Optimal processing chain for waste power plant

This study is done to examine waste power plant’s optimal processing chain and it is important to consider from several points of view on why one option is better than the other. This is to insure that the right decision is made. Incineration of waste has devel-oped to be one decent option for waste disposal. There are several legislation matters and technical options to consider when starting up a waste power plant. From the tech-niques pretreatment, burner and flue gas cleaning are the biggest ones to consider. The treatment of incineration residues is important since it can be very harmful for the envi-ronment. The actual energy production from waste is not highly efficient and there are several harmful compounds emitted. Recycling of waste before incineration is not very typical and there are not many recycling options for materials that cannot be easily re-cycled to same product. Life cycle assessment is a good option for studying the envi-ronmental effect of the system. It has four phases that are part of the iterative study process. In this study the case environment is a waste power plant. The modeling of the plant is done with GaBi 6 software and the scope is from gate-to-grave. There are three different scenarios, from which the first and second are compared to each other to reach conclusions. Zero scenario is part of the study to demonstrate situation without the power plant. The power plant in this study is recycling some materials in scenario one and in scenario two even more materials and utilize the bottom ash more ways than one. The model has the substitutive processes for the materials when they are not recycled in the plant. The global warming potential results show that scenario one is the best option. The variable costs that have been considered tell the same result. The conclusion is that the waste power plant should not recycle more and utilize bottom ash in a number of ways. The area is not ready for that kind of utilization and production from recycled materials.

Vähempiarvoisen kalan käsittelyketjut ja hiilijalanjälki

The condition of Baltic Sea has weakened considerably because of eutrophication which has caused massive increase of devalued fish. The condition of Baltic Sea can be helped by fishing these fish. This study handles three different ways to approach those fish utilizations and counts carbon footprint for those three chains. Environmental point of views are also examined. There are three different fish processing chains. Every processing chain begins with fishing the fish in Baltic Sea. After that the fishes are prepared by crushing and some formic acid is added to ensure preservation. In the first processing chain the fishes are processed as biodiesel. The waste from the biodiesel process is taken to the anaerobic digestion and the forming methane is used as energy. In the second chain the fishes are taken straight to the anaerobic digestion after preparing. In the third chain, the fish will be first prepared and then taken to fur farms as forage. The carbon footprint has been calculated for 1000 kg fish. The carbon footprint in the first chain is 164-178 kg CO2e, in the second chain 313 – 333 kg CO2e and in the third chain 363 kg CO2e. In the processing chains the bioenergy is produced from the biodiesel, anaerobic digestion and from the glycerol, which is by-product of the biodiesel. The energy produced from the biodiesel is so-called emission neutral, which is not taken into account when calculating emissions. The energy is used to compensate the emissions caused by fossil fuels. The PAS 2050 was used to calculate the carbon footprint. Only carbon dioxide and methane were used when calculating the carbon footprint.

Suunnittelulla ja ruo´on hyötykäytöllä tehokkuutta rantojen hoitoon : Tuloksia ja kokemuksia VELHO-hankkeesta

