Modeling of local scour depth downstream hydraulic structures in trapezoidal channel using GEP and ANNs

AbstractLocal scour downstream stilling basins is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour depth. Lack estimation of local scour can endanger to stability of hydraulic structure and can cause risk of failure. This paper presents Gene expression program (GEP) and artificial neural network (ANNs), to simulate local scour depth downstream hydraulic structures. The experimental data is collected from the literature for the scour depth downstream the stilling basin through a trapezoidal channel. Using GEP approach gives satisfactory results compared with artificial neural network (ANN) and multiple linear regression (MLR) modeling in predicting the scour depth downstream of hydraulic structures

Supporting Decision on Energy vs. Asset Cost Optimization in Drinking Water Distribution Networks

AbstractOne of the challenges for water utilities is the optimal asset design (i.e. maximum power of pump systems, tank volumes and pipe diameters) of water distribution networks (WDN) while optimizing operational efficiency (i.e. energy consumption and cost). Besides the classical minimization of capital cost while providing sufficient supply service, the operational sustainability is an emerging issue. As the reduction of each component of capital and energy costs are conflicting with each other, the optimization problem is multi-objective. This work presents the study of the robustness of solutions of the Pareto set as a further element to support the decision

An Integrated Modeling Approach to Optimize the Management of a Water Distribution System: Improving the Sustainability While Dealing with Water Loss, Energy Consumption and Environmental Impacts

Research article There is a strong link between water and energy in municipal water systems then the Alliance to Save Energy coined the term "Watergy" [1]. Each component of the integrated water system contributes differently to the energy balance. With regard to urban water distribution systems (WDS), the pumping energy cost represents the single largest part of the total operational cost, also magnified by every litre of water lost to leaks. Even a small increase in operational efficiency may result in significant cost savings to the water industries. Therefore the inefficient management of water distribution systems results not only into depletion of water resources but also into energy consumption that increase CO2 emissions related also to the treatment of water volumes greater than needed, with use of excessive chemical components and consequent higher environmental global impact. The research outlined in this contribution was born with the aim to develop appropriate methodologies and tools to support the optimization of the WDS performance, in terms of water saving and reduction of energy consumptions and consequently environmental impacts. The integration of advanced WDS hydraulic modelling with a material and energy flow analysis is proposed herein, where the output of the hydraulic simulations permits to get more accurate input for a metabolic analysis of the system Next phases of this research will test the integrated model under different scenarios, aimed at quantifying the environmental impact of different WDS management solutions by means of selected indicator

INPUT SELECTION BY EPR-MOGA

The growing availability of field data, from information and communication technologies (ICTs) in "smart'' urban infrastructures, allows data modeling to understand complex phenomena and to support management decisions. Among the analyzed phenomena, those related to storm water quality modeling have recently been gaining interest in the scientific literature. Nonetheless, the large amount of available data poses the problem of selecting relevant variables to describe a phenomenon and enable robust data modeling. This paper presents a procedure for the selection of relevant input variables using the multi-objective evolutionary polynomial regression (EPR-MOGA) paradigm. The procedure is based on scrutinizing the explanatory variables that appear inside the set of EPR-MOGA symbolic model expressions of increasing complexity and goodness of fit to target output. The strategy also enables the selection to be validated by engineering judgement. In such context, the multiple case study extension of EPR-MOGA, called MCS-EPR-MOGA, is adopted. The application of the proposed procedure to modeling storm water quality parameters in two French catchments shows that it was able to significantly reduce the number of explanatory variables for successive analyses. Finally, the EPR-MOGA models obtained after the input selection are compared with those obtained by using the same technique without benefitting from input selection and with those obtained in previous works where other data-modeling techniques were used on the same data. The comparison highlights the effectiveness of both EPR-MOGA and the input selection procedure

