Multi-objective optimization of rolling schedules on aluminum hot tandem rolling

Hu, Ziyu; Yang, Jingming; Zhao, Zikai; Sun, Hao; Che, Haijun

doi:10.1007/s00170-015-7909-1

Cited by 24 publications

(7 citation statements)

References 6 publications

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“…In recent years, the production optimization in the aluminum industry has attracted some researchers' attention, and some research has been done in the production scheduling of aluminum casting [58,59] and aluminum electrolytic cell [60,61]. However, the research regarding the production scheduling in aluminum hot rolling is still in its infancy, and bounded literature can be found [8,9,28,36,51,52]. The aluminum industry is an important component of the metallurgical industry.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Cao et al [35] formulated a model for work roll wear prediction, a hot roll profile model, and a three-dimensional finite element model of the roll system and strip steel with MATLAB and ABAQUS software. For deep learning methods, Hu et al [36] and Bagheripoor and Bisadi [37] created rolling force prediction models with adaptive neural networks and artificial neural networks, respectively, based on the classification systems in the aluminum and steel industries. Wang et al [38] formulated a model for the prediction of the bending force of hot-rolled strip steel using an artificial neural network optimized by the genetic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, how to consider the uncertainty of demand in the rolling horizon and realize the global optimization regarding the production in the furnace hot rolling section, still remains to be an interesting and promising topic. Thirdly, researchers focus on such methods as robust optimization [29,30], machine learning [31][32][33][34][35], deep learning [36][37][38] and fuzzy planning [39] to describe uncertain factors in the production scheduling of hot rolling. However, due to the intrinsic characteristics of the methods themselves, they are not suitable for describing the uncertainty of demand in rolling horizon.…”

Section: Introductionmentioning

confidence: 99%

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Sustainable Scheduling of the Production in the Aluminum Furnace Hot Rolling Section with Uncertain Demand

et al. 2021

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In order to reduce the energy consumption of furnaces and save costs in the product delivery time, the focus of this paper is to discuss the uncertainty of demand in the rolling horizon and to globally optimize the sustainability of the production in the aluminum furnace hot rolling section in environmental and economic dimensions. First, the triples α/β/γ are used to describe the production scheduling in the aluminum furnace hot rolling section as the scheduling of flexible flow shop, satisfied to constraints of demand uncertainty, operation logic, operation time, capacity and demand, objectives of minimizing the residence time of the ingot in the furnace and minimizing the makespan. Second, on the basis of describing the uncertainty of demand in rolling horizon with the scenario tree, a multi-objective mixed integer linear programming (MILP) optimization model for sustainable production in the aluminum furnace hot rolling section is formulated. Finally, an aluminum alloy manufacturer is taken as an example to illustrate the proposed model. The computational results show that when the objective weight combination takes the value of α=0.7, β=0.3, the sustainability indicators of the environmental and economic dimensions can be optimized to the maximum extent possible at the same time. Increasingly, managerial suggestions associated with the trade-off between environmental and economic dimensions are presented. Scheduling in the rolling horizon can optimize the production process of the aluminum furnace hot rolling section globally, indicating that it is more conducive to the sustainable development of the environment and economic dimensions than scheduling in a single decision time period.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sustainable Scheduling of the Production in the Aluminum Furnace Hot Rolling Section with Uncertain Demand

et al. 2021

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“…The rolling schedule for five-stand tandem cold mill was established in [22]. The rolling power model is expressed as:…”

Section: A the Objective Functions Of The Rolling Schedulementioning

confidence: 99%

Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill

Wang

Yin

et al. 2020

IEEE Access

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The optimization of rolling schedule is the main content of tandem cold rolling which will affect the quality of products directly. A rolling schedule with the objectives of minimum energy consumption, relative power margin and slippage preventing is established. First, in order to make the rolling schedule more accurate in the calculation process, a mathematical model combines with deep neural network is proposed to calculate the rolling force. Second, a multi-objective particle swarm optimizer with dynamic opposition-based learning is proposed to optimize the rolling schedule. It has a new particle learning strategy to update the moving position of particles. Moreover, opposition-based learning is proposed to make the particles jump out of local optima. Finally, the experiments are carried out based on the field data. Simulation results demonstrate that the accuracy of the rolling force is greatly improved. The proposed algorithm has a promising performance on both diversity and convergence. At the same time, the optimized rolling schedule can well balance the rolling power and prevent slipping between five stands comparing with the original rolling schedule. INDEX TERMS Multi-objective optimization, tandem cold rolling, deep neural network, rolling force, rolling schedule.

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“…As a result, the novel diagnosis method is based on PSO and SVM defined as PSO-SVM. Generally, the SVM technique based on the statistical learning theory has been proven to be an effective tool to predict natural parameters from very different fields (Zhang and Wang, 2015), such as rolling schedule optimization (Hu et al , 2016), hydraulic pressing system identification (Yu et al , 2012), strip surface defects recognition (Hu et al , 2014) and so on.…”

Section: Introductionmentioning

confidence: 99%

A novel forecast model based on CF-PSO-SVM approach for predicting the roll gap in acceleration and deceleration process

Sun

Peng

et al. 2020

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Purpose In the cold rolling process, friction coefficient, oil film thickness and other factors vary dramatically with the change in the rolling speed, which seriously affects the strip thickness deviation. This paper aims to study the law among the parameters in the rolling process to improve the strip control precision. Design/methodology/approach In this paper, a novel forecasting model of the roll gap based on support vector machine (SVM) optimized by particle swarm optimization with compression factor (CF-PSO) is proposed. Based on lots of online data, the roll gap models regressed by PSO-SVM, genetic algorithm (GA)-SVM and CF-PSO-SVM are obtained and verified through evaluating the performances with the decision coefficient (R2), mean absolute error and root mean square error. In addition, with the good forecasting performances of CF-PSO-SVM, a roll gap compensation model is studied. Findings The results indicate that the proposed CF-PSO-SVM has excellent learning regression ability compared with other optimization algorithms. And the obtained roll gap compensation model based on the rolling speed and plastic coefficient have been applied in product, which is validated and gets a good product effect. Originality/value In this paper, the SVM algorithm is combined with traditional rolling technology to solve the problems in actual production, which has great supporting significance for the improvement of production efficiency.

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Multi-objective optimization of rolling schedules on aluminum hot tandem rolling

Cited by 24 publications

References 6 publications

Sustainable Scheduling of the Production in the Aluminum Furnace Hot Rolling Section with Uncertain Demand

Sustainable Scheduling of the Production in the Aluminum Furnace Hot Rolling Section with Uncertain Demand

Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill

A novel forecast model based on CF-PSO-SVM approach for predicting the roll gap in acceleration and deceleration process

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