Varsinais-Suomen ELY-keskuksen toteuttamassa VELHO-hankkeessa kehitettiin kustannustehokkaita ratkaisuja ranta-alueiden umpeenkasvun aiheuttamiin ongelmiin luomalla uusi konsepti ranta-alueiden monikäyttösuunnitteluun, edistämällä järviruo’on hyötykäyttöä ja valmistelemalla esityksiä uuteen maaseudun kehittämisohjelmaan. Tässä julkaisussa esitellään työn tulokset ja johtopäätökset. Hankkeessa laadittiin kolme ranta-alueiden monikäyttösuunnitelmaa: Mynälahden Sarsalanaukko ja Musta-aukko, Oukkulanlahti – Naantalinaukko ja Eurajoen - Luvian rannikko. Suunnitelmissa sovitettiin yhteen ranta-alueiden eri käyttömuotoja ja pyrittiin löytämään optimaalinen verkosto hyötykäyttöön leikattavien ruovikoiden, avoimena pidettävien merenrantaniittyjen ja säilytettävien ruovikoiden välille. Kustannustehokkuuteen pyrittiin kohdentamalla hoitotoimet laajoihin kokonaisuuksiin sekä järviruo’on hyötykäytöllä. Suunnitelmat laadittiin laajassa osallistavassa prosessissa. Hankkeessa laadituissa ranta-alueiden monikäyttösuunnitelmissa esitettiin erilaisia maankäyttötavoitteita ja hoitosuosituksia yli 2000 hehtaarille. Ruovikoiden ja rantaniittyjen lisäksi suunnittelun kohteena olivat myös rantojen läheiset peltoalueet, reunavyöhykkeet ja muut perinnebiotoopit. Hoitotoimilla tavoitellaan alueiden luonnon monimuotoisuuden ja vesien tilan paranemista, maiseman avartumista ja virkistyskäytön helpottumista. Ruovikoiden erilaisia leikkuumenetelmiä (talvileikkuut, vesileikkuut, maaleikkuut) testattiin 90 hehtaarin alalla. Rantaniittyjen kunnostuksessa testattiin maaleikkuun lisäksi ruovikon niittomurskausta. Ruokomassan hyötykäyttökokeissa testattiin kahden eri ruokolaadun eli tuoreen kesäruo’on ja kuivan talviruo’on esikäsittelyä ja hyötykäyttöä energiantuotannossa (poltto, biokaasutus) ja maataloudessa (maanparannusaine, viherlannoite, kuivike, katemateriaali). Maaseudun kehittämisohjelmaan tehtiin esityksiä tukimuotojen kehittämiseksi: rantaniittyjen kunnostuksen lisääminen ja hoidon laadun parantaminen, ruovikoiden vesileikkuut ravinteiden poistajina sekä ruokomassojen käyttö maan orgaanisen aineen lisääjänä. Hankkeen kokemusten mukaan yksi kustannustehokkaimmista hoito- ja käyttöketjuista on ruovikon leikkuu loppukesällä ja siitä kertyvän massan käyttö ranta-alueiden läheisillä pelloilla viherlannoitteena ja maanparannusaineena. Yhden hehtaarin ruovikon kesäleikkuulla poistetaan keskimäärin 80 kg typpeä ja 7 kg fosforia. Vesiensuojelullisten hyötyjen lisäksi leikkuulla parannetaan umpeenkasvusta kärsivien lajien elinoloja, lisätään rantojen vetovoimaisuutta ja edistetään luonnonhoitoyrittäjyyden edellytyksiä. Peltokäytössä käsittelyketju on lyhyt eikä se vaadi pitkiä kuljetusmatkoja. Ruokomassa kierrättää ravinteita takaisin pelloille ja parantaa maan rakennetta. Järviruo’on hyötykäytöllä ei pystytä kattamaan koko leikkuu- ja käyttöketjun kustannuksia. Leikkuusta ja hyötykäytöstä saatavien monien eri aineellisten ja aineettomien ekosysteemipalveluhyötyjen vuoksi toimintaan on tarpeen suunnata yhteiskunnan tukea ja luoda käytännön toteutusta edistäviä tukimuotoja. Kustannustehokkuutta voidaan edelleen parantaa laitteita ja menetelmiä kehittämällä.

Modelling ecological values in heterogeneous and dynamic landscapes with geospatial data

