Artificially Intelligent and Adaptive Methods for Prediction and Analysis of Superheater Fireside Corrosion in Fluidized Bed Boilers

Superheater corrosion causes vast annual losses for the power companies. With a reliable corrosion prediction method, the plants can be designed accordingly, and knowledge of fuel selection and determination of process conditions may be utilized to minimize superheater corrosion. Growing interest to use recycled fuels creates additional demands for the prediction of corrosion potential. Models depending on corrosion theories will fail, if relations between the inputs and the output are poorly known. A prediction model based on fuzzy logic and an artificial neural network is able to improve its performance as the amount of data increases. The corrosion rate of a superheater material can most reliably be detected with a test done in a test combustor or in a commercial boiler. The steel samples can be located in a special, temperature-controlled probe, and exposed to the corrosive environment for a desired time. These tests give information about the average corrosion potential in that environment. Samples may also be cut from superheaters during shutdowns. The analysis ofsamples taken from probes or superheaters after exposure to corrosive environment is a demanding task: if the corrosive contaminants can be reliably analyzed, the corrosion chemistry can be determined, and an estimate of the material lifetime can be given. In cases where the reason for corrosion is not clear, the determination of the corrosion chemistry and the lifetime estimation is more demanding. In order to provide a laboratory tool for the analysis and prediction, a newapproach was chosen. During this study, the following tools were generated: · Amodel for the prediction of superheater fireside corrosion, based on fuzzy logic and an artificial neural network, build upon a corrosion database developed offuel and bed material analyses, and measured corrosion data. The developed model predicts superheater corrosion with high accuracy at the early stages of a project. · An adaptive corrosion analysis tool based on image analysis, constructedas an expert system. This system utilizes implementation of user-defined algorithms, which allows the development of an artificially intelligent system for thetask. According to the results of the analyses, several new rules were developed for the determination of the degree and type of corrosion. By combining these two tools, a user-friendly expert system for the prediction and analyses of superheater fireside corrosion was developed. This tool may also be used for the minimization of corrosion risks by the design of fluidized bed boilers.

Soft Computing Methods in Metallurgy

In metallurgic plants a high quality metal production is always required. Nowadays soft computing applications are more often used for automation of manufacturing process and quality control instead of mechanical techniques. In this thesis an overview of soft computing methods presents. As an example of soft computing application, an effective model of fuzzy expert system for the automotive quality control of steel degassing process was developed. The purpose of this work is to describe the fuzzy relations as quality hypersurfaces by varying number of linguistic variables and fuzzy sets.

Asiantuntijajärjestelmä kattilakonseptin valintaan

Polttoaine asettaa puitteet kattilasuunnittelulle. Kiertoleijukattilakonseptin valinta kytkeytyy kiinteästi mitoitusarvoihin ja polttoaineen ominaisuuksiin. Asiakkaan vaatimuk-set kattilalle asettavat lähtökohdan kattilasuunnittelulle. Suorituskyky, kustannukset ja luotettavuus ovat asiakaslähtöisiä tekijöitä, joiden painotukset vaikuttavat kattilakonseptin valintaan. Korkeat lämpötilat tulistimien alueella tekevät tulistinjärjestelystä vaikean ja määräävän osan kattilakonseptin valintaa. Konvektiotulistimien altistuminen kuumille savukaasuille tekee niistä herkkiä likaantumiselle ja korroosiolle. Mitoitusarvojen ja tulistimien rakenteen oikeanlaisella valinnalla voidaan näitä polttoaineperäisiä ongelmia ehkäistä. Lisäksi kiertoleijukattiloissa käytetyt tulipesän ulkopuoliset tulistimet soveltuvat konvektiotulistimia korkeammille lämpötiloille huonolaatuisillakin polttoaineilla. Tässä työssä rakennettu asiantuntijajärjestelmä valitsee alustavan kattilakonseptin mitoitusta varten käyttäjän antamien vähäisten lähtötietojen pohjalta.

