Recent literature has recommended that life cycle assessments (LCA) of forest bioenergy supply chains consider the impact of biomass harvest on ecosystem carbon stocks as well as the net emissions arising from combustion of various forms of biofuels compared with reference fossil fuel systems. The present study evaluated the magnitude and temporal variation of ecosystem C stock changes resulting from harvest of roadside residues and unutilized whole trees for bioenergy. The Carbon Budget Model (CBM-CFS3) was applied to the Gordon Cosens Forest, in northeastern Ontario, along with the Biomass Opportunity Supply Model (BiOS-Map), for cost analysis of different types of biomass comminution. Natural gas (NG) steam and electricity, grid electricity, and coal electricity reference systems were analyzed for a pulp and paper mill. The findings showed that the forested landscape becomes a net sink for carbon following the 20th year of roadside residue harvest, compared to whole-tree harvest, where the forested landscape remained a net source of carbon over the entire 100 year rotation. The cumulative ecosystem carbon loss from whole-tree harvest was 11 times greater compared to roadside residue harvest. BiOS-Map analysis suggested that due to technical and operational limits, between 55%-59% and 16%-24% of aboveground biomass was not recovered under roadside residue and whole-tree harvest respectively. The cost of delivering roadside residues was estimated at $52.32/odt–$57.45/odt, and for whole trees $92.63/odt–$97.44/odt. The Life Cycle Assessment (LCA) analysis showed break-even points of 25, 33 and 6 years for roadside residues displacing NG steam, NG electricity, and coal, respectively. No GHG reduction was achieved when forest biomass was used to displace grid electricity that is generated in Ontario. Whole-tree bioenergy resulted in no GHG reduction for NG displacement, and a break-even point of 70-86 years for coal. A net GHG reduction of 67% and 16% was realized when roadside residues and whole trees were used to displace coal, compared to 45% and 38% when roadside residues were used to displace NG steam and NG electricity, respectively. Therefore, it is recommended that bioenergy deployment strategies focus on the utilization of roadside residues, if the main goal is GHG mitigation.

