检索结果-内蒙古大学图书馆

Predicting quality parameters of wastewater treatment plants using artificial intelligence techniques

JOURNAL OF CLEANER PRODUCTION 2023年第1期405卷

作者： Aghdam, Ehsan Mohandes, Saeed Reza Manu, Patrick Cheung, Clara Yunusa-Kaltungo, Akilu Zayed, Tarek Hong Kong Polytech Univ Fac Construct & Environm FCE Dept Bldg & Real Estate BRE Hung HomKowloon Hong Kong Peoples R China Univ Manchester Sch Engn Dept Mech Aerosp & Civil Engn Manchester M13 9PL England

Estimating wastewater treatment plants' (WWTPs) influent parameters such as 5-day biological oxygen demand (BOD5) and chemical oxygen demand (COD) is vital for optimizing electricity and energy consumption. Against this backdrop, the existing body of knowledge is bereft of a study employing Artificial Intelligence-based techniques for the prediction of BOD5 and COD. Thus, in this study, gene expression programming (GEP), multilayer perception neural networks, multi-linear regression, k-nearest neighbors, gradient boosting, and regression trees -based models were trained for predicting BOD5 and COD, using monthly data collected from the inflow of 7 WWTPs over a three-year period in Hong Kong. Based on different statistical parameters, GEP provides more accurate estimations, with R2 values of 0.784 and 0.861 for BOD5 and COD respectively. Furthermore, results of sensitivity analysis undertaken by monte Carlo simulation revealed that both BOD5 and COD were mostly affected by concentrations of total suspended solids, and a 10% increase in the value of TSS resulted in a 7.94% and 7.92% increase in the values of BOD5 and COD, respectively. It is seen that the GEP modeling results complied with the fundamental chemistry of the wastewater quality parameters and can be further applied on other sewage sources such as industrial sewage and leachate. The promising results obtained pave the way for forecasting the operational parameters during sludge processing, leading to an extensive energy savings during the wastewater treatment processes.

关键词： gene expression programming AI models Wastewater quality parameters Artificial neural networks Monte Carlo simulation

来源：评论

学校读者我要写书评

暂无评论

Mathematical modeling for a new portfolio selection problem in a bubble condition using a new risk measure

引用

SCIENTIA IRANICA 2021年第5期28卷 2812-2829页

作者： Ghahtarani, A. Sheikhmohammady, M. Najafi, A. A. Tarbiat Modares Univ Fac Ind & Syst Engn Tehran Iran KN Toosi Univ Technol Fac Ind Engn Tehran Iran

A portfolio selection model is developed in this study using a new risk measure. The proposed risk measure is based on the fundamental value of stocks. For this purpose, a mathematical model is developed and transformed into an integer linear programming. In order to analyze its efficiency, the actual data of the Tehran Stock Exchange market are used in 12 scenarios to solve the proposed model. In order to evaluate the scenarios, data mining approaches are employed. Data mining methods used in this paper include Adaptive Neuro-Fuzzy Inference System (ANFIS), decision tree, random forest, Fisher Discriminant Analysis (FDA), and gene expression programming (GEP). The best method for scenario evaluation is GEP based on numerical results. Hence, the market values are evaluated by this algorithm. Software packages like MATLAB, GEP xpero tools, and LINGO are used to solve the model. Different trends of market value and fundamental value volatility in the optimum stock portfolio are determined. It is possible to examine the optimum portfolio profitability in different scenarios. By using real-world data, trends are extracted and analyzed. Results show that the developed model can be effectively applied in bubble situations. (C) 2021 Sharif University of Technology. All rights reserved.

关键词： Decision tree Financial bubble Fundamental value gene expression programming Portfolio selection problem Risk measure

来源：评论

学校读者我要写书评

暂无评论

Grey-box solution for predicting thermo-mechanical response of rocks

引用

GEOTHERMICS 2024年 124卷

作者： Nawaz, Muhammad Naqeeb Natl Univ Sci & Technol NUST NUST Inst Civil Engn Islamabad 44000 Pakistan

