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Billet production is for the most part completed in a continuous casting machine. To produce a billet of suitable quality, the secondary cooling control system needs to provide an appropriate value of water flow rate. At present, most secondary cooling control methods are based on a heat transfer model for the billet, the accuracy of which can directly impact the cooling effect and further affect its quality. In practice, it is difficult to determine the heat flux at the billet’s boundaries due to the complexity of continuous casting. Therefore, this study focuses on identifying the boundary conditions for the 3D heat transfer model of a billet with a linear coefficient. First, we transform the identification of boundary conditions into an optimization problem, prove the Lipschitz continuity of the cost function of the optimization problem, and obtain the Lipschitz constant. Second, based on the Lipschitz continuity of the cost function, we present a modified hybrid conjugate gradient algorithm (MHCGA) to solve the optimization problem and then prove the global convergence of this MHCGA. Compared with other methods, the results of the simulation experiments clearly show that this MHCGA can reduce running time and iteration number. Third, to eliminate the ill-posedness of the inverse problem for identifying the boundary condition, we combined a regularization method with MHCGA. Simulation experiments confirmed that this method can effectively estimate the boundary conditions. Finally, the experimental data from a steel plant verified the validity of our method, and the prediction results of the shell thickness were confirmed by the nail shooting method.

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. It proposes a method of ladle re-scheduling in the production process of steelmaking–refining–continuous casting, including two parts: plan re-scheduling and ladle optimisation scheduling. Firstly, a re-scheduling optimisation model of the steelmaking and continuous casting production is built, which 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. The re-scheduling 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 by the rule reasoning of the minimum general generalisation. The ladle optimisation scheduling method consisting 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, 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, it was actually verified based on a large-scale steel company in Shanghai, China. Results showed that the production efficiency of steelmaking-refining-continuous casting was improved.

Wind load is one of the main factors of plastic greenhouse collapse. To solve the dynamic response problem of greenhouses under wind load and determine the dangerous section of a skeleton structure, the investigated lump method is presented for the dynamic response analysis of a plastic greenhouse, considering pulsating wind on the basis of Timoshenko beam theory. First, the investigated lump is designed according to the Timoshenko beam microbody concept. On the basis of Timoshenko beam theory, the governing equations of the skeleton structure of the greenhouse are derived, and the realization process of the algorithm is also provided. Second, the accuracy and effectiveness of the proposed numerical method are verified by an example in which the bending wave of a variable cross section beam with free ends propagates. Finally, the dynamic response of the steel skeletons of plastic greenhouses is analyzed under the effect of the simulation wind speed, and the spatial distribution of the maximum node displacement and the section maximum stress of the steel skeleton are obtained. Computational results show that the displacement peak is near the top of the plastic greenhouse. The most dangerous section of the top chord in the steel skeleton is near the leeward bottom, which has a maximum stress of 219.4 MPa, and the most dangerous section of the bottom chord is near the 1 m height on the leeward side of the plastic greenhouse, which has a maximum stress of 248.5 MPa. Bending stress is the main factor of the rapid increase of stress at the bottom of the skeleton. The maximum node displacement and cross-sectional stress caused by fluctuating wind loads are higher than those caused by average wind loads. The fluctuating wind load should be considered in the wind-induced response analyses of plastic greenhouses.

Steelmaking-refining-Continuous Casting (SCC) is a key process in iron and steel production. SCC scheduling is to determine an optimal schedule for the SCC process, which is a worldwide and important problem. High-quality SCC scheduling methods will help to allocate production resources effectively and increase the productivity. However, dynamic events (e.g. machine breakdown) may happen in the realistic SCC process, which will make the SCC schedule inexecutable or not optimal. In this case, SCC rescheduling is essential in order to obtain a new optimal schedule suitable for the current production environments. The SCC rescheduling can be modeled as hybrid flowshop rescheduling. In this paper, an Improved Imperialist Competitive Algorithm (IICA) is proposed to address the SCC rescheduling. In the proposed IICA, an empire initialization is first devised for constructing an initial population with diversity and certain quality. Moreover, multiswap-based local search and imperialist competition are designed to improve the exploitation ability of the IICA, while revolution and restart strategy are devised to enhance the exploration ability of the IICA. Comparison experiments with three kinds of ICA have shown the efficiency of the IICA.

