Model-based identification and analysis of hot metal desulphurisation

Abstract Sulphur is considered one of the main impurities in steel. Hot metal desulphurisation serves as the main unit process for sulphur removal in the production of steel. The main objective of this thesis is to identify the relevant phenomena and attributes needed to construct a mathematical model suitable for online use. The study also includes a detailed literature review on the modelling of hot metal desulphurisation, which considers a categorisation of the existing models for the process, but also outlines the main uncertainties in the process that may decrease the prediction performance of the existing models. In this study, model-based process identification techniques are studied. More specifically, the objective is to study different techniques, both to explain the variance and to predict the end content of sulphur in the process. To do this, a modelling framework exploiting data-driven and mechanistic modelling techniques is proposed. The model identification procedure is divided into variable construction, variable selection, model structure selection, and model parameter identification steps. The model identification procedure considers both manual and automatic model identification techniques. The thesis focuses on grey box and black box model structures. In automatic model identification, the focus is on evolutionary search strategies, particularly genetic algorithms. The results of this study show that in the case of lime-based hot metal desulphurisation, the major factors inducing variance in the end content of sulphur are related to the properties of the reagent, i.e. to the rate of the transitory contact reaction. If the particle size distribution is known a priori or can be assumed constant, the prediction accuracy of the models can be improved considerably. In addition, the parameterisation of the reaction models improves the prediction performance. It was also found that physically meaningful descriptions for the uncertain phenomena may help to constrain the search of parameters. In addition, in-depth phenomena-based analysis and automatic model identification strategies may assist in model selection.Tiivistelmä Rikki on keskeisimpiä raakarautaan liuenneita epäpuhtauksia. Hiiliteräksen valmistusketjussa raakaraudan rikinpoisto on prosessi, jossa rikki pääasiallisesti poistetaan. Tämän työn tavoitteena on tunnistaa prosessin kannalta merkityksellisiä ilmiöitä ja tekijöitä, joita tarvitaan on-line käyttöön soveltuvien matemaattisten mallien luomiseen. Työ sisältää myös yksityiskohtaisen kirjallisuusselvityksen, jonka tavoitteena on kategorisoida kirjallisuudessa esitetyt mallit, mutta myös tarkastella mallien suorituskykyyn liittyviä epävarmuustekijöitä prosessin näkökulmasta. Menetelmällisesti työ perustuu prosessin mallipohjaiseen analyysiin ja mallien valintaan. Tarkempana tavoitteena on tarkastella systemaattisia tapoja selittää prosessin loppurikkipitoisuuden vaihtelua, mutta myös ennustaa loppupitoisuutta luotettavasti saatavilla olevan aineiston perusteella. Tätä varten tehtiin mallinnuskehys, joka hyödyntää sekä täysin datapohjaisia, mutta myös mekanistisiin ilmiöihin pohjautuvia dataa hyödyntäviä malleja. Mallin vallinta jaotellaan ennustemuuttujien rakenteluun, ennustemuuttujien valintaan, mallin rakenteen valintaan sekä malliparametrien estimointiin. Valinnassa käytetään sekä automaattisia, että asiantuntijatietoon perustuvia tekniikoita. Mallit ovat rakenteellisesti joko harmaa- tai mustalaatikko filosofiaan pohjautuvia. Automaattisessa mallien valinnassa tarkastellaan eniten erityisesti geneettisten algoritmien toimintaa. Tämän työn tulokset näyttävät, että reagenssin ominaisuuksilla kuten partikkelikokojakaumalla sekä kaasua injektoivien lisäaineiden määrällä on vaikutus rikkipitoisuuden vaihteluun erityisesti partikkelien ja rautasulan välillä tapahtuvan reaktion nopeuden näkökulmasta. Yleisesti mallien suorituskykyä voidaan parantaa, kun partikkelikokojakauman vaihtelu tunnetaan, tai sen voidaan otaksua olevan vakio. Malliparametrien optimointia helpottavat fysikaaliset reunaehdot ja prosessituntemus. Automaattisten mallin valintatekniikoiden käyttäminen voi auttaa mallintajaa tarkoituksenmukaisen mallin valinnassa, mutta asiantuntijatiedon merkitystä mallinnuksessa ei voi kuitenkaan korostaa liikaa

A review of modelling hot metal desulphurisation

Abstract Hot metal desulphurisation serves as the main unit process for removing sulphur in blast‐furnace based steelmaking. This study reviews the available body of literature on modelling hot metal desulphurisation to provide an in‐depth analysis of the approaches employed and results obtained. The mathematical models for reaction kinetics have evolved from simplistic rate equations to more complex phenomenon‐based models, which provide useful information on the effect of physico‐chemical properties and operating parameters on desulphurisation efficiency. Data‐driven approaches with varying levels of phenomenological basis have also been proposed with the aim of achieving better predictive performance in industrial scale applications. Bath mixing was studied using physical and numerical modelling to optimise mixing conditions in ladles and torpedo cars. The coupling of gas‐particle jets and their penetration into the liquid have been a focal point of physical and numerical modelling. In recent years, the fluid flow phenomena in mechanically stirred ladles has been studied extensively using physical and numerical modelling. These studies have focused on the fluid flow field, reagent dispersion and bubble dispersion

