Fault tolerant control system design for distillation processes

PhD ThesisThe complexity and sophistication of modern control systems deployed in the re nery operation, particularly the crude distillation unit as a result of increasing demand for higher performance and improved safety, are on the increase. This growing complexity comes with some level of vulnerabilities, part of which is the potential failure in some of the components that make up the control system, such as actuators and sensors. The interplay between these components and the control system needs to have some built-in robustness in the face of actuator and sensor faults, to guarantee higher reliability and improved safety of the control system and the plant respectively, which is fundamental to the economy and operation of the system. This thesis focuses on the application of frugally designed fault tolerant control systems (FTCS) with automatic actuator and sensor faults containment capabilities on distillation processes, particularly atmospheric crude distillation unit. A simple active actuator FTCS that used backup feedback signal, switchable references and restructurable PID controllers was designed and implemented on three distillation processes with varying complexities { methanol-water separation column, the benchmark Shell heavy oil fractionator, and an interactive dynamic crude distillation unit (CDU) to accommodate actuator faults. The fault detection and diagnosis (FDD) component of the actuator FTCS used dynamic principal component analysis (DPCA), a data-based fault diagnostic technique, because of its simplicity and ability to handle large amount of correlated process measurements. The recon gurable structure of the PID controllers was achieved using relative gain array (RGA) and dynamic RGA system interaction analysis tools for possible inputs { outputs pairing with and without the occurrence of actuator faults. The interactive dynamic simulation of CDU was developed in HYSYS and integrated with MATLAB application through which the FDD and the actuator FTCS were implemented. The proposed actuator FTCS is proved being very e ective in accommodating actuator faults in cases where there are suitable inputs { outputs pairing after occurrence of an actuator fault. Fault tolerant inferential controller (FTIC) was also designed and implemented on a binary distillation column and an interactive atmospheric CDU to accommodate sensor faults related to the controlled variables. The FTIC used dynamic partial least squared (DPLS) and dynamic principal component regression (DPCR) based soft sensor techniques to provide redundant controlled variable estimates, which are then used in place of faulty sensor outputs in the feedback loops to accommodate sensor faults and maintain the integrity of the entire control system. Implementation issues arising from the e ects of a sensor fault on the secondary variables used for soft sensor estimation were addressed and the approach was shown to be very e ective in accommodating all the sensor faults investigated in the distillation units. The actuator FTCS and the FTIC were then integrated with the DPCA FDD scheme to form a complete FTCS capable of accommodating successive actuator and sensor faults in the distillation processes investigated. The simulation results demonstrated the e ectiveness of the proposed approach. Lastly, fault tolerant model predictive control (FTMPC) with restructurable inputs { outputs pairing in the presence of actuator faults based on preassessed recon gurable control structures was proposed, and implemented on an interactive dynamic CDU. The FTMPC system used a rst order plus dead time (FOPDT) model of the plant for output prediction and RGA and DRGA tools to analyse possible control structure recon guration. The strategy helped improve the availability and performance of control systems in the presence of actuator faults, and can ultimately help prevent avoidable potential disasters in the re nery operation with improved bottom line { Pro t. Overall, the proposed approaches are shown to be e ective in handling actuator and sensor faults, when there are suitable manipulated variables and redundant analytical signals that could be used to contain the e ects of the faults on the system.University of Lagos, Nigeria & Petroleum Technology Development Fund (PTDF) for the Scholarship award at the later stage of my research programme

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Data-based fault-tolerant model predictive controller an application to a complex dearomatization process

