Monitoring and Fault Diagnosis for Chylla-Haase Polymerization Reactor

The main objective of this research is to develop a fault detection and isolation (FDI) methodologies for Cylla-Haase polymerization reactor, and implement the developed methods to the nonlinear simulation model of the proposed reactor to evaluate the effectiveness of FDI methods. The first part of this research focus of this chapter is to understand the nonlinear dynamic behaviour of the Chylla-Haase polymerization reactor. In this part, the mathematical model of the proposed reactor is described. The Simulink model of the proposed reactor is set up using Simulink/MATLAB. The design of Simulink model is developed based on a set of ordinary differential equations that describe the dynamic behaviour of the proposed polymerization reactor. An independent radial basis function neural networks (RBFNN) are developed and employed here for an on-line diagnosis of actuator and sensor faults. In this research, a robust fault detection and isolation (FDI) scheme is developed for open-loop exothermic semi-batch polymerization reactor described by Chylla-Haase. The independent (RBFNN) is employed here when the system is subjected to system uncertainties and disturbances. Two different techniques to employ RBF neural networks are investigated. Firstly, an independent neural network is used to model the reactor dynamics and generate residuals. Secondly, an additional RBF neural network is developed as a classifier to isolate faults from the generated residuals. In the third part of this research, a robust fault detection and isolation (FDI) scheme is developed to monitor the Chylla-Haase polymerization reactor, when it is under the cascade PI control. This part is really challenging task as the controller output cannot be designed when the reactor is under closed-loop control, and the control action will correct small changes of the states caused by faults. The proposed FDI strategy employed a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. In this research, an independent MLP neural network is implemented here to generate residuals for detection task. And another RBF is applied for isolation task performing as a classifier. The fault diagnosis scheme is developed for a Chylla-Haase reactor under open-loop and closed-loop control system. The comparison between these two neural network architectures (MPL and RBF) are shown that RBF configuration trained by (RLS) algorithm have several advantages. The first one is greater efficiency in finding optimal weights for field strength prediction in complex dynamic systems. The RBF configuration is less complex network that results in faster convergence. The training algorithms (RLs and ROLS) that used for training RBFNN in chapter (4) and (5) have proven to be efficient, which results in significant faster computer time in comparison to back-propagation one. Another fault diagnosis (FD) scheme is developed in this research for an exothermic semi-batch polymerization reactor. The scheme includes two parts: the first part is to generate residual using an extended Kalman filter (EKF), and the second part is the decision making to report fault using a standardized hypothesis of statistical tests. The FD simulation results are presented to demonstrate the effectiveness of the proposed method. In the lase section of this research, a robust fault diagnosis scheme for abrupt and incipient faults in nonlinear dynamic system. A general framework is developed for model-based fault detection and diagnosis using on-line approximators and adaptation/learning schemes. In this framework, neural network models constitute an important class of on-line approximators. The changes in the system dynamics due to fault are modelled as nonlinear functions of the state, while the time profile of the fault is assumed to be exponentially developing. The changes in the system dynamics are monitored by an on-line approximation model, which is used for detecting the failures. A systematic procedure for constructing nonlinear estimation algorithm is developed, and a stable learning scheme is derived using Lyapunov theory. Simulation studies are used to illustrate the results and to show the effectiveness of the fault diagnosis methodology. Finally, the success of the proposed fault diagnosis methods illustrates the potential of the application of an independent RBFNN, an independent MLP, an Extended kalman filter and an adaptive nonlinear observer based FD, to chemical reactors

Instrumentation and control of anaerobic digestion processes: a review and some research challenges

The final publication is available at Springer via http://dx.doi.org/10.1007/s11157-015-9382-6[EN] To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes require advanced instrumentation and control tools. Over the years, research on these topics has evolved and followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid mainly industrial then municipal organic fraction of solid waste and agricultural residues. Time constants of the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. Since fast closed loop control is needed for short time constant processes, human operator is now included in the loop when taking decisions to optimize anaerobic digestion plants dealing with complex solid waste over a long retention time. Control objectives have also moved from the regulation of key variables measured online to the prediction of overall process perfor- mance based on global off-line measurements to optimize the feeding of the processes. Additionally, the need for more accurate prediction of methane production and organic matter biodegradation has impacted the complexity of instrumentation and should include a more detailed characterization of the waste (e.g., biochemical fractions like proteins, lipids and carbohydrates)andtheirbioaccessibility andbiodegradability characteristics. However, even if in the literature several methodologies have been developed to determine biodegradability based on organic matter characterization, only a few papers deal with bioaccessibility assessment. In this review, we emphasize the high potential of some promising techniques, such as spectral analysis, and we discuss issues that could appear in the near future concerning control of AD processes.The authors acknowledge the financial support of INRA (the French National Institute for Agricultural Research), the French National Research Agency (ANR) for the "Phycover" project (project ANR-14-CE04-0011) and ADEME for Inter-laboratory assay financial support.Jimenez, J.; Latrille, E.; Harmand, J.; Robles Martínez, Á.; Ferrer Polo, J.; Gaida, D.; Wolf, C.... (2015). Instrumentation and control of anaerobic digestion processes: a review and some research challenges. Reviews in Environmental Science and Biotechnology. 14(4):615-648. doi:10.1007/s11157-015-9382-6S615648144Aceves-Lara CA, Latrille E, Steyer JP (2010) Optimal control of hydrogen production in a continuous anaerobic fermentation bioreactor. 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Recursive neuro fuzzy techniques for online identification and control

