Kinetics of pentachlorophenol co-metabolism removal by micro-aeration sequencing batch reactor process

Jianhui Wang,Guolong Xie,Xin Qi,Ruifeng Ming,Bin Zhang,Hai Lu 한국화학공학회 2022 Korean Journal of Chemical Engineering Vol.39 No.6

Four carbon sources (including trehalose, glucose, acetic acid, and yeast extract) were used as the co-metabolicmatrix of pentachlorophenol (PCP). The effect of the carbon sources on the process of acclimatization and degradationof PCP was investigated. The acclimatization rate of carbon sources with different substrates, the activities ofmicrobial enzymes in the co-metabolism process, and the control of co-metabolism reaction conditions were evaluated. The kinetic model of co-metabolic degradation of PCP in micro aerated sequencing batch reactor (SBR) wasestablished based on the Monod equation. The model was applied to fit the operating conditions of the micro aeratedSBR process in this study. The experimental results showed that the type and concentration of metabolic matrix greatlyinfluenced the degradation rate of PCP, and its trehalose, glucose, and acetic acid enhanced the degradation of PCP. Inparticular, the strengthening effect of trehalose was pronounced. When trehalose was used as a co-metabolic carbonsource, the time required for PCP degradation to a predetermined degree was shortened to one-fifth of the original,PCP removal rate exceeded 95%. At the same time, yeast extract inhibited the biodegradation of PCP when it was usedas an additional matrix carbon source. After the co-metabolism carbon source was added to the system, the proliferationrate of the microorganism was increased, and the key enzymes of PCP degradation were induced in the system. When the co-metabolic carbon source concentration was high, it accelerated active enzymes’ induction and maintainedhigh activity; 2,3,5-triphenyltetrazolium chloride-electron transport system (TTC-ETS) activity reached about 7.6mgTF/(gTSS·H), and 2-(p-iodophenyl)-3-(p-nitrophenyl)-5-phenyl Tetrazolium chloride-electron transport system(INT-ETS) activity reached 63.5mgINTF/(gTSS·H). When the concentration of co-metabolism carbon source wasextremely high, the co-degradation of toxic organic compounds was inhibited, leading to a decrease in the co-degradationrate. The kinetic model optimized the co-metabolism substrate. The degradation rate of PCP was increased by54.9% by micro-aeration-co-metabolism. The kinetic model was used to fit the microaerobic reaction process of microaeration SBR. The relevant result was in agreement with the experimental result by 97.6%.

A deep and multiscale network for pavement crack detection based on function-specific modules

Allen A. Zhang,Guolong Wang,Kelvin C. P. Wang,Guangwei Yang 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.3

Using 3D asphalt pavement surface data, a deep and multiscale network named CrackNet-M is proposed in this paper for pixel-level crack detection for improvements in both accuracy and robustness. The CrackNet-M consists of four function-specific architectural modules: a central branch net (CBN), a crack map enhancement (CME) module, three pooling feature pyramids (PFP), and an output layer. The CBN maintains crack boundaries using no pooling reductions throughout all convolutional layers. The CME applies a pooling layer to enhance potential thin cracks for better continuity, consuming no data loss and attenuation when working jointly with CBN. The PFP modules implement direct down-sampling and pyramidal upsampling with multiscale contexts specifically for the detection of thick cracks and exclusion of non-crack patterns. Finally, the output layer is optimized with a skip layer supervision technique proposed to further improve the network performance. Compared with traditional supervisions, the skip layer supervision brings about not only significant performance gains with respect to both accuracy and robustness but a faster convergence rate. CrackNet-M was trained on a total of 2,500 pixel-wise annotated 3D pavement images and finely scaled with another 200 images with full considerations on accuracy and efficiency. CrackNet-M can potentially achieve crack detection in real-time with a processing speed of 40 ms/image. The experimental results on 500 testing images demonstrate that CrackNet-M can effectively detect both thick and thin cracks from various pavement surfaces with a high level of Precision (94.28%), Recall (93.89%), and F-measure (94.04%). In addition, the proposed CrackNet-M compares favorably to other well-developed networks with respect to the detection of thin cracks as well as the removal of shoulder drop-offs.

Multi-focus Image Fusion using Fully Convolutional Two-stream Network for Visual Sensors

( Kaiping Xu ),( Zheng Qin ),( Guolong Wang ),( Huidi Zhang ),( Kai Huang ),( Shuxiong Ye ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.5

We propose a deep learning method for multi-focus image fusion. Unlike most existing pixel-level fusion methods, either in spatial domain or in transform domain, our method directly learns an end-to-end fully convolutional two-stream network. The framework maps a pair of different focus images to a clean version, with a chain of convolutional layers, fusion layer and deconvolutional layers. Our deep fusion model has advantages of efficiency and robustness, yet demonstrates state-of-art fusion quality. We explore different parameter settings to achieve trade-offs between performance and speed. Moreover, the experiment results on our training dataset show that our network can achieve good performance with subjective visual perception and objective assessment metrics.

Synthesis and Characterization of Magnetic Nanoparticles and Its Application in Lipase Immobilization

Jiakun Xu,Caixia Ju,Jun Sheng,Fang Wang,Quan Zhang,Guolong Sun,Mi Sun 대한화학회 2013 Bulletin of the Korean Chemical Society Vol.34 No.8

We demonstrate herein the synthesis and modification of magnetic nanoparticles and its use in the immobilization of the lipase. Magnetic Fe3O4 nanoparticles (MNPs) were prepared by simple co-precipitation method in aqueous medium and then subsequently modified with tetraethyl orthosilicate (TEOS) and 3- aminopropyl triethylenesilane (APTES). Silanization magnetic nanoparticles (SMNP) and amino magnetic nanomicrosphere (AMNP) were synthesized successfully. The morphology, structure, magnetic property and chemical composition of the synthetic MNP and its derivatives were characterized using transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR) analysis, X-ray diffraction, superconducting quantum interference device (SQUID) and thermogravimetric analyses (TGA). All of these three nanoparticles exhibited good crystallization performance, apparent superparamagnetism, and the saturation magnetization of MNP, SMNP, AMNP were 47.9 emu/g, 33.0 emu/g and 19.5 emu/g, respectively. The amino content was 5.66%. The AMNP was used to immobilize lipase, and the maximum adsorption capacity of the protein was 26.3 mg/g. The maximum maintained activity (88 percent) was achieved while the amount of immobilized lipase was 23.7 mg g−1. Immobilization of enzyme on the magnetic nanoparticles can facilitate the isolation of reaction products from reaction mixture and thus lowers the cost of enzyme application.

Synthesis and Characterization of Magnetic Nanoparticles and Its Application in Lipase Immobilization

Xu, Jiakun,Ju, Caixia,Sheng, Jun,Wang, Fang,Zhang, Quan,Sun, Guolong,Sun, Mi Korean Chemical Society 2013 Bulletin of the Korean Chemical Society Vol.34 No.8

We demonstrate herein the synthesis and modification of magnetic nanoparticles and its use in the immobilization of the lipase. Magnetic $Fe_3O_4$ nanoparticles (MNPs) were prepared by simple co-precipitation method in aqueous medium and then subsequently modified with tetraethyl orthosilicate (TEOS) and 3-aminopropyl triethylenesilane (APTES). Silanization magnetic nanoparticles (SMNP) and amino magnetic nanomicrosphere (AMNP) were synthesized successfully. The morphology, structure, magnetic property and chemical composition of the synthetic MNP and its derivatives were characterized using transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR) analysis, X-ray diffraction, superconducting quantum interference device (SQUID) and thermogravimetric analyses (TGA). All of these three nanoparticles exhibited good crystallization performance, apparent superparamagnetism, and the saturation magnetization of MNP, SMNP, AMNP were 47.9 emu/g, 33.0 emu/g and 19.5 emu/g, respectively. The amino content was 5.66%. The AMNP was used to immobilize lipase, and the maximum adsorption capacity of the protein was 26.3 mg/g. The maximum maintained activity (88 percent) was achieved while the amount of immobilized lipase was 23.7 mg $g^{-1}$. Immobilization of enzyme on the magnetic nanoparticles can facilitate the isolation of reaction products from reaction mixture and thus lowers the cost of enzyme application.