Ozonation of trace organic compounds in different municipal and industrial wastewaters : kinetic-based prediction of removal efficiency and ozone dose requirements

For the wide application of ozonation in (industrial and municipal) wastewater treatment, prediction of trace organic compounds (TrOCs) removal and evaluation of energy requirements are essential for its design and operation. In this study, a kinetics approach, based on the correlation between the second order reaction rate constants of TrOCs with ozone and hydroxyl radicals ((OH)-O-center dot) and the ozone and (OH)-O-center dot exposure (i.e., integral (sic)O-3(sic)dt and integral [(OH)-O-center dot]dt, which are defined as the time integral concentration of O-3 and (OH)-O-center dot for a given reaction time), was validated to predict the elimination efficiency in not only municipal wastewaters but also industrial wastewaters. Two municipal wastewater treatment plant effluents from Belgium (HB-effluent) and China (QG-effluent) and two industrial wastewater treatment plant effluents respectively from a China printing and dyeing factory (PD-effluent) and a China lithium-ion battery factory (LZ-effluent) were used for this purpose. The (OH)-O-center dot scavenging rate from the major scavengers (namely alkalinity, effluent organic matter (EfOM) and NO2-) and the total (OH)-O-center dot scavenging rate of each effluent were calculated. The various water matrices and the (OH)-O-center dot scavenging rates resulted in a difference in the requirement for ozone dose and energy for the same level of TrOCs elimination. For example, for more than 90% atrazine (ATZ) abatement in HB-effluent (with a total (OH)-O-center dot scavenging rate of 1.9 x 10(5) s(-1)) the energy requirement was 12.3 x 10(-2) kWh/m(3), which was lower than 30.1 x 10(-2) kWh/m(3) for PD-effluent (with the highest total (OH)-O-center dot scavenging rate of 4.7 x 10(5) s(-1)). Even though the water characteristics of selected wastewater effluents are quite different, the results of measured and predicted TrOCs abatement efficiency demonstrate that the kinetics approach is applicability for the prediction of target TrOCs elimination by ozonation in both municipal and industrial wastewater treatment plant effluents

Hydrolytic denitrification and decynidation of acrylonitrile in wastewater with Arthrobacter nitroguajacolicus ZJUTB06-99

Abstract Acrylonitrile (C3H3N) widely used in chemical raw materials has biological toxicity with –CN bond, so it is the key to removal of cyanide from acrylonitrile wastewater. In our previous research and investigation, a strain was identified as Arthrobacter nitroguajacolicus named ZJUTB06-99 and was proved to be capable of degrading acrylonitrile. In this paper, the strain ZJUTB06-99 was domesticated with acrylonitrile-containing medium and its decyanidation and denitrification in simulated acrylonitrile wastewater were studied. The intermediate product of acrylonitrile in degradation process was identified through gas chromatography–mass spectrometer, as well as the biodegradation pathway of acrylonitrile in wastewater was deduced tentatively. The kinetics equation of biodegradation of acrylonitrile was lnC = − 0.1784t + 5.3349, with the degradation half-life of acrylonitrile in wastewater by 3.885 h. The results of this study showed that the optimum levels of temperature, pH and bacteria concentration to attain the maximum biodegradation were obtained as 30 °C, 6 and 100 g/L, respectively. The disadvantages of the biodegradation with this strain and its possible enhanced method to degrade acrylonitrile in wastewater were also discussed

Structured Light Three-Dimensional Measurement Based on Machine Learning

The three-dimensional measurement of structured light is commonly used and has widespread applications in many industries. In this study, machine learning is used for structured light 3D measurement to recover the phase distribution of the measured object by employing two machine learning models. Without phase shift, the measurement operational complexity and computation time decline renders real-time measurement possible. Finally, a grating-based structured light measurement system is constructed, and machine learning is used to recover the phase. The calculated phase of distribution is wrapped in only one dimension and not in two dimensions, as in other methods. The measurement error is observed to be under 1%

Removal of nitrogen components, bulk organics, and fluorophores during one-stage partial nitrification-Anammox treatment of landfill leachate

