Accident prevention is one of the most desired and challenging goals in process industries. For accident prevention, fault detection and diagnosis (FDD) is critical. FDD has been an active area of ...research for decades. The focus of the current review is on the data-driven techniques as we are now in a digital era and data analytics is getting more emphasis in all areas including process industries. The analysis is done to address the following fundamental questions: (i) How are the leading areas evolving? (ii) Who are the contributing authors? (iii) What are the key sources and domains of publications? (iv) Which countries are active in this research area? Furthermore, we briefly described four techniques, principal component analysis, partial least-squares, independent component analysis, and the Gaussian mixture model, to represent the state-of-the-art algorithms from different periods. It was observed that significant work in this field is being carried out throughout the world, including both developed and the developing countries. China is emerging as the leading contributor to the total number of publications while Singapore is the country with the highest per-capita publication. Finally, the link between different types of publications, especially between the engineering journals and the industrial journals, is growing. This indicates that these techniques are gaining industrial importance. It can be concluded that the data-based process monitoring is developing rapidly and being applied in process industries; nevertheless, the pace of application in the process industries is not at par with the pace of theoretical development.
The article considers the evolution of the general education goals in particular physics education, changes through this process the understanding of the subject «Physics» content. Soviet education ...aimed at preparing a comprehensively harmonically developed personality through the knowledge paradigm and the content of education was fully consistent with this goal. Later it takes place a rethinking approaches of understanding the content of education and the content of a subject. At the present goals of education are represented in the form of educational results: personal, subject and metasubject, which implies a change in the content of the subject «Physics» in the direction of increasing the importance of methodological, interdisciplinary, evaluative knowledge and strengthening the procedural component. This trend reflects in the textbooks: the procedural component is expanded, there are problems and tasks that involve conducting experimental research in the classroom and at home, solving creative tasks, and completing educational projects. The article substantiates the role of the physics textbook in improving educational outcomes, provides examples of the organization of educational content in school textbooks of physics in Russia and Singapore.
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•A distributed renewable energy system with heterogeneous end-users is proposed.•Demand flexibility of different sectors is reflected in system modelling.•Interaction among various ...users is studied with non-cooperative game.•Uncertainty of renewable generation is handled with receding horizon optimization.•Smart contracts and decentralized identifiers are implemented with blockchain.
This paper studies the design and management of a distributed energy system incorporating renewable energy generation and heterogeneous end-users from residential, commercial and industrial sectors. A hierarchical framework is first proposed for the energy demand side management through peer-to-peer exchange of energy information in the real-time market. The heterogeneity of demand flexibility associated with different types of users is reflected in the framework with inter-sectorial interactions modelled by a non-cooperative game. The inherent uncertainty of renewable generation is also considered with the technique of receding horizon optimization. Subsequently, smart contracts and decentralized identifier guaranteed by blockchain technologies are implemented to create a seamless, secured and efficient distributed energy system. Finally, a Singapore-based case study is conducted and the results show that with effective interactions among participants, electricity consumption of the entire energy system closely tracks the generation pattern of renewable resources, resulting in a significantly flattened schedule of grid electricity procurement.
Recycling water from municipal wastewater offers a reliable and sustainable solution to cities and regions facing shortage of water supply. Places including California and Singapore have developed ...advanced water reuse programs as an integral part of their water management strategy. Membrane technology, particularly reverse osmosis, has been playing a key role in producing high quality recycled water. This feature paper highlights the current status and future perspectives of advanced membrane processes to meet potable water reuse. Recent advances in membrane materials and process configurations are presented and opportunities and challenges are identified in the context of water reuse.
Municipal solid waste (MSW) management is becoming a serious issue in all over the world. Anaerobic digestion (AD) is one of the technologies to convert that waste into useful form of energy. But ...megacities like Singapore having limited resources, cannot fulfill the cow dung or other animal manure requirements in AD. Therefore there is a need to study critically the operating parameters and also the pretreatment technologies available for treating the substrate so that one can get the maximum output with limited input. To fulfill the need, the present paper deals with the review of various operating parameters and their effects on AD. This paper also reviews different pretreatment methods including mechanical, thermal, chemical and biological methods to improve the effectiveness of AD of MSW.
