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A new kinetic model for Ni(II) ion flotation based on gene expression programming

SEPARATION SCIENCE AND TECHNOLOGY 2019年第15期54卷 2528-2535页

作者： Hoseinian, Fatemeh Sadat Rezai, Bahram Kowsari, Elaheh Amirkabir Univ Technol Dept Min & Met Engn Tehran Iran Amirkabir Univ Technol Dept Chem Tehran Iran

The Ni(II) ion flotation kinetics with sodium dodecylsulfate as collector was studied with the aim of developing new models to predict the flotation rate constant (k) of Ni(II) ions and water removal during the process of Ni(II) ion flotation. In this regard, the most influential parameters on Ni(II) ion flotation efficiency including the ratio of collector concentration on the Ni(II) ion concentration, impeller speed, activity coefficient, and pH were used to develop predictive models. The results show that the proposed gene expression programming models can be used to predict the flotation rate constant of Ni(II) ions and water removal.

关键词： gene expression programming ion flotation kinetic Ni(II) removal prediction

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Computation of energy dissipation across the type-A piano key weir by using gene expression programming technique

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WATER SUPPLY 2022年第8期22卷 6715-6727页

作者： Singh, Deepak Kumar, Munendra Delhi Technol Univ Dept Civil Engn Delhi India

Energy dissipation across the weir and dam structures is a vital economic and technical solution for designing the downstream morphology of any hydraulic system. Accurately estimating the energy over any hydraulic system using traditional empirical formulas is tedious and challenging. Consequently, employing new and precise techniques still in high demand is crucial. In this study, the authors developed an empirical model for estimating the residual energy downstream of the type-A PKW using gene-expression programming (GEP) by considering six non-dimensional parameters: headwater ratio, magnification ratio, inlet to outlet width ratio, inlet to outlet key bottom slopes, inlet to outlet overhang portions and the number of cycles. The performance of the proposed models has been compared to empirical equations using the statistical factors coefficient of determination (R-2), concordance coefficient (CC), and root mean square error (RMSE). The computed relative residual energy values using the proposed models are within +/- 10% of the observed ones. The proposed GEP model predicted the relative residual energy satisfactorily, with coefficients of determination (R-2 = 0.978 for training, 0.980 for testing) and root mean square errors (RMSE) of 0.032, 0.029 for the training and testing datasets, respectively.

关键词： energy dissipation gene expression programming low head efficient structure residual energy

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Toward gene expression programming for accurate prognostication of the critical oil flow rate through the choke: correlation development

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ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING 2017年第6期12卷 884-893页

作者： Rostami, Alireza Ebadi, Hojatollah PUT Dept Petr Engn Ahvaz Iran

In this study, gene expression programming (GEP) was employed to develop a new equation for calculating choke flow rate as a function of fluid properties and wellhead choke characteristics. For this reason, a comprehensive databank was adopted from the petroleum industry, and then, the databank was split into three groups of training set for model construction (70% of database), test set for model evaluation (15% of database), and validation set for avoiding over-fitting problem (15% of database). For assessing of GEP model, numerous statistical and graphical tools were applied, and then, it was compared with widely applied literature correlations. Consequently, the proposed tool in this study gives the best fit with the actual flow rate data with the average absolute relative deviation of 11.0872% and determination coefficient (R-2) of 0.9695. Additionally, cumulative frequency plot demonstrates that the GEP model has the highest frequency over the database. The outcomes of the GEP model were also compared with the outputs of the artificial neural network (ANN) revealing the higher precision of the ANN than the proposed GEP model in this study. At the end, it is worthwhile that the suggested model can be used as reliable tool for choke performance modeling. (c) 2017 Curtin University and John Wiley & Sons, Ltd.

关键词： choke flow rate gene expression programming Gilbert empirically derived correlation error analysis

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Hybrid wavelet-gene expression programming and wavelet-support vector machine models for rainfall-runoff modeling

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WATER SCIENCE AND TECHNOLOGY 2022年第12期86卷 3205-3222页

作者： Lakshmi, Potharlanka Jhansi Apaza, Ruben Apaza Alkhayyat, Ahmed Marhoon, Haydar Abdulameer Alameri, Ameer A. A. Vignans Fdn Sci Technol & Res Comp Sci & Engn Guntur India Jose Maria Arguedas Natl Univ Acad Dept Engn & Informat Technol Andahuaylas Peru Islamic Univ Coll Tech Engn Najaf Iraq Al Ayen Univ Sci Res Ctr Informat & Commun Technol Res Grp Thi Qar Iraq Univ Kerbala Coll Comp Sci & Informat Technol Karbala Iraq Univ Babylon Coll Sci Dept Chem Babylon Iraq

It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an increase in the use of machine learning approaches to model rainfall-runoff. In this study, the accuracy of rainfall-runoff modeling approaches such as Support Vector Machine (SVM), gene expression programming (GEP), Wavelet-SVM (WSVM), and Wavelet-GEP (WGEP) is evaluated. Python is used to run the simulation. The research area is the Yellow River basin in central China, and in the west of the region, the Tang-Nai-Hai hydrometric station has been selected. The train state data ranges from 1950 to 2000, while the test state data ranges from 2000 to 2020. The analysis looks at two differ-ent types of rainy and non-rainy days. The WGEP simulation performed best, with a Nash-Sutcliffe efficiency (NSE) of 0.98, while the WSVM, GEP, and SVM simulations performed poorly, with NSEs of 0.94, 0.89, and 0.77, respectively. As a result, combining hybrid methods with Wavelet improved simulation accuracy, which is now the highest for the WGEP method.

