Management of Metals in the Pulping Process

Diplomityön tarkoituksena oli löytää keino korkean mangaanipitoisuuden hallintaan ECF-valkaisussa. Kirjallisuusosassa käsiteltiin eri metallien ja kuidun vuorovaikutuksia sekä niiden vaikutuksia prosessiin. Lisäksi käytiin läpi sellunvalmituksen yleisimpiä metallienhallintamenetelmiä. Työn kokeellisessa osassa tehtiin esikokeina laboratoriokokeita, jotta löydettiin oikeat kelatointistrategiat tehdasmittakaavan koeajoille. Laboratoriovalkaisut suoritettiin kuudella eri kemikaalilla käyttäen DD3-pesurin jälkeistä massaa ja samanlaisia parametrejä kuin tehdasvalkaisussa. Kolmesta eri valkaisusekvenssistä paras tulos saavutettiin D0-QEP-sekvenssillä. Tehdasmittakaavan koeajojen tavoitteena oli saavuttaa alle 1 mg/kg jäännösmangaanipitoisuus valkaistussa massassa ja korkeampi vaaleus EOP-vaiheessa pienemmällä klooridioksidin kulutuksella. Koeajoissa käytettiinDTPA:ta ja EDTA:ta kahdeksassa eri koepisteessä. Pienimpiin jäännöspitoisuuksiin päästiin koepisteissä, joissa kelatointiaine annosteltiin ennen valkaisun viimeistä pesuvaihetta tai sen jälkeen. Samanlaisia tuloksia saavutettiin koepisteissä, joissa kelatointiaine lisättiin suoraan EOP-vaiheeseen. Tällöin kelatointiaineen käyttö johti myös korkeampaan vaaleuteen EOP-vaiheessa pienemmällä kappakertoimella kuin referenssissä. Säästöt klooridioksidin kulutuksessa eivät olleet kuitenkaan tarpeeksi suuret kattaakseen kelatointiaineiden käytön kustannuksia. Kustannustehokkain tapa kontrolloida jäännösmangaanipitoisuutta oli EDTA:n annostelu D2 DD-pesurin jälkeen. Haittapuolena tälläisessä kelatoinnissa oli metallikompleksien palautuminen valkaisuun kuivauskoneen kiertoveden mukana. Tärkeimmät onnistuneeseen kelatointiin vaikuttavat parametrit olivat lajittelussa käytetyn rikkihapon annos, D0-vaiheen pH ja D0 DD-pesurin pesutehokkuus.

Use of Statistical Methods in Demand Planning

In a very volatile industry of high technology it is of utmost importance to accurately forecast customers’ demand. However, statistical forecasting of sales, especially in heavily competitive electronics product business, has always been a challenging task due to very high variation in demand and very short product life cycles of products. The purpose of this thesis is to validate if statistical methods can be applied to forecasting sales of short life cycle electronics products and provide a feasible framework for implementing statistical forecasting in the environment of the case company. Two different approaches have been developed for forecasting on short and medium term and long term horizons. Both models are based on decomposition models, but differ in interpretation of the model residuals. For long term horizons residuals are assumed to represent white noise, whereas for short and medium term forecasting horizon residuals are modeled using statistical forecasting methods. Implementation of both approaches is performed in Matlab. Modeling results have shown that different markets exhibit different demand patterns and therefore different analytical approaches are appropriate for modeling demand in these markets. Moreover, the outcomes of modeling imply that statistical forecasting can not be handled separately from judgmental forecasting, but should be perceived only as a basis for judgmental forecasting activities. Based on modeling results recommendations for further deployment of statistical methods in sales forecasting of the case company are developed.

On-line determination of residual collector concentration in flotation process

Valuable minerals can be recovered by using froth flotation. This is a widely used separation technique in mineral processing. In a flotation cell hydrophobic particles attach on air bubbles dispersed in the slurry and rise on the top of the cell. Valuable particles are made hydrophobic by adding collector chemicals in the slurry. With the help of a frother reagent a stable froth forms on the top of the cell and the froth with valuable minerals, i.e. the concentrate, can be removed for further processing. Normally the collector is dosed on the basis of the feed rate of the flotation circuit and the head grade of the valuable metal. However, also the mineral composition of the ore affects the consumption of the collector, i.e. how much is adsorbed on the mineral surfaces. Therefore it is worth monitoring the residual collector concentration in the flotation tailings. Excess usage of collector causes unnecessary costs and may even disturb the process. In the literature part of the Master’s thesis the basics of flotation process and collector chemicals are introduced. Capillary electrophoresis (CE), an analytical technique suitable for detecting collector chemicals, is also reviewed. In the experimental part of the thesis the development of an on-line CE method for monitoring the concentration of collector chemicals in a flotation process and the results of a measurement campaign are presented. It was possible to determine the quality and quantity of collector chemicals in nickel flotation tailings at a concentrator plant with the developed on-line CE method. Sodium ethyl xanthate and sodium isopropyl xanthate residuals were found in the tailings and slight correlation between the measured concentrations and the dosage amounts could be seen.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.