Approaches based on LAMDA control applied to regulate HVAC systems for buildings

The control of HVAC (Heating Ventilation and Air Conditioning) systems is usually complex because its modeling in certain cases is difficult, since these systems have a large number of components. Heat exchangers, chillers, valves, sensors, and actuators, increase the non-linear characteristics of the complete model, so it is necessary to propose new control strategies that can handle the uncertainty generated by all these elements working together. On the other hand, artificial intelligence is a powerful tool that allows improving the performance of control systems with inexact models and uncertainties. This paper presents new control alternatives for HVAC systems based on LAMDA (Learning Algorithm for Multivariable Data Analysis). This algorithm has been used in the field of machine learning, however, we have taken advantage of its learning characteristics to propose different types of intelligent controllers to improve the performance of the overall control system in tasks of regulation and reference change. In order to perform a comparative analysis in the context of HVAC systems, conventional methods such as PID and Fuzzy-PID are compared with LAMDA-PID, LAMDA-Sliding Mode Control based on Z-numbers (ZLSMC), and Adaptive LAMDA. Specifically, two HVAC systems are implemented by simulations to evaluate the proposals: an MIMO (Multiple-input Multiple-output) HVAC system and an HVAC system with dead time, which are used to compare the results qualitatively and quantitatively. The results show that ZLSMC is the most robust controller, which efficiently controls HVAC systems in cases of reference changes and the presence of disturbances.European CommissionAgencia Estatal de InvestigaciónJunta de Comunidades de Castilla-La Manch

Application of model predictive control to heating and cooling of off-grid shelters

An increasing concern at off-grid forward operating bases (FOBs), disaster relief camps, refugee aid camps and other encampments is the rising cost of supplying and sustaining shelters to accommodate the occupant(s). This is due to high risk burdened costs of liquid fuel deliveries, needed for local electricity generation in remote, and often hostile regions, where such contingency bases are located. A significant part of the non-combat mission related energy consumption in such bases is towards the heating and cooling of shelters. The Environmental Control Unit (ECU) for a shelter, consisting of the components and controls of a packaged terminal air conditioner & heat pump, is operated with a simple set-point temperature control. For such shelters, more efficient use of energy can be accomplished by applying a model predictive control (MPC) approach to the ECU. MPC selects the most fuel efficient operation of the shelter ECU, based on shelter size, materials and construction, internal thermal loads, weather profile, including wind speed, solar insolation, infiltration, and ground coupling. The thesis demonstrates a first-of-its-kind, more energy-efficient and more thermally comfort application of the MPC approach on an Alaska soft shell shelter, equipped with an ECU, by performing a combination of MATLAB and EnergyPlus modeling.M.S

L'intertextualité dans les publications scientifiques

La base de données bibliographiques de l'IEEE contient un certain nombre de duplications avérées avec indication des originaux copiés. Ce corpus est utilisé pour tester une méthode d'attribution d'auteur. La combinaison de la distance intertextuelle avec la fenêtre glissante et diverses techniques de classification permet d'identifier ces duplications avec un risque d'erreur très faible. Cette expérience montre également que plusieurs facteurs brouillent l'identité de l'auteur scientifique, notamment des collectifs de chercheurs à géométrie variable et une forte dose d'intertextualité acceptée voire recherchée

An intelligent engine condition monitoring system

The main focus of the work reported here is in the design of an intelligent condition monitoring system for diesel engines. Mechanical systems in general and diesel engines in particular can develop faults if operated for any length of time. Condition monitoring is a method by which the performance of a diesel engine can be maintained at a high level, ensuring both continuous availability and design-level efficiency. A key element in a condition monitoring program is to acquire sensor information from the engine, and use this information to assess the condition of the engine, with an emphasis on monitoring causes of engine failure or reduced efficiency. A Ford 70PS 4-stroke diesel engine has been instrumented with a range of sensors and interfaced to a PC in order to facilitate computer controlled data acquisition and data storage. Data was analyzed to evaluate the optimum use of sensors to identify faults and to develop an intelligent algorithm for the engine condition monitoring and fault detection, and in particular faults affecting the combustion process in the engine. In order to investigate the fault-symptom relationships, two synthetic faults were introduced to the engine. Fuel and inlet air shortage were selected as the faults for their direct relationship to the combustion process quality. As a subtask the manually operated hydraulic brake was adapted to allow automatic control to improve its performance. Two modes of controlling were designed for the developed automatic control of the hydraulic brake system. A robust mathematical diesel engine model has been developed which can be used to predict the engine parameters related to the combustion process in the diesel engine, was constructed from the basic relationships of the diesel engine using the minimum number of empirical equations. The system equations of a single cylinder engine were initially developed, from which the multi-cylinder diesel engine model was validated against experimental test data. The model was then tuned to improve the predicted engine parameters for better matching with the available engine type. The final four-cylinder diesel engine model was verified and the results show an accurate match with the experimental results. Neural networks and fuzzification were used to develop and validate the intelligent condition monitoring and fault diagnosis algorithm, in order to satisfy the requirements of on-line operation, i. e. reliability, easily trained, minimum hardware and software requirements. The development process used a number of different neural network architecture and training techniques. To increase the number of the parameters used for the engine condition evaluation, the Multi-Net technique was used to satisfy accurate and fast decision making. Two neural networks are designed to operate in parallel to accommodate the different sampling rate of the key parameters without interference and with reduced data processing time. The two neural networks were trained and validated using part of the measured data set that represents the engine operating range. Another set of data, not utilized within the training stage, has been applied for validation. The results of validation process indicate the successful prediction of the faults using the key measured parameters, as well as a fast data processing algorithm. One of the main outcomes of this study is the development of a new technique to measure cylinder pressure and fuel pressure through the measurement of the strain in the injector body. The main advantage of this technique is that, it does not require any intrusive modification to the engine which might affect the engine actual performance. The developed sensor was tested and used to measure the cylinder and fuel pressure to verify the fuel fault effect on the combustion process quality. Due to high sampling rate required, the developed condition monitoring and fault diagnosis algorithm does not utilize this signal to reduce the required computational resources for practical applications.EThOS - Electronic Theses Online ServiceEgyptian GovernmentGBUnited Kingdo