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Urbanisation is leading to a concentration of growing city populations that contribute significantly to economic growth, while becoming epicentres of waste generation, greenhouse gas emissions, and food consumption. Nutrient smart city circular economy is currently an understudied intersection of growing city populations of food consumers, nutrient recovery technologies, Internet of Things (IoT), and agriculture. Meanwhile, machine learning has exploded with popularity over the years, with many circular economy literatures examining its usefulness in its predictive qualities to support management, optimisation, and recovery of useful resources from organic waste. This review paper examines advancements in machine learning for macronutrient recovery in city organic waste systems for a circular economy. The use of ML will greatly improve the scalability, transparency, productivity and accuracy of nutrient: recovery technologies, logistics, dissemination, and reuse. ML can also be combined with hardware to automate tedious waste separation, recovery and agricultural tasks using drones, hydroponics and satellites. Meanwhile, crop yields, nutrient demand-supply efficiencies, food security, environmental soil monitoring, and prosumer involvement could all increase. However, ML applications for urine, anaerobic digestion and prosumer economics are lacking. [Display omitted] • Lack of ML urine, membrane-AD, and prosumer studies for nutrient recovery. • IoT hardware, drones, democratised data sharing to address ML underfitting. • Lack of digital twins; CE BIM; good technologies and economics were barriers. • ML could increase transparency and food security for building consumer trust. • ML IoT to predict nutrients for soil, hydroponics, water treatment and farming. [ABSTRACT FROM AUTHOR]

The European Commission (EC) has launched the European Green Deal communication, setting out the path for a fundamental transformation of Europe. Key element in this policy is a fully sustainable food system outlined in the farm-to-fork strategy. Such strategy requires a systems approach in which all aspects related to the production and consumption of sufficient and healthy food are considered, including economic, environmental (climate, ecosystems) and social aspects. Here, we present the systems approach concept for food production, following the farm-to-fork principle as embraced by the EC, and elaborate on how digitalisation and Artificial Intelligence (AI) can solve the challenges that a sustainable food system imposes. We present a number of research and innovation challenges and illustrate these by some specific examples. It is concluded that AI and digitalisation show great potential to support the transition towards a sustainable food system. This development will impact the roles and interactions of the actors in the entire value chain from farmers to consumers. Policy recommendations are made for a successful future implementation of AI in sustainable food production. • System approach is needed to achieve sustainable food systems. • AI and digitalisation are key to the transition towards sustainable food systems. • Data availability and use is important for actors in all stages from farm to fork. • Policy recommendations for a successful AI implementation in food systems. [ABSTRACT FROM AUTHOR]

Decoupling the physical world and providing standardized service interfaces is still challenging when developing Location Based Services (LBS). This lack also hinders the possibility of developing Intelligent services on top of LBS architectures. In this paper, we propose a multi-layer Digital Twin-based architecture that aims to enable the development of machine learning-based Intelligent LBS (I-LBS) that are able to adapt, evolve, and perform Continual Learning (CL). The platform uses Digital Twins to ensure physical abstraction and provide cyber–physical knowledge to the I-LBSs, which is defined as an execution graph of operation modules. Finally, we simulated a use-case for this platform in the complex scenario of Healthcare organization and management where the I-LBS classifies allowed/not allowed trajectories of users inside a real-existing hospital scenario depending on their role in the organization. The use case is implemented as a Deep Learning-based reconstruction task of high-resolution trajectories processed by the DT architecture that also deploys the I-LBS. The platform is evaluated in terms of physical complexity and computational time on the DT side and on both a traditional machine learning setting and a replay-based CL one for the intelligence side to demonstrate the flexibility and adaptability features introduced by the components for dynamic or unseen scenarios. [ABSTRACT FROM AUTHOR]

Agriculture industry faces the challenge of increasing productivity by 50% from 2012 to 2050 while reducing water usage, given that it currently consumes 69% of the world's freshwater. To achieve this goal, smart technologies such as Artificial Intelligence (AI), Digital Twins (DT), and Internet of Things (IoT) are being increasingly utilized. However, the use of DT in agriculture is still in its early stages. This study proposes a smart irrigation framework inspired by digital twins in an application domain. The irrigation framework's sensors and actuators are linked to their virtual counterparts to create a digital twin. The IoT platform collects, aggregates, and processes data to determine daily irrigation requirements, and the behavior of the irrigation system is simulated. The proposed framework has two main advantages: evaluating the behavior of the digital twin and IoT platform in the context of agriculture before integrating them into the field and comparing various irrigation methods with current farming methods. By providing farmers with information about soil, weather, and crops, the system has the potential to improve farm operations and reduce water consumption. • Development of IoT platform for field tests collecting meteorological and soil data. • Establishing a virtual space through Digital Twin to simulate acquired factors. • Developing an intelligent control system to make decisions about irrigation needs. • Systematical assessment of the proposed approach through real-time experiments. [ABSTRACT FROM AUTHOR]

