Steelmaking-continuous Casting Research Articles

Simulation Model of a Steelmaking–Continuous Casting Process Based on Dynamic-Operation Rules

The steelmaking–continuous casting process (SCCP) is a complex manufacturing process which exhibits the distinct features of process manufacturing. The SCCP involves a variety of production elements, such as multiple process routes, a wide array of smelting and auxiliary devices, and a variety of raw and auxiliary materials. The production-simulation of SCCP holds a natural advantage in being able to accurately depict the intricate production behavior involved, and this serves as a crucial tool for optimizing the production operation of the SCCP. This paper thoroughly considers the various production elements involved in the SCCP, such as the fluctuation of the converter smelting cycle, fluctuation of heat weight, and ladle operation. Based on the Plant Simulation software platform, a dynamic simulation model of the SCCP is established and detailed descriptions are provided regarding the design of an SCCP using dynamic-operation rules. Additionally, a dynamic operational control program for the SCCP is developed using the SimTalk language, one which ensures the continuous operation of the caster in the SCCP, using the discrete simulation platform. The effectiveness of the proposed dynamic simulation model is verified by the total completion time of the production plan, the transfer time of the heat among the different processes, and the frequency of ladle turnover. The simulation’s results indicate that the dynamic simulation model has a satisfactory effect in simulating the actual production process. On this basis, the application effects of different schedules are compared and analyzed. Compared with a heuristic schedule, the optimized schedule based on the “furnace–machine coordinating” mode reduces the weighted value of total completion time by 8.7 min, reduces the weighted value of transfer waiting time by 45.5 min, and the number of rescheduling times is also reduced, demonstrating a better application effect and verifying the optimizing effect of the “furnace–machine coordinating” mode on the schedule.

Deep reinforcement learning for solving steelmaking-continuous casting scheduling problems under time-of-use tariffs

This paper proposes a novel intelligent scheduling method based on deep reinforcement learning (DRL) to solve the multi-objective steelmaking-continuous casting (SCC) scheduling problem, under time-of-use (TOU) tariffs for the first time. The intelligent scheduling system architecture is designed, and a mathematical model is established to minimise the total sojourn time and electricity cost. To effectively reduce production costs by avoiding peak periods of electricity consumption, the ‘start time’ of the system is generated based on the Markov Decision Process (MDP), and heuristic scheduling rules related to power cost are used as the action space, with corresponding reward functions designed according to the characteristics of these two objectives. To satisfy the continuous casting which is a particular SCC constraint, a backward strategy is developed. Additionally, a branching duelling double deep Q-network (BD3QN) is adapted to guide action selection and avoid blind search in the iteration process, and then applied to real-time scheduling. Numerical experiments demonstrate that the proposed method outperforms comparison algorithms in terms of solution quality and CPU times by a large margin.

A novel method with constraints embedded into a cuckoo search for steelmaking–continuous casting scheduling

A novel method with constraints embedded into a cuckoo search for steelmaking–continuous casting scheduling

Two-stage distributionally robust integrated scheduling of oxygen distribution and steelmaking-continuous casting in steel enterprises

In the steel industry, the imbalance between fluctuating oxygen demand and stable supply generally results in excessive oxygen emissions and power waste. Independent optimal scheduling of oxygen distribution (OD) or steelmaking-continuous casting (SCC) has limited ability to handle this issue. To address this challenge, we propose an integrated scheduling model that allows for flexible interaction between OD and SCC. We first develop an SCC scheduling model that applies to machine sharing of duplex and conventional smelting, and derive an OD scheduling model. A critical demand interface is further designed to connect the scheduling models of SCC and OD, resulting in a mixed integer linear programming-based integrated scheduling model. Existing research overlooks the probability information of demand uncertainty posed by unpredictable interferences, which limits the ability to handle uncertain demands. To address this limitation, we embed demand probability distribution via a data-driven ambiguity set and upgrade the deterministic integrated scheduling model via two-stage distributionally robust optimization. Our model was tested with real data and resulted in a 15% reduction in total costs compared to independent scheduling of OD and SCC. Additionally, it effectively reduced uncertainty risk to 8.3% with low conservativeness, demonstrating superior performance compared to recent methods.

