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Impact of p-cresol on hydrogen sulfide and ammonia treatment by biotrickling filter and the production of nitrous oxide

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Chemosphere 2024年 361卷 142568页

作者： Kong, Xianwang Ying, Shihao Cai, Zhen Du, Jianghui Chen, Dongzhi Liu, Dezhao Institute of Agricultural Bio-Environmental Engineering College of Biosystems Engineering and Food Science Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control School of Petrochemical Engineering & Environment Zhejiang Ocean University Zhoushan316004 China Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture from Ministry of Agriculture and Rural Affairs of China Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province Hangzhou310058 China

Biotrickling filter (BTF) is often used for purification of waste gas from swine houses, with vital information still needed regarding interaction effects among multiple gas pollutants removal and also the formation of byproducts especially nitrous oxide (N2O, a strong greenhouse gas) due to the relative high NH3 concentration level compared to other gases. In this study, gas removal and N2O production were compared between two BTFs, where the inlet gas of BTF-1 contained NH3 and H2S while p-cresol was additionally supplied to BTF-2. At inlet load (IL) between 3.67 and 18.91 g m−3 h−1, removal efficiencies of NH3 exceeded 95% for both BTFs. As alternative strategy, adding thiosulfate improved H2S removal. Interestingly, presence of p-cresol to some extent promoted H2S removal at IL of 0.56 g m−3 h−1possibly due to effect on pH value of circulating solution. Similar to NH3, removal efficiencies of p-cresol were higher than 95% at an average IL of 2.98 g m−3 h−1. Gas residence time, pH of circulating solution and inlet loading were identified as key factors affecting BTF performance, but the response of individual gas compound to these factors was not consistent. Overall, p-cresol enhanced N2O generation although the effects were not always significant. High-throughput sequencing results showed that Proteobacteria accounted for the largest proportion of relative abundance and BTF-2 had much richer microbial diversity compared to BTF-1. Thermomonas, Comamonas, Rhodanobacter and other bacterial genus capable of denitrification were detected in both BTFs, and their corresponding abundances in BTF-2 (10.9%, 8.7% and 5.2%) were all greater than those in BTF-1 (0.4%, 0.3% and 2.0%), indicating that more denitrification may occur within BTF-2 and higher N2O could have been generated. This study provided evidence that organic gas components, served as carbon source, may increase the N2O production from BTF when treating waste gases containing NH3. © 2024

关键词： Denitrification

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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-Objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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Chinese control Conference (CCC)

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China System Integration and Industrial Robotics Laboratory Wuhan P. R. China

The rational design of weighting factors in the cost function for finite control set model predictive torque control (FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor (PMSM) based on the adaptive multi-objective black hole algorithm (AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multi-objective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

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Study on Mechanical Properties of Dual-Channel Cryogenic 3d Printing Scaffold for Mandibular Defect Repair

SSRN

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SSRN 2023年

作者： Gao, Lilan Sun, Mengchao Liu, Jie Meng, Lulu Liu, Han Zheng, Xiongfei Liu, Hao Li, Ruixin Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300382 China Tianjin300382 China Beijing Friendship Hospital Capital Medical University 100050 China State Key Laboratory of Robotics Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110017 China Tianjin Key Laboratory of Oral and Maxillofacial Function Reconstruction Tianjin Stomatological Hospital The Affiliated Stomatological Hospital of Nankai University Tianjin300041 China

Mandibular defect repair has always been a clinical challenge, facing technical bottleneck. The new materials directly affect technological breakthroughs in mandibular defect repair field. Our aim is to fabricate a scaffold for mandibular defect repairing with advanced biomaterials. Therefore, a novel dual-channel printed scaffold consisting of silk fibroin/collagen type I /hydroxyapatite (SCH) and polycaprolactone/hydroxyapatite (PCL/HA) was fabricated by 3D printing technology with double nozzles. The mechanical properties and behaviors of dual-channel printed scaffold were investigated by performing uniaxial compression, creep, stress relaxation and ratcheting experiments respectively. The experiments indicated the Young modulus of dual-channel printed scaffold was 60.1 kPa and dual-channel printed scaffold was typical nolinear viscoelastic consistent with bone tissue. Finite Element Analysis (FEA) was employed performing a numerical simulation to evaluate the implantation effect in mandible. The stress distribution of the contact area between scaffold and defect was uniform, the maximum Mises stress of cortical bone and cancellous bone in defect area were 54.52MPa and 3.196MPa, and the maximum displacement of cortical bone and cancellous bone in defect area were 0.1575mm and 0.1555mm, which were distributed in the incisor region. The peak value of the maximum Mises stress for the scaffold implanted was 3.128× MPa and the maximum displacement was 6.453×mm distributed near incisor area. The displacement distribution of the scaffold was consistent with that of cortical and cancellous bone. The scaffold recovered well when the force applied on it disappeared. Above all, dual-channel printed scaffold had excellent bio-mechanical properties in implanting mandible, which provides a new idea for the reconstruction of irregular bone defects in the mandible and has good clinical development prospects. © 2023, The Authors. All rights reserved.

关键词： 3D printing

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Polytope Volume Monitoring Problem: Formulation and Solution via Parametric Linear Program Based control Barrier Function

arXiv

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arXiv 2025年

作者： Wu, Shizhen Dong, Jinyang Fang, Xu Sun, Ning Fang, Yongchun Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China The Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China

Motivated by the latest research on feasible space monitoring of multiple control barrier functions (CBFs) as well as polytopic collision avoidance, this paper studies the Polytope Volume Monitoring (PVM) problem, whose goal is to design a control law for inputs of nonlinear systems to prevent the volume of some state-dependent polytope from decreasing to zero. Recent studies have explored the idea of applying Chebyshev ball method in optimization theory to solve the case study of PVM;however, the underlying difficulties caused by nonsmoothness have not been addressed. This paper continues the study on this topic, where our main contribution is to establish the relationship between nonsmooth CBF and parametric optimization theory through directional derivatives for the first time, so as to solve PVM problems more conveniently. In detail, inspired by Chebyshev ball approach, a parametric linear program (PLP) based nonsmooth barrier function candidate is established for PVM, and then, sufficient conditions for it to be a nonsmooth CBF are proposed, based on which a quadratic program (QP) based safety filter with guaranteed feasibility is proposed to address PVM problems. Finally, a numerical simulation example is given to show the efficiency of the proposed safety filter. © 2025, CC BY.

关键词： Chebyshev filters

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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Chinese control Conference (CCC)

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China System Integration and Industrial Robotics Laboratory Wuhan P. R. China

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network (TL-DNN) was proposed in this paper. Its specific implementation steps include (1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor; (2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models (random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance

arXiv

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arXiv 2022年

作者： Wang, Guangming Feng, Zhiheng Jiang, Chaokang Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China

Scene flow represents the 3D motion of each point in the scene, which explicitly describes the distance and the direction of each point’s movement. Scene flow estimation is used in various applications such as autonomous driving fields, activity recognition, and virtual reality fields. As it is challenging to annotate scene flow with ground truth for real-world data, this leaves no real-world dataset available to provide a large amount of data with ground truth for scene flow estimation. Therefore, many works use synthesized data to pre-train their network and real-world LiDAR data to finetune. Unlike the previous unsupervised learning of scene flow in point clouds, we propose to use odometry information to assist the unsupervised learning of scene flow and use real-world LiDAR data to train our network. Supervised odometry provides more accurate shared cost volume for scene flow. In addition, the proposed network has mask-weighted warp layers to get a more accurate predicted point cloud. The warp operation means applying an estimated pose transformation or scene flow to a source point cloud to obtain a predicted point cloud and is the key to refining scene flow from coarse to fine. When performing warp operations, the points in different states use different weights for the pose transformation and scene flow transformation. We classify the states of points as static, dynamic, and occluded, where the static masks are used to divide static and dynamic points, and the occlusion masks are used to divide occluded points. The mask-weighted warp layer indicates that static masks and occlusion masks are used as weights when performing warp operations. Our designs are proved to be effective in ablation experiments. The experiment results show the promising prospect of an odometry-assisted unsupervised learning method for 3D scene flow in real-world data. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Low-Power Heterogeneous Networking Method Based on NB-IoT and WSN

Low-Power Heterogeneous Networking Method Based on NB-IoT an...

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International Conference on Communication Engineering and Technology (ICCET)

作者： Mo Guan Liangliang Qi Xi Jin Suoling Guan Changqing Xia Chi Xu Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

Digital oilfields generally adopt the multi-hop technology to expand the network scale, and the data generated depends on the large-scale wireless sensor network (WSN) for transmission. However, multi-hop communication will lead to uneven distribution of network energy consumption and premature death of nodes. To prolong the network lifetime of digital oilfields, we extended the narrowband internet of things (NB-IoT) module on some WSN nodes deployed on site to form heterogeneous nodes. Through the heterogeneity of NB-IoT and WSN, the nodes could transmit as little data as possible from other nodes, fundamentally reducing the node energy consumption. Furthermore, an optimization method was proposed that could simultaneously optimize the network lifetime and node expansion cost. The experimental results revealed that the proposed algorithm could greatly prolong the network lifetime while only introducing a small number of nodes.

关键词： Wireless sensor networks Energy consumption Costs Optimization methods Spread spectrum communication Internet of Things Narrowband

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Trajectory planning of a manipulator based on the DDPG algorithm

Trajectory planning of a manipulator based on the DDPG algor...

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2022 International Workshop on Automation, control, and Communication Engineering, IWACCE 2022

作者： Jing, Xiangyu Zang, Chuanzhi Liu, Yuqi Liu, Yu Xia, Changqing Shenyang University of Technology No. 111 Shenliao West Road Economic and Technological Development Zone Shenyang110870 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9781510660915

Trajectory planning is one of the key technologies in the manipulator motion system, and the effectiveness of the manipulator as a whole is directly influenced by this technology. Thus, accurate and effective trajectory planning is crucial. In this study, a reinforcement learning system is used to plan a manipulator's trajectory. A trajectory planning of a manipulator based on the deep deterministic policy gradient (DDPG) method, which is easier to converge on than the Actor-Critic (AC) approach, is proposed in order to address the issue that the AC algorithm is difficult to converge on. A guiding reward function is provided in the reward function area to address the issue that the manipulator can't contact the target rapidly due to blind exploration. This can help the manipulator touch the target more quickly. In this paper, a twodegree of freedom manipulator is selected as the experimental object. The obstacle avoidance reward function based on the AC algorithm, the obstacle avoidance reward function based on the DDPG algorithm, and the obstacle avoidance reward function and guiding reward function based on the DDPG algorithm are compared. The findings demonstrate that the DDPG method converges easier than the AC algorithm, and that the directed reward function can help the manipulator avoid obstacles. © 2022 SPIE. All rights reserved.

关键词： Manipulators

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Temporally-Adaptive Models for Efficient Video Understanding

arXiv

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arXiv 2023年

作者： Huang, Ziyuan Zhang, Shiwei Pan, Liang Qing, Zhiwu Zhang, Yingya Liu, Ziwei Ang, Marcelo H. Advanced Robotics Centre National University of Singapore Singapore DAMO Academy Alibaba Group China S-Lab National Technological University Singapore Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science China

Spatial convolutions are extensively used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in different frames. This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modeling complex temporal dynamics in videos. Specifically, TAdaConv empowers spatial convolutions with temporal modeling abilities by calibrating the convolution weights for each frame according to its local and global temporal context. Compared to existing operations for temporal modeling, TAdaConv is more efficient as it operates over the convolution kernels instead of the features, whose dimension is an order of magnitude smaller than the spatial resolutions. Further, kernel calibration brings an increased model capacity. Based on this readily plug-in operation TAdaConv as well as its extension, i.e., TAdaConvV2, we construct TAdaBlocks to empower ConvNeXt and Vision Transformer to have strong temporal modeling capabilities. Empirical results show TAdaConvNeXtV2 and TAdaFormer perform competitively against state-of-the-art convolutional and Transformer-based models in various video understanding benchmarks. Our codes and models are released at: https://***/alibaba-mmai-research/TAdaConv. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Intracranial Photodynamic Therapy Strategy Based Magnetic Continuum Robots for Glioblastoma Management

SSRN

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SSRN 2024年

作者： Wu, Junfeng Li, Na Lin, Daojing Zhou, Junjian Song, Yingqiu Liu, Lianqing Jiao, Niandong School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang110168 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Radiotherapy Cancer Hospital of China Medical University Shenyang110042 China

Glioblastoma multiforme (GBM), a malignant brain tumor, poses a significant global health challenge because of its high invasiveness and resistance to treatment. Photodynamic therapy (PDT), with its high spatial-temporal selectivity, holds promise for GBM treatment, offering strong local selectivity, minimal systemic side effects, and low resistance. However, delivering photosensitizers across the blood-brain barrier and precisely targeting brain tumors through the skull with a laser present critical challenges to clinical application. Addressing these challenges, the soft, continuous structure and remote magnetic control of magnetic continuum robots (MCRs) offer new possibilities for precise delivery to GBM. Here, we propose an intracranial PDT strategy using MCRs, featuring a magnetic control model based on Gaussian Process Regression and a computer vision-based closed-loop control algorithm. This approach enables automatic delivery of nanomedicines and localized laser irradiation, characterized by simplicity, minimal data needs, and robustness. Additionally, under the combined effects of photodynamic nanoparticles (PNPs) and a laser, both U87 and GL261 cells exhibit high mortality rates, which increase with the concentration of PNPs, laser energy, and duration of exposure. In conclusion, this strategy overcomes PDT's limitations to superficial lesions, offers a novel GBM treatment approach, and constitutes a key milestone for using PDT in GBM treatment. © 2024, The Authors. All rights reserved.

关键词： Photodynamic therapy

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