A Collaborative Optimization Strategy for Energy Reduction in Ironmaking Digital Twin

Zhou, Heng; Yang, Chunjie; Sun, Youxian

doi:10.1109/access.2020.3027544

Cited by 19 publications

(6 citation statements)

References 34 publications

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“…However, the difference is that in the manufacturing industry, the improvements in economic benefits brought about by digital twin applications are more evident, specifically in terms of lower production costs, reduced energy consumption, etc. (Zhou et al, 2020). In construction, although digital twin applications can lead to increased energy efficiency, whether they are effective in reducing construction costs is still to be investigated.…”

Section: Application and The Main Functionsmentioning

confidence: 99%

Digital twin for product versus project lifecycles’ development in manufacturing and construction industries

Abanda,

Jian,

Adukpo

et al. 2024

J Intell Manuf

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Digital twin, as an important enabling tool for digital transformation, has received increasing attention from researchers and practitioners since its definition was formalised. Especially in the global context and exacerbated by Covid-19, the applications of the digital twin have offered opportunities for many industries. While the digital twin has already been widely used in many sectors such as manufacturing and the construction industry—one of the key engines of economic development, is still lagging behind many other sectors. This study uses the systematic literature review to assess the applications of digital twin in manufacturing and construction respectively, the benefits it brings, and the impediments to its application. Based on this, a comparison is made of digital twin applications in the manufacturing and construction industries to draw lessons. This study concluded that although the use of digital twin in manufacturing is better than construction overall, it is still not reaching its full potential. Despite many benefits brought by the digital twin to construction during the project lifecycle, the construction sector faces even greater challenges than manufacturing in digital twin adoption. By comparison, this study drew five lessons to drive better adoption of the digital twin. The construction industry needs to accelerate the deployment of relevant hardware, promote the standard unification of digital twin, explore the whole lifecycle application of the digital twin, enhance data protection, and embrace changes. This study was limited in the scope of data collection. Future research could focus on gathering information from specific case studies, to produce more comprehensive perspectives.

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Section: Application and The Main Functionsmentioning

confidence: 99%

Digital twin for product versus project lifecycles’ development in manufacturing and construction industries

Abanda,

Jian,

Adukpo

et al. 2024

J Intell Manuf

View full text Add to dashboard Cite

show abstract

“…In Equation (31), â is the discrete Fourier transform of a, and k xx is the first-row vector of the kernel matrix K. Thus, the classifier training changes from finding the best ω to finding the optimal a.…”

Section: Optimization System Analysis Of Plant Factory Seedling Pheno...mentioning

confidence: 99%

Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant Factory

et al. 2023

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This work aims to construct an artificial neural network (ANN) ant colony algorithm (ACA)-based fine recognition system for plant factory seedling phenotypes. To address the problems of complexity and high delay of the plant recognition system in plant factories, first, multiple cameras at different positions are employed to collect images of seedlings and construct 3D images. Then, the mask region convolutional neural networks (MRCNN) algorithm is adopted to analyze plant phenotypes. Finally, the optimized ACA is employed to optimize the process timing in the plant factory, thereby constructing a plant factory seedling phenotype fine identification system via ANN combined with ACA. Moreover, the model performance is analyzed. The results show that plants have four stages of phenotypes, namely, the germination stage, seedling stage, rosette stage, and heading stage. The accuracy of the germination stage reaches 97.01%, and the required test time is 5.64 s. Additionally, the optimization accuracy of the process timing sequence of the proposed model algorithm is maintained at 90.26%, and the delay and energy consumption are stabilized at 20.17 ms and 17.71, respectively, when the data volume is 6000 Mb. However, the problem of image acquisition occlusion in the process of 3D image construction still needs further study. Therefore, the constructed ANN-ACA-based fine recognition system for plant seedling phenotypes can optimize the process timing in a more real-time and lower energy consumption way and provide a reference for the integrated progression of unmanned intelligent recognition systems and complete sets of equipment for plant plants in the later stage.

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“…Among the six essential technologies advocated by Li for process industry DT are data intelligent perception, multisource heterogeneous data integration, data-efficient transmission, digital twin creation, enhanced interaction, and transformation application 25 . Zhou streamlined the raw materials delivery schedule at an ironmaking factory with five sintering machines and seven blast furnaces utilizing cloud computing and DT technology to cut production expenses to reduce the mean coke ratio of an ironmaking plant 26 . Soares showed how a digital twin was successfully implemented to simulate a four-stage multi-effect evaporation train from an industrial sugar-cane processing facility 27 .…”

Section: Related Workmentioning

confidence: 99%

Digital twin key technology on rare earth process

Yang

Kuang

Zhu

et al. 2022

Sci Rep

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Digital twin can be defined as a digital equivalent of an object of which it can mirror its behavior and status or virtual replicas of real physical entities in Cyberspace. To an extent, it also can simulate and predict the states of equipment or systems through smart algorithms and massive data. Hence, the digital twin is emerging used in intelligent manufacturing Systems in real-time and predicting system failure and also has introduced into a variety of traditional industries such as construction, Agriculture. Rare earth production is a typical process industry, and its Extraction Process enjoys the top priority in the industry. However, the extraction process is usually characterized by nonlinear behavior, large time delays, and strong coupling of various process variables. In case of failures happened in the process, the whole line would be shut down. Therefore, the digital twin is introduced into the design of process simulation to promote the efficiency and intelligent level of the Extraction Process. This paper proposes the techniques to build the rare earth digital twin such as soft measurement of component content, component content process simulation, control optimization strategy, and virtual workshop, etc. At the end, the validity of the model is verified, and a case study is conducted to verify the feasibility of the whole Digital twin framework.

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A Collaborative Optimization Strategy for Energy Reduction in Ironmaking Digital Twin

Cited by 19 publications

References 34 publications

Digital twin for product versus project lifecycles’ development in manufacturing and construction industries

Digital twin for product versus project lifecycles’ development in manufacturing and construction industries

Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant Factory

Digital twin key technology on rare earth process

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