Detecting anomalies in water distribution networks using EPR modelling paradigm

This is the author accepted manuscript. The final version is available from IWA Publishing via the DOI in this record.Sustainable management of water distribution networks (WDNs) requires effective exploitation of available data from pressure/flow devices. Water companies collect a large amount of such data, which need to be managed correctly and analysed effectively using appropriate techniques. Furthermore, water companies need to balance the data gathering and handling costs with the benefits of extracting useful information. Recent approaches implementing data mining techniques for analysing pressure/flow data appear very promising, because they can automate mundane tasks involved in data analysis process and efficiently deal with sensor data collected. Furthermore, they rely on empirical observations of a WDN behaviour over time, allowing reproducing/predicting possible future behaviour of the network. This paper investigates the effectiveness of the evolutionary polynomial regression (EPR) paradigm to reproduce the behaviour of a WDN using online data recorded by low-cost pressure/flow devices. Using data from a real district metered area, the case study presented shows that by using the EPR paradigm a model can be built which enables the accurate reproduction and prediction of the WDN behaviour over time and detection of flow anomalies due to possible unreported bursts or unknown increase of water withdrawal. Such an EPR model might be integrated into an early warning system to raise alarms when anomalies are detected.The research reported in this paper was founded by two projects of the Italian Scientific Research Program of National Interest PRIN-2012: ‘Analysis tools for management of water losses in urban aqueducts’ and ‘Tools and procedures for advanced and sustainable management of water distribution networks’

Determination of semi-empirical models for mean wave overtopping using an evolutionary polynomial paradigm

The present work employs the so-called Evolutionary Polynomial Regression (EPR) algorithm to build up a formula for the assessment of mean wave overtopping discharge for smooth sea dikes and vertical walls. EPR is a data-mining tool that combines and integrates numerical regression and genetic programming. This technique is here employed to dig into the relationship between the mean discharge and main hydraulic and structural parameters that characterize the problem under study. The parameters are chosen based on the existing and most used semi-empirical formulas for wave overtopping assessment. Besides the structural freeboard or local wave height, the unified models highlight the importance of local water depth and wave period in combination with foreshore slope and dike slope on the overtopping phenomena, which are combined in a unique parameter that is defined either as equivalent or imaginary slope. The obtained models aim to represent a trade-off between accuracy and parsimony. The final formula is simple but can be employed for a preliminary assessment of overtopping rates, covering the full range of dike slopes, from mild to vertical walls, and of water depths from the shoreline to deep water, including structures with emergent toes.This research was funded by European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No.: 792370.Peer ReviewedPostprint (published version

