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A Data-Driven Based Framework of model optimization and Neural Network modeling for Microbial Fuel Cells

IEEE ACCESS 2019年 7卷 162036-162049页

作者： Ma, Fengying Yin, Yankai Pang, Shaopeng Liu, Jiaxun Chen, Wei Qilu Univ Technol Sch Elect Engn & Automat Jinan 250353 Shandong Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Mine Digitizat Engn Res Ctr Minist Educ Xuzhou 221116 Jiangsu Peoples R China Beijing Inst Petrochem Technol Informat Engn Coll Beijing 102617 Peoples R China Peking Univ Sch Earth & Space Sci Beijing 100871 Peoples R China

Microbial fuel cells (MFCs) are devices that transform organic matters in wastewater into green energy. Microbial fuel cells systems have strong nonlinearity and high coupling, which involves control science, microbiology, electrochemistry and other disciplines. According to the requirements of microbial fuel cell system for model robustness and accuracy, we designed a comprehensive model optimization framework. Firstly, the influence of uncertain parameters on system was analyzed by combining global sensitivity analysis with uncertainty analysis. In accordance with analysis results, the uncertain parameters were optimized. Secondly, based on the optimized stochastic model, a simplified model was proposed by combining variable selection with neural networks. The results shown that the proposed framework can deeply analysis the influence of uncertain parameters on output, and provide theoretical basis for experimental research. It fully simplifies the original MFCs model, and has guiding significance for other types of fuel cells.

关键词： Mathematical model Anodes Cathodes Analytical models Fuel cells Biological system modeling Neural networks Microbial fuel cells model optimization variable selection neural networks

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A integrated mechanical vapor compression enrichment system of radioactive wastewater: Experimental study, model optimization and performance prediction

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ENERGY 2023年第1期282卷

作者： Zhang, Huafu Tong, Lige Zhang, Zhentao Song, Yanchang Yang, Junling Yue, Yunkai Wu, Zhenqun Wang, Youdong Yu, Ze Zhang, Junhao Chinese Acad Sci Tech Inst Phys & Chem Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Energy & Environm Engn Beijing 100083 Peoples R China China Natl Light Ind Key Lab Equipment & Energy Saving Technol Food & P Beijing 100190 Peoples R China Tianjin Univ Sci & Technol Coll Mech Engn Tianjin 300222 Peoples R China Beijing Brigeniues Energy & Environm Tech CO LTD Beijing 10089 Peoples R China 29 Zhongguancun East Rd Beijing 100190 Peoples R China

Evaporation technology has unique advantages in the enrichment of radioactive ions, which can deal with the high concentration of radioactive wastewater. A integrated mechanical vapor compression (MVC) enrichment system with high efficiency filter was proposed and studied experimentally for radioactive wastewater, whose decontamination factor was 37107.1 for Co2+, 2741.3 for Sr2+, and 52545.5 for Cs+, which saved energy by 76.7-84.3% and operation cost by 72.0-81.0% when compared to the conventional single effect (SE) evaporation system. Exergy destruction and efficiency of the single screw compressor were 7.26 kW and 34.0%, respectively, which should be prioritized for improvement. Mathematical models based on both the heat transfer coefficient (HTC) and coefficient of performance (COP) were established and optimized experimentally, the ideal running parameters were 90-110 kPa for evaporating pressure, 8.5-11.5 degrees C for heat transfer temperature difference, 1.50-1.60 for compression ratio and 2.0-4.0 kPa for filtration pressure difference, respectively. It will supply the theoretical and data basis for development and design of MVC enrichment system of radioactive wastewater.

