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To achieve the potential of smart cities, there is a strong requirement to use a set of useful, but still accessible services within smart city systems. Interoperability challenges and roadblocks for software developers and integrators are well-known consequences of fragmented semantics across different contexts. Furthermore, in the smart city context, there is a need to ensure the security and identity of real-world services operating on this data through the adoption of access control principles (authorization and authentication). The use of ontologies to unify the diverse semantics of multiple domains is one strategy that has had considerable success in the past. This paper describes an access management ontology in smart cities developed to enable the interoperability of physical built environment assets, sensing and actuation devices and current built environment services with existing security standards, digital twin and Building Information Model (BIM) datasets. It also provides interoperability between user interfaces and the actors who use them in the context of establishing an access management in smart cities framework for the built environment. This has been validated through its implementation in the Cardiff Urban Sustainability Platform (CUSP), deployed to manage a variety of smart services on a university campus. This validation has successfully shown the ability of the ontology to function as intended in the context of a digital twin, thereby offering single sign-on and suitable access control.

Seismic hazards represent a permanent threat to buildings. The paper argues that risk-oriented approaches provide an interesting solution to account for the long-term resilience of buildings, both for the design of new structures and the rehabilitation of existing ones. The proposed research draws upon the standard definition of risk as a function of vulnerability, hazard and exposure to develop an optimization-based methodology for risk appraisal of buildings in seismic conditions. The proposed methodology allows to identify the optimum layout and thickness of shear walls in a reinforced concrete frame, based on a target risk performance. This is achieved through the coupling of an evolutionary computing environment with an object-oriented structural analysis tool, involving its native Application Programming Interface (API). The latter allows to automate the search for the optimum shear wall configuration solution. The research is validated on the Beichuan Hotel building in Old Beichuan (China), heavily affected by the 2008 Wenchuan Earthquake. The paper evidences that the adoption of the proposed methodology leads to a risk reduction of about 80% compared to the as-built scenario, with additional benefits from both a financial and building functionality perspective.

Recent technological developments in the construction industry are seeking to create smart cities by using Cyber-Physical Systems (CPSs) to enhance information models such as BIM. Currently, BIM models are commonly adopted to work with IoT-based systems and embrace smart technologies that offer interoperability in the communication layer. In future, it is envisioned that digital twins will provide new possibilities for cyber-physical systems via monitoring and simulation. However, rarely in this rapidly developing field is security fully considered. This paper reviews the relevant literature regarding the use of the IoT in the built environment and analyses current practices. It also presents examples of cities that use the IoT to improve construction and the lived experience. Finally, it reviews how digital twins factor in multiple layers defined in CPSs, from physical objects to information models. Based on this review, recommendations are provided documenting how BIM specifications can be expanded to become IoT compliant, enhancing standards to support cybersecurity, and ensuring digital twin and city standards can be fully integrated in future secure smart cities.

Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while improving grid stability and meeting service demand. This is possible by adopting next-generation energy systems, which leverage artificial intelligence, the Internet of things (IoT), and communication technologies to collect and analyze big data in real-time and effectively run city services. However, training machine learning algorithms to perform various energy-related tasks in sustainable smart cities is a challenging data science task. These algorithms might not perform as expected, take much time in training, or do not have enough input data to generalize well. To that end, transfer learning (TL) has been proposed as a promising solution to alleviate these issues. To the best of the authors’ knowledge, this paper presents the first review of the applicability of TL for energy systems by adopting a well-defined taxonomy of existing TL frameworks. Next, an in-depth analysis is carried out to identify the pros and cons of current techniques and discuss unsolved issues. Moving on, two case studies illustrating the use of TL for (i) energy prediction with mobility data and (ii) load forecasting in sports facilities are presented. Lastly, the paper ends with a discussion of the future directions.