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresThe main goal of this thesis will be focused on developing an adaptative closed loop control solution, using fuzzy methodologies. A positive theoretical and experimental contribution, regarding modelling and control of fuzzy and neuro fuzzy systems, is expected to be achieved. Proposed non-linear identification solution will use for modelling and control, a recurrent neuro fuzzy architecture. Regarding model solution, a state space approach will be considered during fuzzy consequent local models design. Developed controller will be based on model parameters, being expected not only a stable closed loop solution, but also a static error with convergence towards zero. Model and controller fuzzy subspaces, will be partitioned throughout process dynamical universe, allowing fuzzy local models and controllers commutation and aggregation. With the aim of capturing process under control dynamics using a real time approach, the use of recursive optimization techniques are to be adopted. Such methods will be applied during parameter and state estimation, using a dual decoupled Kalman filter extended with unscented transformation. Two distinct processes one single-input (SISO) other multi-input (MIMO), will be used during experimentation. It is expected from experiments, a practical validation of proposed solution capabilities for control and identification. Presented work will not be completed, without first presenting a global analysis of adopted concepts and methods, describing new perspectives for future investigations

Methods and Systems for Fault Diagnosis in Nuclear Power Plants

This research mainly deals with fault diagnosis in nuclear power plants (NPP), based on a framework that integrates contributions from fault scope identification, optimal sensor placement, sensor validation, equipment condition monitoring, and diagnostic reasoning based on pattern analysis. The research has a particular focus on applications where data collected from the existing SCADA (supervisory, control, and data acquisition) system is not sufficient for the fault diagnosis system. Specifically, the following methods and systems are developed. A sensor placement model is developed to guide optimal placement of sensors in NPPs. The model includes 1) a method to extract a quantitative fault-sensor incidence matrix for a system; 2) a fault diagnosability criterion based on the degree of singularities of the incidence matrix; and 3) procedures to place additional sensors to meet the diagnosability criterion. Usefulness of the proposed method is demonstrated on a nuclear power plant process control test facility (NPCTF). Experimental results show that three pairs of undiagnosable faults can be effectively distinguished with three additional sensors selected by the proposed model. A wireless sensor network (WSN) is designed and a prototype is implemented on the NPCTF. WSN is an effective tool to collect data for fault diagnosis, especially for systems where additional measurements are needed. The WSN has distributed data processing and information fusion for fault diagnosis. Experimental results on the NPCTF show that the WSN system can be used to diagnose all six fault scenarios considered for the system. A fault diagnosis method based on semi-supervised pattern classification is developed which requires significantly fewer training data than is typically required in existing fault diagnosis models. It is a promising tool for applications in NPPs, where it is usually difficult to obtain training data under fault conditions for a conventional fault diagnosis model. The proposed method has successfully diagnosed nine types of faults physically simulated on the NPCTF. For equipment condition monitoring, a modified S-transform (MST) algorithm is developed by using shaping functions, particularly sigmoid functions, to modify the window width of the existing standard S-transform. The MST can achieve superior time-frequency resolution for applications that involves non-stationary multi-modal signals, where classical methods may fail. Effectiveness of the proposed algorithm is demonstrated using a vibration test system as well as applications to detect a collapsed pipe support in the NPCTF. The experimental results show that by observing changes in time-frequency characteristics of vibration signals, one can effectively detect faults occurred in components of an industrial system. To ensure that a fault diagnosis system does not suffer from erroneous data, a fault detection and isolation (FDI) method based on kernel principal component analysis (KPCA) is extended for sensor validations, where sensor faults are detected and isolated from the reconstruction errors of a KPCA model. The method is validated using measurement data from a physical NPP. The NPCTF is designed and constructed in this research for experimental validations of fault diagnosis methods and systems. Faults can be physically simulated on the NPCTF. In addition, the NPCTF is designed to support systems based on different instrumentation and control technologies such as WSN and distributed control systems. The NPCTF has been successfully utilized to validate the algorithms and WSN system developed in this research. In a real world application, it is seldom the case that one single fault diagnostic scheme can meet all the requirements of a fault diagnostic system in a nuclear power. In fact, the values and performance of the diagnosis system can potentially be enhanced if some of the methods developed in this thesis can be integrated into a suite of diagnostic tools. In such an integrated system, WSN nodes can be used to collect additional data deemed necessary by sensor placement models. These data can be integrated with those from existing SCADA systems for more comprehensive fault diagnosis. An online performance monitoring system monitors the conditions of the equipment and provides key information for the tasks of condition-based maintenance. When a fault is detected, the measured data are subsequently acquired and analyzed by pattern classification models to identify the nature of the fault. By analyzing the symptoms of the fault, root causes of the fault can eventually be identified

