Towards A Computational Intelligence Framework in Steel Product Quality and Cost Control

Steel is a fundamental raw material for all industries. It can be widely used in vari-ous fields, including construction, bridges, ships, containers, medical devices and cars. However, the production process of iron and steel is very perplexing, which consists of four processes: ironmaking, steelmaking, continuous casting and rolling. It is also extremely complicated to control the quality of steel during the full manufacturing pro-cess. Therefore, the quality control of steel is considered as a huge challenge for the whole steel industry. This thesis studies the quality control, taking the case of Nanjing Iron and Steel Group, and then provides new approaches for quality analysis, manage-ment and control of the industry. At present, Nanjing Iron and Steel Group has established a quality management and control system, which oversees many systems involved in the steel manufacturing. It poses a high statistical requirement for business professionals, resulting in a limited use of the system. A lot of data of quality has been collected in each system. At present, all systems mainly pay attention to the processing and analysis of the data after the manufacturing process, and the quality problems of the products are mainly tested by sampling-experimental method. This method cannot detect product quality or predict in advance the hidden quality issues in a timely manner. In the quality control system, the responsibilities and functions of different information systems involved are intricate. Each information system is merely responsible for storing the data of its corresponding functions. Hence, the data in each information system is relatively isolated, forming a data island. The iron and steel production process belongs to the process industry. The data in multiple information systems can be combined to analyze and predict the quality of products in depth and provide an early warning alert. Therefore, it is necessary to introduce new product quality control methods in the steel industry. With the waves of industry 4.0 and intelligent manufacturing, intelligent technology has also been in-troduced in the field of quality control to improve the competitiveness of the iron and steel enterprises in the industry. Applying intelligent technology can generate accurate quality analysis and optimal prediction results based on the data distributed in the fac-tory and determine the online adjustment of the production process. This not only gives rise to the product quality control, but is also beneficial to in the reduction of product costs. Inspired from this, this paper provide in-depth discussion in three chapters: (1) For scrap steel to be used as raw material, how to use artificial intelligence algorithms to evaluate its quality grade is studied in chapter 3; (2) the probability that the longi-tudinal crack occurs on the surface of continuous casting slab is studied in chapter 4;(3) The prediction of mechanical properties of finished steel plate in chapter 5. All these 3 chapters will serve as the technical support of quality control in iron and steel production

Material and energy flows of the iron and steel industry: status quo, challenges and perspectives

Integrated analysis and optimization of material and energy flows in the iron and steel industry have drawn considerable interest from steelmakers, energy engineers, policymakers, financial firms, and academic researchers. Numerous publications in this area have identified their great potential to bring significant benefits and innovation. Although much technical work has been done to analyze and optimize material and energy flows, there is a lack of overview of material and energy flows of the iron and steel industry. To fill this gap, this work first provides an overview of different steel production routes. Next, the modelling, scheduling and interrelation regarding material and energy flows in the iron and steel industry are presented by thoroughly reviewing the existing literature. This study selects eighty publications on the material and energy flows of steelworks, from which a map of the potential of integrating material and energy flows for iron and steel sites is constructed. The paper discusses the challenges to be overcome and the future directions of material and energy flow research in the iron and steel industry, including the fundamental understandings of flow mechanisms, the dynamic material and energy flow scheduling and optimization, the synergy between material and energy flows, flexible production processes and flexible energy systems, smart steel manufacturing and smart energy systems, and revolutionary steelmaking routes and technologies

Optimal and Heuristic Lead-Time Quotation For an Integrated Steel Mill With a Minimum Batch Size

This paper presents a model of lead-time policies for a production system, such as an integrated steel mill, in which the bottleneck process requires a minimum batch size. An accurate understanding of internal lead-time quotations is necessary for making good customer delivery-date promises, which must take into account processing time, queueing time and time for arrival of the requisite volume of orders to complete the minimum batch size requirement. The problem is modeled as a stochastic dynamic program with a large state space. A computational study demonstrates that lead time for an arriving order should generally be a decreasing function of the amount of that product already on order (and waiting for minimum batch size to accumulate), which leads to a very fast and accurate heuristic. The computational study also provides insights into the relationship between lead-time quotation, arrival rate, and the sensitivity of customers to the length of delivery promises

Casting Process Improvement by the Application of Artificial Intelligence

On the way to building smart factories as the vision of Industry 4.0, the casting process stands out as a specific manufacturing process due to its diversity and complexity. One of the segments of smart foundry design is the application of artificial intelligence in the improvement of the casting process. This paper presents an overview of the conducted research studies, which deal with the application of artificial intelligence in the improvement of the casting process. In the review, 37 studies were analyzed over the last 15 years, with a clear indication of the type of casting process, the field of application of artificial intelligence techniques, and the benefits that artificial intelligence brought. The goals of this paper are to bring to attention the great possibilities of the application of artificial intelligence for the improvement of manufacturing processes in foundries, and to encourage new ideas among researchers and engineers