Sports facilities are considered complex buildings due to their high energy demand and occupancy profiles. Therefore, their management and optimization are crucial for reducing their energy consumption and carbon footprint while maintaining an appropriate indoor environmental quality. This work is part of the SportE3.Q project, which aims to manage and optimize the operation of sports facilities. A neural network (NN)-based model predictive control (MPC) management and optimization system is proposed for the heating, ventilation, and air conditioning (HVAC) system of a sports hall in the sports and events complex of Qatar University (QU). The proposed approach provides an integrated dynamic optimization method that accounts for future system behavior in the decision-making process, consisting of a prediction element and an optimizer. A NN is used to implement the dynamic prediction element of the MPC system and is compared with other machine learning (ML)-based models, which are support vector regression (SVR), -nearest neighbor (NN), and decision trees (DT). The NN-based model outperforms the other ML models with an average root mean squared error (RMSE) of around 0.06 between the actual and the predicted values, and an average R of 0.99 as NNs are popular for their high accuracy and reliability. Two schemes of the proposed NN-based MPC system are investigated for managing and optimizing the operation of the hall’s HVAC system for enhanced energy use and indoor environment quality, as well as for providing occupancy profile recommendations to aid the facilities’ managers in handling their operation. In alignment with the objective of the SportE3.Q project, up to 46% energy reduction was achieved while jointly optimizing the thermal comfort and indoor air quality. In addition, Scheme 2 of the proposed system provided productive occupancy recommendations for a healthier indoor environment.

Purpose This paper reviews the state-of-the art research in life cycle assessment (LCA) applied to buildings. It focuses on current research trends, and elaborates on gaps and directions for future research. Methods A systematic literature review was conducted to identify current research and applications of LCA in buildings. The proposed review methodology includes (i) identifying recent authoritative research publications using established search engines, (ii) screening and retaining relevant publications, and (iii) extracting relevant LCA applications for buildings and analyzing their underpinning research. Subsequently, several research gaps and limitations were identified, which have informed our proposed future research directions. Results and discussions This paper argues that humans can attenuate and positively control the impact of their buildings on the environment, and as such mitigate the effects of climate change. This can be achieved by a new generation of LCA methods and tools that are model based and continuously learn from real-time data, while informing effective operation and management strategies of buildings and districts. Therefore, the consideration of the time dimension in product system modeling is becoming essential to understand the resulting pollutant emissions and resource consumption. This time dimension is currently missing in life cycle inventory databases. A further combination of life cycle impact assessment (LCIA) models using time-dependent characterization factors can lead to more comprehensive and reliable LCA results. Conclusions and recommendations This paper promotes the concept of semantic-based dynamic (real-time) LCA, which addresses temporal and spatial variations in the local built and environmental ecosystem, and thus more effectively promotes a “cradle-to-grave-to-reincarnation” environmental sustainability capability. Furthermore, it is critical to leverage digital building resources (e.g., connected objects, semantic models, and artificial intelligence) to deliver accurate and reliable environmental assessments.

Abstract The recent adoption of building information modelling (BIM), and the quest to decarbonise our built environment, has impacted several segments of the supply chain, including design and engineering practitioners, prompting the need to redefine the construction personnel positions along with associated skills and competencies. The research informs ways in which practitioners can fully embrace the potential of BIM for energy efficiency to promote sustainable interventions by improving existing training practices and identifying new training requirements as BIM evolves and as practitioners’ ICT (Information and Communications Technology) maturity levels improve. This is achieved by adopting a novel text-mining approach which analyses social media alongside secondary sources of evidence to establish a level of correlation between BIM roles and skills. The use of ontological dependency analysis has helped to understand the degree of correlation of skills with roles as a method to inform training and educational programmes. A key outcome from the research is a semantic web-based mining environment which determines BIM roles and skills, as well as their correlation factor, with an application for energy efficiency. The paper also evidences that (a) construction skills and roles are dynamic in nature and evolve over time, reflecting the digital transformation of the Construction industry, and (b) the importance of socio-organisational aspects in construction skills and related training provision. Graphic abstract

The Irish Seafood agency reports that 15% of global energy is consumed by operations related to refrigeration and air conditioning in the fish industry which stresses the importance of integration with clean renewables and adoption of smart energy management solutions. While fish processing industries have high energy costs with continuous refrigeration, air conditioning and ice making processes, there is a real need to analyse and model energy use in fish ports to understand environmental impacts in terms of CO2 emissions while exploring the potential for integrating renewable energy sources.In this paper, we conduct energy modelling and optimization for the Milford Haven fish processing port in South Wales. We explain how a simulation capability can be developed at the fish industry port level and propose a simulation-based optimization strategy to determine optimized schedules for appliances. The results show that energy consumption can be reduced with the use of optimized appliance schedules developed in relation to the total energy demand as well as a wide range of optimization constraints. Keywords: Energy analysis, Energy modelling, Simulation, Optimization, Renewable energy, Scheduling appliances

Purpose Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making. Methods In this study, previous research on ML for LCA were considered, and a literature review was undertaken. Results The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases. Conclusions The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature.

