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Flexible Water Distribution System Design under Future Demand Uncertainty

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2015年第4期141卷 04014067-04014067页

作者： Basupi, Innocent Kapelan, Zoran Univ Exeter Coll Engn Math & Phys Sci Ctr Water Syst Exeter EX4 4QF Devon England

In order to address the issue of water demand uncertainty due to the effect of climate change and urbanization in the design and management of water distribution systems (WDSs), a flexible methodology that combines sampling techniques (Monte Carlo-MC or Latin Hypercube-LH simulations), decision tree analysis, and genetic algorithm optimization is presented. The methodology gives flexible and optimal decisions as future water demand unfolds. The problem of optimal WDS design under uncertain future water demand is formulated here as a multiobjective optimization problem. The two objectives are as follows: (1) minimisation of total intervention cost;and (2) maximization of WDS end resilience. The decision variables are the conventional design interventions (e.g., pipe duplication and/or replacement of existing pipes with new ones, addition of tanks and pumps, etc.) and the water demand threshold values. The output from the nondominated sorting genetic algorithm (NSGA2) optimisation process is the Pareto front containing staged (developmental) design solutions represented in a decision tree form with an optimal water demand threshold value that results due to the trade-off in terms of the two objectives analyzed over the planning horizon. This methodology was applied on the New York Tunnels and the Anytown network problems. The results show that there is value achieved by building flexibility in design when compared to the deterministic approach in the long-term planning of WDSs under uncertainty. (C) 2014 American Society of Civil Engineers.

关键词： Uncertainty Flexible design genetic algorithm optimization Water distribution systems

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Hybrid vibration control of the membrane antenna experiencing maneuver with cable actuators

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THIN-WALLED STRUCTURES 2023年第1期192卷

作者： Shao, Qi Lu, Yifan Shi, Chuang Yue, Honghao Lv, Liangliang Liu, Rongqiang Fang, Guangqiang Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China China Aerosp Sci & Technol Grp Co Ltd Space Struct & Mech Technol Lab Shanghai 201108 Peoples R China

This work investigates a novel hybrid vibration control approach of a membrane antenna with cable actuators considering the rigid-flexible coupling effect induced by maneuver. The dynamic model of the membrane antenna is first formulated using finite element analysis, incorporating nonlinearity and rigid-flexible coupling. Tension cables are selected as actuators due to their wide influence range and lightweight properties, without the need for additional accessories on the membrane. Based on these foundations, a hybrid control strategy is proposed, comprising two sub-controllers for the during-maneuver and after-maneuver stages. The feedforward sub-controller during maneuver is designed based on the system's transfer function and optimized using the genetic algorithm, with the purpose of minimizing shape deflection. The feedback sub-controller for postmaneuver vibration suppression is designed based on the linear quadratic regulator (LQR), with a general controller expression of cable actuators provided. Finally, numerical simulations demonstrate the robustness and efficiency of the proposed control scheme for the membrane antenna. The proposed scheme leverages the rapid responses of feedforward control and reliable suppression of feedback control, offering a new perspective on control scheme design for maneuver-induced vibration and presenting a general controller expression for cable actuators based on state space equations.

关键词： Vibration control Membrane antenna Cable-driven actuator Hybrid control genetic algorithm optimization

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Biogas maximization using data-driven modelling with uncertainty analysis and genetic algorithm for municipal wastewater anaerobic digestion

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021年 293卷 112875-112875页

作者： Asadi, Mohsen McPhedran, Kerry Univ Saskatchewan Dept Civil Geol & Environm Engn Saskatoon SK Canada

Anaerobic digestion processes create biogases that can be useful sources of energy. The development of datadriven models of anaerobic digestion processes via operating parameters can lead to increased biogas production rates, resulting in greater energy production, through process modification and optimization. This study assessed processed and unprocessed input operating parameter variables for the development of regression models with transparent structures ('white-box' models) to: (1) estimate biogas production rates from municipal wastewater treatment plant (MWTP) anaerobic digestors;(2) compare their performances to artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) models with opaque structures ('black-box' models) using Monte Carlo Simulation for uncertainty analysis;and (3) integrate the models with a genetic algorithm (GA) to optimize operating parameters for maximization of MWTP biogas production rates. The input variables were anaerobic digestion operating parameters from a MWTP including volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate, which were processed via correlation tests and principal component analysis. Overall, the results indicated that the processed data did not improve regression model performances. Additionally, the developed non-linear regression model with the unprocessed inputs had the best performance based on values including R = 0.81, RMSE = 0.95, and IA = 0.89. However, this model was less accurate, but interestingly had less uncertainty, as compared to ANN and ANFIS models which indicates the compromise between model accuracy and uncertainty. Thus, all three models were coupled with GA optimization with maximum biogas production rate estimates of 22.0, 23.1, and 28.6 m3/min for ANN, ANFIS, and nonlinear regression models, respectively.

