检索结果-内蒙古大学图书馆

An Adaptive Surrogate-Assisted simulation-optimization method for Identifying Release History of Groundwater Contaminant Sources

引用

WATER 2022年第10期14卷 1659页

作者： Wu, Mengtian Xu, Jin Hu, Pengjie Lu, Qianyi Xu, Pengcheng Chen, Han Wang, Lingling State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ Coll Agr Sci & Engn Nanjing 210098 Peoples R China

The simulation-optimization method, integrating the numerical model and the evolutionary algorithm, is increasingly popular for identifying the release history of groundwater contaminant sources. However, due to the usage of computationally intensive evolutionary algorithms, traditional simulation-optimization methods always require thousands of simulations to find appropriate solutions. Such methods yield a prohibitive computational burden if the simulation involved is time-consuming. To reduce general computation, this study proposes a novel simulation-optimization method for solving the inverse contaminant source identification problems, which uses surrogate models to approximate the numerical model. Unlike many existing surrogate-assisted methods using the pre-determined surrogate model, this paper presents an adaptive surrogate technique to construct the most appropriate surrogate model for the current numerical model. Two representative cases about identifying the release history of contaminant sources are used to investigate the accuracy and robustness of the proposed method. The results indicate that the proposed adaptive surrogate-assisted method effectively identifies the release history of groundwater contaminant sources with a higher degree of accuracy and shorter computation time than traditional methods.

关键词： simulation-optimization method surrogate modeling inverse contaminant source identification problems

来源：评论

学校读者我要写书评

暂无评论

Optimal design of groundwater pollution monitoring network based on the SVR surrogate model under uncertainty

引用

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第19期27卷 24090-24102页

作者： Fan, Yue Lu, Wenxi Miao, Tiansheng An, Yongkai Li, Jiuhui Luo, Jiannan Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun 130021 Peoples R China Jilin Univ Jilin Prov Key Lab Water Resources & Environm Changchun 130021 Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130021 Peoples R China

The simulation-optimization method is widely used in the design of the groundwater pollution monitoring network (GPMN). The uncertainty of the simulation model will significantly affect the design results of GPMN. When the Monte Carlo method is used to consider the influence of model uncertainty on the optimization results, the simulation model needs to be invoked many times, which will cause a huge amount of calculation. To reduce the calculation load, the study proposed to use the support vector regression (SVR) method to construct the surrogate model to couple the simulation model and the optimization model in the optimal design of GPMN. The optimization goal is to maximize the accuracy of the spatial description of pollution plume in each monitoring period. The study also considered the dynamic changes in the migration and morphological of pollution plumes in the optimization of GPMN. Finally, the West Shechang coal gangue pile in Fushun of China was used as a case study to verify the effectiveness of the above method. The results demonstrate that the SVR surrogate model can fit the input-output relationship of the simulation model to a high degree with less computation. The optimized monitoring network can reveal essential and comprehensive information about pollution plumes. The study provides a stable and reliable method for the design of GPMN.

关键词： Groundwater pollution Groundwater monitoring SVR Surrogate model simulation-optimization method

来源：评论

学校读者我要写书评

暂无评论

simulation-based modeling and optimization for refinery hydrogen network integration with light hydrocarbon recovery

引用

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第7期47卷 4662-4673页

作者： Yang, Minbo Zeng, Siying Feng, Xiao Zhao, Liang Xi An Jiao Tong Univ Sch Chem Engn & Technol Shaanxi Key Lab Energy Chem Proc Intensificat Xian 710049 Shaanxi Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Purge gases from hydrocrackers and hydrotreaters and refinery off-gases are important hydrogen sources. Some of these hydrogen sources are also rich in light hydrocarbons that are valuable energy resources and chemical materials. In this work, a systematic method is proposed to integrate hydrogen networks considering light hydrocarbon recovery. This work first develops a hydrogen network superstructure with light hydrocarbon recovery. Aspen HYSYS is employed for rigorous process and thermodynamic modeling of the light hydrocarbon recovery process, and a simulation-optimization model is then developed. To solve the simulation-optimization model efficiently, the genetic algorithm is used as the global solver to determine the feed to light hydrocarbon recovery unit, and the linprog and fmincon solvers are combined to determine the optimal hydrogen network design. The application and effectiveness of the proposed method is validated through a case study. The results show that fresh hydrogen consumption decreases by 13% and the total annualized cost reduces to 72% because of light hydrocarbon recovery. This method could provide useful guides for the management of hydrogen and light hydrocarbons in refineries. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hydrogen network integration Light hydrocarbon recovery simulation-optimization method Genetic algorithm Total annualized cost

