a lack of research on comprehensive and integrated emergency responses considering multiple types of simulations, types of resource allocations, stages of accident occurrence to response, and criteria for system optimizations. Optimization algorithms are an important part of system optimization and decision-making. Their performance directly affacts the quality of emergency response systems and operations. Thus, how to improve efficiency of emergency response systems becomes urgent and essential for marine oil spill management. The power and potential of integrating intelligent-based modeling of dynamic processes and system optimization have been recognized to better support oil spill responders with more efficient response decisions and planning tools. Meanwhile, response decision-making combined with human factor analysis can help quantitatively evaluate the impacts of multiple causal factors on the overall processes and operational performance after an accident. To address the challenges and gaps, this dissertation research focused on the development and improvement of new emergency response systems and their applications for marine oil spill response in the following aspects: 1) Realization of coupling dynamic simulation and system optimization for marine oil spill responses - The developed Simulation-Based Multi-Agent Particle Swarm Optimization (SA-PSO) modeling investigated the capacity of agent-based modeling on dynamic simulation of spill fate and response, particle swarm optimization on response allocation with minimal time and multi-agent system on information sharing. 2) Investigation of multi-type resource allocation under a complex simulation condition and improvement of optimization performance - The improved emergency response system was achieved by dynamic resource transportation, oil weathering and response simulations and resource allocation optimization. The enhanced particle swarm optimization (ME-PSO) algorithm performed outstanding convergence performance and low computation cost characteristics integrating multi-agent theory (MA) and evolutionary population dynamics (EPD). 3) Analysis and evaluation of influencing factors of multiple stages of spill accidents based on human factors/errors and multi-criteria decision making - The developed human factors analysis and classification system for marine oil spill accidents (HFACS-OS) framework qualitatively evaluated 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). The framework was further coupled with quantitative data analysis by Fuzzy-based Technique for Order Preference by Similarity to Idea Solution (Fuzzy-TOPSIS) to enhance decision-making during response operations under multiple criteria. 4) Development of a multi-criteria emergency response system with the enhanced optimization algorithm, multi-mode resource transportation and allocation and a more complex and realistic simulation modelling - The developed multi-criteria emergency response system (MC-ERS) system integrated dynamic process simulations and weighted multi-criteria system optimization. Total response time, response cost and environmental impacts were regarded as multiple optimization goals. An improved weighted sum optimization function was developed to unify the scaling and proportion of different goals. A comparative PSO was also developed with various algorithm-improving methods and the best-performing inertia weight function. The proposed emergency response approaches in studies were examined by oil spill case studies related to the North Atlantic Ocean and Canada circumstances to analyze the modelling performance and evaluate their practicality and applicability. The developed optimization algorithms were tested by benchmarked functions, other optimization algorithms, and an oil spill case. The developed emergency response systems and the contained simulation and optimization algorithms showed the strong capability for decision-making and emergency responses by recommending optimal resource management or evaluations of essential factors. This research was expected to provide time-efficient, and cost-saving emergency response management approaches for handling and managing marine oil spills. The research also improved our knowledge of the significance of human factors/errors to oil spill accidents and response operations and provided improved support tools for decision making. The dissertation research helped fill some important gaps in emergency response research and management practice, especially in marine oil spill response, through an innovative integration of dynamic simulation, resource optimization, human factor analysis, and artificial intelligence methods. The research outcomes can also provide methodological support and valuable references for other fields that require timely and effective decisions, system optimizations, process controls, planning and designs under complicated conditions, uncertainties, and interactions

Disaster management in industrial areas: perspectives, challenges and future research

Purpose: In most countries, development, growth, and sustenance of industrial facilities are given utmost importance due to the influence in the socio-economic development of the country. Therefore, special economic zones, or industrial areas or industrial cities are developed in order to provide the required services for the sustained operation of such facilities. Such facilities not only provide a prolonged economic support to the country but it also helps in the societal aspects as well by providing livelihood to thousands of people. Therefore, any disaster in any of the facilities in the industrial area will have a significant impact on the population, facilities, the economy, and threatens the sustainability of the operations. This paper provides review of such literature that focus on theory and practice of disaster management in industrial cities. Design/methodology/approach: In the paper, content analysis method is used in order to elicit the insights of the literature available. The methodology uses search methods, literature segregation and developing the current knowledge on different phases of industrial disaster management. Findings: It is found that the research is done in all phases of disaster management, namely, preventive phase, reactive phase and corrective phase. The research in each of these areas are focused on four main aspects, which are facilities, resources, support systems and modeling. Nevertheless, the research in the industrial cities is insignificant. Moreover, the modeling part does not explicitly consider the nature of industrial cities, where many of the chemical and chemical processing can be highly flammable thus creating a very large disaster impact. Some research is focused at an individual plant and scaled up to the industrial cities. The modeling part is weak in terms of comprehensively analyzing and assisting disaster management in the industrial cities. Originality/value: The comprehensive review using content analysis on disaster management is presented here. The review helps the researchers to understand the gap in the literature in order to extend further research for disaster management in large scale industrial cities.Peer Reviewe

