Hot Strip Mill Process Research Articles

Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements

Modern hot strip mill process (HSMP) is increasingly characterized by producing small-batch, multi-specification and high-value-added products. Associated with such suffered complexities, performing valid process monitoring/fault diagnosis (PM-FD) is becoming a challenging task of ensuring process safety and product quality. In this paper, a new PM-FD approach based on principal component regression (PCR) is proposed for quality-relevant fault detection and diagnosis of HSMP. Firstly, the historical multi-batch process and quality variables data sets (three-dimensional) should be appropriately transferred into the availably applied two-dimensional data sets. Then, the presented approach could orthogonally project the process variable space into the quality-relevant part from the -irrelevant part. Next, when there comes a new measurement regarding a new batch from an unknown specification of the strip, it is automatically assigned into its preferential model by the prediction power of PCR integrated with Bayes inference. In depth, the detection and diagnosis are continued by the presented PCR based monitoring scheme. To the end, the new proposed scheme would be practiced with real HSMP data, where the individual steps as well as the complete framework were extensively tested.

New kernel independent and principal components analysis‐based process monitoring approach with application to hot strip mill process

In this article, a new kernel independent and principal components analysis (kernel ICA–PCA) based process monitoring approach is proposed for hot strip mill process (HSMP). HSMP appears widely in iron and steel industry, which runs in an environment with significant nonlinearity, non-Gaussianity and some other uncertainties. The present method, namely kernel ICA–PCA, firstly addresses the nonlinearity via the popular kernel trick, then applies kernel ICA model to isolate the non-Gaussian independent information, finally, utilises kernel PCA model to account for the uncertain part and extract the principal components. To avoid the disadvantage of the original fault detection statistics, a k nearest neighbour data description-based technique is employed into the kernel ICA–PCA for monitoring the variations occurring in the independent and principal components, whereas traditional Q statistic is employed to reflect the disturbance in the residuals. All of their thresholds will be determined by a new emerging bootstrap-based technique. The applicability of the new scheme is represented via hot strip mill process dataset recorded in the iron and steel company.

A new data-driven process monitoring scheme for key performance indictors with application to hot strip mill process

Hot strip mill process (HSMP) plays a pivotal role in steel manufacturing industry, but involves significant complexity. Several faults could cause the decreasing evaluation of the key performance indicators (KPIs). Partial least squares (PLS) model has been popularly accepted for KPI-monitoring tasks, whereas some drawbacks have been reported such as high false alarm rate and strict limitation of Gaussian distribution. In this paper, a new scheme is designed without any distributional priority. The process information is extracted by the independent component analysis (ICA) and principal component analysis (PCA) one after another to obtain the Non-Gaussianity and Gaussianity rooted in process variables. Then the correlation canonical analysis (CCA), a classic tool of analyzing the correlation of two data sets, will be utilized to incorporate the process information and KPIs. Finally, two KPI-related indices are formed respectively, which are both bounded by key density estimation based approach. In the end, application of the new approach in a real steel plant will be demonstrated, where the comparison with PLS based results is covered.

Strip shape modeling and its setup strategy in hot strip mill process

Shape setup model (SSM) plays a critical role to achieve satisfactory precision of strip shape in hot strip mill process (HSMP). However, for the design of shape model, the lack of systematic shape theory restricts the high accuracy of strip shape. In this paper, the procedure of SSM will be generally introduced and practically demonstrated with a real HSMP producing system. The mechanism of shape modeling and design strategy of SSM is introduced. Special concentration is placed on modeling and calculating the thermal extension and wear of the roll, and mathematical model of roll gap profile is set up on this basis. Then the mechanism of strip profile and flatness is introduced by revealing the shape forming process. Furthermore, the setup strategy of SSM is proposed, whose target is to calculate reference values for shape control actuators. The other focus of this paper concerns on the applicable issue of SSM integrated with the presented design approach. An Ansteel 1,700-mm HSMP line will be employed for the experimental background.

Advanced predictive system using artificial intelligence for cleaning of steel coils

This paper presents a system based on data mining and statistical modelling tools that permits the prediction of the development of oxide scale defects in high quality flat products after the steel industry’s hot strip mill process (HSM), but before the coil becomes processed on the pickling line (PL). The economic impact of the improvement provided by such a system can be valued at several million US dollars per year, because it makes it possible to downgrade materials at an early stage, avoiding additional processes like coating, etc. It also enables the speed of the PL, which is usually seen as a bottleneck in these facilities, to be increased. The learning process of the model presented here is based on automatic surface-inspection systems, as well as processing parameters at the HSM and PL to capture the essentials of the cleaning process itself, and also the main factors in scale production. The system proposed currently which is configured as a multi-agent system, is the first for this particular purpose, although the steel industry uses many other models and systems to predict other properties (e.g., mechanical properties) or the best operating parameters (e.g., forces, temperatures) for processes.