Anammox-based processes have been intensively studied for the nitrogen removal from leachate, but less attention was paid to monitoring the evolution of leachate organics. In this study, fluorescence Excitation-Emission Matrix (EEM) measurement coupling with Parallel Factor Analysis (PARAFAC) analysis was used to supplement the routine monitoring in order to reveal more insights into pollutants removal during the leachate treatment by a one-stage partial nitrification-Anammox process (PNA). During this PNA process, up to 96% of total nitrogen and 43% of COD were removed, indicating the potential of PNA process to simultaneously remove nitrogen and organics. Fluorescence intensities and ratios clearly showed the dynamics of influent fluorophores during seasonal variations, where a high amount of protein-like compound was observed during summer months. Protein-like compound was preferentially removed (43-63%) in the PNA process, whereas humic/fulvic-like compounds exhibited recalcitrance to biodegradation. The increase of oxygen supply promoted protein-like compound degradation, which could be associated with the aerobic oxidation pathway. Furthermore, the protein-like compound removal was linearly correlated with the reduction of NH4+-N at limited oxygen conditions (i.e., air flow rate <= 1.6 L-gas/h/L-reactor) = 0.97, n = 65). The fluorescence directly extracted at Ex/Em:230 nm/345 nm was strongly correlated with the biodegradable chemical oxidation demand (BCOD) (r = 0.92, n = 204), showing its high potential as an indicator for biodegradable organics. Given the relatively weak correlation between fluorescence parameters and COD, EEM coupled with multivariate partial least squares (PLS) modeling was proposed and exhibited good predictive power for COD, as exemplified by a mean error of 4.5%

An Efficient CNN Inference Accelerator Based on Intra- and Inter-Channel Feature Map Compression

Deep convolutional neural networks (CNNs) generate intensive inter-layer data during inference, which results in substantial on-chip memory size and off-chip bandwidth. To solve the memory constraint, this paper proposes an accelerator adopted with a compression technique that can reduce the inter-layer data by removing both intra- and inter-channel redundant information. Principal component analysis (PCA) is utilized in the compression process to concentrate inter-channel information. The spatial differences, truncation, and reconfigurable bit-width coding are implemented inside every feature map to eliminate the intra-channel data redundancy. Moreover, a particular data arrangement is introduced to enhance data continuity to optimize PCA analysis and improve compression performance. A CNN accelerator with the proposed compression technique is designed to support the on-the-fly compression process by pipelining the reconstruction, CNN computation, and compression operation. The prototype accelerator is implemented using 28-nm CMOS technology. It achieves 819.2GOPS peak throughput and 3.75TOPS/W energy efficiency with 218.5mW. Experiments show that the proposed compression technique achieves a compression ratio of 21.5%~43.0% (8-bit mode) and 9.8%~19.3% (16-bit mode) on state-of-the-art CNNs with a negligible accuracy loss. </p

Characterization of landfill leachate by spectral-based surrogate measurements during a combination of different biological processes and activated carbon adsorption

Surrogate measurements based on excitation-emission matrix fluorescence spectra (EEMs) and ultraviolet-visible absorption spectra (UV-vis) were used to monitor the evolution of dissolved organic matter (DOM) in landfill leachate during a combination of biological and physical-chemical treatment consisting of partial nitritation-anammox (PN-Anammox) or nitrification-denitrification (N-DN) combined with granular active carbon adsorption (GAC). PN-Anammox resulted in higher nitrogen removal (81%), whereas N-DN required addition of an external carbon source to increase nitrogen removal from 24% to 56%. Four DOM components (C1 to C4) were identified by excitation-emission matrix-parallel factor analysis (EEM-PARAFAC). N-DN showed a greater ability to remove humic-like components (C1 and C3), while the protein-like component (C4) was better removed by PN-Anammox. Both biological treatment processes showed limited removal of the medium molecular humic-like component (C2). In addition, the synergistic effect of biological treatments and adsorption was studied. The combination of PN-Anammox and GAC adsorption could remove C4 completely and also showed a good removal efficiency for C1 and C2. The Thomas model of adsorption revealed that GAC had the maximum adsorption capacity for PN-Anammox treated leachate. This study demonstrated better removal of nitrogen and fluorescence DOM by a combination of PN-Anammox and GAC adsorption, and provides practical and technical support for improved landfill leachate treatment