Our surrounding landscape is in a constantly dynamic state, but recently the rate of changes and their effects on the environment have considerably increased. In terms of the impact on nature, this development has not been entirely positive, but has rather caused a decline in valuable species, habitats, and general biodiversity. Regardless of recognizing the problem and its high importance, plans and actions of how to stop the detrimental development are largely lacking. This partly originates from a lack of genuine will, but is also due to difficulties in detecting many valuable landscape components and their consequent neglect. To support knowledge extraction, various digital environmental data sources may be of substantial help, but only if all the relevant background factors are known and the data is processed in a suitable way. This dissertation concentrates on detecting ecologically valuable landscape components by using geospatial data sources, and applies this knowledge to support spatial planning and management activities. In other words, the focus is on observing regionally valuable species, habitats, and biotopes with GIS and remote sensing data, using suitable methods for their analysis. Primary emphasis is given to the hemiboreal vegetation zone and the drastic decline in its semi-natural grasslands, which were created by a long trajectory of traditional grazing and management activities. However, the applied perspective is largely methodological, and allows for the application of the obtained results in various contexts. Models based on statistical dependencies and correlations of multiple variables, which are able to extract desired properties from a large mass of initial data, are emphasized in the dissertation. In addition, the papers included combine several data sets from different sources and dates together, with the aim of detecting a wider range of environmental characteristics, as well as pointing out their temporal dynamics. The results of the dissertation emphasise the multidimensionality and dynamics of landscapes, which need to be understood in order to be able to recognise their ecologically valuable components. This not only requires knowledge about the emergence of these components and an understanding of the used data, but also the need to focus the observations on minute details that are able to indicate the existence of fragmented and partly overlapping landscape targets. In addition, this pinpoints the fact that most of the existing classifications are too generalised as such to provide all the required details, but they can be utilized at various steps along a longer processing chain. The dissertation also emphases the importance of landscape history as an important factor, which both creates and preserves ecological values, and which sets an essential standpoint for understanding the present landscape characteristics. The obtained results are significant both in terms of preserving semi-natural grasslands, as well as general methodological development, giving support to science-based framework in order to evaluate ecological values and guide spatial planning.

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.

The radio frequency identification technology in the supply chain management: a case of a big logistics company

Nowadays global business trends force the adoption of innovative ICTs into the supply chain management (SCM). Particularly, the RFID technology is on high demand among SCM professionals due to its business advantages such as improving of accuracy and veloc-ity of SCM processes which lead to decrease of operational costs. Nevertheless, a question of the RFID technology impact on the efficiency of warehouse processes in the SCM re-mains open. The goal of the present study is to experiment the possibility of improvement order picking velocity in a warehouse of a big logistics company with the use of the RFID technology. In order to achieve this goal the following objectives have been developed: 1) Defining the scope of the RFID technology applications in the SCM; 2) Justification of the RFID technology impact on the SCM processes; 3) Defining a place of the warehouse order picking process in the SCM; 4) Identification and systematization of existing meth-ods of order picking velocity improvement; 5) Choosing of the study object and gathering of the empirical data about number of orders, number of hours spent per each order line daily during 5 months; 6) Processing and analysis of the empirical data; 7) Conclusion about the impact of the RFID technology on the speed of order picking process. As a result of the research it has been found that the speed of the order picking processes has not been changed as time has gone after the RFID adoption. It has been concluded that in order to achieve a positive effect in the speed of order picking process with the use of the RFID technology it is necessary to simultaneously implement changes in logistics and organizational management in 3PL logistics companies. Practical recommendations have been forwarded to the management of the company for further investigation and procedure.

Effects of feed processing on the apparent ileal digestibility of amino acids in pig diets containing wheat bran or wheat middlings

Selostus: Prosessoinnin vaikutus vehnän sivutuotteita sisältävien rehuseosten aminohappojen ohutsuolisulavuuteen sioilla

Detection of bovine foetal DNA from amniotic fluid using the polymerase chain reaction

Selostus: Sikiön DNA:n tunnistaminen naudan sikiövedestä polymeraasiketjureaktion avulla

The transfer of 137Cs through the soil-plant-sheep food chain in different pasture ecosystems

Selostus: Radiocesiumin kulkeutuminen eri laidunekosysteemien maa-ruoho-lammas -ravintoketjussa

Universally primed polymerase chain reaction analysis of Fusarium avenaceum isolated from wheat and barley in Finland

Selostus: Vehnästä ja ohrasta eristettyjen F. avenaceum -punahomekantojen analysointi UP-PCR-menetelmällä

Optimizing the supply chain strategy of a multi-unit Finnish nursery company

Abstract