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

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Mining Technologies Innovative Development

The present book covers the main challenges, important for future prospects of subsoils extraction as a public effective and profitable business, as well as technologically advanced industry. In the near future, the mining industry must overcome the problems of structural changes in raw materials demand and raise the productivity up to the level of high-tech industries to maintain the profits. This means the formation of a comprehensive and integral response to such challenges as the need for innovative modernization of mining equipment and an increase in its reliability, the widespread introduction of Industry 4.0 technologies in the activities of mining enterprises, the transition to "green mining" and the improvement of labor safety and avoidance of man-made accidents. The answer to these challenges is impossible without involving a wide range of scientific community in the publication of research results and exchange of views and ideas. To solve the problem, this book combines the works of researchers from the world's leading centers of mining science on the development of mining machines and mechanical systems, surface and underground geotechnology, mineral processing, digital systems in mining, mine ventilation and labor protection, and geo-ecology. A special place among them is given to post-mining technologies research

The effect of microwave heating on ore sorting

Today the Mining industry is being challenged to develop methodologies and technology to process the lower grade and mineralogically complex ore types using ore sorting. The potential of microwave driven selective heating as an excitation tool to underpin sorting is possibly not well known in the mining and mineral industries due to very few applications and lack of awareness of the potential users. This thesis investigates the conditions under which this process is technically effective and can be utilised. A detailed investigation was conducted to understand the reasons for selective heating of specific mineral phases and how infrared sensing can be used as an identification technique to discriminate certain particles from others. This thesis also quantifies the impact of other important factors on the sorting process including; particle shape and size, mineral composition and most importantly the textures of the mineral bearing particles which have a tendency to heat quickly when exposed to microwave energy. An extensive assembly of analytical techniques such as optical microscopy, high-resolution X-ray computed tomography and XL Scanning Electron Microscopy (used by the Mineral Liberation Analyser) were utilised to obtain a mineralogical characterisation of the tested ores. The choice of microwave applicators enabled heating to be carried out in multimode and single mode types of cavity. By engineering synthetic samples a more comprehensive investigation was carried out which enabled some focusing questions from the thesis hypothesis to be addressed. The synthetic samples were used to experimentally validate an adopted theoretical approach of investigating the influence of mineral texture upon selective heating. The supplied ore from the Bingham Canyon Mine, USA (operated by Rio Tinto’s Kennecott Utah Copper Corporation) was experimentally tested in two steps of investigations. The first step involved the approach of an “ideal”, theoretical sorter for which rock particles had to be destroyed (necessary to perform assaying analysis). The temperature threshold for economically justifiable sorting was determined from a temperature difference and assayed metal content of heated particles. In the second step, samples were analysed by heating them in two applicators and the temperature threshold was determined as a function of mineral texture which caused selective heating as in contrast to assayed metal content. The results showed from the exposure of synthetic particles (with designed textures of microwave more responsive minerals) that it is advantageous to use both multimode and single mode cavities for better understanding of microwave heating of the ore. It was also shown that the texture of microwave responsive minerals has a significant effect on the formation of the temperature profiles which are used to evaluate selectivity and potential for the separation as opposed to only mineral composition of the ore particles. It was demonstrated that the types of ores studied in this work, will respond to microwave selective heating to the extent that infrared detection can be applied to perform selection between cold and hot particles defined by a set threshold

TREATMENT OF MINERAL-METALLURGICAL RESIDUES FOR THE RECOVERY OF USEFUL SPECIES AND THE REUSE OF PROCESS WASTE

Millions of tons of mining waste now represent a huge ecological challenge, perhaps also an economic opportunity. This paper illustrates and discusses an innovative approach in the reclamation of old mining areas, which is inspired by the principle of circular economy and considers the waste from old mining and mineral processing activities as potential secondary raw materials.The research proposes to apply the technique of flotation to extract from solid mining residues fractions of useful but polluting species, obtaining the double result of downgrading the material below the CSC (Contamination Threshold Concentration) and extracting a concentrate with commercial characteristics. The materials of potential interest are those of which the dumps from the cultivation and processing of the ores of Pb and Zn are composed. The establishment of the Centre of Excellence for Environmental Sustainability (CESA) has enabled an experimental activity based on the treatment of various mining residues in the Sulcis Iglesiente Guspinese area. The results obtained appear to be important in terms of both technological feasibility and costs compared to those of a Permanent Safety Deposit [1] [2]. The project has been developed, with reference to a pilot basin; the studies carried out so far have concerned samples taken from the Montevecchio Levante mud basin on which batch flotation tests were carried out for the reconstruction of a two-section plant flowsheet, one for the recovery of Zn sulphide and the second for the separation of the oxidized fraction from the final waste. Starting from feed concentrations around 2% Zinc, three products were obtained: a commercial Zn sulphide concentrate with 50% content; a final waste with heavy metal concentrations (Zn and Pb) lower than the CSC for industrial sites; and an intermediate concentrate (not marketable) whose residual Pb and Zn content requires inerting or disposal in a collection site. The collaboration with the Geological Survey of Finland (GTK), Europe's leading competence center for the evaluation and sustainable use of geological resources, has allowed the realization of a project aimed at the implementation of high quality data that have highlighted important characteristics of mineralogical composition of the treated material

