A Discrete Process Modelling and Simulation Methodology for Industrial Systems within the Concept of Digital Twins

A generic well-defined methodology for the construction and operation of dynamic process models of discrete industrial systems following a number of well-defined steps is introduced. The sequence of steps for the application of the method as well as the necessary inputs, conditions, constraints and the results obtained are defined. The proposed methodology covers the classical offline modelling and simulation applications as well as their online counterpart, which use the physical system in the context of digital twins, with extensive data exchange and interaction with sensors, actuators and tools from other scientific fields as analytics and optimisation. The implemented process models can be used for what-if analysis, comparative evaluation of alternative scenarios and for the calculation of key performance indicators describing the behaviour of the physical systems under given conditions as well as for online monitoring, management and adjustment of the physical industrial system operations with respect to given rules and targets. An application of the proposed methodology in a discrete industrial system is presented, and interesting conclusions arise and are discussed. Finally, the open issues, limitations and future extensions of the research are considered.

Measuring Energy Efficiency Performance in China: Does Technological and Environmental Concerns Matter for Energy Efficiency?

Considering the significance of green governance in economic restructuring and the green technology revolution, this study examines the impact of China’s recent green governance policies and their implications in various regions; it also examines new models, methods, and development directions for China’s green governance in the future. Green governance efficiency and spatial patterns have been studied through 2008–2018 data using a three-stage generalized panel Data Envelopment Analysis (DEA) model, spatial autocorrelation model, spatial gravity model, and social network analysis. The study summarizes the status and role of each provincial region in green governance based on the social network of green governance efficiency under the network architecture. It concludes that (1) green governance efficiency in China’s provinces has a U-shaped trend, with non-managerial elements in the external environment masking genuine green governance efficiency. (2) During the study period, the overall efficiency of the industrial system improved. The efficiency of the manufacturing and wastewater stages has been substantially enhanced, but no significant gains were observed in the treatment stages of solid and gas waste. (3) Although China has made progress in enhancing the overall efficiency of its industrial system, there is still potential for improvement. (4) China has established a “three horizontal and two vertical grid-type” green governance spatial correlation structure among the sub-stages of the industrial system, and the radiation impact of major provincial areas would increase overall green governance efficiency.

Multimedia Computer-Aided Industrial System Design Based on the Study of Big Data Mining Algorithm

Aiming at the problems of the multimedia computer-aided industrial system, this paper puts forward the application of big data mining algorithm to multimedia computer-aided industrial system design and analyzes in detail the impact of multimedia technology on industrial quality. This paper introduces the advantages of using big data mining algorithm in multimedia computer technology course, shows the operating environment to be met by using the multimedia computer-aided industrial system, follows the guiding principles of the overall design learning theory and artistic conception cognition theory, supplements specific industrial examples, and discusses multimedia industrial design.

Teaching Short - Circuit Calculation with Off - Nominal Turns Ratio Transformers

Teaching short-circuit analysis is conducted primarily through case studies; however, there are not many validated short-circuit studies available on the subject, especially when considering off-nominal turns ratio transformers. In order to improve the teaching of short-circuit analysis, a three phase short-circuit study in an industrial system according to ANSI/IEEE standards by means of Zmatrix method is presented; two case studies are considered: the industrial system with nominal and offnominal turns ratio transformers, in both cases the step by step solution is given in an explicit manner and the analytical results are validated through software simulation.

Diagnostic and Prognostic Models for Predictive Maintenance: Multi-Criteria Comparative Analysis

Predictive maintenance has evolved considerably over the past two decades making this strategy an effective way to monitor the operation of industrial systems, thereby predicting its future states and remaining lifespan. It is therefore developed through a process that begins with the collection of information from the industrial system, the objective of which is its diagnosis or / and its prognosis. This article presents an analysis of single-model and multi-model approaches to the effect of diagnostic and prognostic tasks. This analysis is based on a multi-criteria comparison of the different models in order to provide a clear vision to choose the appropriate approach for predictive maintenance. The relevance of the comparative study is argued by the development of criteria directly impacting performance, reliability, efficiency and mutual cooperation between models. Conclusions are then drawn, in order to identify the appropriate diagnostic and prognostic approach for predictive maintenance.

Dynamic Green Growth Assessment of China’s Industrial System with an Improved SBM Model and Global Malmquist Index

This study proposes a method for resource management and optimisation in the industrial sector of China. Differing from previous research on the green assessment of industrial systems focusing on “black box” evaluation, our approach contributes to the two-stage structure of an industrial system that consists of an industrial production process and a pollution treatment process. The corresponding network slack-based model (SBM) is proposed to analyse the performance of China’s provincial industry sector. Based on our network SBM, the global Malmquist index is built to analyse the total factor productivity changes of system and individual processes to evaluate the consistency of sustainable development where dynamic green growth assessment is realized. The results show that the whole system and its pollution treatment process performance are poor and disorganised, while the industrial production process maintains a stable ranking for the 30 regions in China. We find that the main cause of this phenomenon is the variable technical efficiency change in the 30 regions, which reflects the immaturity of the management of the pollution treatment process. System performance is also highly related to regionalism.

Evaluation of regional industrial cluster innovation capability based on particle swarm clustering algorithm and multi-objective optimization

With the progress of the times and the development of science, industrial clusters have been regarded by all countries in the world as one of the important ways to enhance regional competitiveness, and become an inevitable trend of industrial development. The research on the innovation ability of industrial clusters can not only maintain sustainable development of industrial clusters and obtain sustained competitive advantages, but also provide reference for the government's policy formulation of industrial clusters. This paper aims to study the evaluation of regional industrial clusters' innovation capability based on particle swarm clustering and multi-objective optimization. This paper uses the theory of industrial cluster innovation and takes regional industrial system as the empirical research object to establish a regional industrial system capability evaluation system, which is based on the selection of indicators, combined with analytic hierarchy process and factor analysis to evaluate industrial innovation capability. On this basis, the particle swarm clustering theory is used to verify the innovation ability and evaluation index system of industrial clusters, and provide a reference for the evaluation of the innovation ability of industrial clusters. This paper divides the regional cluster innovation capability into four aspects: innovation input capability, environment support capability, self-development capability and innovation output capability, and systematically analyzes the key elements and in the composition of innovation elements and their relationships. It then constructs the evaluation index system of regional cluster innovation capability. At the same time, this paper introduces clustering analysis algorithm and swarm intelligence algorithm into regional innovation evaluation, combines particle swarm optimization algorithm and K-means clustering algorithm, and optimizes particle swarm clustering algorithm by adjusting adaptive parameters and adding fitness variance. The experimental results of this paper show that from the results of the tested innovation potential of the three industrial clusters, industrial cluster F has the strongest innovation ability, with an evaluation coefficient of 0.851, followed by industrial cluster F, which has a value of 0.623. This result is consistent with the actual innovation status of the selected industry. From this point of view, the established particle swarm clustering model for evaluating the innovation capability of regional industrial clusters is reliable and can be used to evaluate the innovation capability of different industrial clusters.