Many valued algebraic structures as measures of comparison

This thesis studies the properties and usability of operators called t-norms, t-conorms, uninorms, as well as many valued implications and equivalences. Into these operators, weights and a generalized mean are embedded for aggregation, and they are used for comparison tasks and for this reason they are referred to as comparison measures. The thesis illustrates how these operators can be weighted with a differential evolution and aggregated with a generalized mean, and the kinds of measures of comparison that can be achieved from this procedure. New operators suitable for comparison measures are suggested. These operators are combination measures based on the use of t-norms and t-conorms, the generalized 3_-uninorm and pseudo equivalence measures based on S-type implications. The empirical part of this thesis demonstrates how these new comparison measures work in the field of classification, for example, in the classification of medical data. The second application area is from the field of sports medicine and it represents an expert system for defining an athlete's aerobic and anaerobic thresholds. The core of this thesis offers definitions for comparison measures and illustrates that there is no actual difference in the results achieved in comparison tasks, by the use of comparison measures based on distance, versus comparison measures based on many valued logical structures. The approach has been highly practical in this thesis and all usage of the measures has been validated mainly by practical testing. In general, many different types of operators suitable for comparison tasks have been presented in fuzzy logic literature and there has been little or no experimental work with these operators.

Fraud detection in the banking sector : a multi-agent approach

Fraud is an increasing phenomenon as shown in many surveys carried out by leading international consulting companies in the last years. Despite the evolution of electronic payments and hacking techniques there is still a strong human component in fraud schemes. Conflict of interest in particular is the main contributing factor to the success of internal fraud. In such cases anomaly detection tools are not always the best instruments, since the fraud schemes are based on faking documents in a context dominated by lack of controls, and the perpetrators are those ones who should control possible irregularities. In the banking sector audit team experts can count only on their experience, whistle blowing and the reports sent by their inspectors. The Fraud Interactive Decision Expert System (FIDES), which is the core of this research, is a multi-agent system built to support auditors in evaluating suspicious behaviours and to speed up the evaluation process in order to detect or prevent fraud schemes. The system combines Think-map, Delphi method and Attack trees and it has been built around audit team experts and their needs. The output of FIDES is an attack tree, a tree-based diagram to ”systematically categorize the different ways in which a system can be attacked”. Once the attack tree is built, auditors can choose the path they perceive as more suitable and decide whether or not to start the investigation. The system is meant for use in the future to retrieve old cases in order to match them with new ones and find similarities. The retrieving features of the system will be useful to simplify the risk management phase, since similar countermeasures adopted for past cases might be useful for present ones. Even though FIDES has been built with the banking sector in mind, it can be applied in all those organisations, like insurance companies or public organizations, where anti-fraud activity is based on a central anti-fraud unit and a reporting system.

Modelling Cutting States in Rough Turning of 34CrNiMo6 Steel

Rough turning is an important form of manufacturing cylinder-symmetric parts. Thus far, increasing the level of automation in rough turning has included process monitoring methods or adaptive turning control methods that aim to keep the process conditions constant. However, in order to improve process safety, quality and efficiency, an adaptive turning control should be transformed into an intelligent machining system optimizing cutting values to match process conditions or to actively seek to improve process conditions. In this study, primary and secondary chatter and chip formation are studied to understand how to measure the effect of these phenomena to the process conditions and how to avoid undesired cutting conditions. The concept of cutting state is used to address the combination of these phenomena and the current use of the power capacity of the lathe. The measures to the phenomena are not developed based on physical measures, but instead, the severity of the measures is modelled against expert opinion. Based on the concept of cutting state, an expert system style fuzzy control system capable of optimizing the cutting process was created. Important aspects of the system include the capability to adapt to several cutting phenomena appearing at once, even if the said phenomena would potentially require conflicting control action.

Maritime safety in the Gulf of Finland. Evaluation of the regulatory system

Growing traffic is believed to increase the risk of an accident in the Gulf of Finland. As the consequences of a large oil accident would be devastating in the vulnerable sea area, accident prevention is performed at the international, regional and national levels. Activities of shipping companies are governed with maritime safety policy instruments, which can be categorised into regulatory, economic and information instruments. The maritime regulatory system has been criticised for being inefficient because it has not been able to eliminate the violations that enable accidents. This report aims to discover how maritime governance systems or maritime safety policy instruments could be made more efficient in the future, in order to improve the maritime safety level. The results of the research are based on a literature review and nine expert interviews, with participants from shipping companies, interest groups and authorities. Based on the literature and the interviews, a suggestion can be made that in the future, instead of implementing new policy instruments, maritime safety risks should be eliminated by making the existing system more efficient and by influencing shipping companies’ safety culture and seafarers’ safety related attitudes. Based on this research, it can be stated that the development of maritime safety policy instruments should concentrate on harmonisation, automation and increasing national and cross-border cooperation. These three tasks could be primarily accomplished by developing the existing technology. Human error plays a role in a significant number of maritime accidents. Because of this, improving companies’ safety culture and voluntary activities that go beyond laws are acknowledged as potential ways of improving maritime safety. In the future, maritime regulatory system should be developed into a direction where the private sector has better possibilities to take part in decision-making.