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings' performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings' management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings' performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings. 2022, The Author(s). This publication was made possible by NPRP Grant No. NPRP12S-0222-190128 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors. Scopus

Marine activities in seaports account for circa 3% of total carbon emissions worldwide, prompting several initiatives to decarbonise their energy systems and make seaports smarter and greener. This paper provides a thorough and authoritative review of the vast array of research in this field, including past and ongoing initiatives. The study reveals that existing research leverages recent ad-vances in digital technologies while focusing on one or several of the following themes: carbon reduction, use of renewable energy resources, cost-performance optimisation, deployment of smart control technologies, the regulatory land-scape for greening seaports, and implementing green port practices guidelines. As such, the paper provides a critical review of existing technologies and con-cepts that promote and contribute to the decarbonisation of seaports, including Smart Grids and Virtual Power Plants. Several avenues for future research are then discussed, including (a) total life cycle approach to seaport energy manage-ment, (b) Semantic-based modelling, forecasting and optimisation of seaports energy systems, (c) Secure and reliable seaports energy services, and (d) Transi-tion towards prosumer-driven seaport energy communities. The paper concludes by emphasising the importance of an adapted energy regulatory landscape at a national and EU-wide level to meet EU phased energy reduction targets.

The digitalization of the construction industry has been supported recently with various building information modelling (BIM) methods applied to lifecycle stages. This has enabled the transition towards an improved management of information required in the development of different construction project tasks. In a digital construction context, traceability of models and collaboration across disciplines is required especially when dealing with sensitive data and critical infrastructures. Blockchain technology has the potential to advance the automation of processes in the construction industry by offering a higher level of data traceability, incentivising participation, and ensuring transparency with projects. This paper presents how BIM and Blockchain can be utilised to facilitate the establishment and implementation of Digital Twins for construction projects. A highway construction case study is used for demonstrating the implementation of smart contracts for managing tasks and processes across different disciplines involved in a construction project.

Integrating data analytics, optimisation and dynamic control to support energy services has seen significant interest in recent years. Larger appliances used in an industry context are now provided with Internet of Things (IoT)-based interfaces that can be remotely monitored, with some also provided with actuation interfaces. The combined use of IoT and edge computing enables connectivity between energy systems and infrastructure, providing the means to implement both energy efficiency/optimisation and cost reduction strategies. We investigate the economic implications of harnessing IoT and edge/cloud technologies to support energy management for HVAC (Heating, Ventilation and Air Conditioning) systems in buildings. In particular, we evaluate the cost savings for building operations through energy optimisation. We use a real use case for energy optimisation as identified in the EU “Sporte2” project (focusing on the energy optimisation of sports facilities) and explore several scenarios in relation to costs and energy optimisation.

With the rapid growth in clean distributed energy resources involving micro-generation and flexible loads, users can actively manage their own energy and have the capability to enter in a market of energy services as prosumers while reducing their carbon footprint. The coordination between these distributed energy resources is essential in order to ensure fair trading and equality in resource sharing among a community of prosumers. Peer-to-Peer (P2P) networks can provide the underlying mechanisms for supporting such coordination and offer incentives to prosumers to participate in the energy market. In particular, the federation of energy clusters with P2P networks has the potential to unlock access to energy resources and lead to the development of new energy services in a fast-growing sharing energy economy. In this paper, we present the formation and federation of smart energy clusters using P2P networks with a view to decentralise energy markets and enable access and use of clean energy resources. We implement a P2P framework to support the federation of energy clusters and study the interaction of consumers and producers in a market of energy resources and services. We demonstrate how energy exchanges and energy costs in a federation are influenced by the energy demand, the size of energy clusters and energy types. We conduct our modelling and analysis based on a real fish industry case study in Milford Haven, South Wales, as part of the EU H2020 INTERREG piSCES project.

