Facultad Regional San Francisco
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Item Mechanistic insight into hydroxy‑methylation of hardwood kraft lignin(2024-09-24) Peralta, Micaela; Pajer, Nicolò; Crestini, Claudia; Nicolau, Verónica V.In view of developing upcycling strategies for hardwood Kraft lignin, hydroxymethylation of Eucalyptus Kraft lignin under alkaline conditions (pH 9 and 11) at different temperatures (50 °C and 70 °C) was studied in the present effort with the double objective of optimizing the reaction conditions and understanding the functionalization mechanism of C5 in either terminal or internal guaiacyl units during hydroxy-methylation. Formaldehyde consumption was estimated via titration of the oximated free formaldehyde; the hydroxy-methylation degree under the reaction was estimated by calculating the ratio in Condensed hydroxyl/Guaiacyl (Condensed OH/G-OH) via a new difference UV-spectroscopy. The reliability of the difference UV-method results for the analyses of the hydroxy-methylated lignins was statistically analysed and compared with that of vacuum-dried and sonicated samples. Hydroxy-methylated samples were then fully characterised by NMR (31P and HSQC) and GPC. The reaction temperature of 50 °C, pH 11, and period time of one hour resulted as the optimal conditions for the hydroxy-methylation, preventing the side-reactions leading to the formation of dimethylene-glycol addition products.The 31P and 1H–13C HSQC NMR revealed the absence of undesirable formaldehyde Cannizzaro by-products and the lack of hydroxymethyl groups in the aliphatic side chain under the studied conditions. GPC analyses, comparing two methodologies, revealed increases in molar mass of the hydroxy-methylated samples upon the formaldehyde addition. The selective hydroxy-methylation at the C5 guaiacyl site demonstrates that Eucalyptus Kraft lignin is as a promising candidate for resolproductionItem Fault-tolerant predictive control based on linear parameter varying scheme for industrial processes(2021-12-01) Bernardi, Emanuel; Adam, Eduardo J.Background: Process safety is a major concern in the researchers community, both in the past and today. However, the hardware complexity and the involved non-linear dynamics of industrial processes could lead to unsatisfactory behavior of traditional control methods. Methods: To cope with these issues, this paper presents a model-based strategy for fault tolerance in non-lin- ear chemical processes. Specifically, an observer-based fault detection and diagnosis scheme was imple- mented, which generates early and detailed fault information. Therefore, this valuable data was used to compensate the effects induced by actuator and sensor faults throughout the use of an integrated optimiza- tion-based identification and model predictive control technique, which allowed to track a reference even in the presence of faults. Significant Findings: This method reinforces the inherent robustness against faults of linear parameter varying predictive controllers. Moreover, the observers convergence and the controller stability were guaranteed in terms of linear matrix inequalities problems. A simulation based on a typical chemical industrial process, the highly non-linear continuous stirred tank reactor shows that the proposed method can achieve satisfactory performance in fault tolerance.Item Observer-based fault detection and diagnosis strategy for industrial processes(2020-07-30) Bernardi, Emanuel; Adam, Eduardo J.This study presents the design of a fault detection and diagnosis (FDD) scheme, composed from a bank of two types of observers, applied to linear parameter varying (LPV) systems. The first one uses a combination of reduced-order LPV observers to detect, isolate and estimate actuators faults, and the second one consists of a set of full-order LPV unknown input observers (UIO) to detect, isolate and estimate sensors faults. The observers’ design, convergence and its stability conditions are guaranteed in terms of linear matrix inequalities (LMI). Therefore, the main purpose of this work is to provide a novelty model-based observers’ technique to detect and diagnose faults upon non-linear systems. Simulation results, based on two typical chemical industrial processes, are given to illustrate and discuss the implementation and performance of such an approach.Item Fault-tolerant energy management for an industrial microgrid: A compact optimization method(2021-01-10) Bernardi, Emanuel; Morato, Marcelo M.; Costa Mendes, Paulo R.; Adam, Eduardo J.; Normey-Rico, Julio E.This work presents an optimization-based control method for the fault-tolerant energy management task of an industrial energy microgrid, based on a sugarcane power plant. The studied microgrid has several renewable energy sources, such as photovoltaic panels, wind turbines and biomass power generation, being subject to different operational constraints and load demands. The proposed management policy guarantees that these demands are met at every sampling instant, despite eventual faults. This law is derived from the solution of an optimization problem that combines the formalism of a Moving Horizon Estimation (MHE) scheme (to estimate faults) and a Model Predictive Control (MPC) loop (for fault-tolerant control goals); it chooses which energy source to use, seeking maximal profit and increased sustainability. The predictive controller part of the scheme is based on a linear time-varying model of the process, which is scheduled with respect to the fault estimation brought up by the MHE. Via numerical simulations, it is demonstrated that the proposed method, when com- pared to other MPC strategies, exhibits enhanced performances.Item Infraestructura para el desarrollo de laboratorios remotos(2022-06-15) Miretti, Marco; Bernardi, EmanuelEl