Graph Theoretic Approaches to Understand Resilience of Complex Systems

Modern society is critically dependent on a network of complex systems for almost every social and economic function. While increasing complexity in large-scale engineered systems offer many advantages including high efficiency, performance and robustness, it inadvertently makes them vulnerable to unanticipated perturbations. A disruption affecting even one component may result in large cascading impacts on the entire system due to high interconnectedness. Large direct and indirect impacts across national and international boundaries of natural disasters like Hurricane Katrina, infrastructure failures like the Northeast blackout, epidemics like the H1N1 influenza, terrorist attacks like the 9/11, and social unrests like the Arab Spring are indicative of the vulnerability associated with growing complexity. There is an urgent need for a quantitative framework to understand resilience of complex systems with different system architectures. In this work, a novel framework is developed that integrates graph theory with statistical and modeling techniques for understanding interconnectedness, interdependencies, and resilience of distinct large-scale systems while remaining cognizant of domain specific details. The framework is applied to three diverse complex systems, 1) Critical Infrastructure Sectors (CIS) of the U.S economy, 2) the Kalundborg Industrial Symbiosis (KIS), Denmark and 3) the London metro-rail infrastructure. These three systems are strategically chosen as they represent complex systems of distinct sizes and span different spatial scales. The framework is utilized for understanding the influence of both network structure level properties and local node and edge level properties on resilience of diverse complex systems. At the national scale, application of this framework on the U.S. economic network reveals that excessive interconnectedness and interdependencies among CIS significantly amplify impacts of targeted disruptions, and negatively influence its resilience. At the regional scale, analysis of KIS reveals that increasing diversity, redundancy, and multi-functionality is imperative for developing resilient and sustainable IS systems. At the urban scale, application of this framework on the London Metro system identifies stations and rail connections that are sources of functional and structural vulnerability, and must be secured for improving resilience. This framework provides a holistic perspective to understand and propose data-driven recommendations to strengthen resilience of large-scale complex engineered systems

Lifecycle cost analysis (LCCA) of tailor-made building integrated photovoltaics (BIPV) façade: Solsmaragden case study in Norway

In dense urban areas, the use of building integrated photovoltaics (BIPV) façades are becoming popular and they are bringing many advantageous along with the energy-saving features. However, at the same time, they raise tensions in capital investments and overall returns. “Solsmaragden” is one of such a commercial building, that is integrated with BIPV façade with the peak power of 127.5 kW and owned by Union eiendomsutvikling AS in Norway. In this paper, a lifecycle cost analysis (LCCA) of BIPV façade integrated to “Solsmaragden” is investigated based on on-field recorded data after four years of operation (2016–2019). While formulating LCCA, numerous benefits from system power generation, societal and environmental benefits, and financial gains due to three different end-of-life material recovery approaches were also considered. The result based on the field monitored performance showed that the net present value (NPV), discounted payback period, internal rate of return and levelised cost of energy of the system is equal to 478,934 NOK, 22 years, 6% and 1.28 NOK/kWh, respectively. It is observed that the BIPV system as a building envelope material for different orientations of the building skin could reimburse not only all the investment costs but also become a source of income for the buildings. The results also illustrated that the granted subsidy is substantially covering the societal and environmental benefits of this project.publishedVersio

Distributed energy resources and the application of AI, IoT, and blockchain in smart grids

Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability

Improving Overall Equipment Effectiveness by Enabling Autonomous Maintenance Pillar for Integrated Work Systems

Integrated Work System (IWS) and Overall Equipment Effectiveness (OEE) are two popular approaches used by production firms to identify and eliminate production losses. In a highly competitive business environment, companies must increase their efficiency in the manufacturing process to support resilient business continuity. While OEE is widely used as a quantitative tool for measuring the performance of total productive maintenance (TPM), the IWS approach integrates equipment, processes, and involvement of people into a unified approach to reduce costs, improve quality, and increase productivity. Principally, there is an alignment between the two concepts. The IWS has the potential to maximize OEE to eliminate equipment failure and defects, minimize downtime, and maximize productivity with less time, effort, and waste. The purpose of this work is to compare the performance of the OEE with the implementation of the IWS pillar, i.e., autonomous maintenance (AM). The rollout of the AM pillar was carried out on the two identical packaging machines (HLP1) with a speed of 120 packets per minute. The data which is shown in this paper is for both machines during the operational hours. Finally, the analysis showed positive results for both machines within a five-month period, with an increase of 27% and 15% in OEE, respectively. Later in the discussion, the root cause and SWOT analysis were perused for OEE and TPM, respectively, in this paper

Masks for COVID-19.

