Dynamic control of secondary cooling in continuous casting is the key technology to improve the quality and efficiency of the slab. In order to improve the quality and efficiency of the slab, a new control strategy of secondary cooling water distribution based on an improved artificial bee colony algorithm is proposed. Based on the traditional artificial bee colony algorithm, adding the learning factor to the optimal individual learning factor and random individual of the current population and integrating the output overshoot mechanism into the objective function is effective to avoid the great overshoot phenomenon caused by the abruptly change of casting speed and the large fluctuation of casting surface temperature. The transfer function was constructed based on the coupling relationship between the distribution of secondary cooling water and slab surface temperature. The Simulink simulation model was established, which was compared with the PSO-DSWPI Method and conventional PID controller. The results showed that the improved artificial bee colony algorithm has a more minor error, higher control accuracy and more smooth step response, which provided a new method for realizing more accurate control of secondary cooling dynamic water distribution and had practical significance for improving slab quality.

PID Parameter Optimization of Secondary Cooling Water Distribution Model for Continuous Casting Based on Improved Artificial Bee Colony Algorithm

Digitalization of the real manufacturing process according to Industry 4.0 becomes necessary for industrial producers. Digitalization increases their competitiveness on the market through optimization of processes in the production chain. The rise of prices of raw materials, water shortage, and the effort of the society to reduce the CO2 and greenhouse gas emissions must be compensated by lower production costs. Metallurgical processes and steel production are this case. In the last year, the price of iron ore and scrap metal has risen by more than a third, which is directly proportional to the steel price. The consequence is the increase in the construction sector, car production, white goods, etc. There is a strong assumption that in the next years, steel prices will remain relatively high. Significant cost savings in the production can be achieved by the minimization of the number of rejected slabs and billets, which need to be scraped as well as by the minimization of the cooling water consumption. This paper shows the potential of the use of the original solidification model BrDSM. This long-term validated solidification model represents a digital copy of the real continuous casting process. In the future modern steelmaking process, these models will be irreplaceable. 

Solidification model of a casting process and its irreplaceability

In this paper is proposed an evaluation of ten metaheuristic optimization algorithms applied on the inverse optimization of the Interfacial Heat Transfer Coefficient (IHTC) coupled on the solidification phenomenon. It was considered an upward directional solidification system for Al-7wt.% Si alloy and, for IHTC model, a exponential time function. All thermophysical properties of the alloy were considered constant. Scheil Rule was used as segregation model ahead phase-transformation interface. Optimization results from Markov Chain Monte Carlo method (MCMC) were considered as reference. Based on average, quantiles 95% and 5%, kurtosis, average iterations and absolute errors of the metaheuristic methods, in relation to MCMC results, the Flower Pollination Algorithm (FPA) and Moth-Flame Optimization (MFO) presented the most appropriate results, outperforming the other methods in this particular phenomenon, based on these metrics. The regions with the most probable values for parameters in IHTC time function were also determined. 

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Performance Analysis of Metaheuristic Optimizers on the Estimation of the Interfacial Heat Transfer Coefficient in Aluminum–Silicon Permanent Mold Casting

Abstract Digitalisation of metallurgical manufacturing, especially technological continuous casting using numerical models of heat and mass transfer and subsequent solidification has been developed to achieve high manufacturing efficiency with minimum defects and hence low scrappage. It is still challenging to perform adaptive closed-loop process adjustment using high-fidelity computation in real-time. To address this challenge, surrogate models are a good option to replace the high-fidelity model, with acceptable accuracy and less computational time and cost. Based on deep learning technology, here we developed a real-time prediction (ReP) model to predict the three-dimensional (3D) temperature field distribution in continuous casting on millisecond timescale, with mean absolute error (MAE) of 4.19 K and mean absolute percent error (MAPE) of 0.49% on test data. Moreover, by combining the ReP model with machine learning technology—Bayesian optimisation, we realised the rapid decision-making intelligent adaptation of the operating parameters for continuous casting with high predictive capability. This innovative and reliable method has a great potential in the intelligent control of the metallurgical manufacturing process. 

