Predictive Control of a Closed Grinding Circuit System in Cement Industry

Minchala, Luis I.; Zhang, Youmin; Garza-Castañón, Luis E.

doi:10.1109/tie.2017.2762635

Cited by 14 publications

(4 citation statements)

References 18 publications

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“…Cement industry is considered as a strategic industry all over the world (Shen et al, 2017). The operational cost of implementing manufacturing processes in this industry is so high that a few hours of stop of major machinery in production line can result in huge cost (Minchala, Zhang, & Garza-Castañón, 2018). In fact, cement production lines must continuously produce certain products in order to meet the goals set by the organization (Moretti & Caro, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

A New Nonlinear Optimization Model for Grate Cooler Fans Selective Maintenance Problem in Cement Industries Considering with Random and Economic Dependence

Khosravi

Mostafayi

Taheridoustabad

2022

Preprint

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In this paper, a new nonlinear optimization model for multi-state multi-components systems considering with random and economic dependence is presented for Grate Cooler Fans maintenance problem in Cement industries. The objective function minimizes failure unreliability in future missions subject to cost and time constraints. Actually, the applied constraints caused operation time of carrying out maintenance can be extended to the predetermined time in return to pay its penalty. In the proposed model, economic dependence with the positive effect of simultaneous maintenance executions start time in different components is investigated. Furthermore, grate cooler fans performance is considered under the impression of effects of propagated failures with global effect and failure isolation phenomena that the occurrence probability of propagated failures with global effect on both of the drive and dependent components is discussed. Also, existing more than one drive components based on technical structures of grate cooler fans is possible. The computational results are presented on a real-world case study. Furthermore, sensitivity analyses on the main parameters of the problem are performed to derive some managerial insights that can help corresponding decision makers provide suitable and homogeneous maintenance services in the case study environment.

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Section: Literature Reviewmentioning

confidence: 99%

A New Nonlinear Optimization Model for Grate Cooler Fans Selective Maintenance Problem in Cement Industries Considering with Random and Economic Dependence

Khosravi

Mostafayi

Taheridoustabad

2022

Preprint

View full text Add to dashboard Cite

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“…In recent years, many strong grinding process simulation models have been developed to date. These models of industrial grinding circuits can be considered robust and highly accurate [8,9]. The majority of the proposed models reported in the literature are a combination of physical laws of conservation of energy state and empirical correlations.…”

Section: Related Workmentioning

confidence: 99%

Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model

Danishvar

Daneshvar

Souza³

et al. 2021

Applied Sciences

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Milling operations in various production processes are among the most important factors in determining the quality, stability, and consumption of energy. Optimizing and stabilizing the milling process is a non-linear multivariable control problem. In specific processes that deal with natural materials (e.g., cement, pulp and paper, beverage brewery and water/wastewater treatment industries). A novel data-driven approach utilizing real-time monitoring control technology is proposed for the purpose of optimizing the grinding of cement processing. A combined event modeling for feature extraction and the fully connected deep neural network model to predict the coarseness of cement particles is proposed. The resulting prediction allows a look ahead control strategy and corrective actions. The proposed solution has been deployed in a number of cement plants around the world. The resultant control strategy has enabled the operators to take corrective actions before the coarse return increases, both in autonomous and manual mode. The impact of the solution has improved efficiency resource use by 10% of resources, the plant stability, and the overall energy efficiency of the plant.

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“…The technology of embedding the predictive control algorithm in the distributed control system (DCS) layer configuration control software not only comprehensively simulates the control system but also realizes the predictive control of complex industrial process objects at the DCS layer. This has important practical significance for the application of predictive control technology [4][5][6]. Since the field controller in the current electric heating control system usually adopts temperature-based PID closed-loop feedback control, once the temperature sensor or its detection channel fails, the control output may be saturated or vacant [7].…”

Section: Introductionmentioning

confidence: 99%

Simulation of electric heating prediction model by internet of things technology and room thermal performance analysis

Wen

2020

THERM SCI

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The fault diagnosis and fault-tolerant control of electric heating distributed control system are improved by the thermal performance analysis of rooms. The given values are tracked to meet the heating requirements, and the reliability of heating is increased without increasing hardware resources, which improves the reliability and economy of electric heating. From the perspective of energy conservation of electric heating for buildings and rooms, a predictive control model based on loadside three-phase power self-balance is proposed. A fault tolerance method for the electric heating distributed control system control system heating is designed. The load-side three-phase power self-balancing method of the electric heating control system is implemented by using the advantages of the Internet of Things and the heat storage performance of a room, which is its characteristics. Simulation results show that the performance of predictive control for non-minimum phase process is significantly better than that of conventional proportion integral differential control. For complex control problems, predictive control technology can provide better control performance than proportion integral differential control technology. Without increasing any hardware resources, reliable and economical heating is achieved through software.

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Predictive Control of a Closed Grinding Circuit System in Cement Industry

Cited by 14 publications

References 18 publications

A New Nonlinear Optimization Model for Grate Cooler Fans Selective Maintenance Problem in Cement Industries Considering with Random and Economic Dependence

A New Nonlinear Optimization Model for Grate Cooler Fans Selective Maintenance Problem in Cement Industries Considering with Random and Economic Dependence

Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model

Simulation of electric heating prediction model by internet of things technology and room thermal performance analysis

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