Development of monitoring and control systems for biotechnological processes

The field of biotechnology represents an important research area that has gained increasing success in recent times. Characterized by the involvement of biological organisms in manufacturing processes, its areas of application are broad and include the pharmaceuticals, agri-food, energy, and even waste treatment. The implication of living microorganisms represents the common element in all bioprocesses. Cell cultivations is undoubtedly the key step that requires maintaining environmental conditions in precise and defined ranges, having a significant impact on the process yield and thus on the desired product quality. The apparatus in which this process occurs is the bioreactor. Unfortunately, monitoring and controlling these processes can be a challenging task because of the complexity of the cell growth phenomenon and the limited number of variables can be monitored in real-time. The thesis presented here focuses on the monitoring and control of biotechnological processes, more specifically in the production of bioethanol by fermentation of sugars using yeasts. The study conducted addresses several issues related to the monitoring and control of the bioreactor, in which the fermentation takes place. First, the topic concerning the lack of proper sensors capable of providing online measurements of key variables (biomass, substrate, product) is investigated. For this purpose, nonlinear estimation techniques are analyzed to reconstruct unmeasurable states. In particular, the geometric observer approach is applied to select the best estimation structure and then a comparison with the extended Kalman filter is reported. Both estimators proposed demonstrate good estimation capabilities as input model parameters vary. Guaranteeing the achievement of the desired ethanol composition is the main goal of bioreactor control. To this end, different control strategies, evaluated for three different scenarios, are analzyed. The results show that the MIMO system, together with an estimator for ethanol composition, ensure the compliance with product quality. After analyzing these difficulties through numeric simulations, this research work shifts to testing a specific biotechnological process such as manufacturing bioethanol from brewery’s spent grain (BSG) as renewable waste biomass. Both acid pre-treatment, which is necessary to release sugars, and fermentation are optimized. Results show that a glucose yield of 18.12 per 100 g of dried biomass is obtained when the pre-treatment step is performed under optimized conditions (0.37 M H2SO4, 10% S-L ratio). Regarding the fermentation, T=25°C, pH=4.5, and inoculum volume equal to 12.25% v/v are selected as the best condition, at which an ethanol yield of 82.67% evaluated with respect to theoretical one is obtained. As a final step, the use of Raman spectroscopy combined with chemometric techniques such as Partial Least Square (PLS) analysis is evaluated to develop an online sensor for fermentation process monitoring. The results show that the biomass type involved significantly affects the acquired spectra, making them noisy and difficult to interpret. This represents a nontrivial limitation of the applied methodology, for which more experimental data and more robust statistical techniques could be helpful

Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201

Methodology for utilising prior knowledge in constructing data-based process monitoring systems with an application to a dearomatisation process

Global competition is forcing the process industry to optimise the production processes. One key factor in optimisation is effective process state monitoring and fault detection. Another motivator to improve process monitoring systems are the substantial losses of revenue resulting from abnormal process conditions. It has been estimated that the petrochemical industry in the US alone loses 20 billion dollars per year because of unoptimal handling of abnormal process situations. Traditionally, the monitoring systems have been based on first principle models, constructed by specialists with process specific expertise. In contrast, the use of data-based modelling methods require less expertise and offers the possibilities to build and update the monitoring models in a short period of time, thus allowing more efficient development of monitoring systems. The aims of this thesis are to augment data-driven modelling with existing process knowledge, to combine different data-based modelling methods, and to utilise calculated variables in modelling in order to improve the accuracy of fault detection and identification (FDI) and to provide all necessary diagnostic information for fault tolerant control. The suggested improvements are included in a methodology for setting up FDI systems. The methodology has been tested by building FDI systems for detecting faults in two online quality analysers in a simulated and in a real industrial dearomatisation process at the Naantali oil refinery (Neste Oil Oyj). In developing an FDI system, background information about the user requirements for the monitoring system is first acquired. The information is then analysed and suitable modelling methods are selected according to the guidelines given in the methodology. Second, the process data are prepared for the modelling methods and augmented with appropriate calculated variables. Next, the input variable sets are determined with the introduced method and the models are constructed. After the estimation accuracy of the models is validated, the values of the fault detection parameters are determined. Finally, the fault detection performance of the system is tested. The system was evaluated during a period of one month at the Naantali refinery in 2007. The monitoring system was able to detect all the introduced analyser faults and to provide the information needed for a fault tolerant control system, thus validating the methodology. The effects of a number of suggested improvements in data-based modelling are analysed by means of a comparison study