The rigid constraint of using heat in determining electricity for thermal power units is eliminated to improve the absorption capacity of wind power. In this study, heat storage devices and electric boilers are added to the cogeneration system to alleviate the wind curtailment phenomenon. First, the main reasons for wind curtailment are analyzed according to the structural characteristics of the power supply in the northern part of China. Second, a scheduling model of a cogeneration system, including a heat storage device and an electric boiler, is constructed. An improved artificial bee colony algorithm program is also designed and compiled based on MATLAB. Finally, the feasibility of the proposed scheme is verified by simulation examples, and an economic analysis of wind power consumption is performed. Results show that adding electric boilers lessens coal consumption costs and improves economic benefits.

Power consumption prediction is the basis of implementing planned power consumption and preparing production plan. It is one of the main projects in the design of industrial and mining enterprises. It is also an important link to ensure the balance between national economic needs and power supply. Due to the influence of distributed energy and the change of power demand and load characteristics of the user side compared with the past, the power consumption prediction starts to face small-scale users and is more easily disturbed by various influencing factors, so the traditional prediction method is not fully suitable for today's power consumption prediction. Firstly, STL is used to decompose the power consumption sequence of corresponding month into trend component, season component and random component. Secondly, the BP neural network model is used to predict the seasonal component of the month when the seasonal mutation and major festivals are located. ARIMA model is used to predict the trend component. The average value is used to predict the random components. Then, the predicted values of the three components are reconstructed into the final predicted values. Finally, the algorithm is compiled by R language, and the validity of the proposed method is verified by the actual monthly electricity sales data of a University Park in the north. And further consider the prediction method of economic factors.

A hybrid optimization method for the production of work order group furnaces was proposed in this paper. First, a working order group furnace model was constructed including performance index, the constraint condition and the decision variable to fit the problems in working order group furnaces. The hybrid optimization method consists of an optimal priority and a variable neighborhood search algorithm. In the algorithm, we have adopted a lot of rules and corresponding grade limits on stock production. Based on the proposed algorithm’s calculation results, the delivery time deviation, grade deviation and priority deviation of the 20 group furnace production orders are reduced from 58 to 42 with reduction rate of 27.59%. The satisfaction rate for grade preparation is increased from 4 to 6, which has a rate of increase of 50%. In order to prove the effectiveness of the proposed algorithm, the proposed algorithm is compared with other literature algorithms such as a discrete particle swarm algorithm, a variable neighborhood search algorithm and an adaptive variable neighborhood search algorithm.

A Lagrangian relaxation (LR) approach was presented which is with machine capacity relaxation and operation precedence relaxation for solving a flexible job shop (FJS) scheduling problem from the steelmaking-refining-continuous casting process. Unlike the full optimization of LR problems in traditional LR approaches, the machine capacity relaxation is optimized asymptotically, while the precedence relaxation is optimized approximately due to the NP-hard nature of its LR problem. Because the standard subgradient algorithm (SSA) cannot solve the Lagrangian dual (LD) problem within the partial optimization of LR problem, an effective deflected-conditional approximate subgradient level algorithm (DCASLA) was developed, named as Lagrangian relaxation level approach. The efficiency of the DCASLA is enhanced by a deflected-conditional epsilon-subgradient to weaken the possible zigzagging phenomena. Computational results and comparisons show that the proposed methods improve significantly the efficiency of the LR approach and the DCASLA adopting capacity relaxation strategy performs best among eight methods in terms of solution quality and running time.

Power grid is developing towards the direction of intelligence. Nevertheless, the giant grid scale, complicated structure and external natural factors result in the potential of large-scaled power failure. Traditional black start power gives priority to hydropower. In areas with shortage of hydro power resources, suitable black start power is insufficient. The key of black start is able to realize the safe and stable operation of wind power and energy storage cogeneration system. Due to the combination of wind turbines and energy storage is a small system, energy storage, as a black start power source, starts the wind turbines together to supply power for the remote load. Therefore, it is necessary to run the balance and stable operation of a small system. In this paper, PSCAD simulation software is employed to conduct modeling of wind power and energy storage system, and the self-starting process of wind storage cogeneration system is analyzed by using resistance sensor as the load. The result verifies the wind power storage system's initiation process.