A decomposition-based two-stage online scheduling approach and its integrated system in the hybrid flow shop of steel industry

Steelmaking-continuous casting (SCC) is one of the most critical building blocks in the modern steel industry. Many random events occur in the real-world SCC production system. In this paper, we propose a two-stage online scheduling policy that protects the baseline schedule by the slacks provided by intra-flow times and casting speeds. The main task of the two-stage online scheduling model is: (1) to make “here-and-now” decisions for minimizing economic costs and penalties caused by constraint violations; (2) make “wait-and-see” decisions for online scheduling. Afterward, we propose a decomposition-based optimization algorithm that divides the online scheduling problem into a master problem (MP) to seek partial solutions at the last processing stage and a slave problem (SP) to check optimal solutions for upstream processing stages. Then, we employ the IBM ILOG CPLEX to solve MP and use the constraint programming (CP) optimizer to solve SP. Sensitivity analyses and algorithm comparisons are conducted on a set of well-synthetic and realistic instances to validate the proposed model and algorithm. The results show that the proposed online scheduling model and algorithm can solve realistic industrial case studies. Finally, we also develop a scheduling system integrating the proposed model and algorithm.

An Efficient and Effective Approach for the Scheduling of Steelmaking-continuous Casting Process With Multi Different Refining Routes

With the rapid development of market-oriented demand of iron and steel enterprises towards multi variety, small batch and on-time delivery, the functional requirements of steelmaking-continuous casting (SCC) production process with multi different refining routes for the dynamic balance of logistics, resources and time increase sharply. An effective scheduling of SCC process is important significance for improving the efficiency of steelmaking production and reducing the material and energy consumption. This paper develops an improved Lagrangian relaxation (LR) approach to address this problem based on time-indexed formulation while considering SCC scheduling performance indexes: sojourn time and tardiness of each job in the last stage. A direction controllable target estimation level adjustment strategy with tighter lower bound is addressed to overcome the premature convergence and weaken the oscillation in LR iteration. Computational experiment is verified based on the actual steelmaking plant in China. The computational results show that the approach improves the schedule strategy for the actual SCC process, and promotes the development of the schedule theory with the separate structure characteristics.

An integrated production batch planning approach for steelmaking-continuous casting with cast batching plan as the core

The development of the high-efficiency continuous casting technology makes it possible to continuously cast with multi-cast batching plans. The preparation of such steelmaking-continuous casting (SCC) production batching plan has become a new requirement to promote the efficient steel production. From the perspective of overall production system, an integrated approach with the cast batching plan as the core was proposed, which changes the step-by-step optimization way of combination and sequencing for preparing charge batching plan and cast batching plan sequentially. This approach, which can perform integrated combination and sequencing, directly prepares the SCC production batching plan with multiple cast batching plans that can be continuously cast, and takes into account the charge batching plans. Taking minimizing the weight of unplanned slabs, maximizing the production efficiency of continuous casters, and minimizing the penalty value for the change of dimensions of the adjacent slabs as the objectives, a multi-objective mixed-integer programming model was established. An improved NSGA-II combined with a local search strategy was designed, and the practical aspects such as multiple grades continuous casting rules and on-line width adjustable rules were considered. The test result with data from a steel plant in China indicated that the improved algorithm could obtain better slab combination sequence results compared with the NSGA-II and SPEA2. Simultaneously, compared with the manual approach and the traditional approach of preparing the charge batching plan and cast batching plan sequentially, the integrated approach can not only reduce the residual material rate by 4.96% and 0.42%, improve the quality of the solution, but also improve the efficiency of planning.

An enhanced cross-entropy algorithm for the green scheduling problem of steelmaking and continuous casting with uncertain processing time

Under the mainstream trend of global carbon emission reduction, this paper studies an important steelmaking-continuous casting scheduling problem (SCCSP) considering the practical technological requirements in uncertain production environment. This problem can be modeled as the green SCCSP with uncertain processing time (GSCCSP_UPTHP), whose criteria are to minimize the maximum completion time, the average waiting time, and the carbon emissions simultaneously. An enhanced cross-entropy algorithm (ECEA) is proposed for addressing it. In terms of ECEA’s coding and decoding, a two-segment coding strategy and an adaptive decoding strategy are devised according to the problem’s characteristics. In ECEA’s global search, ECEA adopts two probability models with their own self-adjustment mechanisms to collaboratively learn the valuable information from excellent individuals and quickly guide the search to the promising regions. Moreover, the novel competition and assimilation mechanisms are devised to avoid ECEA falling into local optima early. In ECEA’s local search, a problem-dependent local search based on the key points (PDLS_KP) is proposed to sequentially execute the small neighborhood search (SNS) and the large neighborhood search (LNS) starting from the promising region of each selected key point. Meanwhile, a speed-up scanning method is developed to accelerate the search process of the LNS. Extensive experiments based on both the simulation and the real-world instances from an SCC process are constructed to show the effectiveness of TSOA in solving the GSCCSP_UPTHP.