The Strategy of Shape Setup Model and the Obtaining of Several Key Parameters in Hot Strip Mill Process

Shape setup model (SSM) plays a critical role to achieve satisfactory precision of strip shape in hot strip mill process (HSMP). In this paper, the process of SSM is introduced, taking 1700mm HSMP line in Ansteel as the research object. Special attention is focused on the gap crown model, which is the foundation of SSM. And the allocation strategy of reference values for mechanical actuators is presented. Then an off-line simulation method is proposed to obtain several key parameters of the model. SSM has been put into practical use and proved to be effective to control strip flatness and profile by high accuracy.

Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process

In this paper, a nonlinear fault diagnosis scheme is established for the hot strip mill process (HSMP). In HSMP, the faults affecting quality index are denoted as quality-related faults, which should be taken care as soon as possible. Projection to latent structures (PLS) is a basic model for quality-related fault detection in linear processes. In the presented work, a total kernel PLS (T-KPLS) model is utilized for modeling and monitoring HSMP, which is a typical nonlinear process. However, diagnosis tools have not been developed aiming at the nonlinear case based on T-KPLS model. Motivated by the successful use of contribution plot for the linear case, a contribution rate plot is proposed to extend contribution plots to the nonlinear case. In the end of this paper, the proposed method is applied to the hot strip mill process effectively.

Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application

Projection to latent structures (PLS) model has been widely used in quality-related process monitoring, as it can establish a mapping relationship between process variables and quality index variables. To enhance the adaptivity of PLS, kernel PLS (KPLS) as an advanced version has been proposed for nonlinear processes. In this paper, we discuss a new total kernel PLS (T-KPLS) for nonlinear quality-related process monitoring. The new model divides the input spaces into four parts instead of two parts in KPLS, where an individual subspace is responsible in predicting quality output, and two parts are utilized for monitoring the quality-related variations. In addition, fault detection policy is developed based on the T-KPLS model, which is more well suited for nonlinear quality-related process monitoring. In the case study, a nonlinear numerical case, the typical Tennessee Eastman Process (TEP) and a real industrial hot strip mill process (HSMP) are employed to access the utility of the present scheme.

Online electromagnetic monitoring of austenite transformation in hot strip rolling and its application to process optimization

Online detection and monitoring of phase transformation using non-destructive electromagnetic system in industrial hot strip mill processing of steels are reported. The system renders wide range of possibilities for advanced control and fine tuning of the hot rolling and run-out table cooling parameters. These in turn allow for process modifications to better control the microstructure of hot rolled bands. In particular, such modifications can attenuate the adverse effects of uncontrollable cooling of hot band coils that leads to severely non-homogeneous hot band microstructure. An example of application is given demonstrating how reducing the non-homogeneity of hot band microstructure with the aid of online hot strip mill electromagnetic system can help eliminating the off-thickness defects in advanced high strength steel full hard strips thus contributing to lowering the production costs and increasing the yield.

Formation of a scale on the hole surface of cooling nozzles in the hot strip mill process, obstructing a laminar flow of coolant

We investigated on the formation of a scale on the hole surface of brass cooling nozzles in the hot strip mill process of steel manufacturing. High roughness of a nozzle hole surface due to the scale formation obstructs a laminar flow of coolant, which can result in the diminution of cooling efficiency of strips. Morphology and elemental composition of a scale formed on the hole surface of cooling nozzles used in the hot strip mill process for a long time were characterized with an electron microscope equipped with an energy dispersive X‐ray spectroscope. The scale is mainly composed of a foreign substance layer and an oxide layer. In addition, three kinds of cooling water were analyzed to investigate an inflow source for elemental constituents of the foreign substance layer. Copyright © 2012 John Wiley & Sons, Ltd.