New Methods for ferrous raw materials characterization in electric steelmaking

425 p.In the siderurgical sector, the steel scrap is the most important raw material in electric steelmaking,contributing between 70% of the total production costs. It is well-known how the degree of which thescrap mix can be optimized, and also the degree of which the melting operation can be controlled andautomated, is limited by the knowledge of the properties of the scrap and other raw-materials in thecharge mix.Therefore, it is of strategic importance having accurate information about the scrap composition of thedifferent steel scrap types. In other words, knowing scrap characteristics is a key point in order to managethe steel-shop resources, optimize the scrap charge mix/composition at the electric arc furnace (EAF),increase the plant productivity, minimize the environmental footprint of steelmaking activities and tohave the lowest total cost of ownership of the plant.As a main objective of present doctoral thesis, the doctorate will provide new tools and methods of scrapcharacterization to increase the current recycling ration, through better knowledge of the quality of thescrap, and thus go in the direction of a 100% recycling ratio. In order to achieve it, two main workinglines were developed in present research. Firstly, it was analysed not only the different existingmethodologies for scrap characterization and EAF process optimization, but also to develop new methodsor combination of existing, Secondly, it was defined a general recommendations guide for implementingthese methods based on the specifics of each plant

Quantifying the effects of mass transport in the curing and leaching of agglomerated ores using X-ray Microtomography

Agglomeration and subsequent curing are widely used as pre-treatments for ore prior to heap leaching as they both improve the permeability of the heap and bring leaching solution into close contact with the ore, initializing the leaching reactions. In this thesis, a low-grade copper sulphide ore was used for the experiments and two different agglomeration/leaching solutions were tested, namely a more standard sulphuric acid solution including ferric/ferrous ions, and a solution which also contained chloride ions. A novel image processing methodology was developed to track grains over both the curing and leaching process, taking into account the anisometric changes experienced by the agglomerates and the formation and depletion of species. A combination of XMT and SEM/EDX was used to characterise the chemical and mineralogical changes occurring over both processes. The formation and depletion of mineral components were quantified and tracked beyond the typical time scales used industrially, highlighting that the presence of chloride ions makes a substantial difference to the chemical and structural evolution of the agglomerates. Over the curing process, at least 20 days are required to perceive a significant degree of dissolution. Reprecipitation of metal containing species was observed, especially near the agglomerate surfaces. These precipitates are water-soluble species, and 50% of the initial sulphides were extracted from the agglomerates containing chloride ions, but only 20% from the other agglomerates after curing and water washing. A model of the agglomerate behaviour over the curing process is proposed based on the results observed from the XMT measurements. This model considers both the metal dissolution extent, as well as the reprecipitation of species due to water evaporation. The mathematical model is explained together with the computational approach used to solve it, and the simulation results are compared with the experimental results. This model is able to successfully predict the trends seen in the experiments, with the relative reaction and evaporation rates being a controlling factor. The leach performance was assessed for agglomerates leached using the same recipes used for the agglomeration stage. The compaction and changes in microporosity in the sample were quantified, showing that these changes do not significantly influence the leaching performance. By taking advantage of the more selective leaching that takes place when chloride ions are added to the leach solution, the leaching variability in the system was assessed. SEM/EDX measurements were then used to calibrate the XMT quantifications, isolating the dissolution of copper-containing grains from the pyrite dissolution. It was, thus, possible to quantify the surface kinetics of the hundreds of thousands of grains in the sample, with these kinetics being represented by a family of bi-modal curves. It was shown that the mass transport and mineralogical changes occurring throughout the curing and leaching processes could be quantified both at the grain-scale and the macro-scale by using the developed methodology for combining SEM/EDX measurements with XMT. By incorporating this data into particle scale and, ultimately, heap scale leach models, improved predictions and optimisation of leach performance can be made.Open Acces