The control objective of the forced-circulation evaporation process of alumina production is not only to avoid large fluctuations of the level, but also to ensure the product density to track its setpoint quickly. Due to the existence of strong coupling between the level loop and the product density loop, and high nonlinearities in the process, the conventional control strategy cannot achieve satisfactory control performance, and thus the production demand cannot be met. In this paper, an intelligent decoupling PID controller including conventional PID controllers, a decoupling compensator and a neural feedforward compensator is proposed. The parameters of such controller are determined by generalized predictive control law. Real-time experiment results show that the proposed method can decouple the loops effectively and thus improve the evaporation efficiency.

Steelmaking–continuous casting is a complex process. The method of selecting a ladle, which also functions as a storage device, follows a specific process of the production plan. In ladle matching, several ladle attributes are considered. However, matching objectives are difficult to achieve simultaneously. Different molten steel properties have contributed to the complexity of matching constraints, and, thus, matching optimization is regarded a multiconflict goal problem. In the process of optimization, the first-order rule learning method is first used to extract key ladle attributes (performance indicators), including highest temperature, usage frequency, lowest-level material, and outlet. On the basis of a number of indicators, such as ladle temperature, quantity, material, and usage frequency, as well as skateboard quantity, the ladle matching model is established. Second, the rule of ladle selection is determined by the method of least-generalization rule learning. Third, a simulation experiment is carried out according to various scheduling order strategies and matching priority combinations. Finally, the heuristic ladle matching method based on the rule priority (RP) is determined for possible industrial applications. Results show that the accuracy of ladle selection can be improved. In particular, the numbers of ladles and maintenance times are reduced. Consequently, furnace production efficiency is also enhanced.

The quality of rolling unit planning affects production cost, product quality and productivity. The existing research methods are not applied in practice because the surface quality requirement of the hot rolling coil and the integrated production with two-strand caster and hot rolling machine are neglected, and the width of each waiting produced slab (dummy slab) of is pre-determined when it is casted on caster. At present, the average length of hot rolling plan units is smaller by manual method. Considering slab position limited in rolling unit because of the surface quality requirement, the integrated production with two-strand caster and hot rolling machine, and the selectable width of each waiting produced slab, a model for the single mode rolling unit planning problem of direct hot charge rolling (DHCR) is established. And a method based on a hybrid of variable neighborhood search and ant colony optimization is proposed. The experiment with industrial slab data shows that the results are better than those obtained by artificial methods.

Because of different refining route of every charge, it's difficult to make a static scheduling plan for steelmaking-refining-continuous casting (SRCC) production. There are many types of disturbances that upset the scheduling plan including processing time variation, temperature variation, quality variation, flow speed variation of cater, machine failures, and which could cause the static scheduling to become inefficient and even infeasible. It is necessary to adjust the schedule or generate a new executable schedule upon the occurrence of unanticipated disruptions and changes. An optimization scheduling strategy framework is proposed including static scheduling, dynamic scheduling, assistant equipment scheduling. The static scheduling plan is made by using expert systems, programming method, fuzzy evaluation. The dynamic scheduling is including the partial rescheduling and complete rescheduling by using heuristic and programming methods. The assistant equipment scheduling plan is made based on heuristic method. The dynamic intelligent scheduling system was developed with the optimization scheduling strategy, and has been applied to a large steel enterprise steelmaking-refining-continuous casting production.

This paper studies a steelmaking-continuous casting (SCC) rescheduling problem with machine breakdown and processing time variations. Two objectives are considered in this study: the efficiency objective and the stability objective. The former refers to the total weighted completion time and total sojourn time, whereas the latter refers to the number of operations processed on different machines in the initial and revised schedules. We develop a time-index formulation and an effective Lagrangian relaxation (LR) approach with machine capacity relaxation to address the rescheduling problem. The LR approach decomposes the relaxed problem into batch-level subproblems with variable processing times. A polynomial two-stage dynamic programming algorithm is proposed to solve the batch-level subproblems. An efficient subgradient algorithm with global convergence is presented to solve the corresponding Lagrangian dual (LD) problem. Computational experiments based on practical production data show that the proposed approach not only produces a high quality schedule within an acceptable time but also performs much better than a practical SCC rescheduling method from a large iron and steel enterprise in China.