The high pressure flame speeds and high temperature kinetics of 1,3-butadiene mixtures are studied by using spherical flames at fuel rich and lean conditions at 1–18 atm with H2 and O2 additions. H2 and O2 are added in the mixture to perturb concentrations of H/O radicals and the flame speed sensitivity to 1,3-butadiene + O/H reactions. The presently measured 1,3-butadiene/air flame speeds agree with previous studies at ambient pressure, but are lower at 5 atm. Comparison between the new experimental data and the prediction by the recently developed 1,3-butadiene model (Zhou et al., 2018) shows over-prediction of the flame speeds, especially at high pressure and fuel lean conditions. With H2 and O2 sensitization, the discrepancy between the model prediction and experiments becomes even larger. Sensitivity analysis shows that the flame speed is very sensitive to 1,3-butadiene + O/H reactions, especially to the branching and termination reaction ratio of 1,3-C4H6 + O = C2H3 + CH2CHO (R1) and 1,3-C4H6 + O = CH2O + C3H4-a (R2). The sensitivity of the flame speed to the branching ratio of these two reactions increases with the increase of H2 and O2 enrichment. Due to the lack of accurate theoretical calculations of these two reaction rates, the new flame speed data and previously measured ignition delay time were used to assess the uncertainty of the branching ratio and reaction rates of R1 and R2. It shows that the optimized branching ratio significantly improves the flame speed and ignition delay time predictions, especially at high pressure and fuel lean conditions. The present study reveals that H2 and O2 sensitization in flames provides an important way to identify the uncertainties of fuel + O/H reactions and to improve the model predictability for flames.

Social media data analysis in a smart city context can represent an efficacious instrument to inform decision making. The manuscript strives to leverage the power of Natural Language Processing (NLP) techniques applied to Twitter messages using supervised learning to achieve real-time automated event detection in smart cities. A semantic-based taxonomy of risks is devised to discover and analyse associated events from data streams, with a view to: (i) read and process, in real-time, published texts (ii) classify each text into one representative real-world category (iii) assign a citizen satisfaction value to each event. To select the language processing models striking the best balance between accuracy and processing speed, we conducted a pre-emptive evaluation, comparing several baseline language models formerly employed by researchers for event classification. A heuristic analysis of several smart cities and community initiatives was conducted, with a view to define real-world scenarios as basis for determining correlations between two or more co-occurring event types and their associated levels of citizen satisfaction, while further considering environmental factors. Based on Multiple Regression Analysis (MRA), we established the relationships between scenario variables, obtaining a variance of 60%–90% between the dependent and independent variables. The selected combination of supervised NLP techniques leverages an accuracy of 88.5%. We found that all regression models had at least one variable below the 0.05 threshold of the , therefore at least one statistically significant independent variable. These findings ultimately illustrate how citizens, taking the role of active social sensors, can yield vital data that authorities can use to make educated decisions and sustainably construct smarter cities.

The recent ongoing digital transformation of the energy landscape provides new opportunities to decarbonise energy-intensive industries, including the fish processing sector. The paper explores the potential of deploying multi-vector smart micro-grid solutions in fishery ports, sourced from dispatchable renewable generation, including solar energy. This is demonstrated in the Milford Haven Port in South Wales, United Kingdom. The proposed system is modelled using control scenarios developed based on data and energy models of the port. The control scenario makes energy use decisions based on the availability of dispatchable renewable sources and the price of energy from the local energy market. Also, we consider local energy storage by utilising the local electric fishing boat fleet as an alternative energy storage system. The results demonstrate optimised energy use through multi-vector smart micro-grid model by providing more than 70 percent reduction of energy use from grid.

Urban environments are urgently required to become smarter to overcome sustainability and resilience challenges whilst remaining economically viable. This involves a vast increase in the penetration of ICT resources, both physical and virtual, with the requirement to factor in built environment, socio-economic and human artefacts. This paper, therefore, proposes a methodology for eliciting, testing, and deploying, requirements in the field of urban cybernetics. This extends best practice requirements engineering principles to meet the demands of this growing niche. The paper follows a case study approach of applying the methodology in the smart water domain, where it achieves positive results. The approach not only heavily utilises iteration alongside domain experts, but also mandates the integration of technical domain experts to ensure software requirements are met. A key novelty of the approach is prioritising a balance between (a) knowledge engineers’ tenacity for logical accuracy, (b) software engineers’ need for speed, simplicity, and integration with other components, and (c) the domain experts’ needs to invoke ownership and hence nurture adoption of the resulting ontology.

