scholarly journals A review of end-point carbon prediction for BOF steelmaking process

2020 ◽  
Vol 39 (1) ◽  
pp. 653-662
Author(s):  
Zhou Wang ◽  
Qing Liu ◽  
Haitao Liu ◽  
Shizhong Wei
Keyword(s):  
High Efficiency ◽  
Prediction Models ◽  
Critical Role ◽  
Basic Oxygen Furnace ◽  
Detection Methods ◽  
Steelmaking Process ◽  
Dynamic Prediction ◽  
End Point ◽  
Stable Production ◽  
Precise Prediction

AbstractThe precise prediction of end-point carbon content in liquid steel plays a critical role in increasing productivity as well as energy efficiency that can be achieved in the basic oxygen furnace (BOF) steelmaking process. Due to numerous and diversity of the studies on BOF end-point carbon prediction, it seems necessary to provide a comprehensive literature review on state-of-the-art developments in end-point carbon prediction for BOF steelmaking. This paper presents the characteristics of different end-point carbon prediction models. The end-point carbon prediction for BOF steelmaking has initially relied on the experience and skill of the operators. With the development of information technology and auto-detection methods, BOF end-point carbon prediction mainly has gone through three stages, such as static prediction, dynamic prediction, and intelligent prediction. Future contributions to the development and application of intelligent end-point carbon prediction in BOF steelmaking are still arduous tasks. However, it is envisaged that the intelligent end-point carbon prediction will witness more frequent applications and greatly improve the high-quality, high-efficiency, and stable production for BOF steelmaking in the future.

scholarly journals Management of Lime in Steel

Metals ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 686 ◽  
Author(s):  
Sanjeev Manocha ◽  
François Ponchon
Keyword(s):  
Critical Role ◽  
Steel Production ◽  
Developed Countries ◽  
Basic Oxygen Furnace ◽  
Environmental Benefits ◽  
Steelmaking Process ◽  
Iron And Steel ◽  
Refractory Life ◽  
The Impact ◽  
Local Sourcing

The EU28 total lime demand in 2017 was estimated at about 20 million tons, out of which about 40% are consumed in the iron and steel industry. Steel remains the major consumer after environment and construction. The lime industry is quite mature and consolidated in developed countries, with enough reserves and production to serve regional markets while being fragmented in developing nations where steel producers rely on local sourcing. There is relatively very little trade for lime worldwide. Lime has a critical role at different steps of the steelmaking process, and especially to make a good slag facilitating the removal of sulphur and phosphorus, and for providing a safer platform to withstand high intensity arc plasma in the electric arc furnace (EAF), and violent reactions in the basic oxygen furnace (BOF). Lime quality and quantity has a direct effect on slag quality, which affects metallurgical results, refractory life, liquid metal yield, and productivity, and therefore the total cost of the steel production. In this paper, we present the importance of careful selection in the limestone and calcination process, which influences critical lime quality characteristics. We shall further elaborate on the impact of lime characteristics in the optimization of the steelmaking process, metallurgical benefits, overall cost impact, potential savings, and environmental benefits.

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

scholarly journals A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 2742
Author(s):  
Fatih Ünal ◽  
Abdulaziz Almalaq ◽  
Sami Ekici
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Unified Framework ◽  
Short Term ◽  
Planning And Scheduling ◽  
Short Term Load Forecasting

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

The critical role of the coordination sphere in the high-efficiency blue-light emission from aminopyrazine metal-organic polymers

2021 ◽  
Vol 186 ◽  
pp. 109025
Author(s):  
João Humberto Dias Campos ◽  
Meiry Edivirges Alvarenga ◽  
Maykon Alves Lemes ◽  
José Antônio do Nascimento Neto ◽  
Freddy Fernandes Guimarães ◽  
...  
Keyword(s):  
Blue Light ◽  
Coordination Sphere ◽  
High Efficiency ◽  
Light Emission ◽  
Critical Role ◽  
Organic Polymers ◽  
Blue Light Emission ◽  
Metal Organic

scholarly journals Marine Toxins Detection by Biosensors Based on Aptamers

Toxins ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 1 ◽  
Author(s):  
Wei Ye ◽  
Taomei Liu ◽  
Weimin Zhang ◽  
Muzi Zhu ◽  
Zhaoming Liu ◽  
...  
Keyword(s):  
Detection Limit ◽  
Sensitivity And Specificity ◽  
High Efficiency ◽  
Rapid Development ◽  
High Sensitivity ◽  
Detection Methods ◽  
Marine Toxins ◽  
Time Saving ◽  
Low Detection Limit ◽  
The Future