presente trabajo se fundamenta en la enseñanza a través de experiencias remotas. El mismo plantea el desarrollo de una infraestructura de aprendizaje a distancia mediante la construcción de laboratorios remotos de forma versátil, escalable y asequible, al apoyarse en la democratización de la tecnología. Si bien la propuesta es aplicable a un sinnúmero de ramas de la ciencia, con el fin de proveer un prototipo funcional, éste se implementó para la enseñanza de sistemas de control. En consecuencia, éste consiste en la construcción de un sistema dinámico, típico del área. Además, cuenta con una interfaz de programación de aplicación (API) que permite a los estudiantes desarrollar sus propios algoritmos de control, utilizando lenguajes de alto nivel, como son Python y GNU Octave, prescindiendo de competencias afines a los sistemas embebidos y a las redes de comunicación.Item A qLPV Nonlinear Model Predictive Control with Moving Horizon Estimation(2021-10-15) Morato, Marcelo M.; Bernardi, Emanuel; Stojanović, VladimirThis paper presents a Model Predictive Control (MPC) algorithm for Nonlinear systems represented through quasiLinear Parameter Varying (qLPV) embeddings. Input-to-state stability is ensured through parameter-dependent terminal ingredients, computed offline via Linear Matrix Inequalities. The online operation comprises three consecutive Quadratic Programs (QPs) and, thus, is computationally efficient and able to run in real-time for a variety of applications. These QPs stand for the control optimization (MPC) and a Moving-Horizon Estimation (MHE) scheme that predicts the behaviour of the scheduling parameters along the future horizon. The method is practical and simple to implement. Its effectiveness is assessed through a benchmark example (a CSTR system).Item Full-order Output Observer Applied to a Linear Parameter Varying System with Unknown Input(IEEE, 2020-12-01) Bernardi, Emanuel; Pipino, Hugo; Adam, Eduardo J.This short work presents the outline of a set of full-order observers, applied to linear parameter varying systems with unknown input. In particular, this approach is used to constructs a strategy to detect and diagnose sensor faults on industrial processes. The observers’ design and its stability conditions are guaranteed in terms of a linear matrix inequalities framework. As a consequence, the main purpose of this paper is to provide a model-based observers’ technique, to detect, isolate and diagnose sensor faults upon non-linear systems. At last, two numerical simulations of typical chemical industrial processes are given to illustrate its implementation and performance.Item Nonlinear Fault-tolerant Model Predictive Control Strategy for Industrial Processes(IEEE, 2020-10-28) Bernardi, Emanuel; Adam, Eduardo J.This short paper presents a strategy to tolerate the income of additive faults in nonlinear chemical processes. For that, an observed-based fault detection and diagnosis scheme is implemented to generate an early and detailed fault information. Then, this valuable knowledge is used to compensate the effects induced by actuators and sensors faults throughout the use of an integrated optimization-based estimation and model predictive control scheme, which allows to track a reference even in presence of faults. A simulation based on a typical chemical industrial process, the highly non-linear continuous stirred tank reactor, is addressed to illustrate the design process and the performance of such approach.Item Fault-tolerant Model Predictive Control Strategy Applied to Industrial Processes(IEEE, 2019-09-20) Bernardi, Emanuel; Cappelletti, Carlos A.; Adam, Eduardo J.This paper presents a strategy to address the income of actuators faults using a model-based predictive controller scheme, which allows to track a reference even in the presence of actuator faults. The proposed fault-tolerant control system adopts a model predictive control technique to design a reconfigurable fault-tolerant controller and a reduce-order observer to achieve the fault detection and diagnosis function. Simulation results, based on two typical chemical industries processes, are given to illustrate the use and performance of such approach.Item Fault-tolerant Model-based Predictive Control Applied to Industrial Processes(UNL, 2021-10-21) Bernardi, Emanuel; Adam, Eduardo J.Modern plants rely on sophisticated control systems to meet performance and stability requirements. In particular, a conventional feedback control design for a complex system may result in unsatisfactory performance, or even instability, in the event of malfunctions in actuators, sensors or other system components. In view of these aspects, this thesis addresses the design, development and evaluation of fault-tolerant controllers for typical industrial processes, which ensure the compliance of operational constraints despite the presence of faults. To begin with, the current state-of-art and the main specific concepts are introduced. Then, two model-based strategies are presented. On the one side, the design of a novel observer-based fault detection and diagnosis scheme and the development of an adaptive predictive controller are combined to deploy a non-linear active fault-tolerant control system, on the basis of the linear parameter varying system representation. This proposed scheme is evaluated on typical non-linear chemical industrial processes. On the other hand, an optimisation-based fault-tolerant predictive controller was proposed to develop a tertiary-level energy management system, based on a sugarcane distillery power plant. Lastly, it is important to remark that for each proposed scheme a realistic simulation scenario was presented. Enabling vast discussions about its performance and effectiveness, via graphical observations and metric indices.