Preliminary Draft Report: State-of-the-Art Review of Integrated Systems Control in the Steel Industry

This is a preliminary draft version of the report to be issued on the "State-of-the-Art of Integrated Systems Control in the Steel Industry". The draft is incomplete and not necessarily in final form. Its purpose is to provide background material for the IIASA Conference on "Integrated Systems Control in the Steel Industry" scheduled for 30 June to 2 July, 1975. A second purpose is to motivate feedbacks concerning omissions and additions generated by respondents and Conference participants which may be incorporated into the final 'report

Auction-based approach to resolve the scheduling problem in the steel making process

Steel production is an extremely complex process and determining coherent schedules for the wide variety of production steps in a dynamic environment, where disturbances frequently occur, is a challenging task. In the steel production process, the blast furnace continuously produces liquid iron, which is transformed into liquid steel in the melt shop. The majority of the molten steel passes through a continuous caster to form large steel slabs, which are rolled into coils in the hot strip mill. The scheduling system of these processes has very different objectives and constraints, and operates in an environment where there is a substantial quantity of real-time information concerning production failures and customer requests. The steel making process, which includes steel making followed by continuous casting, is generally the main bottleneck in steel production. Therefore, comprehensive scheduling of this process is critical to improve the quality and productivity of the entire production system. This paper addresses the scheduling problem in the steel making process. The methodology of winner determination using the combinatorial auction process is employed to solve the aforementioned problem. In the combinatorial auction, allowing bidding on a combination of assets offers a way of enhancing the efficiency of allocating the assets. In this paper, the scheduling problem in steel making has been formulated as a linear integer program to determine the scheduling sequence for different charges. Bids are then obtained for sequencing the charges. Next, a heuristic approach is used to evaluate the bids. The computational results show that our algorithm can obtain optimal or near-optimal solutions for combinatorial problems in a reasonable computation time. The proposed algorithm has been verified by a case study

Simulation-based Optimized Production Policy for Hybrid MTS/MTO Glass Tube Manufacturing Systems

Glass Tube is one of the main components for fluorescent lamps as it contains all the other components to generate light. Glass tube industry faces a decline in demand in Egypt. This is attributed to two factors: currency floating and new lighting technologies. In response, glass tube manufacturers decided to diversify their products. This required the integration of Make-to-Stock (MTS), which is used usually for glass tube manufacturing, and Make-to-Order (MTO) which is used to fulfill demands for diversified products. In this thesis, Production policy is proposed to plan for MTS & MTO production. This policy determines when to produce broken glass (cullet), MTS product or MTO product. Priority is given to Cullet which is used as raw material in glass making. The second choice is to produce MTS product, and excess capacity is used to produce MTO products. Once MTO order is fulfilled, the choice is made to either produce cullet or MTS product. The policy defines two levels for cullet inventory and MTS product inventory. If cullet inventory reaches the lower level, cullet will be produced until the inventory level reaches higher level. If the cullet reaches the higher level or the level is decreasing towards lower level, products will be produced. The type of product is determined according to the inventory level of MTS product. If the MTS inventory level is lower than high inventory level, MTS product will be produced. Once it reaches high inventory level, MTO product will be produced. A simulation model is developed to simulate glass tube production. The model is divided into three interconnected modules: production, order fulfillment and decision. The model was verified and validated through different cases. Based on the simulation model, an optimization algorithm is applied to select optimum parameters for proposed policy with the objective of minimizing total costs. The proposed production policy proved its effectiveness in reducing total cost in glass tube manufacturing. Sensitivity analysis was performed to show the effect of raw material prices and energy price on the solutions obtained by optimization algorithm. Increase in raw material prices has effect on production parameters; however, it has no effect policy parameters. Increase in energy prices has effect on production parameters and policy parameters

Decomposition of Manufacturing Processes: A Review

Manufacturing is a global activity that started during the industrial revolution in the late 19th century to cater for the large-scale production of products. Since then, manufacturing has changed tremendously through the innovations of technology, processes, materials, communication and transportation. The major challenge facing manufacturing is to produce more products using less material, less energy and less involvement of labour. To face these challenges, manufacturing companies must have a strategy and competitive priority in order for them to compete in a dynamic market. A review of the literature on the decomposition of manufacturing processes outlines three main processes, namely: high volume, medium volume and low volume. The decomposition shows that each sub process has its own characteristics and depends on the nature of the firm’s business. Two extreme processes are continuous line production (fast extreme) and project shop (slow extreme). Other processes are in between these two extremes of the manufacturing spectrum. Process flow patterns become less complex with cellular, line and continuous flow compared with jobbing and project. The review also indicates that when the product is high variety and low volume, project or functional production is applied