Non-linear models for a gypsum kiln. A comparative analysis

INTERNATIONAL FEDERATION OF AUTOMATIC CONTROL. WORLD CONGRESS (15.2002.BARCELONA)This paper presents several non-linear models adjusted in order to capture the dynamics of a gypsum kiln. The behavior of this kind of processes is affected by nonlinear effects caused by the existence of disturbances and the coupling among some variables. The use of second order Volterra and Hammerstein models as appropriate solutions to describe the process dynamics is analyzed. A thorough study of the best model order and structure is performed. Coefficients that best fit real data are also selected. This work aims to obtain a good non-linear model in order to implement a non-linear predictive controller, able to improve the performances of those linear controllers already tested on the plant.Comisión Interministerial de Ciencia y Tecnología (CICYT) 1FD97-083

Robust global state feedback stabilization of cement mills

peer reviewedPlugging is well known to be a major cause of instability in industrial cement mills. A simple nonlinear model able to simulate the plugging phenomenon is presented. It is shown how a nonlinear robust controller can be designed in order to fully prevent the mill from pluggin

Implementation of a black-box Learning-based Nonlinear Model Predictive Control for a clinker production plant

La produzione di cemento svolge un ruolo significativo nelle emissioni globali di CO2. Algoritmi di controllo avanzati potrebbero ridurne l'impatto ambientale tramite un miglioramento dell'efficienza del processo. Il controllo predittivo non lineare (NMPC) è una tecnica particolarmente adatta a questo ruolo, poiché minimizza una funzione di costo soddisfacendo una serie di vincoli. Un elemento chiave richiesto dal NMPC è un modello matematico accurato del sistema controllato. Tuttavia, la sua derivazione può risultare onerosa, soprattutto per sistemi complessi come gli impianti di produzione del cemento. Una possibile alternativa sono gli approcci learning-based, attualmente in fase di studio. Essi sfruttano i dati storici per lo sviluppo dell'intero controllore o di alcuni suoi componenti. In questo elaborato, tecniche di regressione gaussiana vengono utilizzate per ottenere un modello black-box delle variabili chiave di un impianto di produzione di clinker. Un modello CV-informed GP-NOE è stato addestrato su dati storici e confrontato con una funzione di trasferimento MIMO. I risultati mostrano leggeri miglioramenti nelle predizioni multipasso a lungo termine. Il modello sviluppato ha il potenziale per essere implementato all'interno di un approccio learning-based NMPC per il controllo dell'impianto di produzione di clinker.Cement production plays a significant role in global CO2 emissions. Advanced control algorithms could reduce its environmental impact by improving the efficiency of the process. Nonlinear Model Predictive Control (NMPC) is a technique particularly fit for this role, since it minimizes a cost function while satisfying a set of constraints. A key element required by NMPC is an accurate mathematical model of the controlled system. However, its derivation could be challenging, especially for complex systems such as cement production plants. As an alternative, learning-based approaches are being investigated. They leverage historical data to design the entire controller or part of its components. In this work, Gaussian Processes regression is used to obtain a black-box model of key system variables of a clinker production plant. A CV-informed GP-NOE model was trained on historical data and compared to a MIMO transfer function model. The results show slight improvements in long multi-step-ahead predictions. The developed model has the potential to be implemented within a learning-based NMPC framework to control the clinker production plant

Modelagem, simulação e controle da moagem a seco em moinho de bolas

O presente trabalho descreve a modelagem dinâmica de um circuito industrial de moagem de cimento, incluindo as principais perturbações do processo. O modelo dinâmico pôde simular o efeito de situações comuns na indústria cimenteira, como alterações na dureza (ou moabilidade) e também na distribuição de tamanhos de partícula do material alimentado ao circuito. Baseado no modelo dinâmico, diferentes propostas de controle são avaliadas: desde o controle PI descentralizado ao controle preditivo linear. Também são comparados os resultados quando se simula uma situação comum na maioria dos circuitos de moagem a seco no Brasil: a indisponibilidade de resultados instantâneos de granulometria do produto. Os resultados mostram que a perda energética devido à sobremoagem é bastante relevante quando não se dispõe da análise granulométrica do produto online. Por outro lado, o sistema de controle é capaz de manter a produção em um nível máximo, ao mesmo tempo em que se obtém um produto final dentro das especificações desejadas reduzindo o custo energético do sistema

Risk factors and risk prediction models for early complications following total hip arthroplasty