Ladle intelligent re-scheduling method in steelmaking–refining–continuous casting production process based on BP neural network working condition estimation

Frequent delays will be experienced in the start-up of molten steel on the converter equipment during the steelmaking–continuous casting (SCC) production process due to the untimely supply of molten iron or scrap, which may cause conflicts between adjacent heat on the same equipment or in the same casting. The casting machine is cut off, resulting in the failure of the static scheduling plan. SCC production ladle re-scheduling is based on the premise that the production process path remains unchanged, the operation of adjacent heat on the converter and refining furnace does not conflict, and the casting of adjacent heat within the same casting is continuous. The ladle re-scheduling of steelmaking and continuous casting production aims at continuously casting many charges with the same cast and avoiding conflicts of adjacent charges on the same machine. This mechanism proposes a method of ladle re-scheduling in the production process of steelmaking–refining–continuous casting, which is divided into two parts: plan re-scheduling and ladle optimisation scheduling. Firstly, a re-scheduling optimisation model of the steelmaking and continuous casting production is built. This model aims at minimising the waiting time of all charges. The re-scheduling strategy of steelmaking and continuous casting production is proposed by interval processing time of charges and scheduling expert experience. This strategy is composed of two parts: re-scheduling charge decision and charge processing machine decision. Then, the first-order rule learning is used to select the optimisation target to establish the ladle optimal scheduling model. The ladle matching rules are extracted on the basis of the rule reasoning of the minimum general generalisation. The ladle optimisation scheduling method that consists of the optimal selection of the ladle and the preparation of the optimal path of the ladle is proposed. Ladle selection is based on the production process and adopts rule-based reasoning to select decarburised ladle after choosing dephosphorised ladle. Ladle path preparation, which is a multi-priority heuristic method, is designed to decide the path of the ladle from the converter to the refining furnace to the continuous casting machine. Finally, this mechanism was actually verified based on the large-scale data of a steel company in Shanghai, China. Results showed that the production efficiency of steelmaking-refining-continuous casting was improved.

Research on steelmaking-continuous casting production scheduling problem with uncertain processing time based on Lagrangian relaxation framework

Research on steelmaking-continuous casting production scheduling problem with uncertain processing time based on Lagrangian relaxation framework

Research on steelmaking-continuous casting production scheduling problem with uncertain processing time based on Lagrangian relaxation framework

Research on steelmaking-continuous casting production scheduling problem with uncertain processing time based on Lagrangian relaxation framework

An iterated greedy matheuristic for scheduling in steelmaking-continuous casting process

Steelmaking-Continuous Casting (SCC) is a bottleneck in the steel production process and its scheduling has become more challenging over time. In this paper, we provide an extensive literature review that highlights challenges in the SCC scheduling and compares existing solution methods. From the literature review, we collect the essential features of an SCC process, such as unrelated parallel machine environments, stage skipping, and maximum waiting time limits in between successive stages. We consider an SCC scheduling problem with as objective the minimisation of the weighted sum of cast break penalties, total waiting time, total earliness, and total tardiness. We formulate the problem as a mixed-integer linear programming model and develop an iterated greedy matheuristic that solves its subproblems to find a near-optimal solution. Through numerical experiments, we show that our algorithm outperforms two types of genetic algorithms when applied to test instances.

Steelmaking-continuous casting scheduling problem with multi-position refining furnaces under time-of-use tariffs

Steelmaking-continuous casting scheduling problem with multi-position refining furnaces under time-of-use tariffs

Quantitative evaluation of multi-process collaborative operation in steelmaking-continuous casting sections

Quantitative evaluation of multi-process collaborative operation in steelmaking-continuous casting sections

Scheduling of steelmaking-continuous casting process with different processing routes using effective surrogate Lagrangian relaxation approach and improved concave–convex procedure

This paper studies a steelmaking-continuous casting scheduling problem with different processing routes. We model this problem as a mixed-integer nonlinear programming problem. Next, Lagrangian relaxation approach is introduced to solve this problem by relaxing the coupling constraints. Due to the nonseparability in Lagrangian functions, we design an improved concave–convex procedure to decompose the Lagrangian relaxation problem into three tractable subproblems and analyse the convergence of the improved concave–convex procedure under some assumptions. Furthermore, we present an effective surrogate subgradient algorithm with global convergence to solve the Lagrangian dual problem. Lastly, computational experiments on the practical production data show the effectiveness of the proposed surrogate subgradient method for solving this steelmaking-continuous casting scheduling problem.

