Editorial, Vol. 6, No. 2 De Gloria, Alessandro
International journal of serious games,
06/2019, Letnik:
6, Številka:
2
Journal Article
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This edition of the International Journal of Serious Games is mostly dedicated to a special issue on “Gamification of Industrial Systems”, guest edited by Samir Garbaya and Theo Lim. The special ...issue aims at investigating new emerging needs for gamification in industry. The issue features six articles, that address important challenging issues through conducting pilot studies and developing gamified industrial applications. The issue is completed by a regular paper by Batista et Al.
This paper aims to present a guideline for modeling and normative instructions for short-circuit (SC) and transient recovery voltage (TRV) analysis of medium voltage circuit-breakers installed in a ...real industrial system. The Alternative Transient Program (ATP) through its graphical interface ATPDraw was used in the work. The criteria used for modeling the analyzed industrial system are presented in detail. For a better understanding of the technical information required for the modeling employed in TRV and SC studies, a set of detailed and typical data used is presented in order to contribute to the reproduction of this case study in other expanding industrial power systems. The instructions for SC and TRV evaluation of circuit-breakers are based on the limits described by IEC standards and other references. The results are presented by discussing the effects of industrial expansion on the evaluated medium-voltage circuit breakers.
•Normative modeling criteria for SC and TRV studies based on detailed real data.•Significant changes in industrial systems justify the updating of SC and TRV studies.•The violation of TRV/RRRV limits of CB is not only linked to the SC power increase.
In recent years, environmental problems caused by industries in China have drawn increasing attention to both academics and policy makers. This paper assesses the environmental efficiency of Chinese ...regional industrial systems to come up with some recommendations to policy makers. First, we divided each Chinese regional industrial system into a production process and a pollutant treatment process. Then, we built a scientific input–intermediate–output index system by introducing a new network slacks-based model (NSBM) model. This study is the first to combine NSBM with DEA window analysis to give a dynamic evaluation of the environmental efficiency. This enables us to assess the environmental efficiency of Chinese regional industrial systems considering their internal structure as well as China’s policies concerning resource utilization and environmental protection. Hence, the overall efficiency of each regional industrial system is decomposed into production efficiency and pollutant treatment efficiency. Our empirical results suggest: (1) 66.7% of Chinese regional industrial systems are overall inefficient. 63.3 and of 66.7% Chinese regional industrial systems are inefficient in the production process and the pollutant treatment process, respectively. (2) The efficiency scores for the overall system and both processes are all larger in the eastern area of China than those of the central and western areas. (3) Correlation analysis indicates that SO₂ generation intensity (SGI), solid waste generation intensity, COD discharge intensity, and SO₂ discharge intensity have significantly negative impacts on the overall efficiency. (4) The overall inefficiency is mainly due to inefficiency of the pollutant treatment process for the majority of regional industrial systems. (5) In general, the overall efficiency was trending up from 2004 to 2010, indicating that the substantial efforts China has devoted to protecting the environment have yielded benefits.
This study aims to contribute to the sustainable development of blue growth in Europe via cascade biorefineries of native seaweed bioresources by investigating the economic viability and ...environmental sustainability of three conceptual macroalgal biorefinery systems that utilize endemic brown macroalgae Laminaria digitata, Fucus vesiculosus, and Saccharina latissima. The present study conceptualised lab and pilot scale demonstration trials conducted in the MAB4 research project as fully operational biorefinery systems producing food-grade fucoidan and laminarin via sequential extraction and producing feed supplement via side-stream valorisation. The economic analysis of the base case scenario (Part I) identified the membrane use and extraction efficiency as two critical techno-economic barriers for the biorefinery systems. In the improved technology scenarios (Part II), all systems demonstrated promising economic potentials. Over a 15-year project span, pilot scale systems (2–7 metric tons (t) dry matter (dm) feedstock/year) and industrial scale systems (900 t dm/year) obtained a net present value (NPV) of 20–506 k EUR and 186–454 Mio EUR, respectively. Feedstock costs and laminarin and fucoidan sales are the major cost and revenue drivers. A sensitivity analysis of the economic viability of industrial scale systems (Part III) identified the break-even price of 33, 52, and 67 EUR/kg dm feedstock for the F. vesiculosus, S. latissima, and L. digitata system, respectively. The net carbon footprint and net water footprint of the industrial scale systems range from 3.8 to 11 kg CO2eq./kg dm feedstock and from 0.1 to 0.2 m3 water/kg dm feedstock, respectively. Onsite energy consumption for product drying and process heating and the upstream energy use for membrane manufacturing dominate the system-level carbon footprints, with a respective share of 37–70% and 8–61%. Further development towards environmental sustainability can be achieved by internal process and system optimizations and externally by greening the electricity mix.
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•Process heating and product drying are two major energy and carbon hotspots.•Improvements to energy systems are key to enhancing environmental sustainability.•Extraction efficiency and membrane use are identified key techno-economic barriers.•Optimised industrial scale systems reach a net gain of 186–454 k EUR/15 years.•Break-even feedstock prices for industrial scale systems are 33–67 EUR/kg dm.
Integrating digital twin technology in Cyber–Physical Systems and Internet of Things can boost their intelligence. Given the current maturity of digital twin technology (yet in progress), improving ...the models that these systems use is typically achieved off-line, requiring the system to stop and reconfigure to run each new model version. In fact, most works use cloud back-ends to run heavy machine learning algorithms, imposing strict requirements on the data exchange between the physical system and the cloud.