关键词： gene expression programming machine learning rainfall-runoff modeling support vector machine wavelet

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Estimating Shear Strength of Short Rectangular Reinforced Concrete Columns Using Nonlinear Regression and gene expression programming

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STRUCTURES 2017年 12卷 13-23页

作者： Aval, S. B. Beheshti Ketabdari, H. Gharebaghi, S. Asil KN Toosi Univ Technol Civil Engn Fac Tehran Iran

Shear collapse may be considered as the most important reason of the failure of Short Reinforced Concrete Columns (SR-RCCs) subjected to strong earthquake or other extreme loads. Although the current design provisions are mainly based on experimental data, they are too simple to consider all contributing factors. In this study, 30 test results of SR-RCCs from available laboratory tests have been collected. Next, an accurate Finite Element (FE) model is calibrated using 10 out of 30 laboratory tests. It is shown that the calibrated model successfully represents the behavior of SR-RCCs. Subsequently, 53 simulations are performed using the calibrated model. The results are used to establish a gene expression programming (GEP) model, which considers most effective parameters such as axial force, transverse reinforcement ratio, cross-sectional aspect ratio, compressive strength, and ductility. Besides, a Non-linear Regression (NR) is also presented using the same simulation results. Finally, the remaining validation laboratory tests are used to compare the accuracy of the GEP-based and NR models with ACI and EC2 design codes. The validation results in an average error of 15% for GEP, whereas for NR, ACI, and EC2 the average errors are 50%, 45%, and 43%, respectively. It is concluded that the proposed model, developed by GEP, provides more accurate results in the prediction of shear strength of SRRCCs in comparison with the others at least within available test results.

关键词： Short reinforced concrete column genetic algorithm gene expression programming Non-linear regression Finite element analysis

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Force-Displacement Relationship of the Butterfly-Shaped Beams Based on gene expression programming

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2020年第6期20卷 2009-2019页

作者： Farzampour, Alireza Mansouri, Iman Mortazavi, Seyed Javad Hu, Jong Wan Virginia Tech Dept Civil & Environm Engn Blacksburg VA USA Birjand Univ Technol Dept Civil Engn Birjand Iran Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran Incheon Natl Univ Dept Civil & Environm Engn Incheon 22012 South Korea Incheon Natl Univ Incheon Disaster Prevent Res Ctr Incheon 22012 South Korea

Structural steel plates with engineered cut-outs to exhibit a controlled yielding mechanism is recently proposed for desirable structural performance compared to conventional systems. Butterfly-shaped beams with hexagonal cut-outs inside of the beam's web are implemented to better align the bending strength diagram along the link length with the corresponding demand shape of the applied moment diagram. In previous studies, it has been reported that these links have a substantial energy dissipation capability and sufficient ductility which necessities further investigations and structural behavior prediction studies. In this study, a set of 240 nonlinear finite element models are developed for the creation of a database and subsequently calibrated with finite element software packages. The capability of the gene expression programming (GEP) is explored for the prediction of the force-displacement relationship of a butterfly-shaped beam. Two new models are developed based on the reliable generated database. Subsequently, the proposed models are validated with several conducted analyses and statistical parameters, for which the comparisons are shown in detail. The results represent that the proposed models are able to predict the force-displacement relationship of a butterfly-shaped beam with satisfactory accuracy.

关键词： Structural fuses Butterfly-shaped beam Finite element analysis gene expression programming OpenSees

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Drought modeling using classic time series and hybrid wavelet-gene expression programming models

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JOURNAL OF HYDROLOGY 2020年 587卷 125017-125017页

作者： Mehdizadeh, Saeid Ahmadi, Farshad Mehr, Ali Danandeh Safari, Mir Jafar Sadegh Urmia Univ Water Engn Dept Orumiyeh Iran Shahid Chamran Univ Ahvaz Dept Hydrol & Water Resources Engn Ahvaz Iran Antalya Bilim Univ Fac Engn Dept Civil Engn Antalya Turkey Yasar Univ Dept Civil Engn Izmir Turkey

The standardized precipitation evapotranspiration index (SPEI) at three different time scales (i.e., SPEI-3, SPEI-6, and SPEI-12) from six meteorology stations located in Turkey are modeled in this study. To this end, two types of classic time series models, namely linear autoregressive (AR) and non-linear bi-linear (BL) are used. Furthermore, the hybrid models are proposed by coupling the wavelet (W) and gene expression programming (GEP). Five various mother wavelets (i.e., Haar, db4, Symlet, Meyer, and Coifflet), for the first time, are employed and compared for implementing the hybrid W-GEP approach in drought modeling. The modeling results of SPEI droughts via the time series models illustrated that the non-linear BL performs slightly better than the linear AR. Moreover, all the hybrid W-GEP models developed in the study region provide superior performances compared to the standalone GEP. In general, db4 in SPEI-3 modeling and Symlet for modeling the SPEI-6 and SPEI-12 of the studied locations are the optimal wavelets to develop the W-GEP. Finally, the SPEI series at each target station is modeled through applying the SPEI data of the five neighboring stations. It is found that the SPEI data of neighboring stations are appropriate for modeling the SPEI series of the target station when the SPEI data of the target station is not at hand. For this case, the performance of standalone GEP for modeling the SPEI-3 and SPEI-6 of the stations is generally better than the case of utilizing the original SPEI data at each target station.

