Thermal behaviour model identification for three different office buildings

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The thermal behaviour was investigated of three offices positioned in three buildings built in different periods, one academic institute built in 1920 and two modem commercial buildings in London. The buildings chosen for this study are the Rockefeller Building, which is part of University College London (UCL), Portman House in Oxford Street and the Visa Building in Paddington. Due to the lack of specific information related to the structure of the buildings such as windows, doors, building dimensions and other information that would allow the use of physical models, in this project black-box linear and non-linear mathematical models were used. Data relating to room temperature, hot and chilled water temperature, air flow and temperature from air handling units and outside temperature were collected for one year, from the actual building management systems (BMSs) installed in these buildings. The main assumption of the model development in the three buildings was that although occupancy, computers, printers etc cause an additional internal heat gain, their impact is in part indirectly included in the model. The primary objective of the analysis was to identify the inputs (independent variables) that gave good models for the prediction of room temperature for a certain period. Consequently, the process of input selection and period of validity in obtaining models that give good thermal prediction (within the same period) were the key points in season subdivision. The first part of the analysis applied the following linear parametric mathematical models to the three office buildings selected: Box Jenkins (BJ), autoregressivem oving averagew ith exogenousi nput (ARMAX) and output error (OE) structure. The project then deals with non-linear mathematical models. The same inputs selected and assumptions made with linear analysis were used to build, in turn, models with feedforward backpropagation (FFBP), non-linear autoregressive mathematical models with parallel arrangement (NARX) and series-parallel arrangement (NARXSP). The research presented in this project is related to developing models for three real offices positioned in three different buildings whereas previous researchers have applied these models mainly to experimental rooms and HVAC plants, with the purpose of fault detection and diagnostics. Furthermore, in the past, research on thermal model development has been related to one office or HVAC plant, and for a limited period of time (a few weeks or months). In contrast, this study undertakes an overall analysis of thermal model development for three offices and for a period of one year, where the process of input selection is given priority to obtain good models. Thus, previous studies have not utilized these two types of models for such a long period of data collection nor related them to three different buildings. Finally, model development and then validation were pursued utilizing the same week, different weeks and different days (where the first part of the data in each case was used for model estimation and the following part for model validation). This was one within the period that the models gave good results for the prediction of room temperature. The best mathematical models (linear and non-linear) that predict the room temperature, in terms of the inputs selected, has been determined for each season. The procedures for how to choose the best models are based on the following techniques: final prediction error (FPE for linear models), mean squared error (mse for non-linear models), and model fits and errors between measurements and simulated model output. Overall, the results related for the prediction of room temperature with non-linear models, are better than those obtained with linear models, as a result of comparison between models' errors, FPE and mse obtained with linear and non-linear models.Funding was obtained from the Brunel University Department of Mechanical Engineering

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

Intelligent Control Strategies for an Autonomous Underwater Vehicle

The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics are highly non-linear, and the relative similarity between the linear and angular velocities about each degree of freedom means that control schemes employed within other flight vehicles are not always applicable. In such instances, intelligent control strategies offer a more sophisticated approach to the design of the control algorithm. Neurofuzzy control is one such technique, which fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture. Such an approach is highly suited to development of an autopilot for an AUV. Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots. However, the limitation of this technique is that it cannot be used for developing multivariable fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design that can accommodate changing vehicle pay loads and environmental disturbances. Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system design, the well known properties of radial basis function networks (RBFN) offer a more flexible controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form. This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector, Defence Evaluation and Research Agency, Winfrit

Wastewater Treatment and Reuse Technologies

This edited volume is a collection of 12 publications from esteemed research groups around the globe. The articles belong to the following broad categories: biological treatment process parameters, sludge management and disinfection, removal of trace organic contaminants, removal of heavy metals, and synthesis and fouling control of membranes for wastewater treatment

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Teaching Accommodation Task Skills: from Human Demonstration to Robot Control via Artificial Neural Networks

A simple edge-mating task, performed automatically by accommodation control, was used to study the feasibility of using data collected during a human demonstration to train an artificial neural network (ANN) to control a common robot manipulator to complete similar tasks. The 2-dimensional (planar) edge-mating task which aligns a peg normal to a fiat table served as the basis for the investigation. A simple multi-layered perceptron (MLP) ANN with a single hidden layer and linear output nodes was trained using the back-propagation algorithm with momentum. The inputs to the ANN were the planar components of the contact force between the peg and the table. The outputs from the ANN were the planar components of a commanded velocity. The controller was architected so the ANN could learn a configuration-independent solution by operating in the tool-frame coordinates. As a baseline of performance, a simple accommodation matrix capable of completing the edge- mating task was determined and implemented in simulation and on the PUMA manipulator. The accommodation matrix was also used to synthesize various forms of training data which were used to gain insights into the function and vulnerabilities of the proposed control scheme. Human demonstration data were collected using a gravity-compensated PUMA 562 manipulator and using a custom-built planar, low-impedance motion measurement system (PLIMMS)