关键词： Municipal wastewater treatment Anaerobic digestion processes Data-driven modelling Monte Carlo simulation genetic algorithm optimization

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Energy, exergy, economic, environmental (4E) and dynamic analysis based global optimization of chemical looping air separation for oxygen and power co-production

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ENERGY 2022年第PartB期261卷

作者： Tao, Ye Tian, Wende Kong, Lingqi Sun, Suli Fan, Chenyang Qingdao Univ Sci & Technol Coll Chem Engn Qingdao 266042 Peoples R China Qingdao Univ Sci & Technol Coll Marine Sci & Biol Engn Qingdao 266042 Peoples R China

Chemical looping air separation (CLAS) is a promising oxygen generation technology due to its energy conser-vation nature. It can be integrated with power generation system (PGS) to improve energy efficiency by utilizing high waste heat of CLAS system. This work presents a global optimization strategy for revamping the CLAS-PGS system, in which the comprehensive performance of the system is evaluated by a novel energy, exergy, economic, environmental, and dynamic (4E-1D) analysis method. First, two original after-burner reheating (ABR) and mixed heating (MH) technologies are proposed to optimize the CLAS-HI-PGS scheme. Second, all the schemes are simulated to provide steady-state simulation results for global 4E analysis, and optimized by genetic algorithm (GA) with minimum capital investment cost (CIC) as target to further improve energy efficiency of CLAS-PGS. Finally, the dynamic behaviors of the schemes are investigated with effective control schemes designed to ensure safe and stable operation of CLAS-PGS. In order to measure additional losses caused by disturbance, the 4E analysis is also combined with dynamic analysis. The results show that the scheme combing MH with CLAS-PGS (CLAS-MH-PGS) displays best, proving the effectiveness of this proposal and providing a new way for CLAS-PGS industrial application.

关键词： Chemical looping air separation 4E analysis Dynamic simulation genetic algorithm optimization Power generation system

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Vector-dependent Functionally Pooled ARX Models for the Identification of Systems Under Multiple Operating Conditions

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IFAC Proceedings Volumes 2012年第16期45卷 310-315页

作者： Fotis P. Kopsaftopoulos Spilios D. Fassois Mechanical Systems & Automation (SMSA) Laboratory Dept. of Mechanical & Aeronautical Engr. University of Patras 26504 Patras Greece

The identification of stochastic systems operating under multiple conditions is addressed based on data records obtained under a sample of these conditions. The problem is important in many practical applications and is tackled within a recently introduced Functional Pooling framework. The study focuses on the case of operating conditions characterized by several parameters. Global Vector-dependent Functionally Pooled models of the ARX type are postulated, proper estimators based on the Least Squares and Maximum Likelihood principles are formulated, and their strong consistency and asymptotic normality are established. For model structure selection a genetic algorithm based procedure is formulated. The performance characteristics of the methods are assessed via a Monte Carlo study.

关键词： stochastic systems identification algorithms ARX models asymptotic analysis genetic algorithm optimization Monte Carlo simulations

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A novel hysteretic restoring force model for shear link dampers: A machine learning approach

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STRUCTURES 2024年 70卷

作者： Elgammal, Ahmed Ali, Yasmin Sichuan Univ Coll Architecture & Environm Dept Civil Engn Chengdu 610065 Peoples R China

This research presents an enhanced restoring force model for shear link dampers along with a machine learningbased approach to predict its hardening parameters. The conducted work comprised three distinct phases. Initially, a numerical investigation utilizing ANSYS Workbench was conducted on 350 shear link dampers, taking into account the nonlinearity of the material as well as geometric imperfections. The study focused on the impact of geometric properties on key hysteretic parameters, and analytical formulas were derived to estimate shear yield strength, cyclic web shear buckling rotation angle, and elastic stiffness. In the second phase, the original restoring force model from earlier studies was enhanced and further simplified. Subsequently, a genetic algorithm implemented in MATLAB determined the optimal hardening parameters of the proposed model for each specimen, aligning the analytical hysteretic curves with those from the numerical study. Results indicated that the proposed model more accurately simulated shear link damper hysteretic response compared to the original model. The third and final phase involved constructing and training an artificial neural network (ANN) using hardening parameters obtained in the second phase as targets, with shear link damper geometric properties as inputs. The ANN demonstrated robust learning and high accuracy. To validate the efficiency of the proposed analytical model, blind testing against experimentally tested specimens was conducted, confirming that the model effectively mimicked the experimental hysteretic response of shear link dampers.