关键词： Drought modeling Time series models Hybrid models Wavelet analysis gene expression programming

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Water surface profile in converging compound channel using gene expression programming

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WATER SUPPLY 2022年第5期22卷 5221-5236页

作者： Naik, Bandita Kaushik, Vijay Kumar, Munendra Methodist Coll Engn Dept Civil Engn Hyderabad India Delhi Technol Univ Dept Civil Engn Delhi India

Assessment of water surface profile in compound channels is essential for flood defence systems. Agriculture and development activities in floodplains affect the floodplain shape over the length, leading in a converging compound channel. Few laboratory investigations proved overbank flow in converging floodplains. Therefore, innovative and precise approaches are still in great demand. In this paper, new approach has been proposed to forecast the water surface profile of various compound channels with converging floodplains using gene expression programming (GEP). The models are constructed utilising pertinent experimental data from past studies. A new equation is devised to compute water surface profile in such channels using non-dimensional geometric and flow parameters such as converging angle, width ratio, relative distance, relative depth, aspect ratio and bed slope. The findings demonstrate that the GEP-derived water surface profile is in good correlation with the experimental data and data from other studies (R-2 = 0.99 and RMSE = 0.028 for the training data and R-2 = 0.99 and RMSE = 0.027 for the testing data). According to the results of statistically based investigations, the GEP model created for the study of compound channel flow is reliable and can be used in this domain.

关键词： converging compound channel error analysis flow parameters gene expression programming water surface profile

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Research on Production Scheduling Optimization of Flexible Job Shop Production with Buffer Capacity Limitation Based on the Improved gene expression programming Algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2023年第12期24卷 2317-2336页

作者： Chang, Daliang Shi, Haibo Han, Chao Meng, Fu Chinese Acad Sci Key Lab Networked Control Syst Shenyang 110016 Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot Shenyang 110169 Peoples R China Chinese Acad Sci Inst Intelligent Mfg Shenyang 110169 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This study proposes a method to combine the improved gene expression programming (IGEP) and buffer-local scheduling to solve the optimization problem of flexible job shop production scheduling with buffer capacity limitation (BLFJSP). The IGEP algorithm was first used for the overall optimization and later combined with the flexible job shop production mode to design a multi-gene chromosome coding method to realize the overall optimization of the production schedule using the genetic biological evolution function. Furthermore, when the algorithm fell into a local extremum, the Jaccard similarity coefficient method was used to filter the approximate chromosomes, and the historical excellent chromosome backtracking method was used to increase the diversity of the population. Lastly, we designed the local scheduling rules of the job in and out of the buffer area. Based on the IGEP algorithm and considering the influence of buffer capacity limitation on the scheduling scheme, the final production scheduling scheme was generated by dual optimization methods aiming at minimizing blocking time and minimizing maximum completion time. Based on the actual production data of an assembly manufacturing company, multiple sets of simulation programs were designed to validate the IGEP algorithm for solving the optimization problem of BLFJSP.

关键词： Flexible job shop gene expression programming Multi-gene chromosome coding method Jaccard similarity coefficient Local scheduling rules Dual optimization methods

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Solving symbolic regression problems with uniform design-aided gene expression programming

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JOURNAL OF SUPERCOMPUTING 2013年第3期66卷 1553-1575页

作者： Chen, Yunliang Chen, Dan Khan, Samee U. Huang, Jianzhong Xie, Changsheng Huazhong Univ Sci & Technol Sch Comp Sci Wuhan 430074 Hubei Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China N Dakota State Univ Dept Elect & Comp Engn Fargo ND 58105 USA

gene expression programming (GEP) significantly surpasses traditional evolutionary approaches to solving symbolic regression problems. However, existing GEP algorithms still suffer from premature convergence and slow evolution in anaphase. Aiming at these pitfalls, we designed a novel evolutionary algorithm, namely Uniform Design-Aided gene expression programming (UGEP). UGEP uses (1) a mixed-level uniform table for generating initial population and (2) multiparent crossover operators by taking advantages of the dispersibility of uniform design. In addition to a theoretic analysis, we compared UGEP to existing GEP variants via a number of experiments in dealing with symbolic regression problems including function fitting and chaotic time series prediction. Experimental results indicate that UGEP excels in terms of both the capability of achieving the global optimum and the convergence speed in solving symbolic regression problems.

关键词： gene expression programming Uniform design Symbolic regression problem Function fitting Time series prediction

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