Kartoitus kloori-alkalitehtaan tuotantokapasiteetin kasvattamisesta / Kemira Chemicals Oy Joutseno

Kemira Chemicals Oy:n Joutsenon kloori-alkalitehtaalla valmistetaan elektrolyysin avulla lipeää, suolahappoa, natriumhypokloriittia ja vetyä. Tämän työn tavoitteena on kartoittaa kloori-alkalitehtaan tuotantokapasiteetin kasvatuksen yhteydessä esiin tulevat pullonkaulat, lähitulevaisuuden kunnossapitotarpeet sekä parhaat käytettävissä olevat teknologiavaihtoehdot kloori-alkalitehtaan osa-alueille, joihin tuotantokapasiteetin kasvatuspaineet kohdistuvat: elektrolyysi, lipeän haihdutus ja suolahappopolttimet. Pullonkaulojen kartoittaminen toteutettiin rakentamalla taulukkolaskentamalli kloori-alkalitehtaan prosesseista. Mallin avulla simuloitiin elektrolyysin kloorin tuotantoa, jota kasvatettiin asteittain 54 kt:sta/a aina 100 kt:iin/a asti ja tutkittiin prosessien käyttäytymistä. Tarkastelun pohjalta havaittiin, että kloorin tuotantoa kasvattaessa, tulee lisätä myös tuotantokapasiteettia suolahapon valmistukseen, elektrolyysiin, demineralisoidun veden valmistukseen ja lipeän haihdutuslaitokseen sekä suolahapon ja lipeän varastointikapasiteetteihin. Vaihtoehtoiset teknologiat määritettiin kirjallisuudesta ja laitetoimittajien esitteistä. Lähivuosien kunnossapitotarpeet kartoitettiin haastattelemalla tehtaan henkilökuntaa. Työstä eskaloitui useita jatkotutkimuskohteita, joita ovat bipolaari-teknologian soveltuvuus Joutsenon kloori-alkalitehtaalle, uusien HCl-polttimien esisuunnittelu, höyryn käytön tehostaminen nykyisessä lipeän haihdutuslaitoksessa sekä uusien haihdutusteknologioiden soveltuvuus Joutsenon kloori-alkalitehtaalle, höyry- ja jäähdytysverkostojen kartoitukset sekä demineralisoidun veden valmistuskapasiteetin kasvattaminen.

Metsäteollisuuden ympäristöstrategia hallinnon näkökulmasta: Metsäteollisuuden kehitysskenaariot vuoteen 2020

Suomen metsäteollisuus elää voimakasta uusiutumis- ja murrosvaihetta, joka ilmenee muutoksina yksittäisten tehtaiden ja tehdasintegraattien toiminnassa. Monia yksikköjä on poistunut tuotannosta ja tuotannon painotusta on muutettu. Toisaalta metsäteollisuus on suuntaamassa uusille aloille, jolloin tuotteina voivat olla esimerkiksi erilaiset biopolttoaineet, kemianteollisuuden raaka-aineet ja uuden sukupolven paperi- ja kartonkituotteet. Metsäteollisuuden muuttuminen ja laitosten monimutkaistuminen sekä jatkuvasti lisääntyvä tiedontarve asettavat yhä suurempia vaatimuksia sekä toiminnanharjoittajien ympäristövastaaville että viranomaisille. Hallinnon jatkuva muutos ja niukkenevat voimavarat voivat johtaa siihen, että käytännön lupa- ja valvontatyöhön jää yhä vähemmän aikaa. Kaakkois-Suomen ELY-keskuksen koordinoima hanke ”Metsäteollisuuden ympäristöstrategia vuoteen 2020 - hallinnon näkökulma” pyrkii vastaamaan edellä mainittuihin haasteisiin strategiatyön avulla. Hankkeen tarkoituksena oli tarkastella metsäteollisuuden ympäristönäkökohtia niistä lähtökohdista, joihin yritys voi vaikuttaa raaka-aineen tulosta tehtaalle ja tuotteen lähdöstä tehtaalta sekä tehtaan perustamisesta sulkemiseen ja jälkihoitoon asti. Tavoitteena oli määritellä toiminnoille toimiva ympäristöstrategia. Strategiassa pyrittiin löytämään yhteisymmärrys toiminnanharjoittajan ja viranomaisen kanssa mm. siitä, miten otetaan käyttöön parhaat käytännöt niin teollisuudessa kuin hallinnossakin, toimitaan uusien BAT-, IED- ja vesienhoitoperiaatteiden mukaisesti sekä edistetään kestävän kehityksen mukaisten tuotteiden markkinoille tuloa ja otetaan ennakointi tavaksi -lähtökohta käyttöön kaikessa toiminnassa. Hanketyössä esille nousseet haasteet ryhmiteltiin aihelueittain kolmeksi pääkohdaksi tärkeysjärjestyksessä: viranomaisen ja teollisuuden tiedonkulun ja tietämyksen parantaminen, lupa-, valvonta- ja hallintoprosessien parantaminen sekä uusien haasteiden kartoittaminen ja niihin reagointi. Haasteiden ratkaisukeinoiksi etsittiin käytännön toimenpiteitä sekä määriteltiin niille vastuutahot. Toimenpiteiksi esitettiin mm. viranomaisen ja teollisuuden yhteisiä koulutuspäiviä, asiantuntijapaneelin perustamista sekä lupamääräysten antamista myös tehtaan tai tuotantoyksikön sulkemisen tai muuttamisen varalle. Riittävien resurssien ja tietotaidon turvaaminen niin hallinnossa kuin teollisuudessa on ehdoton edellytys toimenpiteiden onnistumiselle

Indicent reporting in shipping. Experiences and best practices for the Baltic Sea