하천 오염물질 혼합 해석을 위한 저장대 모형의 매개변수 산정법 및 경험식 개발

학위논문(석사)--서울대학교 대학원 :공과대학 건설환경공학부,2019. 8. 서일원.Analyses of solute transport and retention mechanism are essential to manage water quality and river ecosystem. As reported by tracer injection studies that have been conducted to identify solute transport mechanism, concentration curves measured in natural stream have steep rising and long tail parts. This phenomenon is due to solute exchange process between transient storage zones and the main river stream. The transient storage model (TSM) is one of the most widely used models for describing solute transport in natural stream, taking transient storage exchange process into consideration. In order to use this model, calibration of four TSM parameters is necessary. Inverse modelling using measured breakthrough curves (BTCs) from tracer injection test is general method for TSM parameter calibration. However, it is not feasible to carry out performing tracer injection tests, for every parameter calibration. For that reasons, empirical formulae with hydraulic data, which is comparatively easier to obtain, have been proposed for the purpose of parameter estimation. This study presents two methods for TSM parameter estimation. At first, inverse modelling method employing global optimization framework Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), that incorporating famous evolutionary algorithms in water resource management field, was suggested. Second, TSM parameter empirical equations were derived adopting Multigene Genetic Programming (MGGP) based symbolic regression library GPTIPS and using Principal Components Regression (PCR). In terms of general performance, equations of this study were superior to published empirical equations.하천의 수질을 관리하기 위해서는 자연하천에서 유입된 물질이 이송되고 지체되는 메카니즘을 규명하고 이해하는 것이 필요하다. 하천에서의 물질 혼합을 이해하기 위해 수행된 추적자 실험 연구들에 따르면 자연하천에서 계측되는 농도곡선에서는 가파른 상승부와 긴 꼬리기 관측되는 것으로 알려졌다. 이러한 현상은 주로 물질이 흐르는 본류대와 잠시 물질이 포획되었다가 재방출되는 본류대와 저장대 간의 물질교환 효과 때문에 일어난다고 알려져 있다. 이러한 저장대 물질교환 효과를 모사하는 저장대모형 중 Transient Storage zone Model (TSM)은 가장 광범위하게 이용되는 모형으로, 이를 이용하기 위해선 네 가지의 저장대 매개변수를 보정하여야 한다. 네 가지 저장대 매개변수를 결정하는 방법으로는 일반적으로 현장실험에서 측정된 농도곡선을 이용한 역산모형이 이용된다. 그러나 매개변수가 필요할 때마다 추적자실험을 수행하여 역산모형을 이용하는 것은 현실적으로 불가능한 경우가 있어 이러한 경우에는 비교적 취득하기 쉬운 수리지형학적 인자들을 이용해 매개변수를 산정하는 방법이 이용될 수 있다. 따라서 본 연구에서는 TSM 매개변수를 결정하기 위해 두 가지 방법을 제시하였다. 첫 번째로, 전역 최적화 프레임워크인 Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL)을 이용한 역산모형 기반 TSM 매개변수 산정 프레임워크를 제시하였다. 둘째로는 기호회귀법 라이브러리인 GPTIPS를 이용한 다중유전자 유전 프로그래밍(Multigene Genetic Programming, MGGP) 과 주성분회귀법(Principal Components Regression, PCR)을 통해 네 가지 매개변수 별로 각 두 개씩의 경험식이 개발되었다. 개발된 경험식들의 성능평가 결과, 선행 연구에서 제시된 저장대 매개변수 식에 비해 본 연구에서 제시된 방법이 대체적으로 우수한 것으로 나타났다. 결과적으로 본 연구에서는 분석을 통해 실무적으로 활용 가능한 TSM 매개변수 산정 프레임워크와 경험식들이 제시되었으며, 이 방법들은 추적자 실험 자료의 유무에 따라 TSM의 매개변수 결정에 유용하게 사용될 것으로 기대된다.Chapter 1. Introduction 1 1.1 Necessity and Background of Research 1 1.2 Objectives 12 Chapter 2. Theoretical Background 15 2.1 Transient Storage Model 15 2.1.1. Mechanisms of Transient Storage 15 2.1.2. Models Accounting for Transient Storage 21 2.1.2.1 The one Zone Transient Storage Model (1Z-TSM) 24 2.1.2.2 The two Zone Transient Storage Model (2Z-TSM) 25 2.1.2.3 The Continuous Time Random Walk Approach (CTRW) 26 2.1.2.4 The Modified Advection Dispersion Model (MADE) 27 2.1.2.5 The Fractional Advection Dispersion Equation Model (FADE) 28 2.1.2.6 The Multirate Mass Transfer Model (MRMT) 29 2.1.2.7 The Advective Storage Path Model (ASP) 30 2.1.2.8 The Solute Transport in Rivers Model (STIR) 31 2.1.2.9 The Aggregate Dead Zone Model (ADZ) 34 2.2 Empirical Equations for Predicting Transient Storage Model Parameters 39 2.3 Parameter Estimation 47 2.3.1. The SC-SAHEL Framework 50 2.3.1.1 Modified Competitive Complex Evolution (MCCE) 52 2.3.1.2 Modified Frog Leaping (MFL) 52 2.3.1.3 Modified Grey Wolf Optimizer (GWO) 53 2.3.1.4 Modified Differential Evolution (DE) 53 2.4 Regression Method 54 2.4.1. The Multi-Gene Genetic Programming (MGGP) 56 2.4.1.1 The Simple Genetic Programming 56 2.4.1.2 Scaled Symbolic Regression via Multi-Gene Genetic Programming 57 2.4.2. Evolutionary Polynomial Regression (EPR) 61 2.4.2.1 Main Flow of EPR Procedure 62 Chapter 3. Model Development 66 3.1 Numerical Model 66 3.1.1. Model Validation 69 3.2 Merger of TSM-SC-SAHEL 73 3.3 Further assessments for the parameter estimation framework 76 3.3.1. Tracer Test Description 76 3.3.2. Grid Independency of Estimation 81 3.3.3. Choice of Optimization Setting 85 Chapter 4. Development of Formulae for Predicting TSM Parameter 91 4.1 Dimensional Analysis 91 4.2 Data Collection via Meta Analysis 95 4.3 Formulae Development 106 Chapter 5. Result and Discussion 110 5.1 Model Performances 110 5.2 Sensitivity Analysis 118 5.3 In-stream Application of Empirical Equations 130 Chapter 6. Conclusion 140 References 144 Appendix. I. The mean, minimum, and maximum values of the model fitness value and number of evolution using the SC-SAHEL with single-EA and multi-EA 159 Appendix. II. Used dimensionless datasets for development of empirical equations 161 국문초록 165Maste