Evaluating the thermo-mechanical response of rocks under high temperature treatments is crucial for various engineering geology projects. Current predictions of rock thermo-mechanical response rely on simplistic mathematical fittings treating temperature as a reduction factor, while existing machine learning algorithms often present practical challenges due to their black-box solutions. In this study, highly practical grey-box solutions, utilizing gene expression programming (GEP) are proposed for forecasting rock strength following hightemperature treatments. The dataset, comprising temperature, rock type, rock density, sample size, crack damage stress, confining pressure, and elastic modulus, serves as input parameters, with rock strength from triaxial compression tests as the output. Three grey-box solutions (mathematical formulations) based on distinct input parameter sets are proposed, all demonstrating excellent accuracy with high R-2-values (R-2 > 0.95) and low error values across both the training and testing phases. Feature importance analysis highlights crack damage stress, confining pressure, and elastic modulus as statistically significant parameters influencing the strength of rocks subjected to high temperatures. External validation of the proposed models indicates strong generalization capabilities, underscoring their ability to perform well beyond the training data. Furthermore, a monotonicity study demonstrates that the proposed models align with the expected physical processes. The proposed formulations offer valuable field implications, effectively addressing the limitations of labor-intensive and costly laboratory processes for evaluating rock thermo-mechanical responses.

关键词： Thermo-mechanical response High temperature treatment machine learning Grey-box solutions gene expression programming

来源：评论

学校读者我要写书评

暂无评论

A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns

引用

MATERIALS 2021年第7期14卷 1792页

作者： Das, Sourav Mansouri, Iman Choudhury, Satyabrata Gandomi, Amir H. Hu, Jong Wan Natl Inst Technol Silchar Dept Civil Engn Silchar 788010 Assam India Birjand Univ Technol Dept Civil Engn Birjand 97175569 Iran Univ Technol Sydney Fac Engn & IT Ultimo NSW 2007 Australia Incheon Natl Univ Dept Civil & Environm Engn Incheon 22012 South Korea Incheon Natl Univ Incheon Disaster Prevent Res Ctr Incheon 22012 South Korea

Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction of the gross stiffness. However, a more precise estimation of the effective stiffness is important as it affects the distribution of forces and various demands and response parameters in nonlinear dynamic analyses. In this study, an evolutionary computation method called gene expression programming (GEP) was used to predict the effective stiffness ratios of RC columns. Constitutive relationships were obtained by correlating the effective stiffness ratio with the four mechanical and geometrical parameters. The model was developed using a database of 226 samples of nonlinear dynamic analysis results collected from another study by the author. Subsequent parametric and sensitivity analyses were performed and the trends of the results were confirmed. The results indicate that the GEP model provides precise estimations of the effective stiffness ratios of the RC frames.

关键词： effective stiffness ratio reinforced concrete columns performance-based design experimental data gene expression programming unified performance-based design

来源：评论

学校读者我要写书评

暂无评论

Application of Eurocode 4 design provisions and development of new predictive models for eccentrically loaded CFST elliptical columns

引用

JOURNAL OF BUILDING ENGINEERING 2022年 48卷

作者： Ipek, Suleyman Guneyisi, Esra Mete Bingol Univ Dept Architecture TR-12000 Bingol Turkey Gaziantep Univ Dept Civil Engn TR-27310 Gaziantep Turkey

The present study mainly aims to propose design models for the prediction of the peak strength of the concrete-filled steel tubular (CFST) elliptical columns subjected to eccentric compressive loading. At that point, the study has gain importance since the design of specifically such elliptical columns is not covered by current design codes. For example, the current provisions of Eurocode 4 provide a simplified method for the design of composite columns having circular or rectan-gular/square cross-sections. To this aim, first of all, the applicability and assessment of Eurocode 4 to the CFST elliptical columns under eccentric loading conditions were performed. Following, a data repository covering the experimental results of eccentrically loaded CFST elliptical columns (having different geometry, material property, load eccentricity in y and z axes, and longitudinal reinforcement ratio) was constituted. This data repository was used in the evaluation of the design guidance in Eurocode 4. Besides, the same experimental results were employed to develop two new numerical models based on gene expression programming (GEP) and artificial neural network (ANN) approaches. Finally, a sensitive statistical analysis was made to compare the prediction performances of Eurocode 4, GEP, and ANN models against experimental results. It was observed that Eurocode 4 design provisions had sufficiently accurate prediction performance, however, the developed GEP and ANN design models yielded consistent and robust prediction performance than the current design provisions by providing a much smaller mean absolute percent error of 7.5 and 1.4, respectively.