Comparison of Single Control Loop Performance Monitoring Methods

Well-performing control loops have an integral role in efficient and sustainable industrial production. Control performance monitoring (CPM) tools are necessary to establish further process optimization and preventive maintenance. Data-driven, model-free control performance monitoring approaches are studied in this research by comparing the performance of nine CPM methods in an industrially relevant process simulation. The robustness of some of the methods is considered with varying fault intensities. The methods are demonstrated on a simulator which represents a validated state-space model of a supercritical carbon dioxide fluid extraction process. The simulator is constructed with a single-input single-output unit controller for part of the process and a combination of relevant faults in the industry are introduced into the simulation. Of the demonstrated methods, Kullback–Leibler divergence, Euclidean distance, histogram intersection, and Overall Controller Efficiency performed the best in the first simulation case and could identify all the simulated fault scenarios. In the second case, integral-based methods Integral Squared Error and Integral of Time-weighted Absolute Error had the most robust performance with different fault intensities. The results highlight the applicability and robustness of some model-free methods and construct a solid foundation in the application of CPM in industrial processes

Development of a surrogate-model based energy efficiency estimator for a multi-step chemical process

Abstract Energy efficiency is increasingly being considered as a critical measure of process performance due to its importance both in production costs and in environmental footprint. In this work, an indirect energy efficiency estimator was developed for the Tennessee Eastman (TE) benchmark process for the first time. The TE model was first modified to provide the reference values of energy efficiency. A sophisticated model selection scheme was then applied to build the surrogate-model. The results indicate reasonable model performance with mean absolute prediction error around 1.7%. The results also highlight the limitations present in the training set, which are, together with other practical implementation issues, discussed in this work

Identification of rate, extent, and mechanisms of hot metal resulfurization with CaO-SiO₂-Na₂O slag systems

Abstract The resulfurization of hot metal has not been comprehensively studied in literature. This study presents an experimental and mathematical modeling study of resulfurization in thermodynamic and kinetic point of view. The rate, extent, and mechanisms of resulfurization were evaluated by analyzing concurrently the physical properties and sulfur-extracting ability of the slag. Experiments were conducted in a chamber furnace in an argon atmosphere, and the hot metal was sampled with pre-defined basis. The experiments were continued until the metal–slag system reached an apparent thermodynamic equilibrium. To obtain a quantitative measure on the effect of system properties on the rate and extent of resulfurization, the results of this study were combined with previous studies handling the sulfide capacities of Na₂O-SiO₂ and CaO-SiO₂-Na₂O slag systems. The sulfide capacities of the slag and corresponding metal–slag sulfur partition ratios were mathematically modeled with data-driven techniques such as multiple linear and non-linear regression and artificial neural networks. Finally, with the help of these, to study the kinetics of resulfurization, a simple mechanistic reaction model was derived. The results suggest that resulfurization of hot metal follows 1st-order kinetics and that the rate and extent can be regulated through the control of the associated thermodynamic driving force and by modifying the physical properties of the slag. The rate-limiting factor was found to be determined by the morphology of the slag phase

Prediction of inclusion state in molten steel by morphology and appearance of inclusions in liquid steel samples

Abstract Inclusions are unwanted but to some extent inevitable in molten and solid steel. Usually solid inclusions are considered to be the most harmful. Inclusions can be converted into a less detrimental form with calcium treatment. The success of calcium treatment can be evaluated by analyzing the state of the inclusion population. The state of inclusions is usually determined by computational thermodynamics making use of the chemical composition of inclusions and system conditions. In this process, liquid and solid inclusions are usually distinguished. Herein, a classification procedure which combines computational thermodynamics and data‐driven reasoning is presented. The objective of this work is to study the predictability of the inclusion state based on its appearance and morphological properties. As a result, Al₂O₃–CaO–MgO–CaS inclusions are classified as liquid and solid ones based on their aspect ratio, equivalent circle diameter, and mean gray value with a recall of 82.7% and precision of 84.9%, by making use of a logistic regression‐based classifier

Application of a genetic algorithm based model selection algorithm for identification of carbide-based hot metal desulfurization

Abstract Sulfur is considered as one of the main impurities in hot metal. Hot metal desulfurization is often carried out with pneumatic injection of a fine-grade desulfurization reagent using a submerged lance. The aim of this study was to develop a data-driven model for the process. The model selection algorithm carries out a simultaneous variable selection and optimization of number of hidden neurons with a combination of binary and integer coded Genetic Algorithm. The objective function applied in the search is repeated Leave-Multiple-Out cross-validation. The model considered is a feedforward neural network with a single hidden layer. In the inner loop of the algorithm, the computational load is reduced by making use of Extreme Learning Machine (ELM) architecture. The final model is trained using the Bayesian regularization. The results show that a well-generalizing data-driven model with good prediction performance can be repeatedly selected based on noisy industrial data with the help of a Genetic Algorithm, provided that the model is validated comprehensively with internal and external data sets