Improving Organic Micropollutant Removal of Activated Carbon by Pre-treatment with Zeolite Adsorbent

Activated carbon (AC) is commonly used in drinking water treatment plants (DWTPs) to remove organic micro-pollutants (OMPs), and it is effective in adsorbing a wide range of OMPs. However, its adsorption efficiency can be affected by natural organic matter (NOM). NOM is a complex matrix and widely exists in water bodies. During the drinking water treatment process, the large size fraction of NOM may block the pore of the granular activated carbon (GAC), and the small size fraction of NOM can compete with OMPs and occupy the sites. The goal of this study is to increase the OMP adsorption efficiency of AC filtration in the drinking water treatment process. The objective was to investigate the feasibility of the competitive NOM removal by zeolite adsorption prior to powdered activated carbon (PAC). Meanwhile, the filtration performance and advantages of nanofiltration (NF) with 1000 Da membranes were found out. Zeolite - AC and NF- Zeolite - AC combined treatments were exterminated. In this study, the properties of zeolites were reviewed and the zeolite with FAU framework was selected as the hydrophobic adsorbent. Batch experiments with FAU and PAC were conducted to examine the adsorption performance of 10 common OMPs in different water samples. Comparative batch tests on UV effluent and NF permeate were carried out with two stages, zeolite pre-treatment and PAC treatment. Ultrapure water and demineralized water were involved as the reference to prove the influence of NOM from adsorption isotherms. Dissolved organic carbon (DOC) measurements were provided by the DWTP. NOM is confirmed to hinder the OMP adsorption on AC according to DOC measurements and adsorption isotherms. NOM smaller than 1000 Da is proved to cause the competition with OMPs. FAU-type zeolite pretreatment was failed to remove the competitive NOM removal.CIE5050-0

Ozone-Based Regeneration of Granular Zeolites Loaded with Organic Micropollutants

The removal of organic micropollutants (OMPs) in the aquatic environment is crucial to avoid health hazards. Zeolites have been confirmed as a selective adsorbent and can effectively remove target OMPs. To achieve sustainable application of adsorbents, regeneration of zeolites is required. The objective of this study was to investigate the regeneration performance of dried OMP-loaded granular zeolites through gaseous ozonation process, and the regeneration feasibility in long-term adsorption-regeneration processes. Three types of zeolites (MOR, MFI and BEA) were applied for target OMP (benzotriazole, methyl-benzotriazole, carbamazepine, diclofenac hydrochlorothiazide, sulfamethoxazole, metoprolol, sotalol, trimethoprim, propranolol, and clarithromycin) removal. A sequential process coupling zeolite adsorption and oxidation by gaseous ozone was established in batch mode. To assess the ozone effect on OMP degradation and zeolite itself, ozone bubbling tests and adsorption isotherm experiments were executed as pre-experiments. The relative adsorption capacity obtained through regeneration was used to demonstrate regeneration performance. Operating conditions, adsorption duration and regeneration duration were determined and applied. Ultimately the regeneration performance in long-term adsorption-regeneration processes was investigated.Experimental results showed that all target OMPs were not resistant to ozonation in the water phase. Gaseous ozone was showed no influence on the adsorption capacities of zeolite granules. 120 hours and 500 mgL-1 zeolite granules were applied in OMP-loading adsorption experiments. Zeolites always showed high adsorption capacities of metoprolol, trimethoprim and sotalol, which regeneration effect was not evidenced. 60 minutes of ozonation was effective and sufficient for regenerating low and medium adsorption OMPs, except for carbamazepine. The regeneration of carbamazepine probably required a longer regeneration duration. In four cycles of adsorption-regeneration experiments, regeneration of sulfamethoxazole could be achieved after four rounds of ozonation. Regarding carbamazepine, diclofenac, benzotriazole, and methyl-benzotriazole, the regeneration performance were significantly reduced after the first cycle of regeneration. The ozonation duration is supposed to be extended above 60 min in long-term regeneration experiments. Intermediates were potentially responsible for the reduction of regeneration performance in ozonation and adsorption processes. Particularly, the effect of intermediates accumulation might be the main factor that hampered the regeneration performance of low and medium adsorption OMPs in long-term operation.Civil Engineering | Environmental Engineerin

Rail break prediction and cause analysis using imbalanced in-service train data

Timely detection and identification of rail breaks are crucial for safety and reliability of railway networks. This paper proposes a new deep learning-based approach using the daily monitoring data from in-service trains. A time-series generative adversarial network (TimeGAN) is employed to mitigate the problem of data imbalance and preserve the temporal dynamics for generating synthetic rail breaks. A feature-level attention-based bidirectional recurrent neural networks (AM-BRNN) is proposed to enhance feature extraction and capture two-direction dependencies in sequential data for accurate prediction. The proposed approach is implemented on a three-year dataset collected from a section of railroads (up to 350 km) in Australia. A real-life validation is carried out to evaluate the prediction performance of the proposed model, where historical data is used to train the model and future ’unseen’ rail breaks along the whole track section are used for testing. The results show that the model can successfully predict 9 out of 11 rail breaks three months ahead of time with a false prediction of non-break of 8.2%. Predicting rail breaks three months ahead of time will provide railroads enough time for maintenance planning. Given the prediction results, SHAP method is employed to perform cause analysis for individual rail break. The results of cause analysis can assist railroads to plan appropriate maintenance to prevent rail breaks.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Railway Engineerin