Incident and near miss reporting is one of the proactive tools of safety management. By analyzing incidents and near misses and by corrective actions, severe accidents can potentially be avoided. Near miss and incident reporting is widely used in many riskprone industries such as aviation or chemical industry. In shipping incident and near miss reporting is required by the mandatory safety management system International Safety Management Code (ISM Code). However, in several studies the conclusion has been that incidents are reported poorly in the shipping industry. The aim of this report is to highlight the best practices for incident reporting in shipping and to support the shipping industry in the better utilization of incident reporting information. The study consists of three parts: 1) voluntary, shared reporting systems in shipping (international experiences), 2) interview study at four shipping companies in Sweden and in Finland (best practices), 3) expert workshop on incident reporting (problems and solutions). Preconditions for a functional reporting system are an existing no blame culture, commitment of the top management, feedback, good communication, training and an easy-to-use system. Although preconditions are met, problems can still appear, for example due to psychological, interpersonal or nationality-related reasons. In order to keep the reporting system functioning, the shipping company must be committed to maintain and develop the system and to tackle the problems. The whole reporting process from compiling, handling and analyzing a report, creating corrective actions and implementing them has to be handled properly in order to gain benefits from the reporting system. In addition to avoiding accidents, the functional reporting system can also offer other benefits by increasing safety awareness, by improving the overall safety and working conditions onboard, by enhancing team work and communication onboard and between ships and the land-based organization of shipping companies. Voluntary shared reporting systems are supported in the shipping industry in principle, but their development in the Baltic Sea is still in its infancy and the potential benefits of sharing the reports have not been realized. On the basis of this study we recommend that a common reporting system be developed for the Baltic Sea area which all the ships operating in the area could use regardless of their flag. Such a wider system could prevent some of the problems related to the current national systems. There would be more incident cases available in the database and this would support anonymity and thus encourage shipping companies to report to a shared database more frequently. A shared reporting system would contribute to the sharing of experiences and to the wider use of incident information in the shipping industry.

The ETH Zurich DOI Desk: DOIs as standard persistent identifier and best practice in Swiss Open Access repositories

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.

Spatial modeling techniques for mapping and characterization of acid sulfate soils

Acid sulfate (a.s.) soils constitute a major environmental issue. Severe ecological damage results from the considerable amounts of acidity and metals leached by these soils in the recipient watercourses. As even small hot spots may affect large areas of coastal waters, mapping represents a fundamental step in the management and mitigation of a.s. soil environmental risks (i.e. to target strategic areas). Traditional mapping in the field is time-consuming and therefore expensive. Additional more cost-effective techniques have, thus, to be developed in order to narrow down and define in detail the areas of interest. The primary aim of this thesis was to assess different spatial modeling techniques for a.s. soil mapping, and the characterization of soil properties relevant for a.s. soil environmental risk management, using all available data: soil and water samples, as well as datalayers (e.g. geological and geophysical). Different spatial modeling techniques were applied at catchment or regional scale. Two artificial neural networks were assessed on the Sirppujoki River catchment (c. 440 km2) located in southwestern Finland, while fuzzy logic was assessed on several areas along the Finnish coast. Quaternary geology, aerogeophysics and slope data (derived from a digital elevation model) were utilized as evidential datalayers. The methods also required the use of point datasets (i.e. soil profiles corresponding to known a.s. or non-a.s. soil occurrences) for training and/or validation within the modeling processes. Applying these methods, various maps were generated: probability maps for a.s. soil occurrence, as well as predictive maps for different soil properties (sulfur content, organic matter content and critical sulfide depth). The two assessed artificial neural networks (ANNs) demonstrated good classification abilities for a.s. soil probability mapping at catchment scale. Slightly better results were achieved using a Radial Basis Function (RBF) -based ANN than a Radial Basis Functional Link Net (RBFLN) method, narrowing down more accurately the most probable areas for a.s. soil occurrence and defining more properly the least probable areas. The RBF-based ANN also demonstrated promising results for the characterization of different soil properties in the most probable a.s. soil areas at catchment scale. Since a.s. soil areas constitute highly productive lands for agricultural purpose, the combination of a probability map with more specific soil property predictive maps offers a valuable toolset to more precisely target strategic areas for subsequent environmental risk management. Notably, the use of laser scanning (i.e. Light Detection And Ranging, LiDAR) data enabled a more precise definition of a.s. soil probability areas, as well as the soil property modeling classes for sulfur content and the critical sulfide depth. Given suitable training/validation points, ANNs can be trained to yield a more precise modeling of the occurrence of a.s. soils and their properties. By contrast, fuzzy logic represents a simple, fast and objective alternative to carry out preliminary surveys, at catchment or regional scale, in areas offering a limited amount of data. This method enables delimiting and prioritizing the most probable areas for a.s soil occurrence, which can be particularly useful in the field. Being easily transferable from area to area, fuzzy logic modeling can be carried out at regional scale. Mapping at this scale would be extremely time-consuming through manual assessment. The use of spatial modeling techniques enables the creation of valid and comparable maps, which represents an important development within the a.s. soil mapping process. The a.s. soil mapping was also assessed using water chemistry data for 24 different catchments along the Finnish coast (in all, covering c. 21,300 km2) which were mapped with different methods (i.e. conventional mapping, fuzzy logic and an artificial neural network). Two a.s. soil related indicators measured in the river water (sulfate content and sulfate/chloride ratio) were compared to the extent of the most probable areas for a.s. soils in the surveyed catchments. High sulfate contents and sulfate/chloride ratios measured in most of the rivers demonstrated the presence of a.s. soils in the corresponding catchments. The calculated extent of the most probable a.s. soil areas is supported by independent data on water chemistry, suggesting that the a.s. soil probability maps created with different methods are reliable and comparable.