Lost in optimisation of water distribution systems? A literature review of system operation

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Optimisation of the operation of water distribution systems has been an active research field for almost half a century. It has focused mainly on optimal pump operation to minimise pumping costs and optimal water quality management to ensure that standards at customer nodes are met. This paper provides a systematic review by bringing together over two hundred publications from the past three decades, which are relevant to operational optimisation of water distribution systems, particularly optimal pump operation, valve control and system operation for water quality purposes of both urban drinking and regional multiquality water distribution systems. Uniquely, it also contains substantial and thorough information for over one hundred publications in a tabular form, which lists optimisation models inclusive of objectives, constraints, decision variables, solution methodologies used and other details. Research challenges in terms of simulation models, optimisation model formulation, selection of optimisation method and postprocessing needs have also been identified

Development and Applications of Self-learning Simulation in Finite Element Analysis

Numerical analysis such as the finite element analysis (FEA) have been widely used to solve many engineering problems. Constitutive modelling is an important component of any numerical analysis and is used to describe the material behaviour. The accuracy and reliability of numerical analysis is greatly reliant on the constitutive model that is integrated in the finite element code. In recent years, data mining techniques such as artificial neural network (ANN), genetic programming (GP) and evolutionary polynomial regression (EPR) have been employed as alternative approach to the conventional constitutive modelling. In particular, EPR offers great advantages over other data mining techniques. However, these techniques require a large database to learn and extract the material behaviour. On the other hand, the link between laboratory or field tests and numerical analysis is still weak and more investigation is needed to improve the way that they matched each other. Training a data mining technique within the self-learning simulation framework is currently considered as one of the solutions that can be utilised to accurately represent the actual material behaviour. In this thesis an EPR based machine learning technique is utilised in the heart of the self-learning framework with an automation process which is coded in MATLAB environment. The methodology is applied to simulate different material behaviour in a number of structural and geotechnical applications. Two training strategies are used to train the EPR in the developed framework, total stress-strain and incremental stress-strain strategies. The results show that integrating EPR based models in the framework allows to learn the material response during the self-learning process and provide accurate predictions to the actual behaviour. Moreover, for the first time, the behaviour of a complex material, frozen soil, is modelled based on the EPR approach. The results of the EPR model predictions are compared with the actual data and it is shown that the proposed model can capture and reproduce the behaviour of the frozen soil with a very high accuracy. The developed EPR based self-learning methodology presents a unified approach to material modelling that can also help the user to gain a deeper insight into the behaviour of the materials. The methodology is generic and can be extended to modelling different engineering materials

Treatment of Aqueous Arsenic Using Chemically and Electrochemically Modified Biomaterials

