Knut Kvaal

Detection and quantification of tread wear particles in the environment have been a challenge owing to lack of a robust method. This study investigated the applicability of a combination of Simultaneous Thermal Analysis (STA), Fourier Transform Infra-Red (FTIR), and Parallel Factor Analysis (PARAFAC) in the detection and quantification of tire particles from formulated sediments. FTIR spectral data were obtained by heating 20 samples in STA. Among the 20 samples, 12 were tire granules in formulated sediments (TGIS) containing 1%, 2%, 5%, and 10% by mass of tire granules, while the remaining eight contained 0.5, 1, 2.5, and 5 mg of tire granules only (TGO). The PARAFAC models decomposed the trilinear data into three components. Tire rubber materials in tire granules (RM) and a combination of water and carbon dioxide were the components identified in all samples. The linear regression analysis of score values from the PARAFAC models showed that the RM quantity predicted were comparable to measured values in both TGIS and TGO. Decomposing the overlying components in the spectral data into different components, and predicting unknown quantity in both sample types, the method proves robust in identifying and quantifying tire particles from sediments.

Considering new legislative and economic restrictions caused by the water crisis, this work focuses on a more efficient wastewater treatment process, which combines biological treatment in a moving bed biofilm system with a membrane bioreactor (BF-MBR) and coagulation, particularly addressing fouling alleviation in the separation stage. The study justifies the positive impact of coagulant dosing in BF-MBR regarding membrane flux and fouling rate. Statistical techniques connect the results of coagulation and membrane separation experiments with properties of mixed liquor, obtained after biotreatment in the representative pilot plant and characteristics of prepolymerized and non-prepolymerized inorganic coagulants. Research results substantiate the need for a pH-controlled coagulation of mixed liquor in BF-MBR depending on coagulant type, which influences charge, hydrophobicity and size of flocs and organic content of the system. It is suggested, that the adsorption/charge neutralization mechanism dominates in flux enhancement in BF-MBR, giving the best results in the case of prepolymerized aluminium coagulants. Together with high quality of permeate, the application of prepolymerized aluminium chloride of medium basicity entails a tenfold increase in filtration time of the membrane separation cycle and increases net membrane flux by 30–56%. The results of the study are practically significant for the development of an automated control system for BF-MBR, optimizing treatment rates together with membrane separation efficiency.

Online communities can be an attractive source of ideas for product and process innovations. However, innovative user‐contributed ideas may be few. From a perspective of harnessing “big data” for inbound open innovation, the detection of good ideas in online communities is a problem of detecting rare events. Recent advances in text analytics and machine learning have made it possible to screen vast amounts of online information and automatically detect user‐contributed ideas. However, it is still uncertain whether the ideas identified by such systems will also be regarded as sufficiently novel, feasible and valuable by firms who might decide to develop them further. A validation study is reported in which 200 posts from an online home brewing community were extracted by an automatic idea detection system. Two professionals from a brewing company evaluated the posts in terms of idea content, idea novelty, idea feasibility and idea value. The results suggest that the automatic idea detection system is sufficiently valid to be deployed for the harvesting and initial screening of ideas, and that the profile of the identified ideas (in terms of novelty, feasibility and value) follows the same pattern identified in studies of user ideation in general.

A novel floc sensor prototype was tested in a Norwegian municipal wastewater treatment plant. The resulting images of flocs, captured using a specially designed software, were analysed by texture image analysis technique—grey level co-occurrence matrix (GLCM). The results of image analysis were merged with the coagulation process measurement data—inlet and outlet wastewater parameters. The data based only on GLCM textural features resulted in 96.6% explained total variance by two principal components and distinguished two classes in the data—low and high outlet turbidity values. The predicted by partial least squares regression (PLSR) coagulant dosages precisely followed the reference dosages, explained Y total variance by 3 factors equals 91.8% for calibration and 77.9% for validation. Results of the studies indicate that the GLCM method and sensor prototype can be used for an improvement of coagulant dosage control. Tested sensor prototype gives a solid basis for development of the low-cost floc sensor.

Cable yarding is a semi-mechanized timber harvesting system that relies on human machine interaction where a considerable share of the work is done by forest workers. The system is used in mountain forests around the world. Automation of one or more functions could contribute to increased productivity, reduced physical workloads and improved safety conditions. This paper presents a method for sensor data fusion in order to automatically distinguish work phases using Partial Least Squares Discriminant Analysis (PLS-DA). The Robot Operation System (ROS) is implemented to allow for real-time data processing with a maximum latency of 0.16 s. Global Positioning System (GPS), Inertial Measurement Unit (IMU) and camera integration provided a robust solution for 78% correct process segmentation. These results provide a basis for further development from which there is a possibility of expanding this approach for semi-automation, remote control, and autonomous operation.

Urban flooding caused by heavy rainfall is expected to increase in the future. The main purpose of this study was to investigate the variables characterizing the placement of a house, which seem to have an impact when it comes to the exposure to floods. From the same region in Norway, data from 347 addresses were derived. All addresses were either associated with insurance claims caused by flooding or were randomly selected. A multivariate statistical model, Partial Least Square Regression (PLS), was used. Among others, the analysis has shown that the upstream, sealed area is the most significant variable for characterizing properties’ exposure to urban flooding. The model confirms that flooding tends to occur near old combined sewer mains and in concave curvature, and houses located in steep slopes seem to be less exposed. Using this method, it is possible to rank and quantify significant exposure variables contributing to urban floods within a region. Results from the PLS-analysis might provide important input to professionals, when planning and prioritizing measures. It can also predict flood-prone areas and make residents aware of the risks, which may induce them to implement preventive measures.

Flocs formation and growth are important characteristics in wastewater coagulation process. The shape and size of flocs highly affect further separation processes, therefore resulting treatment efficiency of wastewater after coagulation. Observed images of flocs tend to show strong relations to coagulation parameters: dose and coagulation time. In this article, three texture recognition techniques were evaluated for the ability to mathematically describe the relationship between the images of flocs and coagulant dosages. The easily computable texture analysis methods were found to be potential techniques for the characterization of the particles images. Ten out of eleven co-occurrence matrix-based grey level co-occurrence matrix (GLCM) texture features were found to be significant for the dosage prediction by a principal component regression model with only one principal component. Two features (Inverse difference moment and Variance) were selected for the multiple linear regression model. Test set prediction accuracy varied from 83 to 96% depending on texture analysis method and multivariate model. Best dosage prediction and image classification results were achieved by GLCM and angle measure technique. The results of image texture analysis coupled with multivariate modelling techniques indicate that it is possible to characterize and relate flocs images, captured during coagulation, with different coagulant dosages, as well as predict those dosages.