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In this study, the household consumption-related ecological footprint of lifestyles linked to panel housing estates and suburban neighbourhoods were compared in the case of Budapest and its suburbs. Our results show that the biggest parts of the ecological footprint are in both study areas the carbon, the cropland and the forest components, in line with earlier calculations. On the whole, the ecological footprint values are bigger in the suburban study area (2.63 gha/capita) compared to the panel housing estates (2.29 gha/capita), mainly because of the differences between the carbon uptake and the built-up land components. Beyond comparing the ecological footprint values of different residential areas, the study also contributes to the literature by addressing the improvement options of the respective areas through a rough model calculation on the reduction opportunities in both cases, resulting 36 percent in case of panel housing estates, and 47 percent in the suburban areas. Although these values have to be considered cautiously, they show significant opportunities in ecological footprint reduction in both types of residential areas supported by individual motivations, as well as by policy measures.

In this study a 2D visualization technique is presented that is suitable for determining the optimal operational parameters of solid waste gasification depending on the intended use of the product. Steam gasification of different wastes (wheat straw, wood, municipal solid waste (MSW), polyethylene (PE), green waste) was modelled in Aspen Plus simulation software, validated with literature data and a MATLAB – Aspen Plus inter software connection was also created to minimize the possibility of errors when the raw material composition and other parameters are changed. Correlation was found between the simulation and literature data; therefore, the model was also suitable for evaluating the effects of process parameters (T = 650–1100 °C, steam rate = 0–1.5 kg/h) on gas composition, lower heating value and H2/CO ratio. The model was also extended to a wide range of domestic waste types, making it possible to determine the optimal process parameters without performing a high number of time- and energy-intensive gasification experiments. Regarding the process parameters it was established that the temperature has a significant effect on the gasification reactions and shifts the chemical reactions towards hydrogen and carbon monoxide formation, but above 800 °C it has a limited effect on the gas composition, lower heating value and H2/CO ratio. The increasing steam rate also facilitated the hydrogen and carbon monoxide formation, but above a certain ratio its effect was opposite due to the water-gas shift reaction and the shorter residence times. The obtained gases can be used for energy purposes or as raw material for Low-Temperature Fischer-Tropsch synthesis, or production of aldehyde, higher alcohol, acetic acid or even polycarbonate for which the optimal temperatures and steam rates were also determined.

Manufacturers use a large number of components in the production of modern rubber products. The selection of the constituents of the rubber recipe is primarily determined by the purpose of use. The different fields of applications of rubbers require the presence of appropriate mechanical properties. In this respect, it can be useful to know which substances forming the rubber recipe have significant influence on the different mechanical properties. In this study, the statistical analysis of the influence of rubber components on the hardness of natural rubber (NR) is proposed based on literature review. Based on the literature data, various statistical analyses, like linear regression, constrained linear regression, Ridge regression, Ridge sparse regression and binary classification decision trees were performed to determine which rubber components have the most significant effect on the hardness. In the statistical analyses, the effect of a total of 42 constituents of rubber compound on hardness was investigated. Most of the applied statistical methods confirmed that the traditional frequently used rubber components, such as carbon black and sulfur, have a primary effect on the hardness. However, the substances forming the rubber compound that are not widely used in practice or newly developed components appear differently in the lists of significant additives obtained by the different statistical methods.

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.

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. [ABSTRACT FROM AUTHOR]

Hydrogen is one of the key components in renewable energy systems. Its storage and transport, however, are challenging. The Liquid Organic Hydrogen Carrier (LOHC) technology is a possible solution for this issue. With suitable organic components, hydrogen can be stored in a chemically bound form which is safer and has a higher energy density than other solutions. Furthermore, the storage and transport of the LOHC component with high hydrogen content can be provided under normal environmental conditions. The LOHC process cycle involves a catalytic hydrogenation and dehydrogenation step where the LOHC component can be recovered and recycled. The paper reviews a few relevant studies regarding the possible LOHC compound pairs, the potential catalyst systems, and the necessary equipment. Then, the paper discusses a simulation study of three LOHC systems, determining suitable operating conditions and estimating costs. The toluene-methylcyclohexane system was found to be the most promising for LOHC application. Two energy integration opportunities were also examined. The first case study was based on separate hydrogenation and dehydrogenation sites, while the second was based on one site utilization. The results show a lower energy demand in the case of a toluene system, with a reduction of 70% in heating and 45% in cooling. [ABSTRACT FROM AUTHOR]

In this study, the household consumption-related ecological footprint of lifestyles linked to panel housing estates and suburban neighbourhoods were compared in the case of Budapest and its suburbs. Our results show that the biggest parts of the ecological footprint are in both study areas the carbon, the cropland and the forest components, in line with earlier calculations. On the whole, the ecological footprint values are bigger in the suburban study area (2.63 gha/capita) compared to the panel housing estates (2.29 gha/capita), mainly because of the differences between the carbon uptake and the built-up land components. Beyond comparing the ecological footprint values of different residential areas, the study also contributes to the literature by addressing the improvement options of the respective areas through a rough model calculation on the reduction opportunities in both cases, resulting 36 percent in case of panel housing estates, and 47 percent in the suburban areas. Although these values have to be considered cautiously, they show significant opportunities in ecological footprint reduction in both types of residential areas supported by individual motivations, as well as by policy measures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]