来源：评论

学校读者我要写书评

暂无评论

Simultaneous identification of groundwater contaminant source and hydraulic parameters based on multilayer perceptron and flying foxes optimization

引用

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第32期30卷 78933-78947页

作者： Li, Yidan Lu, Wenxi Pan, Zidong Wang, Zibo Dong, Guangqi Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun 130021 Peoples R China Jilin Univ Jilin Prov Key Lab Water Resources & Environm Changchun 130021 Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130021 Peoples R China

Groundwater contaminant source identification (GCSI) has practical significance for groundwater remediation and liability. However, when applying the simulation-optimization method to precisely solve GCSI, the optimization model inevitably encounters the problems of high-dimensional unknown variables to identify, which might increase the nonlinearity. In particular, to solve such optimization models, the well-known heuristic optimization algorithms might fall into a local optimum, resulting in low accuracy of inverse results. For this reason, this paper proposes a novel optimization algorithm, namely, the flying foxes optimization (FFO) to solve the optimization model. We perform simultaneous identification of the release history of groundwater pollution sources and hydraulic conductivity and compare the results with those of the traditional genetic algorithm. In addition, to alleviate the massive computational load caused by the frequent invocation of the simulation model when solving the optimization model, we utilized the multilayer perception (MLP) to establish a surrogate model of the simulation model and compared it with the method of backpropagation algorithm (BP). The results show that the average relative error of the results of FFO is 2.12%, significantly outperforming the genetic algorithm (GA);the surrogate model of MLP can replace the simulation model for calculation with fitting accuracy of more than 0.999, which is better than the commonly used surrogate model of BP.

关键词： Groundwater contaminant source identification Flying foxes optimization Multilayer perception simulation-optimization method Surrogate model

来源：评论

学校读者我要写书评

暂无评论

Identification of groundwater contamination sources and hydraulic parameters based on bayesian regularization deep neural network

引用

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第13期28卷 16867-16879页

作者： Pan, Zidong Lu, Wenxi Fan, Yue Li, Jiuhui Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun 130021 Peoples R China Jilin Univ Jilin Prov Key Lab Water Resources & Environm Changchun 130021 Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130021 Peoples R China

Simultaneous identification of various features of groundwater contamination sources and hydraulic parameters, such as hydraulic conductivities, can result in high-nonlinear inverse problem, which significantly hinders identification. A surrogate model was proposed to relieve computational burden caused by massive callings to simulation model in identification. However, shallow learning surrogate model may show limited fitting ability to high nonlinear problem. Thus, in this study, a simulation-optimization method based on Bayesian regularization deep neural network (BRDNN) surrogate model was proposed to efficiently solve high-nonlinear inverse problem. This method identified eight variables including locations and release intensities of two pollution sources and hydraulic conductivities of two partitions. Three hidden layers were employed in the BRDNN surrogate model, which profoundly improved the fitting capacity of nonlinear mapping relationship to the simulation model. Furthermore, Bayesian regularization was applied in the training process of neural network to solve overfitting problem. The results indicated that BRDNN was capable of establishing input-output interplay of high nonlinear inverse problem, which substantially reduced computational cost while ensuring a desirable level of accuracy. The utility of simulation-optimization on the basis of BRDNN surrogate model provided stable and reliable inversion results for groundwater contamination sources and hydraulic parameters.

