Chromium mass balance in sodium chlorate process

Kuudenarvoista kromia käytetään natriumkloraatin valmistuksessa prosessin tuotantotehokkuuden ja turvallisuuden parantamiseksi. Kromia kuitenkin poistuu prosessista muutamaa reittiä pitkin. Koska kuuudenarvoisella kromilla on syöpää aiheuttavia, mutageenisiä sekä lisääntymiselle myrkyllisiä ominaisuuksia, olisi tärkeää ymmärtää, miten kromi kulkeutuu prosessin eri osiin, ja kuinka paljon sitä poistuu prosessista. Tämä on tärkeää, jotta osataan hallita kromin käytöstä aiheutuvat riskit, sekä toisaalta myös tehostaa kromin käyttöä prosessissa. Työn tarkoituksena oli tuottaa tietoa kromin käytöstä natriumkloraattiprosessissa. Työssä tutkittiin kromitasetta prosessin keskeisimmissä yksikköoperaatioissa. Myös kromin saostumista katodien pinnalle arvioitiin määrällisesti. Eri prosessinäytteistä tutkittiin lisäksi kromin hapetusasteita. Edellä mainittuja tutkimuskohteita varten määritettiin prosessinäytteiden kromipitoisuus. Eri prosessioperaatioille suoritettiin lisäksi taselaskelmat. Työn tuloksena esitettiin kromitase sekä yksikköoperaatioille että koko prosessille. Erinäisten epätarkkuustekijöiden vuoksi tasetta ei kuitenkaan pystytty määrittämään halutulla tarkkuudella, ja siksi työssä esitettyä tasetta voidaan pitää vain suuntaa antavana laskelmana. Katodien pinnalle saostunutta kromin määrää pidettiin kuitenkin oikean suuruusluokan tuloksena. Prosessinäytteiden hapetusasteita ei voitu arvioida, sillä saadut kokonaiskromitulokset eivät olleet täysin luotettavia. Huolimatta tulosten epätarkkuudesta, työ tuotti tärkeää tietoa prosessin toiminnasta kromin suhteen. Työtä voidaan hyödyntää jatkossa monin tavoin prosessin kromitaseen seurannassa.

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.

Successful IFRS standard change implementation process after the initial IFRS implementation

The main objective of the study was to form a strategic process model and project management tool to help IFRS change implementation projects in the future. These research results were designed based on the theoretical framework of Total Quality Management and leaning on the facts that were collected during the empirical case study of IAS 17 change. The us-age of the process oriented approach in IFRS standard change implementation after the initial IFRS implementation is rationalized with the following arguments: 1) well designed process tools lead to optimization of resources 2) With the help of process stages and related tasks it is easy to ensure the efficient way of working and managing the project as well as make sure to include all necessary stakeholders to the change process. This research is following the qualitative approach and the analysis is in describing format. The first part of the study is a literature review and the latter part has been conducted as a case study. The data has been col-lected in the case company with interviews and observation. The main findings are a process model for IFRS standard change process and a check-list formatted management tool for up-coming IFRS standard change projects. The process flow follows the main cornerstones in IASB’s standard setting process and the management tool has been divided to stages accordingly.