Influence of Carbon Sorbent Quantity on Breakthrough Time in Absorbent Filters for Antismog Half Mask Application

In this article, we present polymer non-woven fabrics with the addition of carbon sorbents being tested to estimate the breakthrough time and efficient protection against vapors present in smog. For this purpose, three substances were selected, which constitute an inhalation hazard and are smog components: cyclohexane, toluene, and sulfur dioxide. It was demonstrated that an increased quantity of carbon sorbent in polymeric filters significantly prolongs the breakthrough time. However, high sorbent quantities may increase the filter surface mass and air flow resistance. To optimize the protective parameters with functionality, a compromise between the two has to be found. By comparing the breakthrough times for different carbon sorbent quantities, the optimal filter composition was elaborated. The analyzed non-woven fabrics were manufactured by the melt-blown process and filled with ball-milled carbon sorbents supplied directly into the fabric blowing nozzle. Both protective performance and textural properties were analyzed for two commercially available carbon sorbents. Furthermore, it was proven that high values of sorbent-specific surface area translates directly into greater filter performance.

Assessment of Chemical Fiber Air Filter for General Ventilation

Air filters for general ventilation have mainly been used to control the concentration of indoor particulate matter. In this study, the pressure differential, test dust capacity, quality factor and operating life of class F8 pleat–plate and multi-bag type chemical fiber filters were evaluated using an air filter performance test system. The results showed that the resistance increase rate of multi-bag filter (0.49 Pa/g·(cm/s)) was lower than that of pleat–plate filter (1.94 Pa/g·(cm/s)), the quality factor of the multi-bag filter was lower than that of pleat–plate filter, and the dust capacity of the multi-bag filter was much higher than that of the pleat–plate filter. The operating life of the multi-bag filter was 8 times as that of the pleat–plate filter with the measured PM2.5 of outdoor. The energy consumption of the pleat–plate filter was 2.2 times that of the multi-bag filter. Analyzing the electron microscope photos after dust loading, the dust depth of pleat–plate filter into filter material was thinner than that of multi-bag filter. The research results could provide data support for the design optimization and selection of ventilation filters and the treatment of the particulate matter in indoor environments.

A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis

Blind deconvolution (BD) is an effective algorithm for enhancing the impulsive signature of rolling bearings. As a convex optimization problem, the existing BDs have poor optimization performance and cannot effectively enhance the impulsive signature excited by weak faults. Moreover, the existing BDs require manual derivation of the calculation process, which brings great inconvenience to the researcher's personalized design of the maximization criterion. A new BD algorithm based on backward automatic differentiation (BAD) is proposed, which is named BADBD. The calculation process does not require manual derivation so a general solution of BDs based on different maximization criteria is realized. BADBD constructs multiple cascaded filters to filter the raw vibration signal, which makes up for the deficiency of single filter performance. The filter coefficients are determined by Adam algorithm, which improves the optimization performance of the proposed BADBD. BADBD is compared with classic BDs by synthesized and real vibration signals. The results reveal superior capability of BADBD to enhance the impulsive signature and the fault diagnosis performance is significantly better than the classic BDs.

Study on the determination of the parameters of the electric purifier

The article is devoted to the determination of the parameters on which the filter performance depends on the hydrodynamic, electromagnetic and geometrical parameters of the fluid and pollution. These include fluid viscosity and flow rate, magnetizing force, particle size of contamination, gap height, and the distance between the turns.

T-S Fuzzy Model-Based Fault Detection for Continuous Stirring Tank Reactor

Continuous stirring tank reactors are widely used in the chemical production process, which is always accompanied by nonlinearity, time delay, and uncertainty. Considering the characteristic of the actual reaction of the continuous stirring tank reactors, the fault detection problem is studied in terms of the T-S fuzzy model. Through a fault detection filter performance analysis, the sufficient condition for the filtering error dynamics is obtained, which meets the exponential stability in the mean square sense and the given performance requirements. The design of the fault detection filter is transformed into one that settles the convex optimization issue of linear matrix inequality. Numerical analysis shows the effectiveness of this scheme.