关键词： Groundwater contamination BRDNN Surrogate model simulation-optimization method

来源：评论

学校读者我要写书评

暂无评论

Application of stochastic programming in groundwater pollution source identification

引用

ENVIRONMENTAL FORENSICS 2022年第1-2期23卷 170-178页

作者： Shi, Chenxuan Zhao, Ying Lu, Wenxi Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Peoples R China China Univ Geosci Sch Environm Studies Wuhan Peoples R China Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun Peoples R China

Groundwater pollution is a serious threat to the ecological environment and human life. It is necessary to determine the characteristics of pollution sources accurately and efficiently after the occurrence of pollution. The main basis for determining the characteristics of pollution sources is the pollutant concentration data of each observation well. However, due to the layout of the monitoring wells, the noise intensity of observation well concentration and unknown aquifer parameters, inversion results of groundwater pollution sources will be influenced significantly. Therefore, this article focuses on how to reduce its influence on the inversion results. In this article, a stochastic programming optimization model is introduced to explore its ability to control errors in complex situations. Results show that: in homogeneous aquifer, the normalized error (NE%) produced by simulation-optimization method is stable at about 2%, and the NE generated by stochastic programming method in confidence interval of 60%similar to 95% is between 0.20% and 5.42%. Moreover, stochastic programming model can effectively control the influence of noise. In the simulation-optimization model, when the noise intensity is 0.1 similar to 0.5, the NE value is 2.01%similar to 12.68%, and the corresponding NE of stochastic programming model is 0.12%similar to 11.87%. Finally, this article considers the case that aquifer parameters are unknown (simultaneous identification of aquifer parameters and groundwater pollution sources). The results show that with the increase of the number of unknown aquifer parameters, the NE of the simulation-optimization model gradually increases from 1.16% to 8.75%. The NE value of the stochastic programming model decreases by 30% compared with the simulation-optimization model when the confidence level is 80%.

关键词： Groundwater pollution pollution source identification simulation-optimization method stochastic programming method

来源：评论

学校读者我要写书评

暂无评论

Multiobjective optimization of the groundwater exploitation layout in coastal areas based on multiple surrogate models

引用

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第16期27卷 19561-19576页

作者： Fan, Yue Lu, Wenxi Miao, Tiansheng Li, Jiuhui Lin, Jin Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun 130021 Peoples R China Jilin Univ Jilin Prov Key Lab Water Resources & Environm Changchun 130021 Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130021 Peoples R China Nanjing Hydraul Res Inst Nanjing 210029 Peoples R China

Seawater intrusion is a common problem in coastal areas. The rational distribution of groundwater exploitation can minimize the scope of seawater intrusion and maximize groundwater exploitation. In this study, an optimization method for the groundwater exploitation layout in coastal areas was proposed. Based on the numerical simulation model of variable-density groundwater, a multiobjective groundwater management model was constructed with the objectives of maximizing groundwater exploitation and minimizing seawater intrusion. The optimization model was solved by nondominated sorted genetic algorithm-II (NSGA-II). To improve the computational efficiency of the optimization model, the surrogate models of the groundwater simulation model were built by using three different methods: kriging, support vector regression (SVR), and kernel extreme learning machines (KELM). Finally, the above methods were tested in Longkou City of China. The results show that the use of surrogate models can greatly reduce the computing time for solving seawater intrusion management problems. The surrogate model of the variable-density groundwater simulation model based on the SVR method has the best performance. The groundwater exploitation layout optimized by the above method is reasonable and can reflect the actual hydrogeological conditions in the study area. This study provides a reliable way to optimize the groundwater exploitation layout in coastal areas.

关键词： Seawater intrusion Groundwater exploitation simulation-optimization method Surrogate model Multiobjective optimization Kriging SVR KELM

来源：评论

学校读者我要写书评

暂无评论

Study on simultaneous inversion groundwater pollution sources and aquifer parameters based on bilevel programming

引用

ENVIRONMENTAL FORENSICS 2022年

作者： Di, XinHang Wang, Zhi Pang, YanZhe Ai, Miao Ding, Rui Zhao, Ying Lu, Wenxi Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Peoples R China Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun Peoples R China

Groundwater pollution source identification is the first step of groundwater remediation planning and an important link of groundwater pollution source identification. In order to make the inversion result more accurate, the bilevel programming is proposed to test its availability. In this paper, iterative time, optimization algorithm and noise level are used to test the accuracy of bilevel programming. The advantages and disadvantages of genetic algorithm, multi-objective genetic algorithm and simulated annealing algorithms are compared in searching optimal decision variables. The results show that the combination of multi-objective genetic algorithm and bilevel programming has better accuracy and robustness. The bilevel programming can be used to identify the groundwater pollution source and aquifer parameters.

关键词： Groundwater pollution source identification simulation-optimization method bilevel programming

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：