关键词： Enrichment of radioactive wastewater MVC MVR Exergy analysis Experimental study model optimization

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Coagulation sedimentation performance and model optimization of ultra-fine tailings overflow from hydrocyclone

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MINERALS ENGINEERING 2022年第0期186卷

作者： Niu, Fusheng Zhang, Hongmei Zhang, Jinxia Yu, Xiaodong Wang, Wanbo North China Univ Sci & Technol Coll Min Engn Tangshan 063210 Peoples R China Hebei Prov Min Ind Develops Safe Technol Prior Lab Tangshan 063210 Hebei Peoples R China Tangshan Siruier Environm Technol co ltd Tangshan 063210 Hebei Peoples R China

In order to study the sedimentation characteristics of ultra-fine tailings overflow from hydrocyclone in a coagulation environment, the sedimentation velocity of overflow ultra-fine tailings under different coagulant dosage, pulp pH and temperature conditions were analyzed experimentally in this study. The results indicate that with the increase in sedimentation time, the sedimentation velocity of the coagulant first increases, followed by a decline before becoming flat. When the ferric chloride coagulant dosage is 90 mg/L, the pH of the pulp is 9. The sedimentation velocity reaches the maximum value of 5.57 mm/s, when the temperature of the pulp is 60 C. According to the sedimentation process of the coagula, the coagulation sedimentation model of total tailings was extended and modified. The average absolute error between the corrected settlement velocity and the actual velocity was found as 0.0034, with the root mean square error is as 0.0088, which is relatively small. The results obtained from the theoretical calculations are consistent with the experimental data. The modified and optimized coagulation sedimentation model is important in promoting the theoretical research on the coagulation and sedimentation of such ultra-fine mineral particles.

关键词： Overflow ultra-fine tailings Coagulant Settlement performance Settlement model model optimization

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Prediction of the Height of Fractured Water-Conducting Zone: Significant Factors and model optimization

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WATER 2023年第15期15卷 2720页

作者： Gu, Linjun Shen, Yanjun Wang, Nianqin Kou, Haibo Song, Shijie Xian Univ Sci & Technol Shaanxi Prov Key Lab Geol Support Coal Green Explo Xian 710054 Peoples R China Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Peoples R China Xian Univ Sci & Technol Geol Res Inst Coal Green Min Xian 710054 Peoples R China

Predicting the height of the fractured water-conducting zone (FWCZ) can be challenging due to their significant grey characteristics and the difficulty in scientifically selecting relevant influencing factors. To address this issue, we utilized the Pearson correlation analysis method and the grey entropy correlation analysis method to identify the significant factors and their degree of correlation with the height of FWCZ. Based on this, several constructed models were optimized, and the reliability of the best regression model was verified through parameter inversion analysis. The results indicate that the spatial distribution differences of the main coal mining seams contribute to the complex and variable occurrence conditions of coal seams. This is an important factor that contributes to the significant gray characteristics in predicting the height of FWCZ in the study area. A modeling approach has been proposed for predicting the height of FWCZ. This method is based on analyzing significant factors and conducting a multi-level evaluation of the selected prediction models. The order of correlation between significant influencing factors and the height of FWCZ is as follows: comprehensive hardness of overlying rock > average thickness of sandstone > mining depth > mining height. The results of the multi-level evaluation analysis show that, when using small sample high-quality datasets, the GA-Catboost algorithm has better prediction accuracy compared to the MSR and GA-BP algorithms. The results of the parameter inversion analysis for the GA-Catboost regression prediction model indicate that within the mining height range of 2.5-5.5 m, the ratio of fractured/mining height in the main coal seams is primarily concentrated between 20.45-30.59. In addition, a prediction method was developed to determine the limiting mining height by considering water conservation in coal mining. The relevant research results can provide fundamental theoretical support for ensuring safe

关键词： Yushenfu mining area height of fractured water-conducting zone significant factor model optimization parameter inversion

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Research on the Application and model optimization of Blockchain Technology in Green Supply Chain Finance

Frontiers in Economics and Management

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Frontiers in Economics and Management 2023年第11期4卷 411-417页

作者： Yujing Chen Yaling Hu Xinyue Du Anqi Cheng Guanchi Li Deyu Li

In recent years, China's green finance has become increasingly important in economic transformation and ecological civilization strategy construction. At the same time, the innovation and development of China's green financial system also needs the help of blockchain technology. However, based on the current research situation, there are still problems such as slow processing and insufficient experience in the application of blockchain technology to green supply chain finance development. This project aims to explore the possibilities of blockchain technology application and integration innovation in the field of green supply chain finance on the basis of systematically reviewing the development model of green supply chain finance, and propose model optimization suggestions, in order to provide reference for the market to solve financing demand related problems.

