Data reconciliation for mineral and metallurgical processes : Contributions to uncertainty tuning and dynamic balancing : Application to control and optimization

Pour avoir un fonctionnement de l'usine sûr et bénéfique, des données précises et fiables sont nécessaires. D'une manière générale, une information précise mène à de meilleures décisions et, par conséquent, de meilleures actions pour aboutir aux objectifs visés. Dans un environnement industriel, les données souffrent de nombreux problèmes comme les erreurs de mesures (autant aléatoires que systématiques), l'absence de mesure de variables clés du procédé, ainsi que le manque de consistance entre les données et le modèle du procédé. Pour améliorer la performance de l'usine et maximiser les profits, des données et des informations de qualité doivent être appliquées à l'ensemble du contrôle de l'usine, ainsi qu'aux stratégies de gestion et d'affaires. Comme solution, la réconciliation de données est une technique de filtrage qui réduit l'impact des erreurs aléatoires, produit des estimations cohérentes avec un modèle de procédé, et donne également la possibilité d'estimer les variables non mesurées. Le but de ce projet de recherche est de traiter des questions liées au développement, la mise en œuvre et l'application des observateurs de réconciliation de données pour les industries minéralurgiques et métallurgiques. Cette thèse explique d’abord l'importance de régler correctement les propriétés statistiques des incertitudes de modélisation et de mesure pour la réconciliation en régime permanent des données d’usine. Ensuite, elle illustre la façon dont les logiciels commerciaux de réconciliation de données à l'état statique peuvent être adaptés pour faire face à la dynamique des procédés. La thèse propose aussi un nouvel observateur de réconciliation dynamique de données basé sur un sous-modèle de conservation de la masse impliquant la fonction d'autocovariance des défauts d’équilibrage aux nœuds du graphe de l’usine. Pour permettre la mise en œuvre d’un filtre de Kalman pour la réconciliation de données dynamiques, ce travail propose une procédure pour obtenir un modèle causal simple pour un circuit de flottation. Un simulateur dynamique basé sur le bilan de masse du circuit de flottation est développé pour tester des observateurs de réconciliation de données et des stratégies de contrôle automatique. La dernière partie de la thèse évalue la valeur économique des outils de réconciliation de données pour deux applications spécifiques: une d'optimisation en temps réel et l’autre de commande automatique, couplées avec la réconciliation de données. En résumé, cette recherche révèle que les observateurs de réconciliation de données, avec des modèles de procédé appropriés et des matrices d'incertitude correctement réglées, peuvent améliorer la performance de l'usine en boucle ouverte et en boucle fermée par l'estimation des variables mesurées et non mesurées, en atténuant les variations des variables de sortie et des variables manipulées, et par conséquent, en augmentant la rentabilité de l'usine.To have a beneficial and safe plant operation, accurate and reliable plant data is needed. In a general sense, accurate information leads to better decisions and consequently better actions to achieve the planned objectives. In an industrial environment, data suffers from numerous problems like measurement errors (either random or systematic), unmeasured key process variables, and inconsistency between data and process model. To improve the plant performance and maximize profits, high-quality data must be applied to the plant-wide control, management and business strategies. As a solution, data reconciliation is a filtering technique that reduces impacts of random errors, produces estimates coherent with a process model, and also gives the possibility to estimate unmeasured variables. The aim of this research project is to deal with issues related to development, implementation, and application of data reconciliation observers for the mineral and metallurgical industries. Therefore, the thesis first presents how much it is important to correctly tune the statistical properties of the model and measurement uncertainties for steady-state data reconciliation. Then, it illustrates how steady-state data reconciliation commercial software packages can be used to deal with process dynamics. Afterward, it proposes a new dynamic data reconciliation observer based on a mass conservation sub-model involving a node imbalance autocovariance function. To support the implementation of Kalman filter for dynamic data reconciliation, a procedure to obtain a simple causal model for a flotation circuit is also proposed. Then a mass balance based dynamic simulator of froth flotation circuit is presented for designing and testing data reconciliation observers and process control schemes. As the last part of the thesis, to show the economic value of data reconciliation, two advanced process control and real-time optimization schemes are developed and coupled with data reconciliation. In summary, the study reveals that data reconciliation observers with appropriate process models and correctly tuned uncertainty matrices can improve the open and closed loop performance of the plant by estimating the measured and unmeasured process variables, increasing data and model coherency, attenuating the variations in the output and manipulated variables, and consequently increasing the plant profitability