Sports facilities (SFs) consume massive energy given their unique demand profiles and operation requirements. Intelligent and effective solutions are necessary to tackle the matter of facilities’ sustainability and efficiency. Promoting efficient and environmentally sustainable SFs is pivotal towards environmentally friendly and socially resilient cities. There is a lack of systematic literature review focused on the progress in SFs operation, sustainability, and energy optimization. To the best of the authors’ knowledge, this research presents the first review article addressing the research gap in building operation management and optimization for SFs compared to other types of buildings and SFs located in hot climatic zones compared to cold ones. The topic’s significance is highlighted with emphasis on the climatic zone and the characteristics of SFs. A comprehensive review and in-depth discussion of existing solutions are presented. Limited studies covered the management and optimization of SFs for the past five years compared to residential and commercial buildings, and 71% of them were for facilities located in cold regions. About 45% of the surveyed works targeted swimming pools since they are the most popular SFs’ type with the highest energy consumption per usable area. 39% of the reviewed studies employed simulation-based approaches to investigate the subject, 26% used artificial intelligence and machine learning, and 35% utilized optimization algorithms and other standard approaches. The limitations of those works and the prospects in energy and operation optimization of SFs are presented. The latter includes deploying evolving typologies such as deep learning, developing modular solutions that can be integrated into existing technologies, and deploying renewable energy systems for sustainable facilities. Finally, the active role of SFs in energy markets is discussed.

Literature in digital transformation maturity is scarce. Digital transformation in higher education, especially after COVID-19 is seen as inevitable. This research explores digital transformation maturity and challenges within Higher Education. The significance of this study stems from the role digital transformation plays in today’s knowledge economy. This study proposes a new framework based on Deloitte’s 2019 digital transformation assessment framework with Petkovic 2014 mega and major higher education process mapping. The study triangulates the findings of multiple research instruments, including survey, interviews, case study, and direct observation. The research findings show a significant variance between the respondents’ perception of digital transformations maturity levels, and the core requirements of digital transformation maturity. The findings also show the lack of holistic vision, digital transformation competency, and data structure and processing as the leading challenges of digital transformation.

The decentralisation of energy supply and demand can contribute decisively to protecting the environment and climate of the planet by consuming electricity in the proximity of the generation source and avoiding losses in transmission and distribution. Supporting energy transactions with emerging intelligent technologies can advance the development of energy communities and accelerate the integration of renewable sources. Distributed energy solutions play an essential role as they are explicitly designed to produce, store and deliver green energy. Profiting with these benefits is essential, especially in the context of the current debate on stopping climate change. Several technologies such as waste heat recovery with intelligent algorithms can improve the energy distribution and provide significant resource savings. On the other hand, the usage of Blockchain technology in energy markets promises to incentivise the use of renewables and provide a reliable framework to monitor real-time information of energy production and consumption. Blockchain can also enable trading between independent agents and lead to the formation of more secured energy communities. In this paper, we demonstrate how Blockchain can be utilised to support the formation and use of energy communities. We propose a Blockchain-based energy framework as a mean to support energy exchanges in a community of prosumers. We demonstrate how smart contracts can manage energy transactions and enable a more secured trading environment between consumers and producers. We utilise data and models from a real fish processing industrial site in Milford Haven Port, South Wales, based on which we validate our research hypothesis.

Branched actin network supports cell migration through extracellular microenvironments. However, it is unknown how intracellular proteins adapt the elastic properties of the network to the highly varying extracellular resistance. Here we develop a three-dimensional assembling model to simulate the realistic self-assembling process of the network by encompassing intracellular proteins and their dynamic interactions. Combining this multiscale model with finite element method, we reveal that the network can not only sense the variation of extracellular resistance but also self-adapt its elastic properties through remodeling with intracellular proteins. Such resistance-adaptive elastic behaviours are versatile and essential in supporting cell migration through varying extracellular microenvironments. The bending deformation mechanism and anisotropic Poisson’s ratios determine why lamellipodia persistently evolve into sheet-like structures. Our predictions are confirmed by published experiments. The revealed self-adaptive elastic properties of the networks are also applicable to the endocytosis, phagocytosis, vesicle trafficking, intracellular pathogen transport and dendritic spine formation., Xindong Chen et al. simulate the sheet-like branched actin networks used by cells during cell migration using a novel 3D assembling model. They show how intracellular proteins regulate the network’s elastic properties and then affect cell migration, providing greater insight into the fundamental physical mechanisms of the previously published experimental observations.