关键词： Artificial neural network CFST column Eccentric load Elliptical section Eurocode 4 gene expression programming

来源：评论

学校读者我要写书评

暂无评论

An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement

引用

MEASUREMENT 2020年 152卷 107309-107309页

作者： Ashrafian, Ali Gandomi, Amir H. Rezaie-Balf, Mohammad Emadi, Mohammad Tabari Univ Babol Dept Civil Engn POB 47139-75689 Babol Sar Iran Univ Technol Sydney Fac Engn & IT Sydney NSW 2007 Australia Grad Univ Adv Technol Kerman Dept Civil Engn POB 76315-116 Kerman Iran

The construction and maintenance of roads pavement was a critical problem in the last years. Therefore, the use of roller-compacted concrete pavement (RCCP) in road problems is widespread. The compressive strength (f(c)) is the key characteristic of the RCCP caused to significant impact on the cost of production. In this study, an evolutionary-based algorithm named gene expression programming (GEP) is implemented to propose novel predictive formulas for the f(c) of RCCP. The f(c) is formulated based on important factor used in mixture proportion in three different combinations of dimensional form (coarse aggregate, fine aggregate, cement, pulverized fly ash, water, and binder), non-dimensional form (water to cement ratio, water to binder ratio, coarse to fine aggregate ratio and pulverized fly ash to binder ratio) and percentage form of input variables. A comprehensive and reliable database incorporating 235 experimental cases collected from several studies. Furthermore, mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), average absolute error (AAE), performance index (PI), and objective function (OBJ) as the internal standard statistical measures and external validation evaluated proposed GEP-based models. Uncertainty and parametric studies were carried out to verify the results. Moreover, sensitivity analysis to determine the importance of each predictor on f(c) of RCCP revealed that fine aggregate content and water to binder ratio is the most useful predictor in dimensional, non-dimensional and percentage forms, respectively. The proposed equation-based models are found to be simple, robustness and straightforward to utilize, and provide consequently new formulations for f(c) of RCCP. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Evolutionary approach Roller compacted concrete pavement gene expression programming Compressive strength Prediction

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Dynamic Modeling of Urban Water Consumption Using Chaotic Approach (Case Study: City of Kelowna)

引用

WATER 2020年第3期12卷 753-753页

作者： Yousefi, Peyman Courtice, Gregory Naser, Gholamreza Mohammadi, Hadi Univ British Columbia Sch Engn Kelowna BC V1V1V7 Canada Shippensburg Univ Penn Sch Engn Shippensburg PA 17257 USA

This study investigated urban water consumption complexity using chaos theory to improve forecasting performance to help optimize system management, reduce costs and improve reliability. The objectives of this study were to (1) investigate urban water distribution consumption complexity and its role in forecasting technique performance, (2) evaluate forecasting models by periodicity and lead time, and (3) propose a suitable forecasting technique based on operator applications and performance through various time scales. An urban consumption dataset obtained from the City of Kelowna (British Columbia, Canada) was used as a test case to forecast future consumption values using varying lead times under different temporal scales to identify models which may improve forecasting performance. Chaos theory techniques were employed to inform model optimization. This study attempted to address the paucity of studies on chaos theory applications in water consumption forecasting. This was accomplished by applying non-linear approximation, dynamic investigation, and phase space reconstruction for input variables, to improve the accuracy in various periodicity and lead time. To reconstruct the phase space, lag time was calculated using average mutual information for daily resolution as 17 days to reconstruct the phase space. The optimum embedding dimension and correlation exponent for the phase space were 18 and 3.5, respectively. Comparing the results, the non-linear local approximation model provided the best performance. The forecasting horizon for the models was 122 days. Moreover, phase space reconstruction improved the accuracy of the models for the different lead times. The findings of this study may improve forecasting performance and provide evidence to support further investigation of the chaotic behaviour of water consumption values over different time scales.

关键词： water consumption chaos theory local approximation Kelowna gene expression programming

来源：评论

学校读者我要写书评

暂无评论

Prediction of Organic Micropollutant Removal in Soil Aquifer Treatment System Using GEP

引用

JOURNAL OF HYDROLOGIC ENGINEERING 2016年第9期21卷

作者： Sattar, Ahmed M. A. Cairo Univ Fac Engn Dept Irrigat & Hydraul Giza 12613 Egypt