关键词： Supply Chain Finance Blockchain Technology Green Finance model optimization

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In vitro placental model optimization for nanoparticle transport studies

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INTERNATIONAL JOURNAL OF NANOMEDICINE 2012年 7卷 497-510页

作者： Cartwright, Laura Poulsen, Marie Sonnegaard Nielsen, Hanne Morck Pojana, Giulio Knudsen, Lisbeth E. Saunders, Margaret Rytting, Erik Univ Texas Med Branch Dept Obstet & Gynecol Galveston TX 77555 USA UH Bristol NHS Fdn Trust St Michaels Hosp Biophys Res Unit BIRCH Bristol Avon England Univ Copenhagen Fac Hlth Sci Dept Publ Hlth Copenhagen Denmark Univ Copenhagen Fac Pharmaceut Sci Dept Pharmaceut & Analyt Chem Copenhagen Denmark Univ Ca Foscari Venice Dept Environm Sci Informat & Stat Venice Italy

Background: Advances in biomedical nanotechnology raise hopes in patient populations but may also raise questions regarding biodistribution and biocompatibility, especially during pregnancy. Special consideration must be given to the placenta as a biological barrier because a pregnant woman's exposure to nanoparticles could have significant effects on the fetus developing in the womb. Therefore, the purpose of this study is to optimize an in vitro model for characterizing the transport of nanoparticles across human placental trophoblast cells. Methods: The growth of BeWo (clone b30) human placental choriocarcinoma cells for nanoparticle transport studies was characterized in terms of optimized Transwell (R) insert type and pore size, the investigation of barrier properties by transmission electron microscopy, tight junction staining, transepithelial electrical resistance, and fluorescein sodium transport. Following the determination of nontoxic concentrations of fluorescent polystyrene nanoparticles, the cellular uptake and transport of 50 nm and 100 nm diameter particles was measured using the in vitro BeWo cell model. Results: Particle size measurements, fluorescence readings, and confocal microscopy indicated both cellular uptake of the fluorescent polystyrene nanoparticles and the transcellular transport of these particles from the apical (maternal) to the basolateral (fetal) compartment. Over the course of 24 hours, the apparent permeability across BeWo cells grown on polycarbonate membranes (3.0 mu m pore size) was four times higher for the 50 nm particles compared with the 100 nm particles. Conclusion: The BeWo cell line has been optimized and shown to be a valid in vitro model for studying the transplacental transport of nanoparticles. Fluorescent polystyrene nanoparticle transport was size-dependent, as smaller particles reached the basal (fetal) compartment at a higher rate.

关键词： nanoparticles placenta BeWo cells transport model optimization nanotoxicology

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A Machine Learning-Based Approach to Clinopyroxene Thermobarometry: model optimization and Distribution for Use in Earth Sciences

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JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2022年第4期127卷 e2021JB022904-e2021JB022904页

作者： Jorgenson, C. Higgins, O. Petrelli, M. Begue, F. Caricchi, L. Univ Geneva Dept Earth Sci Geneva Switzerland Univ Perugia Dept Phys & Geol Perugia Italy