An efficient adaptive genetic algorithm for energy saving in the hybrid flow shop scheduling with batch production at last stage

AbstractThis article deals with energy saving in the hybrid flow shop scheduling problem with batch production at last stage, which has important application in energy‐intensive steelmaking‐continuous casting (SCC) process. We first establish a mixed integer programming model to reduce extra energy consumption, and then adopt genetic algorithm to solving the scheduling problem. Based on traditional genetic algorithm (TGA), the calculation of the fitness function as well as adaptive crossover and mutation are designed. Due to the complexity of the problem in this article, we then propose an efficient adaptive genetic algorithm (EAGA) to improve the search ability of TGA. The EAGA has new features including layered strategies and enhanced adaptive adjustment method. To evaluate the proposed model and algorithm, we conduct computational experiments under practical background and compare the EAGA with the several algorithms presented previously. The results illustrate that scheduling with our model can greatly reduce the extra energy consumption. Meanwhile, the proposed EAGA is very efficient in comparison.

Charge Plan Model for Steelmaking-Continuous Casting Section

Pursuing the intellectualization of a steelmaking plant and developing a charge plan of the steelmaking-continuous casting section are critical in metallurgy engineering. Herein, we aim to develop a charge plan model based on the operation of the steelmaking-continuous casting section to minimize the penalty values of residual materials; of a contract not selected and the penalty values that is caused by the difference in steel grades, the width and the delivery time between slabs in the same charge. We introduce an improved elitist genetic algorithm (IEGA), define the matching chromosome coding and decoding strategies, and suggest improving the selection, crossover, and mutation operators. Finally, we verify the proposed model and algorithm on the production data of a real enterprise. We clarify the applicability of developing a charge plan based on model analysis and demonstrate the effectiveness of the IEGA through algorithm analysis.

Energy-efficient steelmaking-continuous casting scheduling problem with temperature constraints and its solution using a multi-objective hybrid genetic algorithm with local search

With the increasing energy price and the urgent demand of manufacturing enterprises for energy conservation, energy-efficient scheduling (EES) technology has been widely investigated and applied in industry and academia. The steelmaking-continuous casting (SCC) production is the main energy-consuming sector and the key process for quality control of steel manufacturing. Due to the high-temperature characteristics of SCC production, the temperature drop deriving from non-processing process could directly lead to energy loss and increase the energy consumption of each procedure, which has important influence on the total energy consumption. Therefore, the energy-efficient steelmaking-continuous casting scheduling problem with temperature constraints (EESCCSPT) was concerned and a multi-objective mathematical programming model was introduced to minimize the penalty of due date deviation and the extra energy consumption measured by temperature drop. Comparing to the general scheduling problem, the constraint of minimum casting superheat and the constraint of target tapping temperature generated by the high-temperature technical requirements were directly considered to ensure schedule feasibility in terms of temperature. A multi-objective hybrid genetic algorithm combined with local search (MOHGALS) was presented, in which the enhanced evolutionary mechanisms combined with the improved genetic operators and the local search were also designed. Results of computational experiments showed that MOHGALS was more feasible and effective than NSGA-II and SPEA2 on the EESCCSPT.

Integrated Scheduling for Steelmaking Continuous Casting— Hot Rolling Processes considering Hot Chain Logistics

Steelmaking continuous casting (SCC)—hot rolling (HR) is a key process in the production of steel products. It is also a process with large energy consumption. Energy saving has always been an important goal of production scheduling of this process. In this paper, aiming at integrated scheduling optimization for SCC-HR processes, energy saving objective is converted to minimize waiting time of slabs in slab yard, so as to reduce slab temperature loss and achieve the goal of saving energy. An integrated two-stage mathematical programming model is established for the above problems, and a hybrid algorithm of genetic algorithm and linear programming is designed for the integrated model. The correctness of the model and the validity of the algorithm are verified by computational experiments using simulated instances.

Hybrid Scatter Search Algorithm for Optimal and Energy-Efficient Steelmaking-Continuous Casting

This article studies a steelmaking-continuous casting (SCC) scheduling problem by considering ladle allocation. It takes technological rules in steel manufacturing and ladle-related constraints into account. A scheduling problem is formulated to determine allocation equipment for jobs, production sequence for jobs processed by the same equipment, and modification operations for empty ladles after their service for jobs. To ensure the fastest production and least energy consumption, we present a mixed integer mathematical programming model with the objectives to minimize the maximum completion time, idle time penalties, and energy consumption penalties related to waiting time. To solve it, we develop a two-stage approach based on a combination of scatter search (SS) and mixed integer programming (MIP). The first stage applies an SS algorithm to determine the assignment and sequence variables for charges. For the obtained solution, we construct a temporal constraint network and establish an MIP model at the second stage. We apply ILOG.CPLEX to solve the model and find the final solution. We analyze and compare the performance of the proposed approach with a hybrid method that combines a genetic algorithm with MIP on instances constructed from a real iron-steel plant. To further verify the effectiveness of the proposed algorithm, we compare its results with optimal solutions of the constraint-relaxed original problem. The experimental results show the effectiveness of the proposed approach in solving the SCC-scheduling problem.