On risk management of shipping system in ice-covered waters : Review, analysis and toolbox based on an eight-year polar project

Publisher Copyright: © 2022 The AuthorsWith the climate change, polar sea ice is diminishing. This, on one hand, enables the possibility for e.g., Arctic shipping and relevant resource exploitation activities, but on the other hand brings additional risks induced by these activities. Increasing research focuses have been observed on the relevant topics in the complex and harsh polar environment and its fragile ecosystem. However, from risk management perspective, there is still a lack of holistic analysis and understanding towards safe shipping in the ice-covered waters and its available models applicable for managing risks in the system. Therefore, this paper aims to establish a framework and analysis for better understanding of this gap. The paper targets a comprehensive and long-term project specifically focusing on holistic safe shipping in ice-covered waters as the analysis basis. It firstly creates a holistic framework for the shipping system in ice-covered waters and then implements review and analysis of project publications on their overall features. Quantitative prediction models are selected for a structured applicability analysis. Furthermore, an extensive review outside the project following the elements established for the holistic shipping system is conducted so that this paper provides an overview of models for the shipping system in ice-covered waters, addressing the status of the current toolbox. Moreover, it helps to identify the next scientific steps on risk management of shipping in ice-covered waters.Peer reviewe

Development of an integrated decision support system for supporting offshore oil spill response in harsh environments

Offshore oil spills can lead to significantly negative impacts on socio-economy and constitute a direct hazard to the marine environment and human health. The response to an oil spill usually consists of a series of dynamic, time-sensitive, multi-faceted and complex processes subject to various constraints and challenges. In the past decades, many models have been developed mainly focusing on individual processes including oil weathering simulation, impact assessment, and clean-up optimization. However, to date, research on integration of offshore oil spill vulnerability analysis, process simulation and operation optimization is still lacking. Such deficiency could be more influential in harsh environments. It becomes noticeably critical and urgent to develop new methodologies and improve technical capacities of offshore oil spill responses. Therefore, this proposed research aims at developing an integrated decision support system for supporting offshore oil spill responses especially in harsh environments (DSS-OSRH). Such a DSS consists of offshore oil spill vulnerability analysis, response technologies screening, and simulation-optimization coupling. The uncertainties and/or dynamics have been quantitatively reflected throughout the modeling processes. First, a Monte Carlo simulation based two-stage adaptive resonance theory mapping (MC-TSAM) approach has been developed. A real-world case study was applied for offshore oil spill vulnerability index (OSVI) classification in the south coast of Newfoundland to demonstrate this approach. Furthermore, a Monte Carlo simulation based integrated rule-based fuzzy adaptive resonance theory mapping (MC-IRFAM) approach has been developed for screening and ranking for spill response and clean-up technologies. The feasibility of the MC-IRFAM was tested with a case of screening and ranking response technologies in an offshore oil spill event. A novel Monte Carlo simulation based dynamic mixed integer nonlinear programming (MC-DMINP) approach has also been developed for the simulation-optimization coupling in offshore oil spill responses. To demonstrate this approach, a case study was conducted in device allocation and oil recovery in an offshore oil spill event. Finally, the DSS-OSRH has been developed based on the integration of MC-TSAM, MC-IRFAM, AND MC-DSINP. To demonstrate its feasibility, a case study was conducted in the decision support during offshore oil spill response in the south coast of Newfoundland. The developed approaches and DSS are the first of their kinds to date targeting offshore oil spill responses. The novelty can be reflected from the following aspects: 1) an innovative MC-TSAM approach for offshore OSVI classification under complexity and uncertainty; 2) a new MC-IRFAM approach for oil spill response technologies classification and ranking with uncertain information; 3) a novel MC-DMINP simulation-optimization coupling approach for offshore oil spill response operation and resource allocation under uncertainty; and 4) an innovational DSS-OSRH which consists of the MC-TSAM, MC-IRFAM, MC-DMINP, supporting decision making throughout the offshore oil spill response processes. These methods are particularly suitable for offshore oil spill responses in harsh environments such as the offshore areas of Newfoundland and Labrador (NL). The research will also promote the understanding of the processes of oil transport and fate and the impacts to the affected offshore and shoreline area. The methodologies will be capable of providing modeling tools for other related areas that require timely and effective decisions under complexity and uncertainty

