Designing turbulence promoters with optimal geometry and using them for ultrafiltration systems has been a key challenge in mitigating membrane fouling. In this study, six different turbulence promoters were created using three-dimensional printing technology and applied in dead-end ultrafiltration. Three-dimensional-printed (3DP) turbulence promoter configurations were integrated into a classical batch ultrafiltration cell. The effects of these configurations and the stirring speeds on the permeate filtration flux, organic rejections, and membrane resistances were investigated. The fouling control efficiency of the 3DP promoters was evaluated using two polyethersulfone membranes in a stirred ultrafiltration cell with model dairy wastewater. The Hermia and resistance-in-series models were studied to further investigate the membrane fouling mechanism. Of the Hermia models, the cake layer model best described the fouling in this membrane filtration system. It can be concluded that the 3DP turbulence promoters, combined with intense mechanical stirring, show great promise in terms of permeate flux enhancement and membrane fouling mitigation. Using a well-designed 3DP turbulence promoter improves the hydrodynamic flow conditions on the surface of the stirred membrane separation cells based on computational fluid dynamics modeling. Therefore, the factors effecting the fabrication of 3DP turbulence promoters are important, and further research should be devoted to revealing them.

Dairy factories annually generate an increasing amount of wastewater, which can cause eutrophication due to high concentrations of amino acids and lipids. To address this issue, membrane technology has emerged as a promising solution, but membrane fouling remains a significant challenge, since it can cause decreased flux, decrease membrane rejection performance, and increased energy demand. This study aimed to reduce membrane fouling by integrated a three-dimensional printed (3DP) turbulence promoter into an ultrafiltration dead-end cell and varying stirring speeds. Two mathematical models, Hermia and resistance-in-series, were used to analyze the fouling process. According to both models, the cake layer formation model indicated the most prevalent fouling mechanism. Specific energy demand, permeate flux, membrane rejection, and membrane reversible and irreversible resistances were measured, calculated, and compared. The results suggest that the combination of an integrated 3DP turbulence promoter and high stirring speeds can effectively reduce membrane fouling in a dairy wastewater treatment module.

This study aims to analyze various methods, including AI, that can be used to optimize error analysis in digital twins and highlight the advantages and disadvantages of these analysis methods. Furthermore, the study aims to present an automated solution for error analysis of MATLAB/Simulink-based digital twins. This solution can make the error analysis more efficient without the use of AI, meaning that it can be used even if the digital twin is not appropriately known, which can be a considerable advantage in the current automotive industry, where complex digital twins are commonly used for the development and optimization of E/E systems during different types of in-the-Loop simulations.

This study aims to implement the digital twin of a Li-ion battery by using real measurement data and to create a deep learning-based SOC (state of charge) estimation solution. In the case of the SOC estimator, a special type of deep learning, so-called long short-term memory (LSTM), was used to increase the capabilities of the estimator. The digital twin and the SOC estimator were created by using MATLAB and MATLAB/Simulink. As a result, the implemented system can accurately simulate the non-linearities of the Li-ion battery and provide a satisfactory estimation of the SOC of the battery.

The goal of this paper is to present a methodology developed to detect feedback loops within complex MATLAB/Simulink-based digital twins. Digital twins are developed using mathematical equations that describe the behavior of the given physical system in environments such as MATLAB/Simulink (2024a). During the implementation of these equations as block diagrams following a causal modeling approach, it is almost unavoidable to use feedback loops, which can cause many problems if the digital twin is not properly designed. Using the presented methodology, these loops within the model can be quickly identified, allowing the user to check them immediately and determine whether the source of a model error is associated with the given loop, e.g., a not-a-number (NaN) value stuck in the loop. This can be especially useful if the source of error is already partly localized and the goal is to narrow down the possible sources as fast as possible.