Real time optimization of chemical processes

Due to current changes in the global market with increasing competition, strict bounds on product specifications, pricing pressures, and environmental issues, the chemical process industry has a high demand for methods and tools that enhance profitability by reducing the operating costs using limited resources. Real time optimization (RTO) strategies combine process control and economics, and have gone through much advancement during the last few decades. A typical real time optimization application is model based and requires the solution of at least three (usually) nonlinear programming problems, such as combined gross error detection and data reconciliation, parameter estimation and economic optimization. A successful implementation of RTO requires fast and accurate solution of these stated nonlinear programming problems.Current real time optimization strategies wait for steady state after a disturbance enters the process. If, during this wait, another disturbance enters into the system, it will increase the transition time significantly. An alternative, real time evolution (RTE), calculates the new set-points using only disturbance information and the new set-points are implemented in small step changes to a supervisory control system such as model predictive control (MPC) or can be implemented directly to the regulatory control layer. RTE ignores the important part of data screening therefore there is no surety that the calculated set-points represents current plant conditions. The main contribution of this thesis is to investigate the possibility of implementing new set-points without waiting for steady state. Two case studies, the Williams-Otto reactor and an integrated plant (the Williams-Otto reactor extended to include flash drum and large recycle stream), were used for analysis. The application of RTE, RTO and MPC were discussed and compared for the case studies to evaluate the performance in terms of the theoretical profit achieved.A new strategy, dynamic-RTO (D-RTO), based on modified dynamic data reconciliation (DDR) strategy and translated steady state model, was also developed for systems with significant bias and process noise. In the D-RTO strategy, the residual terms of the steady state model were calculated from the reconciled values. These residual terms were translated subsequently into the steady state model. Due to the translation there is no need for calculating set-point changes in small steps. The formulation of the DDR strategy is based on control vector parameterization techniques. D-RTO was compared with RTE and RTO for the two case studies. The results obtained show that RTE can lead to an unstable control if used without taking into account process and controller dynamics. For measurements having bias, the DDR strategy can be used with the assumption that the variables with bias are unmeasured and are calculated implicitly. The D-RTO strategy is able to deal with constant and changing bias, and is able to decrease profit losses during transitions. D-RTO is a good alternative to steady state RTO, for processes with frequent disturbances, where RTO implementation due to its steady state nature may not be justifiable

Development of Hydraulic Models, Mass Transfer Models, and Dynamic Models of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