关键词： Cyclic loading Hysteretic response Material nonlinearity Finite element genetic algorithm optimization Artificial neural network

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Enhanced Carbon/Oxygen Ratio Logging Interpretation Methods and Applications in Offshore Oilfields

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PROCESSES 2024年第10期12卷 2301页

作者： Zhou, Wei Lin, Yaoting Gao, Gang Wang, Peng Guangdong Ocean Univ Sch Comp Sci & Engn Yangjiang 529500 Peoples R China Guangdong Ocean Univ Yangjiang Res Inst Yangjiang 529500 Peoples R China Yangtze Univ Key Lab Explorat Technol Oil & Gas Resources Minist Educ Wuhan 430100 Peoples R China

As the development of most domestic and international oilfields progresses, many fields have entered a mature phase characterized by high water cut and high recovery, with water cut levels often exceeding 90%. Carbon/oxygen ratio logging has proven to be an indispensable tool for distinguishing oil layers from water layers in complex environments, especially where salinity is low, unknown, or highly variable. This logging method has become one of the most effective techniques for determining residual oil saturation in cased wells, providing critical insights into the oil-water interface. In this study, we evaluate two key interpretation models for carbon/oxygen ratio logging: the fan chart method and the ratio chart method. We optimize the interpretation parameters in the ratio chart model using an improved genetic algorithm, which significantly enhances interpretation precision. The optimized parameters enable a more seamless integration of logging results with reservoir and conventional logging data, reducing the influence of lithological variations and physical property differences on the measurements. This research establishes a robust theoretical foundation for enhancing the interpretation accuracy of carbon/oxygen ratio logging, which is crucial for effectively identifying water-flooded layers. These advancements provide vital technical support for monitoring oil-water dynamics, optimizing reservoir management, and improving production efficiency in oilfield development.

关键词： carbon/oxygen logging fan chart interpretation ratio chart analysis genetic algorithm optimization residual oil saturation

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XGB-SEGA coupled energy saving method for wastewater treatment plants

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APPLIED WATER SCIENCE 2024年第2期14卷 29页

作者： Wang, Zijian Zhou, Xuwen Wang, Hao Huang, Zehan Ji, Jiantao Peng, Zhaoxu Jiang, Kun Beijing Univ Technol Fac Architecture Civil & Transportat Engn Beijing 100124 Peoples R China Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Harbin Inst Technol Sch Civil & Environm Engn Shenzhen 518055 Peoples R China Cent Plains Environm Protect Co Ltd Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Ecol & Environm Zhengzhou 450001 Peoples R China CSD Water Serv CO Ltd Beijing 100124 Peoples R China

To reduce the amount of energy consumed in wastewater treatment plants, nine methods were used to select the key operation parameters that affected energy consumption according to daily operation records, and an intelligent operation management system based on a genetic algorithm was constructed by mapping the relationships between energy consumption and the key operation parameters. The results showed that the prediction and management of energy consumption could be achieved by incorporating the strengthened elastic genetic algorithm into the extreme gradient boosting model. The main parameters affecting energy consumption were the influent flow rate, effluent total nitrogen, NH4+-N loading rate, etc., and the energy consumption could be reduced by 13-27% (with an average of 22%). The parameters were all selected from the daily operation records of the wastewater treatment plant, and no additional complex data acquisition system was needed to collect specific parameters. This study provided a cost-effective strategy to reduce energy consumption in wastewater treatment plants.

关键词： Energy saving Extreme gradient boosting (XGB) genetic algorithm optimization Carbon neutrality Wastewater treatment

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A genetic approach for building different alphabets for peptide and protein classification

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BMC BIOINFORMATICS 2008年第1期9卷 45-45页

作者： Nanni, Loris Lumini, Alessandra Univ Bologna DEIS I-47023 Cesena FC Italy

Background: In this paper, it is proposed an optimization approach for producing reduced alphabets for peptide classification, using a genetic algorithm. The classification task is performed by a multi-classifier system where each classifier (Linear or Radial Basis function Support Vector Machines) is trained using features extracted by different reduced alphabets. Each alphabet is constructed by a genetic algorithm whose objective function is the maximization of the area under the ROC-curve obtained in several classification problems. Results: The new approach has been tested in three peptide classification problems: HIV-protease, recognition of T-cell epitopes and prediction of peptides that bind human leukocyte antigens. The tests demonstrate that the idea of training a pool classifiers by reduced alphabets, created using a genetic algorithm, allows an improvement over other state-of-the-art feature extraction methods. Conclusion: The validity of the novel strategy for creating reduced alphabets is demonstrated by the performance improvement obtained by the proposed approach with respect to other reduced alphabets-based methods in the tested problems.

关键词： genetic algorithm Support Vector Machine Human Leukocyte Antigen Linear Support Vector Machine genetic algorithm optimization

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A domain-based approach to predict protein-protein interactions

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BMC BIOINFORMATICS 2007年第1期8卷 199-199页

作者： Singhal, Mudita Resat, Haluk Pacific NW Natl Lab Computat Biol & Bioinformat Grp Richland WA 99352 USA

Background: Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins. Results: DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis;we conclude that our DomainGA method shows great promise to be applicable across multiple organisms. Conclusion: We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are

关键词： Domain Pair genetic algorithm optimization Interaction Score Detection Rule Protein Data Bank Structure

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