Puun ja turpeen seospolton vaikutus tuhkan hyötykäyttökohteisiin

Työn tavoitteena oli selvittää puun ja turpeen seospolton vaikutukset tuhkien hyötykäyttökohteiden valintaan laitoksella tehtävien koeajojen ja kustannus- sekä SWOT-analyysien avulla. Lisäksi tavoitteena oli selvittää lainsäädännön vaikutukset tuhkien hyötykäyttöön. Kustannusanalyysissä tarkasteltiin tuhkien hyödyntämisen nykytilan lisäksi rakeistuslaitosinvestointia ja selvitettiin eri hyötykäyttövaihtoehtojen etuja ja haittoja. Lainsäädäntö vaikuttaa oleellisesti tuhkien hyötykäyttöön. Hyötykäyttöä säädellään sekä kansallisella että EU-tasolta tulevalla lainsäädännöllä. Ympäristölainsäädännön ja etenkin jätelainsäädännön kokonaisuudistuksen myötä myös hyötykäyttöä koskevat lainsäädäntö uudistui. Energiantuotannon tuhkat tulivat jäteverolain piiriin ja lannoiteasetuksenuudistuksella pyrittiin helpottamaan tuhkien lannoitekäyttöä. Myös jäteluokittelu ja sen päättyminen tuhkien osalta vaikuttaa olennaisesti tuhkien käsittelymenetelmien ja hyödyntämisen kannattavuuteen. Jäteluokituksen päättyminen voi viedä tuhkat kemikaalilainsäädännön piiriin. Kustannus- ja SWOT-analyysissä selvitettiin tuhkien hyödyntämisen kannalta keskeiset kustannustekijät. Rakeistuslaitosinvestoinnissa huomioitiin erilaiset tuhkien käsittelymäärät ja niiden vaikutukset kustannuksiin ja investoinnin kannattavuuteen. SWOT-analyysin avulla selvitettiin hyödyntämisen vahvuudet, heikkoudet, mahdollisuudet ja uhkatekijät. Polttoainesuhteella ei ollut poissulkevaa vaikutusta tuhkien hyötykäyttövalintaan. Lentotuhkat soveltuivat parhaiten lannoitehyötykäyttöön ja pohjatuhkat maarakennushyötykäyttöön. Rakeistuslaitosinvestointi olisi kannattava etenkin suuremmilla tuhkamäärillä.

Variable speed drive in fan system monitoring

Fan systems are responsible for approximately 10% of the electricity consumption in industrial and municipal sectors, and it has been found that there is energy-saving potential in these systems. To this end, variable speed drives (VSDs) are used to enhance the efficiency of fan systems. Usually, fan system operation is optimized based on measurements of the system, but there are seldom readily installed meters in the system that can be used for the purpose. Thus, sensorless methods are needed for the optimization of fan system operation. In this thesis, methods for the fan operating point estimation with a variable speed drive are studied and discussed. These methods can be used for the energy efficient control of the fan system without additional measurements. The operation of these methods is validated by laboratory measurements and data from an industrial fan system. In addition to their energy consumption, condition monitoring of fan systems is a key issue as fans are an integral part of various production processes. Fan system condition monitoring is usually carried out with vibration measurements, which again increase the system complexity. However, variable speed drives can already be used for pumping system condition monitoring. Therefore, it would add to the usability of a variablespeed- driven fan system if the variable speed drive could be used as a condition monitoring device. In this thesis, sensorless detection methods for three lifetime-reducing phenomena are suggested: these are detection of the fan contamination build-up, the correct rotational direction, and the fan surge. The methods use the variable speed drive monitoring and control options for the detection along with simple signal processing methods, such as power spectrum density estimates. The methods have been validated by laboratory measurements. The key finding of this doctoral thesis is that a variable speed drive can be used on its own as a monitoring and control device for the fan system energy efficiency, and it can also be used in the detection of certain lifetime-reducing phenomena.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.