To accelerate the development and commercial deployment of CO2 capture technologies, computational tools and models are being developed under the auspices of the U.S. Department of Energy\u27s Carbon Capture Simulation Initiative (CCSI). The CCSI process modeling team was tasked with developing a gold-standard model that will serve as a definitive reference for benchmarking the performance of solvent-based CO2 capture systems under steady-state and dynamic conditions over a large operating-range. The main three areas that this work focused on are: development of the hydrodynamic and mass transfer submodels for a monoethanolamine (MEA) solvent system, uncertainty quantification of these submodels, development of a dynamic model for this system, and development of a dynamic design of experiment methodology for model validation and parameter estimation of this system.;For the gold-standard model, it was desired that the pressure drop and holdup models must be applicable over a wide range of operating conditions. In this work, a large range of liquid and gas flowrates, and wide range of viscosity and density for the liquid phase are considered and an optimal model is developed. The pressure drop and holdup models are also evaluated with data from numerous process scales.;Typically the mass transfer models and their parameters such as the liquid and gas-side mass transfer coefficients, diffusivity, and interfacial area are regressed using the data obtained from different experimental set-ups and scales, often in a sequential and sub-optimal way. In this work, a novel methodology is developed where parameters of the mass transfer models are simultaneously regressed by using the data from the wetted wall column, and packed towers, simultaneously. It is observed that the technique helps to improve the predictive capability of the process model.;Uncertainty in process models and their parameters are unavoidable. A Bayesian uncertainty quantification technique is applied for the first time to quantify the parametric uncertainty of the hydraulic and mass transfer models.;Dynamic models of CO2 capture solvent systems are very few in the existing literature. Model validation with the dynamic data from pilot plant has been scarcely reported. In this project, dynamic models are developed in Aspen Plus DynamicsRTM. Approximate pseudo random binary sequences are designed for the input signals and applied to the National Carbon Capture Center (NCCC) pilot plant during the 2014 MEA campaign. The pilot plant data were found to be noisy, did not satisfy mass and energy balances. In addition, some key variables were not measured. Preprocessing of the data followed by solution of a dynamic data reconciliation problem showed that the model could predict the transient response reasonably well.;For the first time, a dynamic design of experiments (DDoE) is developed for solvent-based CO2 capture processes using pseudo-random binary sequence and Schroeder-phased input techniques. The design ensured plant friendliness and could be successfully implemented in NCC during the 2017 campaign. The transient data are used to solve a dynamic data reconciliation and parameter estimation problem. Due to the computational expense and large dimensionality of the underlying problem, only the parameters corresponding to the holdup model could be estimated. It is observed that the holdup parameters could be optimally estimated by using the dynamic data collected over only a day. The parameters are slightly superior to those that have been regressed by using a large amount of the steady-state data collected over weeks. The techniques shows promise for the model development and parameter estimation by using the dynamic data that can be collected very quickly as opposed to the traditionally used steady-state data that take months thereby saving considerable resources

Model Adaptation for Real-Time Optimization in Energy Systems

Real-time optimization (RTO) is widely used in industry to operate processes close to their maximum performance. The models used for RTO need to be adapted using real-time data to ensure feasibility of the model optimal inputs and convergence to the real plant optimal point. Heat and power systems are suitable for being optimized in real-time because of their fast dynamics and the benefits achievable by reacting to changes in power prices and steam demand. This work proposes a modifier adaptation strategy that exploits the structure of certain problems to make the adaptation faster and more reliable, which is proven to be particularly useful for heat and power systems. The adaptation is performed in the equations that predict efficiencies or performance of unit operations. By identifying the variables that modify each performance factor, the number of data sets needed for gradient correction is reduced. This makes the proposed strategy suitable for real-time optimization of processes with a large number of inputs. Two alternatives are proposed to implement the approach: gradient calculation by finite differences and quadratic regression using current and past data. The features and behavior of this approach are shown through two case studies: (i) a simple model with three processes, and (ii) a heat and power system of a sugar and ethanol plant. A comparison with other existent approaches shows a better performance in terms of operating cost and sensitivity to noise.Fil: Serralunga, Fernán José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina;Fil: Mussati, Miguel Ceferino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina;Fil: Aguirre, Pio Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina

Plant level irreversible investment and equilibrium business cycles

This paper evaluates the importance of microeconomic irreversibilities for aggregate dynamics using a general equilibrium approach. To this end a real business cycle model of establishment level dynamics is formulated and analyzed. Investments decisions are subject to irreversibility constraints and consequently, are of the (S,s) variety. This complicates the analysis since the state of the economy is described by an endogenous distribution of agents. The paper develops a computational strategy that makes this class of (S,s) economies fully tractable. Contrary to what the previous literature has suggested, investment irreversibilities are found to have no effects on aggregate business cycle dynamics.Business cycles ; Investments

