Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems

Diseño de un sistema de control neural para el monitoreo y control de calidad en una columna de destilación de multicomponentes

Los sistemas de destilación, desde hace muchas décadas, vienen siendo ampliamente usados en la industria de procesos químicos, especialmente en refinerías y procesos de acondicionamiento y tratamiento de gas natural. Los objetivos típicos en estos sistemas están asociados al cumplimiento de especificaciones sobre la calidad de los productos, y para lo cual usualmente se cuenta con analizadores online para monitoreo de estas especificaciones como es el caso de cromatógrafos, así como análisis en laboratorio mediante técnicas específicas.Tesi

plant-wide control of industrial processes using rigorous simulation and heuristics

Ph.DDOCTOR OF PHILOSOPH

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

Intelligent techniques for dynamic and transient analysis of multi stage desalination plant

This thesis is concerned with dynamic and transient analysis of MSF desalination plants. The technique is developed using artificial neural networks (ANN) approach for the purpose of prediction, analysis, modelling, and control of MSF desalination plant. The applicability of the method to predict an approximation of the transient operating conditions as well as the control action are shown satisfactory. The network architecture and learning algorithm are developed based on the Multilayered Feed forward Networks (MFN) with the Back Propagation (BP) learning algorithm. It was shown that the approach could intelligently capture the dynamics of the system. An improved technique is developed for the BP learning algorithm based on Global Error Node Evaluation (GENE) approach for MFN to retains the function approximation requirements for a nonlinear dynamic behaviour. However, by using this approach considerable improvement for the generalization capability could be obtained for the case study under consideration. The technique provides the necessary dynamic learning, behaviour required for MFN. This approach appears to be effective for the input - output dynamic modelling of complex process systems and therefore on-line adaptation is possible (when the characteristic of the system is changing or when more test data are available for another operating range). The developed algorithm is used for the development and validation of an empirical multi-controller structure for MSF desalination plant. Satisfactory results are obtained from practical examples with the additional training ability