Thermobarometry is a fundamental tool to quantitatively interrogate magma plumbing systems and broaden our appreciation of volcanic processes. Developments in random forest-based machine learning lend themselves to a data-driven approach to clinopyroxene thermobarometry, allowing users to access large experimental data sets that can be tailored to individual applications in Earth Sciences. We present a methodological assessment of random forest thermobarometry using the R freeware package extraTrees. We investigate the model performance, the effect of hyperparameter tuning, and assess different methods for calculating uncertainties. Deviating from the default hyperparameters used in the extraTrees package results in little difference in overall model performance (<0.2 kbar and <3 degrees C difference in standard error estimate, SEE). However, accuracy is greatly affected by how the final value from the distribution of trees in the random forest is selected (mean, median, or mode). Using the mean value leads to higher residuals between experimental and predicted P and T, whereas using median values produces smaller residuals. Additionally, this work provides two scripts for users to apply the methodology to natural data sets. The first script permits modification and filtering of the model calibration data set. The second script contains premade models, where users can rapidly input their data to recover PT estimates (SEE clinopyroxene-only model: 3.2 kbar, 72.5 degrees C and liquid-clinopyroxene model: 2.7 kbar, 44.9 degrees C). Additionally, the scripts allow the user to estimate the uncertainty for each analysis, which in some cases is significantly smaller than the reported SEE. These scripts are open source and can be accessed at .

关键词： machine learning random forest clinopyroxene thermobarometry model optimization

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A NOVEL DEEP model WITH STRUCTURE optimization FOR SCENE UNDERSTANDING

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INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2021年第6期36卷 392-401页

作者： Zheng, Hengyi Sun, Chuan Yin, Hongpeng Chongqing Univ Management Sci & Engn Chongqing 400044 Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 400044 Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China

Scene understanding is a fundamental problem in the field of robots, which provides a reference for the decision-making layer of the robot. Recently, the encoder-decoder image caption model based on the deep model was quite capable of accomplishing scene understanding. However, the deep model generally has a large scale, and the high time and space complexity restrict its implementation in the field of robots. In this paper, a hybrid optimization approach based on network pruning and tensor decomposition is presented for the basic components of "encoder-decoder", aiming to reduce the temporal and spatial complexity. First, the data-driven global supervised iterative method is used to decompose the convolutional layer, then the convolution kernels and neurons are sorted according to the important evaluation criteria proposed, and finally, the relatively unimportant convolution kernel or neurons are pruned. Through experiments on public datasets, the results compared with the state-of-the-art methods show that the proposed approach can quickly and accurately understand the image. Moreover, the proposed optimization method can effectively reduce the time and spatial complexity of the deep model.

关键词： Scene understanding robots image caption deep model model optimization

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Application Research of Three-dimensional model optimization Technology in Virtual Drilling Training System

Application Research of Three-dimensional Model Optimization...

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2011 International Conference on Information and Intelligent Computing（ICIIC 2011）

作者： FuHua Shang ,ShiJun Chu and HongTao Xie School of Computer & Information Technology Northeast Petroleum University Daqing, China

Virtual drilling training is an important way to achieve standardized drilling training. While as drilling well site and operations are complex, the amount of three-dimensional models created and their scenes are large which seriously affects the speed of roaming and interacting in the scene. Be aimed at this problem, from simplifying model numbers and model sides to optimizing model texture were adopted in modeling process as well as other methods. And integrating with Virtools, this article realized the usage of LOD technology in the three- dimensional scene, improved the efficiency of optimizing three-dimensional model, increased the speed of operation of the system and met the requirements of virtual drilling training.

关键词： Three-dimensional model model optimization LOD Drilling training

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Analysis of PV Array Fault Based on Multi-peak model optimization under Partial Shading Conditions

Analysis of PV Array Fault Based on Multi-peak Model Optimiz...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Chen Yu Han Wie Kong Sheng-li State Grid Henan Electric Power Research Institute Zhengzhou China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

In the practical application of PV array, equipment aging, partial shading and other factors will lead to failure of PV array. In order to improve the output power of PV array, this paper proposes to optimize the multi-peak model. To improve the output power of PV array, the bypass diode with parameter correction is introduced. The output characteristics of multi-peak model optimization is analyzed by MATLAB simulation, which verified that the model optimization can reduce the power loss of PV array effectively.

关键词： PV array fault partial shading model optimization parameter correction power loss photovoltaic array power loss Output power Bypass Diode output characteristic artificial shade

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