The tightening global competition during the last few decades has been the driving force for the optimisation of industrial plant operations through the use of advanced control methods, such as model predictive control (MPC). As the occurrence of faults in the process measurements and actuators has become more common due to the increase in the complexity of the control systems, the need for fault-tolerant control (FTC) to prevent the degradation of the controller performance, and therefore the better optimisation of the plant operations, has increased. Traditionally, the most actively studied fault detection and diagnosis (FDD) components of the FTC strategies have been based on model-based approaches. In the modern process industries, however, there is a need for the data-based FDD components due to the complexity and limited availability of mechanistic models. Recently, active FTC strategies using fault accommodation and controller reconfiguration have become popular due to the increased computation capacity, easier adaptability and lower overall implementation costs of the active FTC strategies. The main focus of this thesis is on the development of an active data-based fault-tolerant MPC (FTMPC) for an industrial dearomatization process. Three different parallel-running FTC strategies are developed that utilise the data-based FDD methods and the fault accommodation- and controller reconfiguration-based FTC methods. The performances of three data-based FDD methods are first compared within an acknowledged testing environment. Based on the preliminary performance testing, the best FDD method is selected for the final FTMPC. Next, the performance of the FTMPC is validated with the simulation model of the industrial dearomatization process and finally, the profitability of the FTMPC is evaluated based on the results of the evaluation. According to the testing, the FTMPC performs efficiently and detects and prevents the effects of the most common faults in the analyser, flow and temperature measurements, and the controller actuators. The reliability of the MPC is increased and the profitability of the dearomatization process is enhanced due to the lower off-spec production

Integration of Process Design, Scheduling, and Control Via Model Based Multiparametric Programming

The conventional approach to assess the multiscale operational activities sequentially often leads to suboptimal solutions and even interruptions in the manufacturing process due to the inherent differences in the objectives of the individual constituent problems. In this work, integration of the traditionally isolated process design, scheduling, and control problems is investigated by introducing a multiparametric programming-based framework, where all decision layers are based on a single high fidelity model. The overall problem is dissected into two constituent parts, namely (i) design and control, and (ii) scheduling and control problems. The proposed framework was first assessed on these constituent subproblems, followed by the implementation on the overall problem. The fundamental steps of the framework consists of (i) developing design dependent offline control and scheduling strategies, and (ii) exact implementation of these offline rolling horizon strategies in a mixed-integer dynamic optimization problem for the optimal design. The design dependence of the offline operational strategies allows for the integrated problem to consider the design, scheduling, and control problems simultaneously. The proposed framework is showcased on (i) a binary distillation column for the separation of toluene and benzene, (ii) a system of two continuous stirred tank reactor, (iii) a small residential heat and power network, and (iv) two batch reactor systems. Furthermore, a novel algorithm for large scale multiparametric programming problems is proposed to solve the classes of problems frequently encountered as a result of the integration of rolling horizon strategies

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

Distributed Control for Cyber-Physical Systems

Networked Cyber-Physical Systems (CPS) are fundamentally constrained by the tight coupling and closed-loop control and actuation of physical processes. To address actuation in such closed-loop wireless control systems there is a strong need to re-think the communication architectures and protocols for maintaining stability and performance in the presence of disturbances to the network, environment and overall system objectives. We review the current state of network control efforts for CPS and present two complementary approaches for robust, optimal and composable control over networks. We first introduce a computer systems approach with Embedded Virtual Machines (EVM), a programming abstraction where controller tasks, with their control and timing properties, are maintained across physical node boundaries. Controller functionality is decoupled from the physical substrate and is capable of runtime migration to the most competent set of physical controllers to maintain stability in the presence of changes to nodes, links and network topology. We then view the problem from a control theoretic perspective to deliver fully distributed control over networks with Wireless Control Networks (WCN). As opposed to traditional networked control schemes where the nodes simply route information to and from a dedicated controller, our approach treats the network itself as the controller. In other words, the computation of the control law is done in a fully distributed way inside the network. In this approach, at each time-step, each node updates its internal state to be a linear combination of the states of the nodes in its neighborhood. This causes the entire network to behave as a linear dynamical system, with sparsity constraints imposed by the network topology. This eliminates the need for routing between “sensor → channel → dedicated controller/estimator → channel → actuator”, allows for simple transmission scheduling, is operational on resource constrained low-power nodes and allows for composition of additional control loops and plants. We demonstrate the potential of such distributed controllers to be robust to a high degree of link failures and to maintain stability even in cases of node failures

Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution