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In this article, the authors focus on the introduction of a hybrid method for risk-based fault detection (FD) using dynamic principal component analysis (DPCA) and failure method and effect analysis (FMEA) based Bayesian networks (BNs). The FD problem has garnered great interest in industrial application, yet methods for integrating process risk into the detection procedure are still scarce. It is, however, critical to assess the risk each possible process fault holds to differentiate between non-safety-critical and safety-critical abnormalities and thus minimize alarm rates. The proposed method utilizes a BN established through FMEA analysis of the supervised process and the results of dynamical principal component analysis to estimate a modified risk priority number ( RPN ) of different process states. The RPN is used parallel to the FD procedure, incorporating the results of both to differentiate between process abnormalities and highlight critical issues. The method is showcased using an industrial benchmark problem as well as the model of a reactor utilized in the emerging liquid organic hydrogen carrier (LOHC) technology.

The properties of 3D-printed bodies are an essential part of both the industrial and research sectors, as the manufacturers try to improve them in order to make this now additive manufacturing method more appealing compared to conventional manufacturing methods, like injection moulding. Great achievements were accomplished in both 3D printing materials and machines that made 3D printing a viable way to produce parts in recent years. However, in terms of printing parameters, there is still much room for advancements. This paper discusses four of the 3D printing parameters that affect the properties of the final products made by chopped glass fibre-filled nylon filaments; these parameters are the printing temperature, nozzle diameter, layer height, and infill orientation. Furthermore, a polynomial function was fitted to the measured data points, which made it possible to calculate the tensile strength, flexural strength, and Young's modulus of the 3D-printed samples based on their printing parameters. A Pearson correlation analysis was also carried out to determine the impact of each parameter on all three mechanical properties studied. Both the infill orientation and printing temperature had a significant effect on both strengths and Young's modulus, while the effect of nozzle diameters and layer heights were dependent on the infill orientation used. Also, a model with excellent performance was established to predict the three mechanical properties of the samples based on the four major parameters used. As expected from a fibre-reinforced material, the infill orientation had the most significant effect on the tensile strength, flexural strength, and Young's modulus. The temperature was also quite significant, while the nozzle diameters and layer height effect were situational. The highest values for the tensile strength, flexural strength, and Young's modulus were 72 MPa, 78.63 MPa, and 4243 MPa, respectively, which are around the same values the manufacturer states.

Waterbirds depend on a dispersed network of wetlands for their annual life cycle during migration. Climate and land use changes raise new concerns about the sustainability of these habitat networks, as water scarcity triggers ecological and socioeconomic impacts threatening wetland availability and quality. During the migration period, birds can be present in large enough numbers to influence water quality themselves linking them and water management in efforts to conserve habitats for endangered populations. Despite this, the guidelines within laws do not properly account for the annual change of water quality due to natural factors such as the migration periods of birds. Principal component analysis and principal component regression was used to analyze the correlations between the presence of a multitude of migratory waterbird communities and water quality metrics based on a dataset collected over four years in the Dumbrăvița section of the Homoród stream in Transylvania. The results reveal a correlation between the presence and numbers of various bird species and the seasonal changes in water quality. Piscivorous birds tended to increase the phosphorus load, herbivorous waterbirds the nitrogen load, while benthivorous duck species influenced a variety of parameters. The established PCR water quality prediction model showed accurate prediction capabilities for the water quality index of the observed region. For the tested data set, the method provided an R 2 value of 0.81 and a mean squared prediction error of 0.17., (© 2023. The Author(s).)