Treatment of end-stage hip osteoarthritis was revolutionized in the 1960s with the newly invented low-friction total hip arthroplasty (THA). Since then, an increasing number of both primary and revision THAs have been performed annually, especially over the past two decades. To achieve better outcomes, orthopedic surgeons should carefully select optimal patients and appropriate methods and devices. Risk prediction models have been developed to inform the surgeon and patient more precisely about the expected outcomes of the surgery. The use of such a tool could engage patients more closely in the decision-making process and guide surgeons in avoiding unnecessary risk. The aims of this doctoral thesis were: 1) to determine the risk factors for revision due to dislocation after primary THA; 2) to determine the risk factors for revision due to periprosthetic joint infection (PJI) after primary THA; 3) to develop risk prediction models for assessing the risk of the most common adverse outcomes after primary THA, based on versatile registry data from Finland; and 4) to develop risk prediction models for early revisions and death, and to evaluate the predictive potential of various machine learning algorithms for complications following primary THA, based on the Nordic Arthroplasty Register Association (NARA) dataset. ,, We found that posterior approach, fracture diagnosis, and American Society of Anesthesiologists class III–IV were associated with an increased risk of revision for dislocation after primary THA. The use of a 36 mm femoral head size decreased the risk of revision for dislocation. For PJI, we identified several modifiable variables increasing and decreasing the risk of revision. Especially patients with a high body mass index may be at even higher risk of developing infection than previously reported. We also successfully developed preoperative risk prediction models for PJI, dislocation, periprosthetic fracture, and death after primary THA. Based on the NARA dataset, we were able to demonstrate that complex risk prediction methods are not required to achieve maximum predictive potential. Hence, simpler models can improve usability. All the developed models can easily be used in clinical practice to serve individual risk estimations for adverse outcomes.--- Pitkälle edenneen lonkan nivelrikon hoito mullistui, kun moderni lonkan tekonivelleikkaus yleistyi 60-luvulla. Lonkan tekonivelen ensi- ja uusintaleikkausten määrät ovat kasvaneet merkittävästi erityisesti kahden viimeisen vuosikymmenen aikana. Uusintaleikkausten välttämiseksi ortopedien tulisi huolellisesti valita ensileikkaukseen sopivat potilaat sekä parhaat mahdolliset leikkausmenetelmät ja komponentit. Viime aikoina onkin kehitetty riskilaskureita, jotta sekä kirurgien että potilaiden ymmärrys odotettavissa olevasta lopputuloksesta paranisi. Riskilaskureiden avulla potilaat voidaan ottaa paremmin mukaan yhteiseen päätöksentekoon. Tässä väitöskirjatutkimuksessa selvitettiin riskitekijöitä lonkan tekonivelleikkauksen jälkeisille uusintaleikkauksille. Erityishuomion kohteena olivat tekonivelen sijoiltaanmenot sekä infektiot. Lisäksi kehitimme riskilaskurimalleja ennustamaan potilaskohtaista riskiä tyypillisimmille komplikaatioille ja kuolemalle lonkan ensitekonivelleikkauksen jälkeen. Tämä väitöskirja perustuu uudistetun Suomen Endoproteesirekisterin ja Pohjoismaisen tekonivelrekisterin tietoihin. Tutkimuksessa havaittiin taka-avauksen, reisiluun kaulan murtumadiagnoosin ja anestesiariskiluokkien III-IV altistavan uusintaleikkaukselle tekonivelen sijoiltaanmenon vuoksi. Käytettäessä 36 mm:n halkaisijan omaavia nuppeja sijoiltaanmenoriski oli matala. Lisäksi tunnistimme useita muuttujia, jotka olivat yhteydessä tekonivelen infektoitumiseen. Erityisesti potilaat, joilla on korkea painoindeksi, saattavat olla alttiimpia tekonivelinfektiolle, kuin mitä aikaisemmin on raportoitu. Kehitimme myös onnistuneesti riskilaskurimallit ennustamaan riskiä tekonivelen uusintaleikkaukselle infektion, sijoiltaanmenon ja periproteettisen murtuman johdosta sekä kuolemalle lonkan ensitekonivelleikkauksen jälkeen. Tärkeä havainto riskilaskurimallien kehityksessä oli myös se, että yksinkertaisilla menetelmillä pystytään ennustamaan riskiä yhtä hyvin kuin monimutkaisilla menetelmillä. Kaikkia kehittämiämme malleja voi käyttää kliinisen päätöksenteon tukena arvioimaan potilaskohtaista riskiä leikkauksen jälkeiselle epäsuotuisalle päätetapahtumalle

Control, optimization and monitoring of Portland cement (Pc 42.5) quality at the ball mill

Thesis (Master)--Izmir Institute of Technology, Chemical Engineering, Izmir, 2006Includes bibliographical references (leaves: 77-78)Text in English; Abstract: Turkish and Englishxi, 89 leavesIn this study, artificial neural networks (ANN) and fuzzy logic models were developed to model relationship among cement mill operational parameters. The response variable was weight percentage of product residue on 32-micrometer sieve (or fineness), while the input parameters were revolution percent, falofon percentage, and the elevator amperage (amps), which exhibits elevator charge to the separator. The process data collected from a local plant, Cimenta Cement Factory, in 2004, were used in model construction and testing. First, ANN (Artificial Neural Network) model was constructed. A feed forward network type with one input layer including 3 input parameters, two hidden layer, and one output layer including residue percentage on 32 micrometer sieve as an output parameter was constructed. After testing the model, it was detected that the model.s ability to predict the residue on 32-micrometer sieve (fineness) was successful (Correlation coefficient is 0.92). By detailed analysis of values of parameters of ANN model.s contour plots, Mamdani type fuzzy rule set in the fuzzy model on MatLAB was created. There were three parameters and three levels, and then there were third power of three (27) rules. In this study, we constructed mix of Z type, S type and gaussian type membership functions of the input parameters and response. By help of fuzzy toolbox of MatLAB, the residue percentage on 32-micrometer sieve (fineness) was predicted. Finally, It was found that the model had a correlation coefficient of 0.76. The utility of the ANN and fuzzy models created in this study was in the potential ability of the process engineers to control processing parameters to accomplish the desired cement fineness levels. In the second part of the study, a quantitative procedure for monitoring and evaluating cement milling process performance was described. Some control charts such as CUSUM (Cumulative Sum) and EWMA (Exponentially Weighted Moving Average) charts were used to monitor the cement fineness by using historical data. As a result, it is found that CUSUM and EWMA control charts can be easily used in the cement milling process monitoring in order to detect small shifts in 32-micrometer fineness, percentage by weight, in shorter sampling time interval

Dynamic simulation of industrial grinding circuits : mineral liberation, advanced process control, and real-time optimisation

