Formulation of Shrinkage Rate of Sinter during Softening Process

Yasuda, Naoto; Nishioka, Koki; Nomura, Seiji

doi:10.2355/isijinternational.isijint-2019-817

Cited by 3 publications

(7 citation statements)

References 16 publications

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“…Shown in Figure 9 below is the temperature-dependant GRD determined from the CFD model in comparison with a range of experimental conditions used previously [7,14,15,21–27,36] (shaded red stripes). The range of GRD is shown only for the carbon component of the previous S&M test gas conditions as the reduction degree of the hydrogen component at the inlet is always one (as no steam is used).…”

Section: Resultsmentioning

confidence: 99%

“…S&M experiment temperature profiles from comparative studies [1,7,15,21–27] are shown below in Figure 3. These temperature profiles generally follow a similar trend, with an initial fast heating rate, followed by a slow heating rate (simulating the thermal reserve zone), and ending with a fast heating rate.…”

Section: Methodsmentioning

confidence: 99%

“…The gas composition is presented in its most generalised form (as a function of temperature) so as to allow well-established S&M experiment temperature profiles to dictate their time dependence. S&M experiment temperature profiles from comparative studies [1,7,15,[21][22][23][24][25][26][27] are shown below in Figure 3. These temperature profiles generally follow a similar trend, with an initial fast heating rate, followed by a slow heating rate (simulating the thermal reserve zone), and ending with a fast heating rate.…”

Section: Development Of Sandm Testing Conditionsmentioning

confidence: 99%

“…With the gas conditions being imposed in the S&M experiment, the GRD provides a more natural term for understanding the driving force behind the reduction reactions occurring. Shown in Figure 9 below is the temperature-dependant GRD determined from the CFD model in comparison with a range of experimental conditions used previously [7,14,15,[21][22][23][24][25][26][27]36] (shaded red stripes). The range of GRD is shown only for the carbon component of the previous S&M test gas conditions as the reduction degree of the hydrogen component at the inlet is always one (as no steam is used).…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

See 3 more Smart Citations

Development of softening and melting testing conditions simulating blast furnace operation with hydrogen injection

Barrett

Mitra

Doostmohammadi

et al. 2023

Ironmaking & Steelmaking

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Softening and Melting (S&M) experiments have evolved alongside the blast furnace as a crucial tool for burden characterisation and optimisation. Modern blast furnaces derive a base load of hydrogen from various sources. However, with hydrogen-enrichment of the blast furnace being considered to mitigate emissions, new S&M test conditions are required. In this study, a 2-D axisymmetric CFD model is used to simulate the internal conditions of a modern blast furnace operation, and a future operation with tuyere level hydrogen injection. The model results are used to guide the development of novel S&M test conditions, inclusive of H 2 , H 2 O, CO, CO 2 and N 2 . The maximum hydrogen concentration under hydrogen enrichment was 20%, with the hydrogenous fraction of the gas primarily replacing nitrogen. A particular focus was given to the importance of including water vapour in the inlet gas, andits impact on reactions occurring in the S&M test.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Development Of Sandm Testing Conditionsmentioning

confidence: 99%

Section: Comparison With Previous Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Development of softening and melting testing conditions simulating blast furnace operation with hydrogen injection

Barrett

Mitra

Doostmohammadi

et al. 2023

Ironmaking & Steelmaking

View full text Add to dashboard Cite

show abstract

“…In the article, the RNN algorithm is designed with four chromosomes so that different neural nets can be described. Designing these four chromosomes allows the optimal structure of the search space of the RNN algorithm [ 44 , 45 ]. The parameters set in the article are as follows: the maximum number of hidden layer neurons is 144, the minimum number of hidden layer neurons is 6, the maximum hidden layers number is 5, and the minimum hidden layers number is 2.…”

Section: Comparison Of Lstm and Ga-rnn Algorithmsmentioning

confidence: 99%

A Model Study on Raw Material Chemical Composition to Predict Sinter Quality Based on GA-RNN

Zhang

Zhu

et al. 2022

Computational Intelligence and Neuroscience

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The quality control process for sintered ore is cumbersome and time- and money-consuming. When the assay results come out and the ratios are found to be faulty, the ratios cannot be changed in time, which will produce sintered ore of substandard quality, resulting in a waste of resources and environmental pollution. For the problem of lagging sinter detection results, Long Short-Term Memory and Genetic Algorithm-Recurrent Neural Networks prediction algorithms were used for comparative analysis, and the article used GA-RNN quality prediction model for prediction. Through correlation analysis, the chemical composition of the sintered raw material was determined as the input parameter and the physical and metallurgical properties of the sintered ore were determined as the output parameters, thus successfully establishing a GA-RNN-based sinter quality prediction model. Based on 150 sets of original data, 105 sets of data were selected as the training sample set and 45 sets of data were selected as the test sample set. The results obtained were compared to the real value with an average prediction error of 1.24% for the drum index, 0.92% for the low-temperature reduction chalking index (RDI), 0.95% for the reduction index (RI), 0.40% for the load softening temperature T10%, and 0.43% for the load softening temperature T40%, with all within the running time thresholds. The study of this model enables the prediction of the quality of sintered ore prior to sintering, thus improving the yield of sintered ore, increasing corporate efficiency, saving energy, and reducing environmental pollution.

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Utilization and impacts of hydrogen in the ironmaking processes: A review from lab-scale basics to industrial practices

Deng

Wang

et al. 2021

International Journal of Hydrogen Energy

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Formulation of Shrinkage Rate of Sinter during Softening Process

Cited by 3 publications

References 16 publications

Development of softening and melting testing conditions simulating blast furnace operation with hydrogen injection

Development of softening and melting testing conditions simulating blast furnace operation with hydrogen injection

A Model Study on Raw Material Chemical Composition to Predict Sinter Quality Based on GA-RNN

Utilization and impacts of hydrogen in the ironmaking processes: A review from lab-scale basics to industrial practices

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