An intelligent decision-making strategy based on the forecast of abnormal operating mode for iron ore sintering process

Du, Sheng; Wu, Min; Chen, Luefeng; Cao, Weihua; Pedrycz, Witold

doi:10.1016/j.jprocont.2020.11.001

Cited by 17 publications

(7 citation statements)

References 29 publications

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“…(2) Feed-forward control, Kou [22] proposed a method to predict the sintering end point by using the rising point of the exhaust gas temperature of the air bellows, and using the temperature change of the central air bellows as a feed-forward signal to adjust the speed of the trolley, so that the sintering end point is finally controlled at the position required by the process. (3) Predictive control, Du et al [23]…”

Section: Optimal End Point Controlmentioning

confidence: 99%

“…After the actual position of the sintering end point has been determined, it can be effectively controlled. There are three main control strategies for the sintering end point: Feedback control, where Zhou et al [21] calculate the sintering end point based on the exhaust gas temperature method and then continuously adjust the machine speed through fuzzy control methods to stabilize the sintering end point within the set range. Feed-forward control, Kou [22] proposed a method to predict the sintering end point by using the rising point of the exhaust gas temperature of the air bellows, and using the temperature change of the central air bellows as a feed-forward signal to adjust the speed of the trolley, so that the sintering end point is finally controlled at the position required by the process. Predictive control, Du et al [23] used fuzzy rules to develop an intelligent decision and control model for the sintering end point position, which sequentially adjusted the essential influencing factors of the sintering end point and reduced the abnormal sintering end point by 12%. By analysing the relevance of the sintering end point in different periods, Du et al [24] established a time series end point advance prediction model based on fuzzy theory, and applied this method to predict the sintering end point position in advance within 100 min with an error of ± 0.2 in a more stable period. …”

Section: Status Of Research On Intelligent Manufacturing In Sintering...mentioning

confidence: 99%

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Research and application of the sintering whole process intelligent manufacturing system

Liu

Zhang

Zhao

et al. 2023

Ironmaking & Steelmaking

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Stable production processes, high efficiency and good product quality are the main objectives of the sintering industry. Taking the whole sintering production process as the research object, cutting-edge intelligent algorithms and classical sintering theories are applied to establish a sintering batch optimization model, a sinter layer permeability prediction model, a sintering end point optimization control model and a sinter quality prediction and evaluation model respectively, supported by a large amount of historical data, so that the model has reasonably accurate results as well as strong practicality. At the same time, a sintering whole process intelligent manufacturing system based on the above four models is constructed using Java and Python programming languages. The system realizes the functions of real-time analysis of key parameters, intelligent early warning, decision optimization and fault tracing, which provides reasonable line optimization suggestions for field operators, and significantly improves the intelligence and production efficiency of sintering process.

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Section: Optimal End Point Controlmentioning

confidence: 99%

Section: Status Of Research On Intelligent Manufacturing In Sintering...mentioning

confidence: 99%

Research and application of the sintering whole process intelligent manufacturing system

Liu

Zhang

Zhao

et al. 2023

Ironmaking & Steelmaking

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“…e proposed method was compared with the two common methods of customer service data-aided decision-making mentioned in the Introduction section (method 1 and common method 2 [10,11], resp.) to compare the throughput performance and transmission efficiency of data sharing.…”

Section: Experimental Demonstration and Analysismentioning

confidence: 99%

“…An overview of the IoT and AI-enabled system along with four capabilities are provided, and then the review of the literature and its analysis was shown. Du et al [11] presented an intelligent decision strategy for the iron ore sintering process based on abnormal condition prediction. Initially, the model of running mode prediction was established by using the fuzzy rules model, and the input was selected by one-way ANOVA.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Assistant Decision-Making Method for Power Enterprise Customer Service Based on IoT Data Acquisition

Long

Liu

et al. 2021

Mobile Information Systems

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The prevailing era of the Internet of Things (IoT) has renewed all fields of life in general, but, especially with the advent of artificial intelligence (AI), has drawn the attention of researchers into a new paradigm of life standards. This revolution has been accepted around the world for making life comfortable with the use of intelligent devices. AI-enabled machines are more intelligent and capable of completing a specific task which saves a lot of time and resources. Currently, diverse methods are available in the existing literature to handle different issues of real life based on AI and IoT systems. The role of decision-making has its prominence in the AI-enabled and IoT systems. In this article, an AI- and IoT-based intelligent assistant decision-making method is presented for power enterprise customer service. An intelligent model of the customer service data network is designed, and the method of collecting data from IoT to assist decision-making is presented. Then, the semantic relationship of customer service data is defined, and the sharing scope of data transmission and resources are determined to realize intelligent assistant decision-making of customer service in power enterprises. Simulation results show that the proposed method improves the decision data transmission speed and shortens the transmission delay, and the network performance of data interaction is better than that of the existing methods.

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“…Statistics Process Control (SPC) techniques are used at the specified periods at the substance preparation foundation from which datum is obtained to increase the production's quality and output [7,8]. So, some important easiness is provided in terms of time control and practicability.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Iron Ore Sinter RUL Test Results

Bölükbaşı¹

2022

JCHAR

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The sintering process is to bring the powdered iron ores to the desired particle size, strength and gas permeability for the blast furnace. During sintering, important events such as bonding between particles, size change and coarsening of the microstructure take place. Reduction Under Load (RUL) test is performed to determine the metallurgical properties of the sinter sample. Statistical process control techniques are used for iron and steel plants in which data are measured for certain periods to increase physical, chemical and reducibility properties of sinter production. Thus, when some important advantages are provided in terms of control and applicability. In this study, static and dynamic test low temperature reduction disintegration and reduction under load of the samples taken out regularly from sinter samples were investigated to determine capability of the process. For each parameter, X-R control charts using moving range method were drawn. Thereby, deviations of lower and upper limit values were determined It was concluded that the amount and grade of raw ore in the sinter blend should be fixed by using appropriate amounts.

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An intelligent decision-making strategy based on the forecast of abnormal operating mode for iron ore sintering process

Cited by 17 publications

References 29 publications

Research and application of the sintering whole process intelligent manufacturing system

Research and application of the sintering whole process intelligent manufacturing system

Intelligent Assistant Decision-Making Method for Power Enterprise Customer Service Based on IoT Data Acquisition

Analysis of Iron Ore Sinter RUL Test Results

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