We address the online improvement of digital twin models in cyber–physical systems by supporting model refinement without disrupting nor stopping the normal operation of the system. This improves the dynamicity of the system that may turn into a major competitive advantage in a number of industrial scenarios. Precisely, we exploit the collaborative expectation of next generation cyber–physical systems based on highly-connected cells enabled by 5G and 6G networking; and on top of these, we design a shared space properly managed to deliver the needed temporal behaviour required by cyber–physical systems.
For this, we present the design of CoTwin framework as a middleware that allows cells to collectively improve digital twin models seamlessly. CoTwin manages the interaction of cells with a blockchain-based collaborative space offering a built-in trusted storage model. We integrate neural network algorithms as they provide fast execution that meet the time-sensitivity requirements of cyber–physical systems. Our contribution is validated by means of its implementation and deployment on an actual blockchain network, and an exhaustive set of experiments to analyse the resulting overhead and temporal behaviour. Results show that CoTwin achieves stable execution times across all its functional pieces; and it exhibits stable service time for large sets of cells.
•Design of a shared space for collaborative training and improvement of digital twin models•A middleware to manage the trained digital twin models in the blockchain network•Formal specification of middleware, processes, and involved smart contracts•Framework that meets temporal requirements of cyber-physical systems and IoT•Experimental validation that yields low overhead of all relevant operations
In complex industrial systems, causal inference plays a crucial role in improving production and tracing faults. The causal inference of industrial systems encompasses two main steps. First, it aims ...to discover causal relationships between variables. Second, it also attempts to determine time delays between variables that are causally linked, caused by factors such as industrial responses and pipeline transport. However, existing causal inference methods have certain limitations when applied to complex process industrial systems with highly dynamic and noisy environments. Therefore we propose the Causal-Transformer, a causal model based on the deep structure of the transformer. In our model, a multi-head causal attention mechanism is designed to discovery causal relationships, remove indirect coupling effects in causality, and eliminate the effects of confounding factors in causality through a causal verification step. To the best of our knowledge, we propose for the first time a novel delay discovery method to address the dynamic delay problem caused by the environment. In summary, our approach addresses the problem of causal inference in complex industrial systems. We conducted extensive numerical experiments to verify the effectiveness of our model. Moreover, we applied the model to the actual polymerization process of polyester fiber, further validating its performance in a real industrial system.
Anomaly detection has attracted considerable attention from the research community in the past few years due to the advancement of sensor monitoring technologies, low-cost solutions, and high impact ...in diverse application domains. Sensors generate a huge amount of data while monitoring the physical spaces and objects. These huge collected data streams can be analyzed to identify unhealthy behaviors. It may reduce functional risks, avoid unseen problems, and prevent downtime of the systems. Many research methodologies have been designed and developed to determine such anomalous behaviors in security and risk analysis domains. In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas. We focus on the studies published from 2000 to 2018 in the application areas of intelligent inhabitant environments, transportation systems, health care systems, smart objects, and industrial systems. We have identified a number of research gaps related to the data collection, the analysis of imbalanced large datasets, limitations of statistical methods to process the huge sensory data, and few research articles in abnormal behavior prediction in real scenarios. Based on our analysis, researchers and practitioners can acquaint themselves with the existing approaches, use them to solve real problems, and/or further contribute to developing novel techniques for anomaly detection, prediction, and analysis.
Reliability and safety are two important concepts in industrial applications. Thus, the development of monitoring tools, which are able to ensure the continuity of service by predicting faults, ...should improve competitiveness. This paper presents two probabilistic methods based on hidden Markov models (HMMs) for the prediction of impending faults. This paper shows that a prediction of faults is not limited to the estimation of the remaining useful life but is also extended to the estimation of the risk of an imminent appearance of faults in the future. The first method consists in modeling the degradation process of the studied system by a single HMM. A probabilistic model is proposed to predict an imminent appearance of a fault. The second method consists in modeling the degradation states by a set of HMMs. Another probabilistic model is proposed to predict an imminent appearance of a fault. An experimental application is proposed to demonstrate their applicability. The obtained results show their effectiveness to predict the imminent appearance of faults.
•We arrived at the conclusion that the neo-fuzzy neuron is a tool offering great advantages for the modeling of complex systems thanks to the simplicity of its structure that consists of a single ...neuron.•The adaptive-neuro fuzzy inference system needs to change the number of layers, the number of neurons in each layer, and the membership function to find the structure hat gives a good fit for the problem to model.•The artificial ants algorithm makes possible to highlight classes grouping similar observations. This improves classification and allows the identification of the degradation states.
As they are a part of the energy transmission chain, bearings are considered as important mechanical components in rotating machines. This importance requires a special attention in order to avoid expensive production shutdown due to the appearance of failures. It is therefore necessary to anticipate the appearance of faults by implementing an appropriate prediction model. There exist in the literature several examples of prediction models able to estimate the remaining useful life (RUL) of bearings. These models are based on the principle of the long-term prediction without considering the degradation state of bearings or by defining a degradation threshold arbitrarily. It should be noted that, in addition to a reliable prediction model, identifying the degradation states is an important parameter in the estimation of the RUL. The presented paper proposes a particular approach based on the artificial intelligence (AI) principle. The proposed approach is composed of two model-based AI inspired by the reasoning of the human for the RUL estimation. This reasoning is modeled via the neural networks for time-series prediction. These models are called the adaptive-neuro fuzzy inference system and the neo-fuzzy neuron. The proposed approach is also composed by a third model-based AI. This model is inspired by the behavior of ants to identify the different degradation states of bearings. The combination of these two types of AI provides reliable and robust prediction results.
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