Environmental hazard identification, assessment and control for a sustainable maritime transportation system

A demand exists to contribute towards the widening awareness of the need for sustainable maritime development and for coordinated maritime policies worldwide. Maritime shipping is considered the most eco-efficient mean of transportation and yet, is responsible for negative environmental impacts. This dissertation focuses on developing data-driven decision support tools to evaluate the sustainable performance of MTS by focusing on the elements of the MTS that place stress on the environment. The first research contribution is a System Dynamics simulation model that examines the MTS resiliency after an extreme event and determines the sequence needed to restore the ocean-going port to its pre-event state. The second is a Decision-Making in Complex Environments (DMCE) tool developed by integrating fuzzy logic with a combination of Analytic Hierarchy Process (FAHP) and Techniques for Order Performance by Similarity to Ideal Solution (FTOPSIS) to quantify and rank preferred environmental impact indicators within MTS. The third is an extension to this DMCE tool by the integration of a Monte Carlo simulation in order to have a better understanding of the risks associated with the resulting rankings of those preferred environmental indicators. And, the fourth is a predictive model for the monitoring of vegetation changes near-port areas and to understand the long-term impacts that maritime activity has towards the environment. The developed models address the impacts MTS has on the natural environment and help achieve environmental sustainability of this complex system by evaluating the sustainability performance of the MTS --Abstract, page iv

Paths forward for sustainable maritime transport : A techno-economic optimization framework for next generation vessels

Climate change is omnipresent in our society. It is known that climate change is occurring, and that additional warming is unavoidable. Therefore, the decarbonization of industrial sectors has gained increased importance in the last years. The maritime transport sector is one of the most targeted industries as it contributes to approximately 3% of global GHG emissions. Nevertheless, maritime transport accounts for up to 80% of the global trade volume, underlying its importance for the world economy. A technical feasible and reliable solution is, thus, essential for the shipping industry to reach the ambitious climate goals established by the Paris Agreement. In the past, the maritim sector has been highly reliant on fossil fuels, using heavy fuel oil as the major energy input. Heavy fuel oil has been the most dominant fuel in the industry due to its cost advantage and high energy density. Recent developments in the maritime industry promote the emergence of dual fuel engines (e.g. LNG and HFO). Even though increased efficiencies and low carbon fuels can reduce maritime pollution, they cannot achieve carbon neutrality. In the long-term, it will be necessary to implement zero emission fuels including green hydrogen, ammonia, methanol, and LNG. The implementation of new sustainable technologies and fuels in the maritime sector will however depend on their economic competitiveness compared to alternative solutions. Therefore, the following research question arises: When can sustainable maritime transport achieve cost parity compared to conventional technologies? The master thesis investigates the break-even point of sustainable shipping technologies in order to achieve climate targets. Thereby, the focus is set on the life cycle costs of different maritime technologies. A techno-economic framework is necessary to decide on the most suitable options for the industry in prospective years. The framework should be able to analyze current as well as prospective technologies, and guide during the technological decision-making process. Therefore, the definition of key performance indicators (KPI) is essential to set a standard for further assessments. The KPIs will be the main value to compare technologies from an economic perspective. In order to answer the research question a case study is developed. The case study is formed by an extensive literature review on current and next-generation sustainable energy systems for vessels. A priority lies on potential carbon neutral technologies and engines such as fuel cells and battery systems based on a predetermined shipping route and shipping class. In a first step, a simulation model for the developed case is established. The output of the simulation model will then be used in the techno-economic framework, connecting components of the system through thermodynamic and physical properties. In a last step, cost functions translate the systems behavior into economic behavior. Once the case study is analyzed, a statistical model is applied on the results in order to evaluate the system under varying boundary conditions. This sensitivity approach is further necessary to underline the impact of the aforementioned KPIs. By that, the robustness of the framework is tested and secured. Finally, the results of the analysis are explained and interpreted with regard to the research question. A conclusion is drawn regarding the potential economic benefits of sustainable maritime transport technologies within the light of potential market access.The results of the thesis are to be documented in a scientifically appropriate manner and discussed within the context of existing literature and regulatory targets for the industry

Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications

[EN] The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems have been designed with the main goal of reducing their monetary cost while offering a specified quality of service. During the last decade, however, sustainability concepts are also being considered as a critical component of transportation systems, i.e., the environmental and social impact of transportation activities have to be taken into account when managers and policy makers design and operate modern transportation systems, whether these refer to long-distance carriers or to metropolitan areas. This paper reviews the existing work on different scientific methodologies that are being used to promote Sustainable Transportation Systems (STS), including simulation, optimization, machine learning, and fuzzy sets. This paper discusses how each of these methodologies have been employed to design and efficiently operate STS. In addition, the paper also provides a classification of common challenges, best practices, future trends, and open research lines that might be useful for both researchers and practitioners.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Torre-Martínez, MRDL.; Corlu, CG.; Faulin, J.; Onggo, BS.; Juan-Pérez, ÁA. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications. Sustainability. 13(3):1-21. https://doi.org/10.3390/su1303155112113

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Incentivising Bioenergy Production: Economic and Environmental Insights from a Regional Optimization Methodology

