Measurements, Models, Systems and Design

531 s.

Review of air fuel ratio prediction and control methods

Air pollution is one of main challenging issues nowadays that researchers have been trying to address.The emissions of vehicle engine exhausts are responsible for 50 percent of air pollution. Different types of emissions emit from vehicles including carbon monoxide, hydrocarbons, NOX, and so on. There is a tendency to develop strategies of engine control which work in a fast way. Accomplishing this task will result in a decrease in emissions which coupled with the fuel composition can bring about the best performance of the vehicle engine.Controlling the Air-Fuel Ratio (AFR) is necessary, because the AFR has an enormous impact on the effectiveness of the fuel and reduction of emissions.This paper is aimed at reviewing the recent studies on the prediction and control of the AFR, as a bulk of research works with different approaches, was conducted in this area.These approaches include both classical and modern methods, namely Artificial Neural Networks (ANN), Fuzzy Logic, and Neuro-Fuzzy Systems are described in this paper.The strength and the weakness of individual approaches will be discussed at length

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Past, present and future mathematical models for buildings (i)

This is the first of two articles presenting a detailed review of the historical evolution of mathematical models applied in the development of building technology, including conventional buildings and intelligent buildings. After presenting the technical differences between conventional and intelligent buildings, this article reviews the existing mathematical models, the abstract levels of these models, and their links to the literature for intelligent buildings. The advantages and limitations of the applied mathematical models are identified and the models are classified in terms of their application range and goal. We then describe how the early mathematical models, mainly physical models applied to conventional buildings, have faced new challenges for the design and management of intelligent buildings and led to the use of models which offer more flexibility to better cope with various uncertainties. In contrast with the early modelling techniques, model approaches adopted in neural networks, expert systems, fuzzy logic and genetic models provide a promising method to accommodate these complications as intelligent buildings now need integrated technologies which involve solving complex, multi-objective and integrated decision problems

Reliability assessment of nuclear power plant fault-diagnostic systems using artificial neural networks

The assurance of the diagnosis obtained from a nuclear power plant (NPP) fault-diagnostic advisor based on artificial neural networks (ANNs) is essential for the practical implementation of the advisor to transient detection and identification. The objectives of this study are to develop a validation and verification technique suitable for ANNs and apply it to the fault-diagnostic advisor. The validation and verification is realized by estimating error bounds on the advisor\u27s diagnoses. The two different partition criteria are developed to create computationally effective partitions for generating the error information associated with the advisor performance. The bootstap partition criterion (BPC) and the modified bootstap partition criterion (MBPC) can alleviate the computational requirements significantly. In addition, a new error-bound prediction scheme called error estimation by series association (EESA) is constructed not only to infer error-bounds but also to alleviate the training complexity of an error predictor neural network. The EESA scheme is applied to validate the outputs of the ANNs modeled for a simple nonlinear mapping and more complicated NPP fault-diagnostic problems. Two independent sets of data simulated at San Onofre Nuclear Generating Station, a pressurized water reactor, and Duane Arnold Energy Center, a boiling water reactor, are used to design the fault-diagnostic advisor systems and to perform the reliability assessment of the advisor systems. The results of this research show that the fault-diagnostic systems developed using ANNs with EESA are effective at producing proper diagnoses with predicted error even when degraded by noise. In general, EESA can also be used to verify an ANN system by indicating that the ANN system requires training on more data in order to increase generalization. The EESA scheme developed in this study can be implemented to any ANN system regardless of ANN learning paradigm