This research hypothesizes that agricultural residues can be transformed into value-added adsorbents with the ability to remove arsenic from water effectively. Modifying these residues is expected to enhance adsorption capacity, providing a promising solution to address the global issue of arsenic contamination in water. Arsenic (As) contamination in drinking water poses a significant global water health hazard. To address this issue, adsorption has gained increasing attention as a promising technology for As removal, offering improved cost efficiency and simplified treatment operations. The utilization of agricultural biomass residues as As adsorbents has emerged as a promising approach. However, untreated biomasses exhibit limitations such as low sorption capacity, coloration issues in treated waters, increased BOD, COD, and TOC levels due to the release of soluble organic compounds, poor solubility and stability, and their unsuitability for conventional process equipment due to their fragile nature and swelling. It is evident that modification of biomasses is necessary to overcome these drawbacks, enabling their application at commercial and industrial scales. Metal oxides, such as iron oxide, have demonstrated high efficacy as As adsorbents. However, due to their small particle size, using them in adsorption systems presents challenges. In the last two decades, the use of the terms engineered- or modified-biomass/biochar has been gaining momentum in the literature. These terms refer to chemically treated biomasses/biochars produced to improve adsorption capacities. Generally, the modification process consists of impregnating the biomass/biochar with a metal agent with a high affinity to the target contaminant (e.g., As). The resulting composite material consists of the original biomass or biochar acting as a support structure for the metal oxide, which serves as the active site for adsorption. This modification process can significantly improve the adsorption capacity, selectivity, and stability of the material, making it a more effective adsorbent for water treatment applications. The modification process is typically conducted by mixing the untreated biomass/biochar materials in a chemical solution. However, this process can be time-consuming, is difficult to control product formation, and results in potentially unstable and unreproducible physicochemical properties of the modified materials. Clearly, it would be beneficial to have a better method for this modification process. The electrochemical modification offers a promising and simplified approach to overcome the limitations associated with chemical-based methods for preparing modified biomass/biochar adsorbents. This technique involves a two-electrode system within an electrochemical cell, where a sacrificial anode (such as Fe) eliminates the need for external chemical modifiers. The modification process begins by passing an electric current through the electrodes in an electrolyte solution, while the biomass/biochar materials are continuously stirred within the solution between the electrodes. As the sacrificial anode dissolves, the modifying agent ions (e.g., Fe2+) are released and undergo a series of reactions, resulting in the deposition of the desired modifying agent (e.g., iron oxide) onto the surface of the biomass/biochar. The overall objective of the conducted research over three years was the development of biomass/biochar-based adsorbents with high As adsorption capacities using chemical and electrochemical modification methods. Seven different adsorbents, including chemically iron-loaded biomass (ICS), electrochemically iron-loaded biomass and biochar (OBM and OBC), microwave-assisted and electrochemically iron and manganese-loaded biochar (MnBC and FeMnBC), and microwave-assisted and electrochemically aluminum and zinc-loaded biochar (ZnBC, and AlZnBC) were developed and used for As adsorption. The effects of parameters affecting the As adsorption capacity of the modified adsorbents were investigated, and the modification conditions were optimized. The concept of the shrinking core model was applied to develop a mathematical model to characterize the As adsorption process for OBM and OBC. A diffusional mass transfer model (DMTM) was developed to identify the external mass transfer coefficient (kf), effective pore volume diffusion coefficient (Dp), and surface diffusion coefficient (Ds) in the adsorption of As onto MnBC and FeMnBC. Finally, a model was developed to study temperature distribution inside the biomass during pyrolysis for making ZnBC. Overall, the modified adsorbents prepared through the applied modification methods exhibited exceptional performance in removing both As(III) and As(V) from water. However, their adsorption capacities varied depending on the optimization considerations for the modification conditions, resulting in higher efficiency for either As(III) or As(V) removal. The combination of microwave pyrolysis and electrochemical treatment demonstrated significant potential as an alternative to traditional pyrolysis and chemical treatment processes, showcasing its viability for preparing biomass/biochar-based adsorbents. Moreover, extensive investigations were conducted to assess the influence of different adsorption conditions on the As adsorption capacity of the modified adsorbents. Parameters such as pH, temperature, initial As concentration, and adsorption time were systematically studied, revealing their significant impact on the adsorption capacity. By analyzing the effects of these adsorption conditions, valuable insights were obtained regarding the underlying mechanisms governing the adsorption process. Notably, chemisorption was found to be the dominant and rate-limiting mechanism in most cases. This understanding of the adsorption mechanisms is crucial for optimizing the adsorption process and designing effective As removal strategies using the modified adsorbents. This research provides a substantial contribution to the field by offering valuable insights into the conversion of agricultural residues into value-added and efficient adsorbents for As removal. By exploring different modification methods and optimizing the adsorption conditions, this study paves the way for the development of enhanced water treatment approaches to address the challenges posed by As contamination. Moreover, the findings have the potential to be extended to the development of adsorbents for other contaminants. By harnessing the potential of biomass-based adsorbents, this research not only expands our knowledge regarding the utilization of agricultural residues but also provides practical solutions for mitigating water pollution issues