I dagsläget finns det ingen standardiserad metod för att mäta en enhets tillstånd medhjälp av dofter. Vid tillståndsövervakning av rullningslager är vibrationsmätning denmest dominanta metoden. I samband med vibrationsmätning används i vissa falltemperaturövervakning för att få en bättre insikt på rullningslagrets tillstånd. I det härarbetet undersöks de om en elektronisk näsa kan avgöra ett rullningslagers tillstånd.Innan några mätningar påbörjas monterades en elektronisk näsa ihop i ett hölje sombestår av ett kretskort, metalloxid-sensorer och en fläkt för att styra dofter med ettkonstant flöde mot sensorerna. Den elektroniska näsan styrs av en Arduino Nanomikrokontroller. Utöver e-näsan sättes en enhet ihop tillhörande två temperaturgivareoch en luftfuktighetsgivare som styrs av en Arduino UNO. Enhetens syfte är att kunnakontrollera de rådande förhållandena vid mätningar och för att leta någon form avkorrelation mot e-näsan vid eventuella utslag. Förstörande prover av kullager utfördesför att se om e-näsan reagerar innan ett lagerhaveri. Testerna gjordes i en öppen samtsluten miljö och tre stycken olika oljor används för att smörja lagret. Detta för att seom e-näsan reagerar olika beroende på vilken olja som används. En undersökningutförs ifall den elektroniska näsan kan separera på de tre oljorna som används ilagertesterna. För att utvärdera mätresultaten används Excel och Minitab, därprincipalkomponentanalyser genomförs på all mätdata. Efter att alla lagerprover harverkställts utfördes en uppföljning av rullningslagrena för att studera deras tillstånd,detta genom ett optiskt mikroskop.Det framgår i rapporten att med hjälp av analysmetoden PCA syns det att denelektroniska näsan kunde skilja på hydraulolja, motorolja och växellådsolja. Utslag iPCA för de olika mätserierna blev inte identiska men det blev tydligaklusterindelningar hos samtliga mätserier. Genomförd studie visade att med delagerhaveri samt temperaturer går det inte att avgöra ett kullagers tillstånd med hjälpav en elektronisk näsa. Eftersom att de specifika gas-sensorerna som användes till enäsaninte gav någon form av utslag vid mätningarna. Den elektroniska näsanreagerade däremot vid totalhaveri av kullager, vilket är för sent i ett förebyggandeunderhållsperspektiv. Detta medförde att den elektroniska näsan inte kan användas förtillståndsövervakning av det specifika kullagret som användes vid denna studie. / At present, there is no standardized method of measuring a device's condition with thehelp of odors. In condition monitoring of rolling bearings, vibration measurement isthe most dominant method. In case of vibration measurement, temperature monitoringis used in some cases to get a better insight into the condition of the bearing. In thiswork, it is investigated whether an electronic nose can determine the condition of arolling bearing.Before any measurements began, an electronic nose is assembled in a housingconsisting of a circuit board, metal oxide sensors and a fan for stearing odors with aconstant flow towards the sensors. The electronic nose is controlled by an ArduinoNano which is a microcontroller. In addition to the e-nose, a unit is connected to twotemperature sensors and a humidity sensor controlled by an Arduino UNO. The unit'spurpose is to monitor the status and to look for any kind of correlation with the e-nosein case of any possible findings. Destructive specimens of ball bearings are performedto see if the e-nose responds prior to a bearing failure. Tests are conducted in an openand closed environment and three different oils are used to lubricate the bearings.This to see if the e-nose acts differently depending on the oil that is used. Aninvestigation is conducted if the electronic nose can separate the three different typesof oils that is used in the destructive bearing tests. To evaluate the measurementresults, Excel and Minitab are used, where principal component analysis is performedon all measurement data. After all bearing tests have been performed, a follow-up ofthe rolling bearings condition is performed, this through an optical microscope.The report shows that using the PCA analysis method, it appears that the electronicnose could distinguish between hydraulic oil, engine oil and gear oil. In the PCA forthe different measurement series the results did not become identical, but clusterdivisions became clear in all measurement series. Completed study showed that withthese bearing failures and temperatures, it is not possible to determine the condition ofthis ball bearer using an electronic nose. Because the specific gas sensors used for thee-nose did not give any kind of impact during the measurements. On the other hand,the electronic nose responded to a total failure of a ball bearing, which is too late in apreventative maintenance perspective. Therefore, the electronic nose cannot be usedfor condition monitoring of the specific ball bearing used in this study.

In this thesis it has been studied how operational and workshop data can be used to improve the handling of field quality (FQ) issues for electronic units. This was done by analysing how failure rates can be predicted, how failure mechanisms can be detected and how data-based lifetime models could be developed. The work has been done on an electronic control unit (ECU) that has been subject to a field quality (FQ) issue, determining thermomechanical stress on the solder joints of the BGAs (Ball Grid Array) on the PCBAs (Printed circuit board assembly) to be the main cause of failure. The project is divided into two parts. Part one, "PCBA" where a laboratory study on the effects of thermomechanical cycling on solder joints for different electrical components of the PCBAs are investigated. The second part, "ECU" is the main part of the project investigating data-driven solutions using operational and workshop history data. The results from part one show that the Weibull distribution commonly used to predict lifetimes of electrical components, work well to describe the laboratory results but also that non parametric methods such as kernel distribution can give good results. In part two when Weibull together with Gamma and Normal distributions were tested on the real ECU (electronic control unit) data, it is shown that none of them describe the data well. However, when random forest is used to develop data-based models most of the ECU lifetimes of a separate test dataset can be correctly predicted within a half a year margin. Further using random survival forest it was possible to produce a model with just 0.06 in (OOB) prediction error. This shows that machine learning methods could potentially be used in the purpose of condition based maintenance for ECUs.

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