Abstract
Most countries have implemented restrictions on mobility to prevent the spread of Coronavirus disease-19 (COVID-19), entailing considerable societal costs but, at least initially, based on ...limited evidence of effectiveness. We asked whether mobility restrictions were associated with changes in the occurrence of COVID-19 in 34 OECD countries plus Singapore and Taiwan. Our data sources were the
Google Global Mobility Data Source,
which reports different types of mobility, and COVID-19 cases retrieved from the dataset curated by
Our World in Data.
Beginning at each country’s 100th case, and incorporating a 14-day lag to account for the delay between exposure and illness, we examined the association between changes in mobility (with January 3 to February 6, 2020 as baseline) and the ratio of the number of newly confirmed cases on a given day to the total number of cases over the past 14 days from the index day (the potentially infective ‘pool’ in that population), per million population, using LOESS regression and logit regression. In two-thirds of examined countries, reductions of up to 40% in commuting mobility (to workplaces, transit stations, retailers, and recreation) were associated with decreased cases, especially early in the pandemic. Once both mobility and incidence had been brought down, further restrictions provided little additional benefit. These findings point to the importance of acting early and decisively in a pandemic.
•Traces the history of downtown congestion pricing.•Describes systems in Singapore, London, Stockholm, Milan and Gothenburg.•Outlines proposals for future systems.•Identifies four recurring themes.
...“Downtown congestion pricing” (DCP) is what this paper calls the practice of relieving congestion by charging tolls, or requiring the purchase of supplementary licenses, to drive in a city’s central areas. Five major cities have implemented DCP: Singapore, London, Stockholm, Milan and Gothenburg. This paper reviews the history of DCP, reporting on each system’s implementation, design, transportation impacts and finances. In addition, the paper provides historical context, discusses four recurring themes and outlines plans for future systems.
•LCA carried out for plastic waste recycling/recovery options in Singapore.•Combination of options considered capacities and sizes of recycling plants available.•Total of 8 scenarios were compared ...for 822,200 tonnes of plastic waste.•Waste options included mechanical recycling, pyrolysis and gasification.•Normalization and weighted scores done to select best option.
In Singapore, 822,200 tonnes of plastic waste was generated in 2016, with only 7% recycled. Due to the complex nature of plastic waste mixtures, mechanical recycling is often inefficient, leading to a majority of waste being incinerated. In this article, alternative solutions are introduced to address the waste problem, such as recovering valuable fuels from plastics via thermochemical methods. Life cycle assessment (LCA) was adopted to investigate 8 scenarios of plastic waste management options. In a nation with land scarcity, the scales and sizes of each plastic waste recycling/recovery method is also taken into consideration. The results demonstrated how different combinations of four plastics valorization technologies, and associated capacities, affected the potential environmental benefits and drawbacks of plastic waste treatment systems. In order to enable selecting the best option among the 8 scenarios, normalization and weighting was carried out.
•An end-to-end deep learning framework based on attention sequence to sequence model is proposed to predict the short-term passenger flow in large-scale metro system.•The proposed framework ...outperforms other baselines in multiple look-ahead time windows prediction.•Seq2seq-attention based architecture is more effective in capturing long-range dependencies.•The proposed frame has good scalability and it can be generalized to other similar application scenarios.
The accurate short-term passenger flow prediction is of great significance for real-time public transit management, timely emergency response as well as systematical medium and long-term planning. In this paper, we propose an end-to-end deep learning framework that can simultaneously make multi-step predictions for all stations in a large scale metro system. A sequence to sequence model embedded with the attention mechanism forms the backbone of this framework. The sequence to sequence model consists of an encoder network and a decoder network, making it good at modeling sequential data with varying lengths and the attention mechanism further enhances its ability to capture long-range dependencies. We use the proposed framework to predict the number of passengers alighting at each station in the near future, given the number of passengers boarding at each station in the last few short-term periods. The large quantities of real-world data collected from Singapore’s metro system are used to validate the proposed model. In addition, a set of comparisons made among our model and other classical approaches evidently indicates that the proposed model is more scalable and robust than other baselines in making multi-step and network-wide predictions for short-term passenger flow.
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