Sustainable solutions on fabricating and using a face mask to block the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread during this coronavirus pandemic of 2019 (COVID-19) are required as society is directed by the World Health Organization (WHO) toward wearing it, resulting in an increasingly huge demand with over 4 000 000 000 masks used per day globally. Herein, various new mask technologies and advanced materials are reviewed to deal with critical shortages, cross-infection, and secondary transmission risk of masks. A number of countries have used cloth masks and 3D-printed masks as substitutes, whose filtration efficiencies can be improved by using nanofibers or mixing other polymers into them. Since 2020, researchers continue to improve the performance of masks by adding various functionalities, for example using metal nanoparticles and herbal extracts to inactivate pathogens, using graphene to make masks photothermal and superhydrophobic, and using triboelectric nanogenerator (TENG) to prolong mask lifetime. The recent advances in material technology have led to the development of antimicrobial coatings, which are introduced in this review. When incorporated into masks, these advanced materials and technologies can aid in the prevention of secondary transmission of the virus

Sustainable biosurfactant production from secondary feedstock—recent advances, process optimization and perspectives

Biosurfactants have garnered increased attention lately due to their superiority of their properties over fossil-derived counterparts. While the cost of production remains a significant hurdle to surpass synthetic surfactants, biosurfactants have been anticipated to gain a larger market share in the coming decades. Among these, glycolipids, a type of low-molecular-weight biosurfactant, stand out for their efficacy in reducing surface and interfacial tension, which made them highly sought-after for various surfactant-related applications. Glycolipids are composed of hydrophilic carbohydrate moieties linked to hydrophobic fatty acid chains through ester bonds that mainly include rhamnolipids, trehalose lipids, sophorolipids, and mannosylerythritol lipids. This review highlights the current landscape of glycolipids and covers specific glycolipid productivity and the diverse range of products found in the global market. Applications such as bioremediation, food processing, petroleum refining, biomedical uses, and increasing agriculture output have been discussed. Additionally, the latest advancements in production cost reduction for glycolipid and the challenges of utilizing second-generation feedstocks for sustainable production are also thoroughly examined. Overall, this review proposes a balance between environmental advantages, economic viability, and societal benefits through the optimized integration of secondary feedstocks in biosurfactant production

Graph theoretic approaches to understand resilience of complex systems

Modern society is critically dependent on a network of complex systems for almost every social and economic function. While increasing complexity in large-scale engineered systems offer many advantages including high efficiency, performance and robustness, it inadvertently makes them vulnerable to unanticipated perturbations. A disruption affecting even one component may result in large cascading impacts on the entire system due to high interconnectedness. Large direct and indirect impacts across national and international boundaries of natural disasters like Hurricane Katrina, infrastructure failures like the Northeast blackout, epidemics like the H1N1 influenza, terrorist attacks like the 9/11, and social unrests like the Arab Spring are indicative of the vulnerability associated with growing complexity. There is an urgent need for a quantitative framework to understand resilience of complex systems with different system architectures. In this work, a novel framework is developed that integrates graph theory with statistical and modeling techniques for understanding interconnectedness, interdependencies, and resilience of distinct large-scale systems while remaining cognizant of domain specific details. The framework is applied to three diverse complex systems, 1) Critical Infrastructure Sectors (CIS) of the U.S economy, 2) the Kalundborg Industrial Symbiosis (KIS), Denmark and 3) the London metro-rail infrastructure. These three systems are strategically chosen as they represent complex systems of distinct sizes and span different spatial scales. The framework is utilized for understanding the influence of both network structure level properties and local node and edge level properties on resilience of diverse complex systems. At the national scale, application of this framework on the U.S. economic network reveals that excessive interconnectedness and interdependencies among CIS significantly amplify impacts of targeted disruptions, and negatively influence its resilience. At the regional scale, analysis of KIS reveals that increasing diversity, redundancy, and multifunctionality is imperative for developing resilient and sustainable IS systems. At the urban scale, application of this framework on the London Metro system identifies stations and rail connections that are sources of functional and structural vulnerability, and must be secured for improving resilience. This framework provides a holistic perspective to understand and propose data-driven recommendations to strengthen resilience of large-scale complex engineered systems