With constrained Nile water and the cruel drop in per capita freshwater share in Egypt, soil aquifer treatment (SAT) appears an attractive, low-cost, unconventional water resource that is environmentally friendly and relatively easy to operate. Nevertheless, organic micropollutants (OMPs) in treated wastewater pose environmental and health risks if not properly attenuated through vadose zone infiltration. Thus, determination of OMP removal in a SAT system is important for sustainable groundwater management. In this study, a new, simple equation for the prediction of organic micropollutant removal in SAT systems was developed using gene expression programming (GEP). A wide range of 15OMPs and aquifer conditions were examined along with various real operational aspects of SAT systems, including hydraulic loading rate and dry/wet ratio. The effect of spatial heterogeneity on saturated hydraulic conductivity was considered. Developed GEP models had an average coefficient of determination R2 of 0.92. Monte Carlo simulation (MCS) with 50,000 realizations was used to propagate uncertainty in SAT parameters in order to generate stochastic inputs for the GEP model. It was found that the removal of OMPs in SAT systems is mostly affected by biodegradation rate and soil-saturated hydraulic conductivity, in addition to dry/wet ratio. Finally,the developed GEP models were applied to enhance the criteria for selecting potential sites for SAT systems in Egypt considering OMP. It was shown that a SAT system would perform well at three sites, with a OMP removal efficiency reaching 100%, whereas it would have a removal of only 50% in the other two sites. Uncertainties in predictions were quantified with an average value of 35%. The developed GEP models can serve as basis for preliminary SAT site selection and design, and can substitute for complex commercial modeling software, especially for practitioners and decision makers in feasibility studies. However, for SAT implementation in a

关键词： Soil aquifer treatment Vadose zone Organic micropollutants HYDRUS gene expression programming Egypt

来源：评论

学校读者我要写书评

暂无评论

Multimodel Approach Using Neural Networks and Symbolic Regression to Combine the Estimated Discharges of Rainfall-Runoff Models

引用

JOURNAL OF HYDROLOGIC ENGINEERING 2016年第8期21卷

作者： Phukoetphim, Phanida Shamseldin, Asaad Y. Adams, Keith Univ Auckland Dept Civil & Environm Engn Private Bag 92019 Auckland 1142 New Zealand

The aim of this study is to compare the performance of a symbolic regression combination method based on gene expression programming (GEP) with different neural network combination methods when used in the development of multimodel systems. The two different neural network combination methods used in this study are the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN). The methods were used to combine the results from different types of rainfall-runoff models to test the multimodel combination system in catchments located in Thailand and New Zealand. Comparison of the results revealed that the GEP performed better than neural network methods in the case of the catchment located in New Zealand. Nevertheless, the RBFNN method outperformed the GEP and the MLPNN combination method in the case of the catchment located in Thailand. However, which combination method produces better results in the multimodel combination is not conclusive. The results suggest that the selection of the best combination method to be used in conjunction with the multimodel approach may depend on the catchment type.

关键词： Multimodel approach Neural networks gene expression programming Combination methods Multilayer perceptron neural network Radial basis function neural network

来源：评论

学校读者我要写书评

暂无评论

Stochastic assessment of rockburst potential in underground spaces using Monte Carlo simulation

引用

ENVIRONMENTAL EARTH SCIENCES 2022年第18期81卷 447-447页

作者： Kadkhodaei, Mohammad Hossein Ghasemi, Ebrahim Sari, Mehmet Isfahan Univ Technol Dept Min Engn Esfahan *** Iran Aksaray Univ Dept Min Engn TR-68100 Aksaray Turkey

Rockburst (RB) is known as one of the deadliest and most destructive geotechnical events in deep underground spaces under high stresses. The complexity of the RB phenomenon and uncertainty arising from variability in geotechnical and geomechanical conditions makes its prediction very difficult. The current study is an attempt to address the suitability of the stochastic modeling approach for the evaluation of RB potential and to assess the effects of the contributing parameters on the phenomenon. To do this study, a database containing the major effective parameters on RB potential (i.e. stress concentration coefficient, brittleness coefficient, and elastic energy index) compiled from 335 case histories of RB in various underground projects worldwide was applied. Using this database, first, a new deterministic mathematical relation for predicting RB potential was developed using gene expression programming (GEP) method. Then, the RB phenomenon was simulated by the Monte Carlo (MC) method. The results reveal that stochastic modeling is a good means for modeling and evaluating the effects of the variability of contributing parameters on RB. Finally, sensitivity analysis was conducted to analyse the effects of the contributing parameters on RB potential. Sensitivity analysis showed that stress concentration coefficient, brittleness coefficient, and elastic energy index have a direct relationship with RB potential. Furthermore, the elastic energy index was found to be the most effective parameter on the RB potential with regression coefficient of + 0.50.

关键词： Rockburst Underground spaces Stochastic modeling gene expression programming Monte Carlo simulation

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：