Deep Bayesian survival analysis of rail useful lifetime

Reliable estimation of rail useful lifetime can provide valuable information for predictive maintenance in railway systems. However, in most cases, lifetime data is incomplete because not all pieces of rail experience failure by the end of the study horizon, a problem known as censoring. Ignoring or otherwise mistreating the censored cases might lead to false conclusions. Survival approach is particularly designed to handle censored data for analysing the expected duration of time until one event occurs, which is rail failure in this paper. This paper proposes a deep Bayesian survival approach named BNN-Surv to properly handle censored data for rail useful lifetime modelling. The proposed BNN-Surv model applies the deep neural network in the survival approach to capture the non-linear relationship between covariates and rail useful lifetime. To consider and quantify uncertainty in the model, Monte Carlo dropout, regarded as the approximate Bayesian inference, is incorporated into the deep neural network to provide the confidence interval of the estimated lifetime. The proposed approach is implemented on a four-year dataset including track geometry monitoring data, track characteristics data, various types of defect data, and maintenance and replacement (M&R) data collected from a section of railway tracks in Australia. Through extensive evaluation, including Concordance index (C-index) and root mean square error (RMSE) for evaluating model performance, as well as a proposed CW-index for evaluating uncertainty estimations, the effectiveness of the proposed approach is confirmed. The results show that, compared with other commonly used models, the proposed approach can achieve the best concordance index (C-index) of 0.80, and the estimated rail useful lifetimes are closer to real lifetimes. In addition, the proposed approach can provide the confidence interval of the estimated lifetime, with a correct coverage of 81% of the actual lifetime when the confidence interval is 1.38, which is more useful than point estimates in decision-making and maintenance planning of railroad systems.Railway Engineerin

Removal of organic micropollutants by well-tailored granular zeolites and subsequent ozone-based regeneration

Advanced technologies to remove organic micropollutants (OMPs) from municipal wastewater have gained much attention over the last decades. Adsorption by zeolites is one of these technologies. In this study, the regeneration performance of well-tailored granular zeolites loaded with OMPs was evaluated. The selected OMPs were categorized into three groups due to the adsorption performance: high, medium and low adsorbance. Gaseous ozone was directly applied to regenerate dried zeolite granules at an ozone concentration of 30 mg/L and a gas flow rate of 0.2 L/min (0.04 m/s). For the high and medium adsorbing OMPs, 45 min of ozonation was long enough to fully restore their adsorption capacity. For the low adsorbing OMPs, the regeneration efficiency reached 60% after 60 min of ozonation. Interestingly, their recovered adsorption capacities firstly decreased and subsequently increased along with the ozonation duration. The dramatically decrease was most probably due to the presence of the transformation products generated from the ozonation of some selected OMPs. In seven sequential adsorption-regeneration cycles, the adsorption capacity for 75% of the selected OMPs was fully recovered at an ozonation duration of 60 min in each regeneration. The assumed accumulation of the ozonation transformation products only influenced the adsorption of low adsorbing OMPs in 7 cycles.Sanitary Engineerin

TypeEvalPy: A Micro-benchmarking Framework for Python Type Inference Tools

In light of the growing interest in type inference research for Python, both researchers and practitioners require a standardized process to assess the performance of various type inference techniques. This paper introduces TypeEvalPy, a comprehensive microbenchmarking framework for evaluating type inference tools. Type- EvalPy contains 154 code snippets with 845 type annotations across 18 categories that target various Python features. The framework manages the execution of containerized tools, transforms inferred types into a standardized format, and produces meaningful metrics for assessment. Through our analysis, we compare the performance of six type inference tools, highlighting their strengths and limitations. Our findings provide a foundation for further research and optimization in the domain of Python type inference.Software Engineerin

A Self-Bias-Flip with Charge Recycle Interface Circuit with No External Energy Reservoir for Piezoelectric Energy Harvesting Array

This article presents a piezoelectric energy harvesting (PEH) interface circuit using a new self-bias-flip with the charge recycle (SBFR) technique without employing any additional energy reservoir. Traditional designs, including synchronous-switch harvesting on inductor (SSHI), synchronous-switch harvesting on capacitor (SSHC), synchronous electric charge extraction (SECE), etc., require additional capacitors or inductors to reverse the voltage on the PEH at the zero-crossing point. This design innovatively uses the inherent capacitors of the piezoelectric harvesters as the flipping capacitors. In order to improve the extract efficiency of the interface, the zero-crossing state is split into a charge recycle stage and a voltage-flip stage. For a piezoelectric array with 2^n PEHs, a configuration with (n-1) phases in the charge recycle stage is adopted to reduce the loss caused by direct charge neutralization. The charge redistribution loss is reduced by employing (2n+1) phases in the voltage-flip stage. The proposed principle has been implemented with discrete components and is verified by three different prototypes. The measurement results show that a flipping efficiency of 67% is achieved by utilizing SBFR with four PEHs. And the proposed interface can provide up to 5.2x improvement when compared with the full-bridge rectifier (FBR).Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Electronic Instrumentatio