Marine toxins cause great harm to human health through seafood, therefore, it is urgent to exploit new marine toxins detection methods with the merits of high sensitivity and specificity, low detection limit, convenience, and high efficiency. Aptasensors have emerged to replace classical detection methods for marine toxins detection. The rapid development of molecular biological approaches, sequencing technology, material science, electronics and chemical science boost the preparation and application of aptasensors. Taken together, the aptamer-based biosensors would be the best candidate for detection of the marine toxins with the merits of high sensitivity and specificity, convenience, time-saving, relatively low cost, extremely low detection limit, and high throughput, which have reduced the detection limit of marine toxins from nM to fM. This article reviews the detection of marine toxins by aptamer-based biosensors, as well as the selection approach for the systematic evolution of ligands by exponential enrichment (SELEX), the aptamer sequences. Moreover, the newest aptasensors and the future prospective are also discussed, which would provide thereotical basis for the future development of marine toxins detection by aptasensors.

scholarly journals A Coordinated Wheeled Gas Pipeline Robot Chain System Based on Visible Light Relay Communication and Illuminance Assessment

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2322 ◽  
Author(s):  
Wen Zhao ◽  
Mitsuhiro Kamezaki ◽  
Kento Yoshida ◽  
Kaoru Yamaguchi ◽  
Minoru Konno ◽  
...  
Keyword(s):  
Visible Light ◽  
High Efficiency ◽  
Gas Pipeline ◽  
Detection Methods ◽  
Preliminary Evaluation ◽  
Communication Quality ◽  
Chain System ◽  
Error Ratio ◽  
Relay Communication ◽  
Data Error

The gas pipeline requires regular inspection since the leakage brings damage to the stable gas supply. Compared to current detection methods such as destructive inspection, using pipeline robots has advantages including low cost and high efficiency. However, they have a limited inspection range in the complex pipe owing to restrictions by the cable friction or wireless signal attenuation. In our former study, to extend the inspection range, we proposed a robot chain system based on wireless relay communication (WRC). However, some drawbacks still remain such as imprecision of evaluation based on received signal strength indication (RSSI), large data error ratio, and loss of signals. In this article, we thus propose a new approach based on visible light relay communication (VLRC) and illuminance assessment. This method enables robots to communicate by the ‘light signal relay’, which has advantages in good communication quality, less attenuation, and high precision in the pipe. To ensure the stability of VLRC, the illuminance-based evaluation method is adopted due to higher stability than the wireless-based approach. As a preliminary evaluation, several tests about signal waveform, communication quality, and coordinated movement were conducted. The results indicate that the proposed system can extend the inspection range with less data error ratio and more stable communication.

scholarly journals Dynamic prediction of cardiovascular disease using improved LSTM

2019 ◽  
Vol 3 (1) ◽  
pp. 14-25
Author(s):  
Kuang Junwei ◽  
Hangzhou Yang ◽  
Liu Junjiang ◽  
Yan Zhijun
Keyword(s):  
Cardiovascular Disease ◽  
Large Scale ◽  
Prediction Models ◽  
Short Term Memory ◽  
Classification Performance ◽  
Time Interval ◽  
Dynamic Prediction ◽  
Content Type ◽  
Period Data ◽  
Long Short Term Memory

Purpose Previous dynamic prediction models rarely handle multi-period data with different intervals, and the large-scale patient hospital records are not effectively used to improve the prediction performance. This paper aims to focus on the prediction of cardiovascular disease using the improved long short-term memory (LSTM) model. Design/methodology/approach A new model based on the traditional LSTM was proposed to predict cardiovascular disease. The irregular time interval is smoothed to obtain the time parameter vector, and it is used as the input of the forgetting gate of LSTM to overcome the prediction obstacle caused by the irregular time interval. Findings The experimental results show that the dynamic prediction model proposed in this paper obtained a significant better classification performance compared with the traditional LSTM model. Originality/value In this paper, the authors improved the LSTM by smoothing the irregular time between different medical stages of the patient to obtain the temporal feature vector.

Short-Term Wind Power Dynamic Prediction Based on GA-BP Neural Network

2014 ◽  
Vol 986-987 ◽  
pp. 524-528 ◽  
Author(s):  
Ting Jing Ke ◽  
Min You Chen ◽  
Huan Luo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Model Parameters ◽  
Short Term ◽  
Dynamic Prediction ◽  
Power Dynamic ◽  
Prediction Approach

This paper proposes a short-term wind power dynamic prediction model based on GA-BP neural network. Different from conventional prediction models, the proposed approach incorporates a prediction error adjusting strategy into neural network based prediction model to realize the function of model parameters self-adjusting, thus increase the prediction accuracy. Genetic algorithm is used to optimize the parameters of BP neural network. The wind power prediction results from different models with and without error adjusting strategy are compared. The comparative results show that the proposed dynamic prediction approach can provide more accurate wind power forecasting.

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