A supervisory fuzzy control of back-end temperature of rotary cement kilns

Fallahpour, Marzieh; Fatehi, Alireza; Araabi, Babak Nadjar; Azizi, Mohammad

doi:10.1109/iccas.2007.4406944

Cited by 17 publications

(5 citation statements)

References 8 publications

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“…Fuzzy inference models (FIM) are an example of heuristic-based methods. However, expert knowledge has a direct impact on determining the fuzzy inference rules that maximize the manufacturing process's key performance indicators [27]. In order to reduce the dependency on domain expert intervention, automatic artificial intelligence (AI) based learning methods have also been used to infer FIM [28], [29].…”

Section: E Heuristic-based Approachmentioning

confidence: 99%

Real-Time Event-Driven Learning in Highly Volatile Systems: A Case for Embedded Machine Learning for SCADA Systems

Goncalves

Sousa²,

Mendes

et al. 2022

IEEE Access

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Extracting key system parameters and their impact on state transition is a necessity for knowledge and data engineering. In Decision Support Systems, the quest for yet more efficient and faster methods of sensitivity analysis (SA) and feature extraction in complex and volatile systems persists. A new improved event tracking methodology, the fastTracker, for real-time SA in large scale complex systems is proposed in this paper. The main feature of fastTracker is its high-frequency analytics using meager computational cost. It is suitable for data processing and prioritization in embedded systems, Internet of Things (IoT), distributed computing (e.g. lEdge computing) applications. The presented algorithm's underpinning rationale is event driven; its objective is to correctly and succinctly quantify the sensitivity of observable changes in the system (output) with respect to the input variables. To demonstrate the performance of the proposed fastTracker methodology, fastTracker was deployed in the Supervisory control and data acquisition (SCADA) system from real cement industry. fastTracker has been verified by system experts in real industrial application. Its performance was compared with other real-time event-based SA techniques. The comparison revealed savings of 98.8% in processing time per sensitivity index and 20% in memory usage when compared with EventTracker, its closest rival. The proposed methodology is more accurate and 80.9% faster than an entropy-based method. Its application is recommended for reinforced learning and/or formulating system key performance indicators from raw data.

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Section: E Heuristic-based Approachmentioning

confidence: 99%

Real-Time Event-Driven Learning in Highly Volatile Systems: A Case for Embedded Machine Learning for SCADA Systems

Goncalves

Sousa²,

Mendes

et al. 2022

IEEE Access

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“…A part of the combustion gases is the Co gas produced here. Finally, the cement crystals are made and go out from the kiln as the clinker [1][2][3].. The burning zone temperature (BZT zt B ) in cement rotary kiln is a very important production index and has a significant role on the quality of the clinker.…”

Section: Process Description Of Cement Rotary Kilnmentioning

confidence: 99%

“…Rotary kiln is a kind of large scale sintering device widely used in various process industries, such as metallurgical, cement, refractory materials, chemical and environment protection [1][2][3]. In the cement production industry, the cement rotary kiln decomposition is the most important unit, and its operating status serious affects the output, quality, energy consumption, and environment pollution.…”

Section: Introductionmentioning

confidence: 99%

Intelligent dynamic modeling for online estimation of burning zone temperature in cement rotary kiln

Zhou

Yuan

2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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Cement rotary kiln is a complex multivariable, large-disturbances and nonlinear system which is full of mass transfer, heat transfer, and physical and chemical reactions. The burning zone temperature (BZT) in cement rotary kiln is a very important production index and has a significant role on the quality of the clinker. However, the BZT is generally difficult to be measured online using the conventional instruments. Although the BZT can be detected by using the expensive infrared pyrometer which located at the kiln head hood, it generally loses veracity due to the complex dynamics of the cement rotary kiln. Obviously, such an inaccurate measurement may guide the operator to do some improper operations in practice. To attack such a practical engineering problem, an intelligence-based dynamic soft-sensor modeling approach is proposed to online estimate the BZT in cement rotary kiln in this paper. The proposed approach mainly includes two digital filters which are used to pre-process the original measurement data, and an intelligent CBR soft-sensor system which is adapted to online predict the BZT in time, according to the measured secondary variables. At last, industrial tests have been performed to demonstrate the good estimation performance of the proposed method for a real cement rotary kiln process. Index Terms -Burning zone temperature, Cement rotary kiln, Dynamic soft-sensor, Case-based reasoning, Digital filter.

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“…A fuzzy control in Greece TITAN cement plant, aiming at reducing NOx emission to atmosphere was introduced by Batzias [16] from Piraeus University in 2006. Fuzzy supervision controller and MLP neural network was designed by Fallahpour et al [17] of Iran Tehran Science, Technology University in 2007. Many research work focus on neural network due to nonlinear, mapping, self learning ability [18].…”

Section: Introductionmentioning

confidence: 99%

Design of Neural Network Based Expert System for Automated Lime Kiln System

Anand¹,

Mamatha²,

Reddy³

et al. 2019

JESA

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Rotary lime kiln is an important pulp production facility of paper mills and cement factories. To achieve fully-automated lime kiln production, this paper firstly carries out a thorough review of the production processes of lime kiln, revealing that the shell temperature of lime kiln and furnace oil consumption are two key parameters to the working efficiency and product quality of the kiln. Next, an expert system was designed based on artificial neural network to optimize the parameters in kiln application. The functions and operations of the intelligent controllers in the system were also detailed. After that, the designed system was adopted to predict the shell temperature and furnace oil consumption using moisture in lime mud. The comparison between the predicted results and the real-time data of a plant shows that our system can predict the two key parameters accurately, enabling the operator to make sound decisions on many production indices. The research findings lay the basis for low-cost and high-quality lime kiln operations.

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A supervisory fuzzy control of back-end temperature of rotary cement kilns

Cited by 17 publications

References 8 publications

Real-Time Event-Driven Learning in Highly Volatile Systems: A Case for Embedded Machine Learning for SCADA Systems

Real-Time Event-Driven Learning in Highly Volatile Systems: A Case for Embedded Machine Learning for SCADA Systems

Intelligent dynamic modeling for online estimation of burning zone temperature in cement rotary kiln

Design of Neural Network Based Expert System for Automated Lime Kiln System

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