Artificial intelligent based energy scheduling of steel mill gas utilization system towards carbon neutrality

Han, Xiao; Wu, Xiao; Sha, Peng

doi:10.1016/j.apenergy.2021.117069

Cited by 44 publications

(13 citation statements)

References 38 publications

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“…Pena et al [16] proposed different time series prediction models considering the difference between continuous and discrete processes, after which a MILP model is used to construct a dynamic regulation model in a moving horizon way. Focusing on the demand for carbon capture, utilization, and storage in iron and steel enterprises, Xi et al [17] used a gradient lifting regression tree as the proxy model of the energy conversion system and then used the PSO algorithm to find the optimal distribution decision. These studies all assumed the decision-maker has limited information but can accurately forecast critical parameters via machine learning techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization

Jiang¹,

Wang²,

Bogle³

2023

Preprint

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Byproduct gas is one of the most important energy resources of the modern iron and steel industry because it is crucial in supplying energy to manufacturing processes as well as converting energies, such as stream and electricity. The optimal distribution of byproduct gases in an iron and steel plant can significantly reduce energy costs and carbon emissions. However, the balance between supply and demand is easily threatened by the quantityand quality-related uncertainties from manufacturing processes. Following the supply-storage-conversion-demand network, this study developed an optimal gas distribution model considering uncertain supply and proposed a two-stage robust optimization (TSRO) model including "here-and-now" decisions, which minimize the start-stop cost of conversion units, and making "wait-and-see" decisions, which minimize the operating costs of gasholders and demand penalties. To implement the TSRO model in practice, this study proposes a "first quantify, then optimize" method: (1) quantify the uncer-

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Section: Literature Reviewmentioning

confidence: 99%

Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization

Jiang¹,

Wang²,

Bogle³

2023

Preprint

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“…The AI technology reduces information entropy and finds possible certainty from countless uncertainties. By comparison, AI technology analyzes and compares the historical data of groups and individuals based on a full data sampling analysis, and then predicts the psychological state of the respondents using intelligent methods, such as the DL algorithms (Xi et al, 2021 ).…”

Section: College Students' Entrepreneurial Psychologymentioning

confidence: 99%

The Effect of Law Students in Entrepreneurial Psychology Under the Artificial Intelligence Technology

Zhang

2021

Front. Psychol.

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With the increasingly serious employment situation in China, the government and schools encourage college students to start businesses to alleviate employment pressure. College student's successful entrepreneurship depends on national preferential policies, social support, and, most importantly, their healthy and solid psychological quality and entrepreneurial psychological quality. The purpose is to understand the entrepreneurial psychology of college students and study the entrepreneurial psychological effect. Firstly, the four aspects of entrepreneurial psychology are summarized, including entrepreneurial awareness, entrepreneurial volition, entrepreneurial ability, and entrepreneurial personality. Secondly, the research status of college students' entrepreneurial psychology is reviewed, and the existing problems are pointed out. Thirdly, the combined model of wavelet transform and Neural Network (NN) is proposed, and the feasibility of the proposed model is evaluated through the analysis of college students' entrepreneurial psychology. The wavelet NN is used in experimental design to predict college students' entrepreneurial psychology, and the predicted results are compared with the actual value. From the perspective of the prediction results of entrepreneurial psychology, the combination of wavelet algorithm and neural network is more accurate for entrepreneurial psychology prediction and evaluation results of law students. Overall, the difference between the predicted value and the actual value is within 0.3 points, which is relatively stable. According to the analysis of single-factor results, the scores of students of different majors in the four dimensions of entrepreneurial psychology are all higher than 3.5, but there is no significant difference among the four dimensions (P > 0.05), indicating that the major has no significant impact on entrepreneurial psychology; law students with different educational backgrounds have significant differences in entrepreneurial psychology (P < 0.05), among which students with a master's degree have the strongest entrepreneurial will, while doctoral students have the lowest entrepreneurial will; in terms of entrepreneurial psychological capital, men's self-efficacy is higher than women's, and the difference is significant (P < 0.05). The difference between males and females in the scores of entrepreneurial psychological factors' four aspects is not very obvious. In terms of entrepreneurial psychological capital, males' self-efficacy is significantly higher than females' (P < 0.05). Artificial Intelligence (AI) technology has great application prospects in the prediction and evaluation of college students' entrepreneurial psychology, and college students' entrepreneurial psychology is highly correlated with gender and education.

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“…ese have laid a solid foundation for the great development of the garden landscape. In recent years, computer-aided technology has made great progress, the existing assistive technologies and platforms have been optimized, and some of them have become special technologies for landscape design, such as sketch masters [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Application of Computer 3D Modeling Technology in the Simulation Design of Modern Garden Ecological Landscape

Tian

2022

Mathematical Problems in Engineering

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Modern landscape design not only needs creativity but also needs auxiliary tools that can predict the design effect, so as to ensure that the deficiencies can be found through the renderings before the landscape is completed, and targeted rectification can be carried out. At present, the research of landscape planning and design assisted by virtual reality technology in China is basically in its infancy. The rapid development of information-based computer technology, powerful 3D modeling, and solid rendering and animation functions have created a good environment for landscape design, so landscape design is inseparable from the help of computer-aided design technology. However, the conventional method to model and simulate the landscape is rather time-consuming. Based on the research on the application of computer-aided design technology in landscape design, the computer-aided design technology is briefly explained, and the application of computer-aided landscape design is explored step by step.

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Artificial intelligent based energy scheduling of steel mill gas utilization system towards carbon neutrality

Cited by 44 publications

References 38 publications

Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization

Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization

The Effect of Law Students in Entrepreneurial Psychology Under the Artificial Intelligence Technology

Application of Computer 3D Modeling Technology in the Simulation Design of Modern Garden Ecological Landscape

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