In conjunction with the European Union (EU) targets, the United Kingdom (UK) Government has introduced a range of mechanisms to foster the development and deployment of low carbon energy technologies and markets. This study focuses on the three main financial incentive schemes to promote renewable energy sector in the UK for electricity, heat and fuel production from renewables, namely feed-in tariff (FiT), Renewable Heat Incentive (RHI) and Renewables Obligation Certificate (RoC), considering the fact that optimal policy design depends on effective analyses of the impacts of incentives on the performance of renewable energy systems. The effects of potential changes in these incentive schemes on the economic and environmental performance of bioenergy sector are investigated using an analytical methodology. The methodology integrates fuzzy decision making and multi objective mathematical modelling in the same framework to capture uncertainties in the system parameters as well as economic and environmental sustainability aspects. Computational experiments are performed on bioenergy production using the entire West Midlands Region in the UK as case study region. The results reveal that the changes in incentive policies have a significant impact on the profitability of the supply chain, whereas environmental performance of the supply chain in terms of total GHG emissions is the least affected performance indicator by the changes in the incentive policies

Ship Lifecycle

In an effort to contribute to global efforts by addressing the marine pollution from various emission types, this Special Issue of Ship Lifecyle for Journal of Marine Science and Engineering was inspired to provide a comprehensive insight for naval architects, marine engineers, designers, shipyards, and ship-owners who strive to find optimal ways to survive in competitive markets by improving cycle time and the capacity to reduce design, production, and operation costs while pursuing zero emission. In this context, this Special Issue is devoted to providing insights into the latest research and technical developments on ship systems and operation with a life cycle point of view. The goal of this Special Issue is to bring together researchers from the whole marine and maritime community into a common forum to share cutting-edge research on cleaner shipping. It is strongly believed that such a joint effort will contribute to enhancing the sustainability of the marine and maritime activities. This Special Issue features six novel publications dedicated to this endeavor. First of all, as a proactive response to transitioning to cleaner marine fuel sources, numerous aspects of the excellence of fuel-cell based hybrid ships were demonstrated through four publications. In addition, two publications demonstrated the effectiveness of life cycle assessment (LCA) applicable to marine vessels

Integration of e-business strategy for multi-lifecycle production systems

Internet use has grown exponentially on the last few years becoming a global communication and business resource. Internet-based business, or e-Business will truly affect every sector of the economy in ways that today we can only imagine. The manufacturing sector will be at the forefront of this change. This doctoral dissertation provides a scientific framework and a set of novel decision support tools for evaluating, modeling, and optimizing the overall performance of e-Business integrated multi-lifecycle production systems. The characteristics of this framework include environmental lifecycle study, environmental performance metrics, hyper-network model of integrated e-supply chain networks, fuzzy multi-objective optimization method, discrete-event simulation approach, and scalable enterprise environmental management system design. The dissertation research reveals that integration of e-Business strategy into production systems can alter current industry practices along a pathway towards sustainability, enhancing resource productivity, improving cost efficiencies and reducing lifecycle environmental impacts. The following research challenges and scholarly accomplishments have been addressed in this dissertation: Identification and analysis of environmental impacts of e-Business. A pioneering environmental lifecycle study on the impact of e-Business is conducted, and fuzzy decision theory is further applied to evaluate e-Business scenarios in order to overcome data uncertainty and information gaps; Understanding, evaluation, and development of environmental performance metrics. Major environmental performance metrics are compared and evaluated. A universal target-based performance metric, developed jointly with a team of industry and university researchers, is evaluated, implemented, and utilized in the methodology framework; Generic framework of integrated e-supply chain network. The framework is based on the most recent research on large complex supply chain network model, but extended to integrate demanufacturers, recyclers, and resellers as supply chain partners. Moreover, The e-Business information network is modeled as a overlaid hypernetwork layer for the supply chain; Fuzzy multi-objective optimization theory and discrete-event simulation methods. The solution methods deal with overall system parameter trade-offs, partner selections, and sustainable decision-making; Architecture design for scalable enterprise environmental management system. This novel system is designed and deployed using knowledge-based ontology theory, and XML techniques within an agent-based structure. The implementation model and system prototype are also provided. The new methodology and framework have the potential of being widely used in system analysis, design and implementation of e-Business enabled engineering systems

Sustainable Freight Transport

This Special Issue of Sustainability reports on recent research aiming to make the freight transport sector more sustainable. The sector faces significant challenges in different domains of sustainability, including the reduction of greenhouse gas emissions and the management of health and safety impacts. In particular, the intention to decarbonise the sector’s activities has led to a strong increase in research efforts—this is also the main focus of the Special Issue. Sustainable freight transport operations represent a significant challenge with multiple technical, operational, and political aspects. The design, testing, and implementation of interventions require multi-disciplinary, multi-country research. Promising interventions are not limited to introducing new transport technologies, but also include changes in framework conditions for transport, in terms of production and logistics processes. Due to the uncertainty of impacts, the number of stakeholders, and the difficulty of optimizing across actors, understanding the impacts of these measures is not a trivial problem. Therefore, research is not only needed on the design and evaluation of individual interventions, but also on the approach of their joint deployment through a concerted public/private programme. This Special Issue addresses both dimensions, in two distinct groups of papers—the programming of interventions and the individual sustainability measures themselves