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In this work, the authors provide a case study of time series regression techniques for water quality forecasting. With the constant striving to achieve the Sustainable Development Goals (SDG), the need for sensitive and reliable water management tools has become critical. Continuous online surface water quality monitoring systems that record time series data about surface water parameters are essential for the supervision of water conditions and proper water management practices. The time series data obtained from these systems can be used to develop mathematical models for the prediction of the temporal evolution of water quality parameters. Using these mathematical models, predictions can be made about future trends in water quality to pinpoint irregular behaviours in measured data and identify the presence of anomalous events. We compared the performance of regression models with different structures for the forecasting of water parameters by utilizing a data set collected from the Kis-Balaton Water Protection System (KBWPS) wetland region of Hungary over an observation period of eleven months as a case study. In our study, autoregressive integrated moving average (ARIMA) regression models with different structures have been compared based on forecasting performance. Using the resulting models, trends of the oxygen saturation, pH level, electrical conductivity, and redox potential of the water could be accurately forecast (validation data residual standard deviation between 0.09 and 20.8) while in the case of turbidity, only averages of future values could be predicted (validation data residual standard deviation of 56.3). [ABSTRACT FROM AUTHOR]

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]

Safety regulations demand the elimination of random mistakes and the reliable operation of production units. However, the control and maintenance of batch and semi-continuous processes haalways been difficult. In this paper, a way of preventing malfunctions in batch and semi-continuous processes is presented by using appropriately designed buffer tanks. A stochastic model was investigated in which batch and continuous subsystems were linked by an intermediate storage tank. The main concern was the reliability of the system. Reliable operation was defined as neither the exhaustion of raw materials nor the excessive accumulation of them. The counting processes that describe the random batch-input and random batch-output processes are supposed to be independent homogeneous Poisson processes with different rates. By introducing a function that describes the material in storage, reliable operation is defined as when this function satisfies two inequalities for a time interval of any duration. By applying probabilistic methods, an integral equation is set up for the the reliability. Nevertheless, its analytical solution cannot be determined, hence the values according to a Monte Carlo simulation are approximated. By applying this method, a link could be identified between the necessary initial buffer and tank capacities that belong to a reliability level. Economic investigations were conducted to help determine the optimal initial buffer and tank capacities that satisfy the appointed reliability level.

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]

In recent years the topic of additive manufacturing has become quite popular in industrial applications and scientific studies. However, the rapid development in this field, especially the slicer and machine software, makes research difficult because these can become obsolete due to software improvements. Another impeding aspect is the lack of knowledge and interest in the field of 3D printing or additive manufacturing process and materials; primarily, 3D printing is seen as a tool to achieve some results by researchers. Due to these facts, most studies do not discuss 3D printing parameters properly, making reproducing results almost impossible. This paper discusses three 3D printing parameters that affect the properties of final products made by different chopped fibre filled- and generic nylon filaments; these parameters are the printing temperature, nozzle diameter, layer height, infill orientation and wall line count (or perimeter count). The family of nylon filaments is mainly used for functional 3D printing, especially in replacing aluminium and steel parts, thanks to their mechanical and thermal properties. Furthermore, a polynomial function was fitted to the measured data points, which made it possible to calculate the tensile strength and ductility of the 3D-printed samples based on their printing parameters. [ABSTRACT FROM AUTHOR]

The role of homogenization in industrial systems is to provide proper feedstock and product properties to ensure the smooth operation of industrial systems. In this paper, an external recirculation mixing tank with unique construction for the homogenization of stratified liquids is investigated. The main focus of the work is to determine the relationship between tank geometry and dead volumes present in the unit. The unit's geometry was altered by changing the volume of the tank during experimental work. Residence time based methods were applied for the investigation of the system. For a thorough investigation of the unit, computational fluid dynamics methods were employed in COMSOL Multiphysics to obtain data about the velocity field in the unit. Experimental and modeling data were compared and used to understand the impact of the tank geometry on the flow characteristics of the system, the dead volumes present and thus on the required homogenization time. Optimal tank geometry was chosen for the mixing process to minimize dead volume ratios and to allow more profitable operation. It was observed that the increase in the tank's volume caused more significant amounts of dead volume to form with an increase in dead volume ratio from 20% to 27% for the smallest and largest geometries respectively. Thus, it was concluded that the geometry with the smallest volume is optimal for mixing since it encompasses the largest ratio of active mixing volumes. The methods provided can be used to analyze and optimize other mixing units in which stratified fluids are to be homogenized. [ABSTRACT FROM AUTHOR]

In recent years the fault detection and isolation (FDI) problems have garnered increasing interest due to concerns of environmental protection, stricter regulations and optimization of processes within industrial settings. The use of data-based methods such as Neural Networks (NNs) for purposes of FDI has received significant research interest in the scientific community. However while these data-driven methods present relatively robust and sensitive means of fault detection and isolation they require large amounts of process data for their training so they can work reliably. Within the chemical industry rather than experimental investigations various modeling techniques such as computational fluid dynamics (CFD) may be used to generate this training data. However many of these methods are computationally expensive and less efficient for producing large data sets. In this paper we wish to present an identification algorithm for the development of compartment models (CM) based on CFD results which can be used to reliably generate process data for FDI purposes in a computationally more efficient way. [ABSTRACT FROM AUTHOR]

Fault detection and isolation have become increasingly important problems over time, due to the more complex and larger scale industrial systems. The last few decades have seen a rise in research focused on developing robust and sensitive fault detection methods. Approaches using mathematical models, qualitative logic or operation data driven solutions were developed and while they all performed well overall some lacked in robustness and others in flexibility or sensitivity. In this study a hybrid fault detection approach using both parity relation methods and a Fuzzy Expert System (FES) to analyse and detect process faults in a distillation unit is introduced. The combination of the two schemes was used to handle the detection and classification of additive and multiplicative faults. The effectiveness of the hybrid method in alleviating the shortcomings of the single techniques has been verified by simulation and experimental tests. [ABSTRACT FROM AUTHOR]