NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

A flexible approach to the estimation of water budgets and its connection to the travel time theory.

The increasing impacts of climate changes on water related sectors are leading the scientists' attentions to the development of comprehensive models, allowing better descriptions of the water and solute transport processes. "Getting the right answers for the right reasons", in terms of hydrological response, is one of the main goals of most of the recent literature. Semi-distributed hydrological models, based on the partition of basins in hydrological response units (HRUs) to be connected, eventually, to describe a whole catchment, proved to be robust in the reproduction of observed catchment dynamics. 'Embedded reservoirs' are often used for each HRU, to allow a consistent representation of the processes. In this work, a new semi-disitrbuted model for runoff and evapotranspiration is presented: five different reservoirs are inter-connected in order to capture the dynamics of snow, canopy, surface flow, root-zone and groundwater compartments. The knowledge of the mass of water and solute stored and released through different outputs (e.g. discharge, evapotranspiration) allows the analysis of the hydrological travel times and solute transport in catchments. The latter have been studied extensively, with some recent benchmark contributions in the last decade. However, the literature remains obscured by different terminologies and notations, as well as model assumptions are not fully explained. The thesis presents a detailed description of a new theoretical approach that reworks the theory from the point of view of the hydrological storages and fluxes involved. Major aspects of the new theory are the 'age-ranked' definition of the hydrological variables, the explicit treatment of evaporative fluxes and of their influence on the transport, the analysis of the outflows partitioning coefficients and the explicit formulation of the 'age-ranked' equations for solutes. Moreover, the work presents concepts in a new systematic and clarified way, helping the application of the theory. To give substance to the theory, a small catchment in the prealpine area was chosen as an example and the results illustrated. The rainfall-runoff model and the travel time theory were implemented and integrated in the semi-distributed hydrological system JGrass-NewAge. Thanks to the environmental modelling framework OMS3, each part of the hydrological cycle is implemented as a component that can be selected, adopted, and connected at run-time to obtain a user-customized hydrological model. The system is flexible, expandable and applicable in a variety of modelling solutions. In this work, the model code underwent to an extensive revision: new components were added (coupled storages water budget, travel times components); old components were enhanced (Kriging, shortwave, longwave, evapotranspiration, rain-snow separation, SWE and melting components); documentation was standardized and deployed. Since the Thesis regards in wide sense the building of a collaborative system, a discussion of some general purpose tools that were implemented or improved for supporting the present research is also presented. They include the description and the verification of a software component dealing with the long-wave radiation budget and another component dealing with an implementation of some Kriging procedure

Engineering Parallel Transmit/Receive Radio-Frequency Coil Arrays for Human Brain MRI at 7 Tesla

Magnetic resonance imaging is widely used in medical diagnosis to obtain anatomical details of the human body in a non-invasive way. Clinical MR scanners typically operate at a static magnetic field strength (B0) of 1.5T or 3T. However, going to higher field is of great interest since the signal-to-noise ratio is proportional to B0. Therefore, higher image resolution and better contrast between the human tissues could be achieved. Nevertheless, new challenges arise when increasing B0. The wavelength associated with the radio-frequency field B1+ has smaller dimensions - approx. 12 cm for human brain tissues - than the human brain itself (20 cm in length), the organ of interest in this thesis. The main consequence is that the transmit field distribution pattern (B1+) is altered and the final MR images present bright and dark signal spots. These effects prevent the ultra-high field MR scanners (>= 7T) to be used for routine clinical diagnosis. Parallel-transmit is one approach to address these new challenges. Instead of using an RF coil connected to a single power input as it is commonly done at lower magnetic fields, multiple RF coils are used with independent power inputs. The subsequent distinct RF signals can be manipulated separately, which provides an additional degree of freedom to generate homogeneous B1+-field distributions over large or specific regions in the human body. A transmit/receive RF coil array optimized for whole-brain MR imaging was developed and is described in this thesis. Dipoles antennas were used since they could provide a large longitudinal (vertical axis-head to neck) coverage and high transmit field efficiency. Results demonstrated a complete coverage of the human brain, and particularly high homogeneity over the cerebellum. However, since the receive sensitivity over large field-of-views is related to the number of channels available to detect the NMR signal, the next work was to add a 32-channel receive loop coil array to the transmit coil array. The complete coverage of the human brain was assessed with a substantial increase in signal-to-noise compared to the transmit/receive dipole coil array alone. Moreover, acquisition time was shortened since higher acceleration factors could be used. To optimize the individual RF fields and generate an homogeneous B1+-field, a method was developed making use of the particle-swarm algorithm. A user-friendly graphical interface was implemented. Good homogeneity could be achieved over the whole-brain after optimization with the coil array built in this study. Moreover, the optimization was shown to be robust across multiple subjects. The last project was focused on the single transmit system. Local volume coils (single transmit) present pronounced transmit field inhomogeneities in specific regions of the human brain such as the temporal lobes. A widely used approach to address locally these challenges is to add dielectric pads inside the volume coils to enhance the local transmit field efficiency. It was shown in this thesis that constructing dedicated surface coils is a valuable alternative to the dielectric pads in terms of transmit field efficiency and MR spectroscopy results. Two RF coil setups were developed for the temporal and frontal lobes of the human brain, respectively. This thesis provides extensive insight on MR engineering of RF coils at ultra-high field and the potential of parallel-transmit to address the future needs in clinical applications

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Sample-Efficient Reinforcement Learning of Robot Control Policies in the Real World

abstract: The goal of reinforcement learning is to enable systems to autonomously solve tasks in the real world, even in the absence of prior data. To succeed in such situations, reinforcement learning algorithms collect new experience through interactions with the environment to further the learning process. The behaviour is optimized by maximizing a reward function, which assigns high numerical values to desired behaviours. Especially in robotics, such interactions with the environment are expensive in terms of the required execution time, human involvement, and mechanical degradation of the system itself. Therefore, this thesis aims to introduce sample-efficient reinforcement learning methods which are applicable to real-world settings and control tasks such as bimanual manipulation and locomotion. Sample efficiency is achieved through directed exploration, either by using dimensionality reduction or trajectory optimization methods. Finally, it is demonstrated how data-efficient reinforcement learning methods can be used to optimize the behaviour and morphology of robots at the same time.Dissertation/ThesisDoctoral Dissertation Computer Science 201