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Real-time prediction and adaptive adjustment of continuous casting based on deep learning

Thermal imaging-based state estimation of a Stefan problem with application to cell thawing

The paper presents the comparison of optimization-regulation algorithms applied to the secondary cooling zone in continuous steel casting where the semi-product withdraws most of its thermal energy In steel production, requirements towards obtaining defect-free semi-products are increasing day-by-day and the products, which would satisfy requirements of the consumers a few decades ago, are now far below the minimum required quality To fulfill the quality demands towards minimum occurrence of defects in secondary cooling as possible, some regulation in the casting process is needed The main concept of this paper is to analyze and compare the most known metaheuristic optimization approaches applied to the continuous steel casting process Heat transfer and solidification phenomena are solved by using a fast 25D slice numerical model The objective function is set to minimize the surface temperature differences in secondary cooling zones between calculated and targeted surface temperatures by suitable water flow rates through cooling nozzles Obtained optimization results are discussed and the most suitable algorithm for this type of optimization problem is identified Temperature deviations and cooling water flow rates in the secondary cooling zone, together with convergence rate and operation times needed to reach the stop criterium for each optimization approach, are analyzed and compared to target casting conditions based on a required temperature distribution of the strand The paper also contains a brief description of applied heuristic algorithms Some of the algorithms exhibited faster convergence rate than others, but the optimal solution was reached in every optimization run by only one algorithm

Comparison of Optimization-Regulation Algorithms for Secondary Cooling in Continuous Steel Casting

Nowadays, people are increasingly interested in renewable energy sources and accumulation of energy for its efficient use. The use of non-renewable resources is progressively decreasing due to their adverse changes in climate conditions and high production of CO2 emissions. This work deals with the problem of heat accumulation by means of the phase change of a material using the Stefan problem, which serves to describe the temperature distribution in the medium and to determine the location of the interface between the solid and liquid phase. This approach is used to determine desired properties and thermal behaviour of the material under different accumulation requirements. The main objective was to create and solve an optimization model in order to determine heat transfer conditions and other parameters to ensure the extrema of thermal behaviour characterization.

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Optimization of Material Properties of Phase Change Materials for Latent Heat Thermal Energy Storage

A micro cogeneration unit based on a three-cylinder internal combustion engine, Skoda MPI 1.0 L compressed natural gas (CNG), with an output of 25 kW at 3000 RPM is proposed in this paper. It is a relatively simple engine, which is already adopted by the manufacturer to operate on CNG. The engine life and design correspond to the original purpose of use in the vehicle. A detailed dynamic model was created in the GT-SUITE environment and implemented into an energy balance model that includes its internal combustion engine, heat exchangers, generator, battery storage, and water storage tank. The 1D internal combustion engine model provides us with information on engine start-up time, actual effective power, friction power, and the amount of heat going to the cooling system and exhaust pipe. The catalytic converter was removed from the exhaust pipe, and the engine was always operating at full load; thus, engine power control is not considered. An energy storage system for an island operation of the entire power unit for a large, detached house was designed to withstand accumulated energy for a few days in the case of a breakout. To reach a low initial system cost, the possible implementation of worn-out battery packs toward emission reduction in terms of the second life of the battery is proposed. The energy and emission balance are carried out, and the service life of the engine is also discussed.

https://www.mdpi.com/1996-1073/14/8/2102/pdf

Small Cogeneration Unit with Heat and Electricity Storage

Steelmakers focus on producing high quality steel to minimize defects, as this strategy maximizes their productivity and profit. The paper describes how to create a macro model of solidification of continuously cast steel. The described model is fully 3D, non-stationary and is implemented in a form suitable for GPU (Graphics Processing Unit). The formation of blank cracks follows the continuous casting of steel from the beginning and is a problem for the production of some steel grades. The various criteria that can predict the occurrence of defects in continuous steel casting can serve to optimize the casting process, control the blank cooling, and then predict the quality of the resulting blank.

Michal Brezina

Papers

Comparison of Optimization-Regulation Algorithms for Secondary Cooling in Continuous Steel Casting

Optimization of Material Properties of Phase Change Materials for Latent Heat Thermal Energy Storage

Small Cogeneration Unit with Heat and Electricity Storage

The real-time macro-solidification macro model for crack prediction