Improvement of Polytetrafluoroethylene Membrane High-Efficiency Particulate Air Filter Performance with Melt-Blown Media

Polytetrafluoroethylene (PTFE) membrane filters are widely used in low-load application areas, such as industrial cleanrooms, due to their low initial pressure drop. In this study, melt-blown (MB) nonwoven was introduced as a pre-filtration layer at the front end of a high-efficiency particulate air (HEPA) filter to improve the filter performance of the PTFE membrane. Pre-filtration reduces the average particle size, which reaches the PTFE membrane and reduces the dust load on the HEPA filters. A comparative analysis of the HEPA filters by composite MB and PTFE was conducted. Regarding the MB composite on the PTFE, low-weight and high-weight MB composites were effective in increasing dust filtration efficiency, and the dust loading capacity of the PTFE composite with high-weight MB increased by approximately three times that of the PTFE membrane. In addition, the filter was installed on an external air conditioner in an actual use environment and showed a high efficiency of 99.984% without a change in differential pressure after 120 days.

Translation and validation of PubMed and Embase search filters for identification of systematic reviews, intervention studies, and observational studies in the field of first aid

Objective: The aim of this project was to validate search filters for systematic reviews, intervention studies, and observational studies translated from Ovid MEDLINE and Embase syntax and used for searches in PubMed and Embase.com during the development of evidence summaries supporting first aid guidelines. We aimed to achieve a balance among recall, specificity, precision, and number needed to read (NNR).Methods: Reference gold standards were constructed per study type derived from existing evidence summaries. Search filter performance was assessed through retrospective searches and measurement of relative recall, specificity, precision, and NNR when using the translated search filters. Where necessary, search filters were optimized. Adapted filters were validated in separate validation gold standards.Results: Search filters for systematic reviews and observational studies reached recall of ≥85% in both PubMed and Embase. Corresponding specificities for systematic review filters were ≥96% in both databases, with a precision of 9.7% (NNR 10) in PubMed and 5.4% (NNR 19) in Embase. For observational study filters, specificity, precision, and NNR were 68%, 2%, and 51 in PubMed and 47%, 0.8%, and 123 in Embase, respectively. These filters were considered sufficiently effective. Search filters for intervention studies reached a recall of 85% and 83% in PubMed and Embase, respectively. Optimization led to recall of ≥95% with specificity, precision, and NNR of 49%, 1.3%, and 79 in PubMed and 56%, 0.74%, and 136 in Embase, respectively.Conclusions: We report validated filters to search for systematic reviews, observational studies, and intervention studies in guideline projects in PubMed and Embase.com.

Investigating the Filtration Characteristics of Direct Driven Hydraulics

Direct driven hydraulics (DDH) is receiving increasing attention due to its advantages such as high energy efficiency, easy maintenance, and compactness. However, its oil contamination management has not been surveyed in detail, whereas uncontrolled oil contamination might result in extra maintenance efforts or even downtime. Therefore, this research paper analyzes the oil filtration in direct driven hydraulics through modeling and simulation to predict the filtration effects and support the filtration design. Firstly, model blocks of the filtration characteristics are built to be added to the basic DDH dynamic model. The model can hereby include the coupling effects between fluid degradation and component wear, the filtering process, and the time-varying filter performance. Secondly, the proposed model is applied to a DDH that incorporates filtration and simulated under a duty cycle for 960 hours. The DDH efficiency and control performance when considering the contaminant are revealed. Thirdly, the results are compared between different filter selections and a filtration solution is finally proposed. In conclusion, this paper illustrates the filtration effects on the efficiency, control performance, and oil contaminant level of DDH by simulation, which can hereby support the design of the DDH filtration solution. According to the simulations, the filtered DDH resulted in 96% degradation of the pump’s volumetric efficiency after 960-hour running compared to 92% when not using any filters. The extra pressure drop introduced by the filter is below 2 bar, suggesting that the introduction of a filter with 6 μm filtration level is beneficial.