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.

Imaging of the Vulnerable Atherosclerotic Plaque. Pre-Clinical Evaluation of PET Tracers for Vascular Inflammation

Atherosclerosis is a vascular inflammatory disease causing coronary artery disease, myocardial infarct and stroke, the leading causes of death in Finland and in many other countries. The development of atherosclerotic plaques starts already in childhood and is an ongoing process throughout life. Rupture of a plaque and the following occlusion of the vessel is the main reason for myocardial infarct and stroke, but despite extensive research, the prediction of rupture remains a major clinical problem. Inflammation is considered a key factor in the vulnerability of plaques to rupture. Measuring the inflammation in plaques non-invasively is one potential approach for identification of vulnerable plaques. The aim of this study was to evaluate tracers for positron emission tomography (PET) imaging of vascular inflammation. The studies were performed with a mouse model of atherosclerosis by using ex vivo biodistribution, autoradiography and in vivo PET and computed tomography (CT). Several tracers for inflammation activity were tested and compared with the morphology of the plaques. Inflammation in the atherosclerotic plaques was evaluated as expression of active macrophages. Systematic analysis revealed that the uptake of 18F-FDG and 11C-choline, tracers for metabolic activity in inflammatory cells, was more prominent in the atherosclerotic plaques than in the surrounding healthy vessel wall. The tracer for αvβ3 integrin, 18Fgalacto- RGD, was also found to have high potential for imaging inflammation in the plaques. While 11C-PK11195, a tracer targeted to receptors in active macrophages, was shown to accumulate in active plaques, the target-to-background ratio was not found to be ideal for in vivo imaging purposes. In conclusion, tracers for the imaging of inflammation in atherosclerotic plaques can be tested in experimental pre-clinical settings to select potential imaging agents for further clinical testing. 18F-FDG, 18F-galacto-RGD and 11C-choline choline have good properties, and further studies to clarify their applicability for atherosclerosis imaging in humans are warranted.

Prediction of neurodevelopment and neuromotor trajectories in very preterm born children up to 11 years of age: PIPARI Study

Very preterm birth is a risk for brain injury and abnormal neurodevelopment. While the incidence of cerebral palsy has decreased due to advances in perinatal and neonatal care, the rate of less severe neuromotor problems continues to be high in very prematurely born children. Neonatal brain imaging can aid in identifying children for closer follow-up and in providing parents information on developmental risks. This thesis aimed to study the predictive value of structural brain magnetic resonance imaging (MRI) at term age, serial neonatal cranial ultrasound (cUS), and structured neurological examinations during the longitudinal follow-up for the neurodevelopment of very preterm born children up to 11 years of age as a part of the PIPARI Study (The Development and Functioning of Very Low Birth Weight Infants from Infancy to School Age). A further aim was to describe the associations between regional brain volumes and long-term neuromotor profile. The prospective follow-up comprised of the assessment of neurosensory development at 2 years of corrected age, cognitive development at 5 years of chronological age, and neuromotor development at 11 years of age. Neonatal brain imaging and structured neurological examinations predicted neurodevelopment at all age-points. The combination of neurological examination and brain MRI or cUS improved the predictive value of neonatal brain imaging alone. Decreased brain volumes associated with neuromotor performance. At the age of 11 years, the majority of the very preterm born children had age-appropriate neuromotor development and after-school sporting activities. Long-term clinical follow-up is recommended at least for all very preterm infants with major brain pathologies.