This research extends Hasan Fathy’s (1986) principle of vernacular architecture by focusing on the Rawshan through an investigation of two criteria: aesthetics and energy efficiency. The paper discusses the views of both the Saudi public and key decision-makers on reviving vernacular architecture in the context of Saudi Arabia’s rapidly developing economy, characterized by relatively high rates of energy consumption and CO2 emissions. This research explores (a) the interaction in domestic buildings of Saudi occupants with their windows, and how these are perceived as an interface with the external environment; (b) awareness and knowledge of the use of shading elements (such as Rawshans) to reduce the use of artificial lighting while maintaining indoor privacy; (c) Saudi awareness of, and familiarity with, the Rawshan as a vernacular element and a secular architectural tradition; and (d) Saudi views on the revival of traditional architectural elements with a focus on the Rawshan. An online survey (n = 812) was conducted across Saudi Arabia complemented by interviews with expert decision-makers (n = 23) to (a) assess criteria such as privacy, aesthetics, daylight, ventilation, and energy consumption in Saudi residences and (b) investigate the level of acceptance of an optimized retrofitted Rawshan design.

In this research, the Poisson’s ratio of three different types of almost isotropic interpenetrating composites are designed to be either positive, or negative, or zero. As they are strengthened by a self-connected fibre-network and do not contain any pore in their structure, they all are stiffer than the conventional particle composites. In addition, structural hierarchy is also demonstrated to be able to significantly enhance the auxetic behaviour for the three types of interpenetrating composites. Thus, these composites could be used not only as functional materials, but also as structural materials in engineering applications.

Air quality and energy consumption are among the top ten environmental priorities in seaports as stated by the European Sea Ports Organization. Globally, it is estimated that 15% of energy consumption can be attributed to refrigeration and air conditioning systems in fishing activities. There is a real need to understand energy usage in fishery ports to help identify areas of improvements, with a view to optimize energy usage and minimize carbon emissions. In this study, we elaborate on ways in which a simulation capability can be developed at the community level with a fishery port, using a real-world case study seaport in Milford Heaven (Wales, UK). This simulation-based strategy is used to investigate the potential of renewable energy, including local solar farms, to meet the local power demand. This has informed the development of a simulation-based optimization strategy meant to explore how smart energy communities can be formed at the port level by integrating the smart grid with the local community energy storage. The main contribution of the paper involves a co-simulation environment that leverages calibrated energy simulation models to deliver an optimization capability that (a) manages electrical storage within a district an environment, and (b) promotes the formation of energy communities in a fishery port ecosystem. This is paving the way to policy implications, not only in terms of carbon and energy reduction, but also in the formation and sustained management of energy communities.

The low temperature oxidation of n-pentane with nitric oxide (NO) addition has been investigated at 500–800 K in an atmospheric jet stirred reactor (JSR). The molar fraction of NO in the mixture is varied between 0 to 1070 ppm to study its chemical sensitization effect on low temperature oxidation of both fuel lean and rich n-pentane/oxygen mixtures. N-pentane, O2, CO, CO2, CH2O, C2H4, CH3CHO, NO, and NO2 are quantified simultaneously, in-situ by using an electron impact molecular beam mass spectrometer (MBMS), a micro-gas chromatograph (µ-GC), and a sensitive mid-IR dual-modulation faraday rotation spectrometer (DM-FRS). The experimental results reveal that NO addition delays the onset temperature of low temperature oxidation of n-pentane between 550–650 K, but reduces the negative temperature coefficient (NTC) behavior in the NTC region (650–750 K) and dramatically shifts the onset of high temperature fuel oxidation to an intermediate temperature (750–800 K). A recently developed n-pentane/NOx model by using Reaction Mechanism Generation (RMG) and a new n-pentane/NOx model in the present work were used to predict the experimental results. The results show that the three distinct temperature-dependent characteristics of NO sensitized n-pentane oxidation are captured appropriately by these two models at both fuel rich and lean conditions, while the onset temperature of low temperature oxidation is not accurately predicted by these two models. It shows that the RMG model has a better prediction of the onset delay of n-pentane oxidation than Zhao's model, while Zhao's model performs better at NTC and intermediate temperature regions. Besides RO2 + NO, additional fuel/NOx reaction pathway, like R + NO2, RO + NO, and RO + NO2, and the interconversion reactions among NO, NO2, and HONO may need to be further studied.