There are many types of disturbances that upset the plan in steelmaking and continuous casting (SCC) production, including processing time variation, temperature variation, quality variation, machine failures, which could cause the static schedule to become inefficient and even infeasible. It is necessary to adjust the schedule or generate a new executable schedule upon the occurrence of unanticipated disruptions and changes. Two rescheduling methods are presented: the partial rescheduling and complete rescheduling. The former refers to the right shift partial rescheduling based on continuous casting, the partial rescheduling based on case-based reasoning and man-computer interaction. The latter refers to the complete rescheduling considering changeable processing time, the complete rescheduling considering changeable processing time and production path. The mathematical models of two class complete rescheduling are formulated, and the solving methods are also given. The intelligent rescheduling system for SCC production with these methods is successfully applied to one large steel plant, and rapidly optimal rescheduling is achieved when disturbances occur.

In steelmaking and continuous casting (SMCC) production process, converter fault can lead to unexpected changes to the pre-specified converter-continuous caster production mode so that the original scheduling plan becomes unrealizable. In this paper, the dynamic scheduling problem in response to converter fault is firstly analyzed. This is then followed by the establishment of a novel multi-objective nonlinear programming model (MONPM) by introducing the production mode parameter I±, production schedule parameter I² and I�. The proposed method considers changes in production mode, production schedule of charge, the interval characteristics of processing time. In specific, a two-stage dynamic optimal scheduling method is proposed including the production path planning of charges (PPP) and the production time scheduling (PTS). As a result, a dynamic optimal scheduling software system (DOSSS) is developed and is successfully applied to the scheduling of the largest iron and steel company (BaoSteel) in China. The real-time application shows that the proposed method can efficiently reduce scheduling time, significantly increase the outputs of converters and dramatically shorten the redundant waiting time for molten steel.

Delayed processing time and machine failures upset the plan in steelmaking-continuous casting (SCC) production, which make production plan infeasible. The manual repairing cannot satisfy objectives of quick response and optimal scheduling. The practical rescheduling with 1-4 refining stages cannot abstract as three stages hybrid flow shop (HFS). A method is presented for the rescheduling problem, which includes two steps. Firstly, the assigning machine strategy and its computing algorithm are given. Secondly, the mathematical models are developed as multi-objective model based on practical production constraints, so as to solve machine conflicts. The dynamic scheduling system for SCC with the rescheduling method has been successfully applied to Shanghai Baoshan steel plant, and realizes rapidly optimal rescheduling.

This paper studies a real-world hybrid flow shop problem arising from the steelmaking continuous casting process, which is the bottleneck of the iron and steel production. There are a variety of features to be taken into account, in particular the batch constraints and the variable processing times in the last stages. Based on a time-index formulation and machine capacity relaxation, three Lagrangian relaxation (LR) approaches are presented for addressing this scheduling problem. The three LR approaches decompose the relaxed problem into job-level problems, batch-level problems and machine-level problems, respectively. These subproblems are solved based on polynomial dynamic programming algorithms. The corresponding Lagrangian dual (LD) problems are solved by an efficient subgradient algorithm with global convergence. Computational results and comparisons demonstrate that the approach adopting job-level decomposition is most efficient among three approaches, whereas the approach adopting batch-level decomposition is most effective.

The steelmaking continuous casting and mould casting is a procedure which is in high temperature, high comanding for real time, with significant time effect and complexity for schedule. For mould casting cannont be replaced by continuous casting in some area, the study in mould casting schedule has significant engineering value. To the actual situation, this paper aims at cracking the hard nut to make scheduling in workshop of steel-making plant.

There will be a variety of unusual circumstances at any time when the steelmaking scheduling plan is in the implementation, so it is the important thing for steelmaking scheduling study the that how to deal with such emergencies and disturbances, coordinated changes in a variety of uncertainties, lower the production costs, reduce losses, balance the logistics and improve the production efficiency. In this thesis, an analysis of the issue of dynamic scheduling steelmaking-related content is made under the background of a steelmaking factory in a large iron and steel enterprise. Deep research on the dynamic scheduling and dynamic scheduling methods for the mixed production of the continuous casting and mold casting is made.