Risk Assessment and Management of Petroleum Transportation Systems Operations

Petroleum Transportation Systems (PTSs) have a significant impact on the flow of crude oil within a Petroleum Supply Chain (PSC), due to the great demand on this natural product. Such systems are used for safe movement of crude and/or refined products from starting points (i.e. production sites or storage tanks), to their final destinations, via land or sea transportation. PTSs are vulnerable to several risks because they often operate in a dynamic environment. Due to this environment, many potential risks and uncertainties are involved. Not only having a direct effect on the product flow within PSC, PTSs accidents could also have severe consequences for the humans, businesses, and the environment. Therefore, safe operations of the key systems such as port, ship and pipeline, are vital for the success of PTSs. This research introduces an advanced approach to ensure safety of PTSs. This research proposes multiple network analysis, risk assessment, uncertainties treatment and decision making techniques for dealing with potential hazards and operational issues that are happening within the marine ports, ships, or pipeline transportation segments within one complete system. The main phases of the developed framework are formulated in six steps. In the first phase of the research, the hazards in PTSs operations that can lead to a crude oil spill are identified through conducting an extensive review of literature and experts’ knowledge. In the second phase, a Fuzzy Rule-Based Bayesian Reasoning (FRBBR) and Hugin software are applied in the new context of PTSs to assess and prioritise the local PTSs failures as one complete system. The third phase uses Analytic Hierarchy Process (AHP) in order to determine the weight of PTSs local factors. In the fourth phase, network analysis approach is used to measure the importance of petroleum ports, ships and pipelines systems globally within Petroleum Transportation Networks (PTNs). This approach can help decision makers to measure and detect the critical nodes (ports and transportation routes) within PTNs. The fifth phase uses an Evidential Reasoning (ER) approach and Intelligence Decision System (IDS) software, to assess hazards influencing on PTSs as one complete system. This research developed an advance risk-based framework applied ER approach due to its ability to combine the local/internal and global/external risk analysis results of the PTSs. To complete the cycle of this study, the best mitigating strategies are introduced and evaluated by incorporating VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and AHP to rank the risk control options. The novelty of this framework provides decision makers with realistic and flexible results to ensure efficient and safe operations for PTSs

Disaster management in industrial areas: Perspectives, challenges and future research

Purpose: In most countries, development, growth, and sustenance of industrial facilities are given utmost importance due to the influence in the socio-economic development of the country. Therefore, special economic zones, or industrial areas or industrial cities are developed in order to provide the required services for the sustained operation of such facilities. Such facilities not only provide a prolonged economic support to the country but it also helps in the societal aspects as well by providing livelihood to thousands of people. Therefore, any disaster in any of the facilities in the industrial area will have a significant impact on the population, facilities, the economy, and threatens the sustainability of the operations. This paper provides review of such literature that focus on theory and practice of disaster management in industrial cities. Design/methodology/approach: In the paper, content analysis method is used in order to elicit the insights of the literature available. The methodology uses search methods, literature segregation and developing the current knowledge on different phases of industrial disaster management. Findings: It is found that the research is done in all phases of disaster management, namely, preventive phase, reactive phase and corrective phase. The research in each of these areas are focused on four main aspects, which are facilities, resources, support systems and modeling. Nevertheless, the research in the industrial cities is insignificant. Moreover, the modeling part does not explicitly consider the nature of industrial cities, where many of the chemical and chemical processing can be highly flammable thus creating a very large disaster impact. Some research is focused at an individual plant and scaled up to the industrial cities. The modeling part is weak in terms of comprehensively analyzing and assisting disaster management in the industrial cities. Originality/value: The comprehensive review using content analysis on disaster management is presented here. The review helps the researchers to understand the gap in the literature in order to extend further research for disaster management in large scale industrial cities.Scopu

Social sustainable supplier evaluation and selection: a group decision-support approach

Organisational and managerial decisions are influenced by corporate sustainability pressures. Organisations need to consider economic, environmental and social sustainability dimensions in their decisions to become sustainable. Supply chain decisions play a distinct and critical role in organisational good and service outputs sustainability. Sustainable supplier selection influences the supply chain sustainability allowing many organisations to build competitive advantage. Within this context, the social sustainability dimension has received relatively minor investigation; with emphasis typically on economic and environmental sustainability. Neglecting social sustainability can have serious repercussions for organisational supply chains. This study proposes a social sustainability attribute decision framework to evaluate and select socially sustainable suppliers. A grey-based multi-criteria decision-support tool composed of the ‘best-worst method’ (BWM) and TODIM (TOmada de Decisão Interativa e Multicritério – in Portuguese ‘Interactive and Multicriteria Decision Making’) is introduced. A grey-BWM approach is used to determine social sustainability attribute weights, and a grey-TODIM method is utilised to rank suppliers. This process is completed in a group decision setting. A case study of an Iranian manufacturing company is used to exemplify the applicability and suitability of the proposed social sustainability decision framework. Managerial implications, limitations, and future research directions are introduced after the application of the model

Data Analysis Methods for Software Systems

Using statistics, econometrics, machine learning, and functional data analysis methods, we evaluate the consequences of the lockdown during the COVID-19 pandemics for wage inequality and unemployment. We deduce that these two indicators mostly reacted to the first lockdown from March till June 2020. Also, analysing wage inequality, we conduct analysis separately for males and females and different age groups.We noticed that young females were affected mostly by the lockdown.Nevertheless, all the groups reacted to the lockdown at some level

Offshore oil spill detection using synthetic aperture radar

Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and ‘look-alike’ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements