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Treatment of effluent from the upflow anaerobic sludge blanket-hollow centered packed bed fermentor by utilizing Chlorella vulgaris in a fed-batch system

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Case Studies in Chemical and Environmental engineering 2024年 9卷

作者： Sidabutar, Rivaldi Trisakti, Bambang Irvan Bani, Okta Nasution, Juan Akmal Khodijah, Putri Alexander, Vikram Daimon, Hiroyuki Takriff, Mohd Sobri Department of Chemical Engineering Faculty of Engineering Universitas Sumatera Utara Indonesia Department of Applied Chemistry and Life Science Toyohashi University of Technology Toyohashi 441-8580 Japan Department of Mechanical and Nuclear Engineering College of Engineering University of Sharjah United Arab Emirates

Palm oil mill effluent (POME) has high levels of contaminants, one of which is Chemical Oxygen Demand (COD) of around 40,000–60,000 mg.L−1. The very high COD levels in POME are harmful in terms of toxicology and aest... 详细信息

Palm oil mill effluent (POME) has high levels of contaminants, one of which is Chemical Oxygen Demand (COD) of around 40,000–60,000 mg.L⁻¹. The very high COD levels in POME are harmful in terms of toxicology and aesthetics to the environment. POME treatment by anaerobic digestion can be carried out through the Upflow Anaerobic Sludge Blanket-Hollow Centered Packed Bed (UASB-HCPB) fermentor to produce biogas, while the liquid phase will end up as effluent. However, these effluent not met the quality threshold so further treatment is required. Aerobic process can be address this issue by utilizing microalgae. Chlorella vulgaris is a microalgae that is widely used in wastewater treatment because of its resistance to pollutants and degradation effectiveness. The purpose of this research is to treat the effluent from the UASB-HCPB fermentor by utilizing Chlorella vulgaris in a fed-batch system. The novelty of this research is the utilization of microalgae of Chlorella vulgaris in photobioreactor for treating the effluent from the UASB-HCPB fermentor so that it meets the predetermined quality standards and safe to be discharged into the environment. In this study, the culturing process of Chlorella vulgaris was carried out in photobioreactor with variations of lighting intensity (12:12) and (24:0), with and without aeration. The cultivation process was carried out in the same of the photobioreactor with variation of fed-batch system with effluent addtion of 450 ***⁻¹, 900 mL.2 day⁻¹, and 1350 mL.3 day⁻¹ with variation of agitation rate of 10 rpm, 20 rpm, 30 rpm, 50 rpm, and 100 rpm. Based on the experimental results in culturing process, it was found that the aeration and lighting process (24:0) increased the growth of Chlorella vulgaris best with an absorbance of 0.759 with the relationship between optical density and biomass dry weight with a value of (***⁻¹) = 0.008 OD + 0.0017 with R² = 0.981. The b

关键词： Chlorella vulgaris Cultivation Effluent UASB-HCPB Fed-batch

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Effects of Gamma Irradiation on the Nutritional Quality and Physicochemical Properties of Morchella Angusticeps

SSRN

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SSRN 2023年

作者： Gao, Peng Meng, Ailian Xu, Pan Jia, Xin He, Jiang Wang, Yan Chen, Qian Zhang, Yanjun Qiu, Yalu Huang, Min Chen, Hao Sichuan Institute of Atomic Energy Sichuan Chengdu610101 China Irradiation Preservation Key Laboratory of Sichuan Province Sichuan Chengdu610101 China College of Life Science and Engineering Southwest University of Science and Technology Sichuan Mianyang621000 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China

This study aimed to evaluate the effects of gamma irradiation on the storage quality of Morchella angusticeps, specifically focusing on nutritional quality and physicochemical properties. The mushrooms were exposed to doses of 1, 2, 3, and 4 kGy, and their properties were analyzed under storage conditions of 4°C. The results indicated that different irradiation doses had varying effects on the nutritional quality and physicochemical properties of Morchella angusticeps during storage. At doses of 1 and 2 kGy, the content of phenolic compounds increased, while the activity of peroxidase and catalase decreased. Consequently, browning and softening were effectively delayed. Furthermore, with increased storage time, soluble protein content exhibited a downward trend. However, the 1 and 2 kGy irradiation doses effectively reduced the decomposition of soluble protein. As the irradiation dose increased, the content of aldehydes initially increased and then decreased. In addition, the fatty acid content exhibited an increase at irradiation doses of 1 and 2 kGy. During the storage period, the release of unsaturated fatty acids was promoted, particularly in the later stages. Furthermore, the shrinkage of Morchella angusticeps increased over time. However, a dose of 1 kGy was found to effectively maintain cell morphology. The experimental results demonstrated that treatment with 1 and 2 kGy irradiation doses effectively delayed the quality loss of the fruiting body and inhibited browning-related enzyme activities. This study highlights the potential of irradiation as a method to preserve the storage quality of Morchella angusticeps. © 2023, The Authors. All rights reserved.

关键词： Irradiation

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Interval Optimization for Integrated Demand Response in A Battery-Equipped Household Multi-Energy System 5

Interval Optimization for Integrated Demand Response in A Ba...

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5th Asian Conference on Artificial Intelligence technology, ACAIT 2021

作者： Hu, Diankun Su, Yongxin Tan, Mao Hunan National Center for Applied Mathematics Xiangtan China College of Automation and Electronic Information Engineering Xiangtan University Xiangtan China Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Xiangtan China

ISBN: (纸本)9781665426305

The promotion of gas-electric devices and electricity storage devices brings necessity to realize integrated demand response (IDR) for household multi-energy systems (HMES). This paper proposed an interval optimization method for load scheduling in HMES for IDR, which addresses uncertainties in the system model and considers dynamic energy-source switching among gas, electricity and storage. Controllable loads include washing machine, air conditioner and battery as electric devices, and water heater and kitchenware as gas-electric devices. For minimizing energy cost, an interval optimization model with tolerance degree is converted into a deterministic model based on the interval order relationship and interval probability. Then genetic algorithm is applied for solving the deterministic problem. Case studies show that the interval optimization with tolerance method is robust to HMES uncertainty. For a battery-equipped HMES, the energy cost savings are up to 14.7% compared with the traditional method without considering interval optimization and tolerance degree. © 2021 IEEE.

关键词： Genetic algorithms

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification

A Novel Attention-based Explainable Deep Learning Framework ...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Gladys W. Muoka Ding Yi Chiagoziem Chima Ukwuoma Moindi Duke Martin Ahmet Arif Aydin Mugahed A. Al-Antari School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China Department of Computer Science Unicaf University Lilongwe Lilongwe Malawi Department of Computer Engineering Faculty of Engineering İnönü University Turkey Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul Korea

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study’s primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics.

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Evaluation of the Fitting-Based Rpt Method for Double Heterogeneity Phenomenon of Plate-Type Fuel in Pwr

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SSRN 2024年

作者： Li, Jiannan Chen, Ling Li, Song Zhang, Yongfa Hao, Jianli Zhang, Yunfei Chen, Guannan College of Nuclear Science and Technology Naval University of Engineering Wuhan430033 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201204 China School of Foreign Languages Central China Normal University Wuhan430079 China

The RPT method transforms the double heterogeneity problem into the traditional single heterogeneity problem, which has the advantages of simplicity and efficiency. However, the current RPT methods mainly focus on rod-type fuel elements rather than plate-type elements. Therefore, an optimization research is conducted for the RPT method applicable to fuel plates containing dispersed particles in this paper. First, a quantitative method is used to assess the sensitivity of the fuel plate parameters to the RPT model, and three important geometrical parameters are identified in relation to the equivalent side length. Next, a rapid method for fitting equivalent side lengths is proposed. It allows direct modeling of the RPT by fitting formula so that the results can be quickly computed with conventional neutronics computational program. Finally, the numerical validation indicates that the fitted RPT method achieves high accuracy for typical plate element dispersed particle fuel problems. © 2024, The Authors. All rights reserved.

关键词： Numerical methods

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Data Enhancement technology Based on I-GOS Algorithm

Data Enhancement Technology Based on I-GOS Algorithm

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2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

作者： Li, Fei Deng, Xiaotong Shi, Feifei Zhou, Xiaoxue Xia, Kaijun Hu, Guangdi Mechanical Engineering Southwest Jiaotong University Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China School of Nuclear Science and Technology Lanzhou University Lanzhou China Jiangsu Xintongda Electronic Technology Co. Ltd Jiangsu China Zhejiang Geely New Energy Commercial Vehicle Group Co. Ltd Zhejiang China

ISBN: (纸本)9781665437554

Although deep learning is developing rapidly and being applied on a large scale, its essence is still a black box model. When evaluating the credibility of the model, the judgment for the prediction of the model is important. Based on the I-GOS algorithm in the field of visual interpretation, we effectively extract the effective information of the sample and then use the sample to retrain a new model to improve the accuracy of the model. Thus, a new data enhancement method is proposed to train the algorithm by using the ImageNet dataset and retrain it by using the I-GOS algorithm to extract effective information. The experiment result shows that compared with the original model, the accuracy of the new model is improved by 5% in the top 1 and 3% in the top 5, which fully proves the effectiveness of this data enhancement method. © 2021 IEEE.

关键词： Image enhancement

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Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System

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Journal of Modern Power Systems and Clean Energy 2021年第2期9卷 285-294页

作者： Jon Martinez-Rico Ekaitz Zulueta Ismael Ruiz de Argandona Unai Femandez-Gamiz Mikel Armendia The Automation and Control Unit Fundacion TeknikerBasque Research and Technology Alliance(BRTA)Ifiaki Goenaga520600 the University of the Basque Country Ing.Torres Quevedo148013 BilbaoSpain the Department of Systems Engineering and Control College of Engineering at Vitoria-GasteizUniversity of the Basque CountryNieves Cano1201006 Vitoria-GasteizSpain the Department of Nuclear and Fluid Mechanics College of Engineering at Vitoria-GasteizUniversity of the Basque CountryNieves Cano1201006 Vitoria-GasteizSpain

Considering the increasing integration of renewable energies into the power grid,batteries are expected to play a key role in the challenge of compensating the stochastic and intermittent nature of these energy ***,the deployment of batteries can increase the benefits of a renewable power *** way to increase the profits with batteries studied in this paper is performing energy *** strategy is based on storing energy at low electricity price moments and selling it when electricity price is *** this paper,a hybrid renewable energy system consisting of wind and solar power with batteries is studied,and an optimization process is conducted in order to maximize the benefits regarding the dayahead production scheduling of the plant.A multi-objective cost function is proposed,which,on the one hand,maximizes the obtained profit,and,on the other hand,reduces the loss of value of the battery.A particle swarm optimization algorithm is developed and fitted in order to solve this non-linear multi-objective *** the aim of analyzing the importance of considering both the energy efficiency of the battery and its loss of value,two more simplified cost functions are *** show the importance of including the energy efficiency in the cost function to ***,it is proven that the battery lifetime increases substantially by using the multi-objective cost function,whereas the profitability is similar to the one obtained in case the loss of value is not ***,due to the small difference in price among hours in the analyzed Iberian electricity market,it is observed that low profits can be provided to the plant by using batteries just for arbitrage purposes in the day-ahead market.

关键词： Battery energy storage system energy arbitrage hybrid renewable energy system particle swarm optimization

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Simple Online Real-Time Tracking Algorithm with Improved YOLOV4 as Extractor

Simple Online Real-Time Tracking Algorithm with Improved YOL...

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2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

作者： Li, Fei Deng, Xiaotong Shi, Feifei Zhou, Xiaoxue Xia, Kaijun Hu, Guangdi Mechanical Engineering Southwest Jiaotong University Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China School of Nuclear Science and Technology Lanzhou University Chengdu China Jiangsu Xintongda Electronic Technology Co. Ltd Jiangsu China Zhejiang Geely New Energy Commercial Vehicle Group Co. Ltd Zhejiang China

ISBN: (纸本)9781665437554

Multiple target tracking algorithm is an important research field of computer vision. We improve the YOLOV4 algorithm by using the MatrixNets feature extraction module to obtain a better target detection and feature extractor and then uses the improved YOLOV4 algorithm to improve the deep sort algorithm to improve the performance of the algorithm to propose the Deep SORT-Y algorithm. The Deep SORT-Y algorithm can track the lost target object through a longer occlusion period, which greatly reduces the target loss rate. Compared with sort and deep sort algorithms, the score is reduced by 61 and 18%. Experimental evaluation shows that this algorithm still maintains the overall competitive advantage compared with other online real-time tracking algorithms at a high frame rate. © 2021 IEEE.

关键词： Target tracking

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Research on Risk Situation Awareness and Prediction Method of Industrial Control Systems

Research on Risk Situation Awareness and Prediction Method o...

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System Reliability and Safety engineering (SRSE), International Conference on

作者： Chao Zhang Sijuan Chen Ming Yang Ruoyu Zhu Jiajun Sun Department of Science and Technology Innovation State Nuclear Power Automation System Engineering Company Shanghai China Institute for Advanced Study in Nuclear Energy & Safety College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen City Guangdong Province China Power Plant Control Center State Nuclear Power Automation System Engineering Company Shanghai China

In the operation process of the power plant, in addition to the operating state information of the power plant process systems and components is monitored by Industrial Control Systems (ICSs), a large number of the operating state information of ICSs and its components themselves can be obtained. These information helps operators to understand the operation safety state of ICSs themselves, and whether ICSs operate reliably, which plays an important role in maintaining the normal operation of the power plant, controlling abnormal events and mitigating the development of accidents. This paper proposes an integrated methodological framework for real-time risk awareness and prediction of ICS based on the particle filtering model whose features are updating monitoring data and predicting the state transition process, and focus on the measurement of risk states, identification of critical risk states and key components, modeling of the state transition process, prediction of risk levels and estimation of remaining arrival time of high-risk residual. The assumption that the transition probability matrix of system states does not change over time in traditional Markov predictions is discarded in this proposed method. Compared with the traditional ICS safety analysis method, this method can provide global and systemic risk insights for ICS risk situation, with a better real-time performance, and it is a kind of more scientific and objective measurement for ICS risk situation.

关键词： Process control Predictive models Particle measurements Real-time systems Data models Time measurement Safety

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Research on CFBG Hydrophone for Vocalization Sensing of Oreochromis Mossambicus During Feeding

Research on CFBG Hydrophone for Vocalization Sensing of Oreo...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Mariano Mahissi Weiming Cai Xianmin Zhang Michel Dossou Xinli Ma Bicong Zheng College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Information Science and Engineering NingboTech University Ningbo China Laboratory of Electrotechnics Telecommunications and Applied Computing (LETIA) University of Abomey-Calavi Abomey-Calavi Benin School of Electrical Engineering and Automation Jiangxi University of Science and Technology Ganzhou China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Underwater detection of acoustic pressure field in measuring vocalization of aquatic fauna, monitoring of seabed mining sites, etc., is a hot topic in fields such as aquaculture, mechanics of deep-sea fishing or aquaculture boats, deep sea mining, etc. By rigidly placing a chirped Bragg grating fiber in an aluminum cylinder structure, and adding an acrylonitrile-butadiene rubber (NBR) coating around the grating, a CFBG hydrophone is proposed. The underwater experiments show that the designed hydrophone responds via shifting its central wavelength of 4.5 pm to 113 Hz sound excitation and 8 pm to fish vocalizations during feeding. The designed sensor shows clear performance at higher order frequencies while the spectrogram of the reference hydrophone lacks clarity at certain times.

关键词： Vibrations Time-frequency analysis Echo sounders Aluminum Fiber gratings Fish Coatings

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