The current Industry 4.0 revolution promotes the use of cyber–physical systems to enhance manufacturing and other industrial processes via automation, real-time analysis, etc. Data communication between individual systems plays an important role in this revolution's success. As defined by researchers, Digital Twin is the digital representation of a physical system that enables predictive maintenance. Due to the increase in environmental pollution, battery-powered electric vehicles (EVs) are regarded as the urgent solution to internal combustion engines in the transportation business, despite obstacles such as safety concerns and range estimation. State of Health (SoH) and State of Charge (SoC) are two battery metrics that, when precisely anticipated, permit safer and longer battery use. Predicting these parameters online is computationally and financially expensive. Alternately, some of these factors could be predicted in the cloud rather than on the vehicle, hence cutting costs. Consequently, the EV business is one example where cloud-to-vehicle data connection saves total costs. A digital twin for an EV battery would aid in the estimate of battery parameters for predictive maintenance. This paper presents a Digital Twin paradigm for EV battery management in which SoH is predicted in the cloud and SoC is estimated on-vehicle. A continuous learning method is also proposed for forecasting SoH, whereas the Kalman filter is used to estimate SoC. The proposed framework predicts the SoH with a mean square error of 0.022. [ABSTRACT FROM AUTHOR]

[Display omitted] • Discusses the concept of building performance simulation versus new digital trends. • Reviews Artificial Intelligence, Machine Learning, Digital Twins, Cyber-Physical Systems, Internet of Things, Data Mining. • Explores common notions about differences, similarities, strengths and limitations of each concept. • Reviews emerging work on the interface between these concepts. • New concepts enjoy traction and energize the building performance research area. In an increasingly digital world, there are fast-paced developments in fields such as Artificial Intelligence, Machine Learning, Data Mining, Digital Twins, Cyber-Physical Systems and the Internet of Things. This paper reviews and discusses how these new emerging areas relate to the traditional domain of building performance simulation. It explores the boundaries between building simulation and these other fields in order to identify conceptual differences and similarities, strengths and limitations of each of these areas. The paper critiques common notions about these new domains and how they relate to building simulation, reviewing how the field of building performance may evolve and benefit from the new developments. [ABSTRACT FROM AUTHOR]

• Construction of the intelligent pipeline is still in the initial stage. • Building digital twin pipelines is an inevitable trend. • Integrating data and mechanisms is a difficulty. • The internet of Things, big data, and multi-objective optimization are crucial. • Need to establish unified intelligent pipeline standard system and data platform. The pipeline is one of the important carriers in the petroleum industry, but facing with the development bottlenecks of low efficiency, high cost and high decision risk. With machine learning and advanced sensor, new technological breakthroughs are being opening up for intelligent pipeline systems to benefit. Nowadays, the intelligent transformation of pipeline is still in the initial stage. To accelerate this process, this paper elaborates the research on intelligent pipeline systems from the perspective of the full life cycle of pipeline. The life cycle is divided into six stages, including exploration, feasibility study, design, construction, operation and scrap. In each stage, the related theoretical research and key technologies have been systematically reviewed. It is observed that full digital construction and operation is an inevitable trend. The Internet of Things, big data, and multi-objective optimization are critical technologies. Finally, some future directions are put forward to prompt a pipeline into intelligence. [ABSTRACT FROM AUTHOR]

• An energy management tool is proposed to coordinate multi-vector energy systems. • Control and machine learning modules enable optimised operation of assets. • Real-time IoT integration with energy networks as part of a smart city is discussed. • A digital twin for design and real-time parallel evaluation is proposed. • The ability of the tool to handle multi-vector district level problems is illustrated. As Internet of Things (IoT) technologies enable greater communication between energy assets in smart cities, the operational coordination of various energy networks in a city or district becomes more viable. Suitable tools are needed that can harness advanced control and machine learning techniques to achieve environmental, economic and resilience objectives. In this paper, an energy management tool is presented that can offer optimal control, scheduling, forecasting and coordination services to energy assets across a district, enabling optimal decisions under user-defined objectives. The tool presented here can coordinate different sub-systems in a district to avoid the violation of high-level system constraints and is designed in a generic fashion to enable transferable use across different energy sectors. The work demonstrates the potential for a single open-source optimisation framework to be applied across multiple energy vectors, providing local government the opportunity to manage different assets in a coordinated fashion. This is shown through case studies that integrate low-carbon communal heating for social housing with electric vehicle charge-point management to achieve high-level system constraints and local government objectives in the borough of Greenwich, London. The paper illustrates the theoretical methodology, the software architecture and the digital twin-based testing environment underpinning the proposed approach. [ABSTRACT FROM AUTHOR]