Étant donné que les minéraux apparaissent fréquemment dans des associations complexes dans la nature, la libération minérale est un aspect clé du traitement de minerais et celle-ci est accomplie par comminution. Cette opération est certainement l’une des plus importantes, mais aussi des plus coûteuses dans l’industrie. La réussite globale d’une usine dépend souvent de la performance du circuit de broyage car il existe un compromis pour atteindre la taille des particules libérant les minéraux ciblés afin d’obtenir des concentrés de haute pureté tout en ayant de faibles coûts d’opération, lesquels sont largement influencés par la consommation énergétique. Dans les années récentes, les entreprises ont été confrontées à des objectifs de performance plus exigeants, une concurrence accrue sur les marchés, et des réglementations environnementales et de sécurité plus strictes. D’autres défis supplémentaires sont inhérents aux circuits de broyage, par exemple les réponses non linéaires, le niveau élevé d’intercorrélation entre les variables et les recirculations de matière. Les problèmes ci-dessus soulignent la pertinence d’avoir des systèmes de contrôle et d’optimisation adéquats pour lesquels les praticiens profitent de plus en plus des approches basées sur des modèles pour y faire face de façon systématique. La modélisation et la simulation sont des outils puissants ayant des avantages significatifs tels que les faibles coûts, les temps requis pour réaliser des expériences relativement courts et la possibilité de tester des conditions opérationnelles extrêmes ainsi que différentes configurations des circuits sans interrompre la production. De toute évidence, la qualité des résultats sera aussi bonne que la capacité du modèle à représenter la réalité, ce qui souligne l’importance d’avoir des modèles précis et des procédures de calibrage appropriées, un sujet fréquemment omis dans la littérature. Un autre aspect essentiel qui n’a pas été rapporté est l’intégration efficace de la libération minérale aux systèmes de contrôle et d’optimisation de procédés. Bien qu’il s’agisse d’une information clé directement liée aux performances de l’étape de concentration, la plupart des stratégies se concentrent exclusivement sur la taille de particule du produit. Ceci est compréhensible étant donné qu’il est impossible de mesurer la distribution de libération présentement. Basée sur une librairie de simulation d’usines de traitement des minerais déjà existante, cette recherche aborde lesdits problèmes en (1) développant un modèle de libération minérale visant à coupler les étapes de broyage et de concentration ; (2) programmant et validant par calibrage un modèle phénoménologique de broyeur autogène/semi-autogène (BA/BSA), nécessaire pour compléter la librairie de simulation ; (3) couplant un simulateur de circuit de broyage à un procédé de concentration avec le modèle de libération, et (4) développant un système de contrôle et d’optimisation qui considère explicitement des données de libération minérale pour évaluer les avantages économiques. Les principaux résultats confirment que le modèle de libération est capable de reproduire avec précision des distributions de libération minérale couramment observées dans l’industrie. Cependant, si les données de calibrage correspondent à un point d’opération unique, la validité pourrait être limitée aux régions voisines proches. Le problème de caractériser l’évolution de la libération minérale aux diverses conditions d’opération ainsi qu’aux régimes transitoires reste à être abordé. Le modèle de libération s’est aussi révélé utile pour coupler des circuits de broyage avec des procédés de concentration, en particulier pour une unité de flottation. Quant au modèle de BA/BSA, celui-ci peut capturer le régime statique ainsi que la dynamique d’un broyeur réel et conjointement avec le reste des équipements dans la librairie de simulation, des circuits de broyage industriels. Ceci a été confirmé par le calibrage à partir des données d’opération d’une usine et des tests en laboratoire, tout en suivant une procédure systématique, contribuant aussi au sujet de l’établissement de méthodologies de calibrage standardisées. Pour terminer, les expériences concernant la stratégie de contrôle et d’optimisation basée sur la libération minérale suggèrent que l’utilisation de cette information peut améliorer la performance globale des circuits de broyage-séparation en réagissant aux variations des caractéristiques de libération, qui à leur tour influencent l’efficacité de séparation. L’étude de cas réalisé révèle que cela peut entraîner une augmentation du débit massique et de la teneur du concentré, de la récupération des métaux et des revenus de l’ordre de +0.5%, +1%, +1% et +5%, respectivement, par rapport au cas où ces informations sont omises.As minerals frequently appear in complex associations in nature, mineral liberation is one of the most relevant aspects in ore processing and is achieved through comminution. This operation is one of the most important, but also one of the most expensive ones in industry. The global efficiency of a plant often depends on the performance of the grinding circuit, since there is a compromise to achieve the particle size liberating the targetted minerals in order to obtain high purity concentrates while maintaining low operating costs, which are largely influenced by the energy consumption. In recent years, companies have been facing more demanding performance targets, stronger competition, and more stringent environmental and safety regulations. Additional challenges are inherent to the grinding circuits themselves, e.g. the nonlinear responses, high degree of intercorrelation of the different variables, and material recirculations. The abovementioned issues highlight the relevance of adequate process control and optimisation, and practitioners rely more often on model-based approaches in order to face them systematically. Modeling and simulation are powerful tools with significant advantages such as low costs, required times for conducting experiments are relatively short, and the possibility of testing extreme operational conditions as well as different circuit configurations without disrupting production. Evidently, the quality of the results will only be as good as the model capacity to represent the reality, which emphasises the relevance of having precise models and proper calibration procedures, the latter being a topic frequently omitted in the literature. Another crucial aspect that has not been reported yet is the effective integration of mineral liberation in control and optimisation schemes. Although it is a key piece of information directly related to the performance of the concentration stage, most strategies focus exclusively on the particle size. This is understandable given that it is currently impossible to measure the liberation distribution online. Based on an existing mineral processing plant simulation library, this research addresses these problems by (1) developing a mineral liberation model aiming at linking the grinding and concentration stages; (2) programming a phenomenological autogenous/semiautogenous (AG/SAG) mill model, required to complement the simulation toolbox, and validating it through calibration; (3) coupling a grinding circuit simulator to a concentration process by means of the liberation model, and (4) developing a plantwide control and optimisation scheme considering mineral liberation data explicitly to evaluate the economic benefits. The main results confirm that the liberation model is capable of reproducing accurately mineral distributions observed in industry. If calibration data correspond to a single operating point, its validity may however be limited to the close neighbourhood. Characterising the evolution of mineral liberation in different operating conditions and transient states remains to be addressed. The liberation model proved to be equally useful in coupling grinding circuits with concentration processes, specifically for flotation. As for the AG/SAG mill model, it can capture the steady state and dynamic behaviour of an actual device and, along with the rest of pieces of equipment in the simulation toolbox, of industrial grinding circuits. This was confirmed through calibration from plant data and laboratory testwork following a systematic procedure, contributing to the endeavour of establishing standard calibration methodologies. Lastly, the results of the designed control and optimisation scheme suggest that using liberation data for control and real-time optimisation can improve the overall performance of grinding-separation circuits by reacting to variations in the liberation characteristics, which in turn influence the concentration performance. The case study reveals that doing so can lead to increases in the concentrate mass flow rate and grade, metal recovery, and global profits in the order of +0.5%, +1%, +1%, and +5%, respectively, compared to the case omitting this information