Advanced decision support through real-time optimization in the process industry

En la industria de procesos se puede obtener un aumento de la eficiencia de las plantas de producción, bien mediante la sustitución de procesos o equipos antiguos por otros más modernos y eficientes, o bien operando de forma más eficiente las instalaciones actuales en lugar de realizar grandes inversiones con tiempos de amortización inciertos. Si nos centramos en esta segunda línea de acción, hoy en día la toma de decisiones es conceptualmente más compleja que en el pasado, debido al rápido crecimiento que ha tenido la tecnología últimamente y a que los sistemas de comunicación han generado un gran número de alternativas entre las que se ha de elegir. Además, una decisión incorrecta o subóptima, con la complejidad estructural de los problemas actuales, a menudo resulta en un aumento de los costes a lo largo de la cadena de producción. A pesar de ello, el uso de sistemas de apoyo a la toma de decisiones (DSS) sigue siendo atípico en las industrias de procesos debido a los esfuerzos que se requieren en términos de desarrollo y mantenimiento de modelos matemáticos y al desafío de formulaciones matemáticas complejas, los exigentes requisitos computacionales y/o la difícil integración con la infraestructura de control o planificación existente. Esta tesis contribuye en la reducción de estas barreras desarrollando formulaciones eficientes para la optimización en tiempo real (RTO) en una planta industrial. En particular, esta tesis busca mejorar la operación de tres secciones interconectadas de una fábrica de producción de fibra de viscosa: una red de evaporación, una de sistema de enfriamiento y una red de recuperación de calor.Departamento de Ingeniería de Sistemas y AutomáticaDoctorado en Ingeniería Industria

Integrated Model-Centric Decision Support System for Process Industries

To bring the advances in modeling, simulation and optimization environments (MSOEs), open-software architectures, and information technology closer to process industries, novel mechanisms and advanced software tools must be devised to simplify the definition of complex model-based problems. Synergistic interactions between complementary model-based software tools must be refined to unlock the potential of model-centric technologies in industries. This dissertation presents the conceptual definition of a single and consistent framework for integrated process decision support (IMCPSS) to facilitate the realistic formulation of related model-based engineering problems. Through the integration of data management, simulation, parameter estimation, data reconciliation, and optimization methods, this framework seeks to extend the viability of model-centric technologies within the industrial workplace. The main contribution is the conceptual definition and implementation of mechanisms to ease the formulation of large-scale data-driven/model-based problems: data model definitions (DMDs), problem formulation objects (PFOs) and process data objects (PDOs). These mechanisms allow the definition of problems in terms of physical variables; to embed plant data seamlessly into model-based problems; and to permit data transfer, re-usability, and synergy among different activities. A second contribution is the design and implementation of the problem definition environment (PDE). The PDE is a robust object-oriented software component that coordinates the problem formulation and the interaction between activities by means of a user-friendly interface. The PDE administers information contained in DMD and coordinates the creation of PFOs and PIFs. Last, this dissertation contributes a systematic integration of data pre-processing and conditioning techniques and MSOEs. The proposed process data management system (pDMS) implements such methodologies. All required manipulations are supervised by the PDE, which represents an important advantage when dealing with high volumes of data. The IMCPSS responds to the need for software tools centered in process engineers for which the complexity of using current modeling environments is a barrier for broader application of model-based activities. Consequently, the IMCPSS represents a valuable tool for process industries, as the facilitation of problem formulation is translated into incorporation of plant data in less error-prone manner, maximization of time dedicated to the analysis of processes, and exploitation of synergy among activities based on process models

Transitory powder flow dynamics during emptying of a continuous mixer

This article investigates the emptying process of a continuous powder mixer, from both experimental and modelling points of view. The apparatus used in this work is a pilot scale commercial mixer Gericke GCM500, for which a specific experimental protocol has been developed to determine the hold up in the mixer and the real outflow. We demonstrate that the dynamics of the process is governed by the rotational speed of the stirrer, as it fixes characteristic values of the hold-up weight, such as a threshold hold-up weight. This is integrated into a Markov chain matrix representation that can predict the evolution of the hold-up weight, as well as that of the outflow rate during emptying the mixer. Depending on the advancement of the process, the Markov chain must be considered as non-homogeneous. The comparison of model results with experimental data not used in the estimation procedure of the parameters contributes to validating the viability of this model. In particular, we report results obtained when emptying the mixer at variable rotational speed, through step changes