Microstructure and Mechanical Properties of Hot-Rolled Low-Carbon Medium-Manganese TRIP Steels

Transformation Induced Plasicity (TRIP) steels have attracted a growing interest in recent years due to their high strength and ductility combination.An alternative alloy and processing concept has been studied to evaluate the feasibility of producing low-carbon medium-manganese TRIP Steels. Conventional hot-rolling, and batch annealing processes were simulated with three laboratory heats of varying manganese content. The steels were found to be fully hardenable with conventional hot-strip mill processing and subsequent batch annealing simulations produced significant retained austenite levels. The combination of the prior martensitic microstructure in the as-hot-rolled condition, and austenite created during annealing,resulted in remarkable combinations of strength and ductility. Optimum properties were found when samples were annealed at approximately 630°C. While this treatment maded the tensile strength to 800-1020 MPa, the total elongation increased to between 27 percent and 35 percent. UTS*TE products exceeding 30,000 MPa*% were observed, making these materials attractive for high strength, high ductility applications.

Low-Carbon Manganese TRIP Steels

The development of TRansformation Induced Plasicity (TRIP) steels has seen much activity in recent years, due to the promise of very high formability combined with high strength. The accepted method for production of as-hot-rolled TRIP steel employs multistage runout table cooling and coiling in the bainitic transformation temperature regime. As an alternative to confronting the production difficulties the accepted strategy presents, a program was begun to evaluate the potential of 0.1C-6.0Mn steels processed in a more conventional manner. Three laboratory heats were melted to consider the effect of manganese content on processing and properties. The steels were found to be fully hardenable with conventional hot-strip mill processing and subsequent batch annealing simulations produced significant retained austenite levels. The combination of the prior martensitic microstructure in the as-hot-rolled condition, and austenite created during annealing, resulted in remarkable combinations of strength and ductility. In the as-hot-rolled condition, tensile strengths exceeding 1400 MPa were observed, with total elongations of approximately 10 percent. Optimum properties were found when samples were annealed at approximately 650°C. While this treatment reduced the tensile strength to 800-1000 MPa, the total elongation increased to between 30 percent and 40 percent. UTS*TE products exceeding 30,000 MPa-% were observed, making these materials attractive for high strength, high ductility applications.

Design Requirements for a Hot Strip Mill Process Computer System

Many computer-controlled hot strip mills have been installed world-wide since the mid-1960's. The basic functions provided for such installations are well-documented in other papers. This paper reviews some basic design requirements felt to be necessary for successful long-term optimal performance of a strip mill control system for a mill willi a large product mix and relatively small order lots. It is based on four years experience with a computer control system retrofitted to an existing hot strip mill.A review of the control system is presented and used to illustrate the reason for the design requirements.A number of critical questions affecting the design requirements are also reviewed. It is shown that in many cases there is a conflict between customer objectives and requirements and vendor objectives.Some of the questions addressed are:a)Under what conditions will on-going modifications and tuning be required to maintain adequate mill performance?b)What is the sensitivity of the control system to various process disturbances (furnace practice, product mix, physical condition of mill and sensors, etc.)?c)What level of skills are available in plant operations, technical and maintenance personnel to provide on-going support?d)What are the software implications if it becomes necessary to add new functions or re-design existing functions?

Structure Computer Automation Systems for Wide Hot Strip Mills

Hot strip mills provided one of the earliest opportunities for the application of process control computers. The large investment, operating complexity - particularly in the multi-stand finishing train, the high product volume coupled with the demand for accurate and uniform product dimensions created favorable economic conditions for computer automation as early as 1961. Since then computer automation systems in hot strip mills have advanced from a highly developmental creation of engineering art to a mature, predictable and repeatable engineering product. Shorter design and implementation cycles, as well as full operator acceptance of computer control of the hot strip mill process, are evidence of the maturity these systems have achieved.The paper describes advances in the structuring of computer automation systems for hot strip mills, which have enhanced or will further enhance system maturity. The structure of a computer system largely determines its ability to perform in the intended environment. While technical discussions of computer automation systems generally concentrate on individual functional components such as a control algorithm or a process model, the greatest progress in terms of cost, performance, and installation time can often be made through careful design of system structure.Experiences of a major computer systems supplier for the metal industry are explored. The structure of recent systems is compared with that of older systems. Differences are noted and resulting benefits are analyzed. For example, present day systems use highly modularized software to minimize the impact of changing equipment configurations. This technique reduces design and programming effort for a new system and increases flexibility of an existing system. Another example is the emergence of test, diagnostic and evaluation features as integral parts of modern hot strip mill computer systems.The paper concludes with a review of system evolution during the first ten years of computer process control in hot strip mills.