Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

The forecasting of future value of water consumption in an urban area is highly complex and nonlinear. It often exhibits a high degree of spatial and temporal variability. It is a crucial factor for long-term sustainable management and improvement of the operation of urban water allocation system. This chapter will study the application of two pre-processing phase space reconstruction (PSR) and wavelet decomposition transform (WDT) methods to investigate the behavior of time series to forecast short-term water demand value of Kelowna City (BC, Canada). The research proposes two pre-process technique to improve the accuracy of the models. Artificial neural networks (ANNs), gene expression programming (GEP) and multilinear regression (MLR) methods are the tools that considered for forecasting the demand values. Evaluation of the tools is based on two steps with and without applying the pre-processing methods. Moreover, autocorrelation function (ACF) is used to calculate the lag time. Correlation dimension is used to study the chaotic behavior of the dataset. The models’ relative performance is compared using three different fitness indexes; coefficient of determination (CD), root mean square error (RMSE) and mean absolute error (MAE). The results showed how pre-processing combination of WDT and PSR improved the performance of the models in forecasting short-term demand values

Basin Effects in Strong Ground Motion: A Case Study from the 2015 Gorkha, Nepal, Earthquake

The term "basin effects" refers to entrapment and reverberation of earthquake waves in soft sedimentary deposits underlain by concave basement rock structures. Basin effects can significantly affect the amplitude, frequency, and duration of strong ground motion, while the cone-like geometry of the basin edges gives rise to large amplitude surface waves through seismic wave diffraction and energy focusing, a well-known characteristic of basin effects. In this research, we study the role of basin effects in the mainshock ground motion data recorded at the Kathmandu Basin, Nepal, during the 2015 M_w7.8 Gorkha earthquake sequence. We specifically try to understand the source of the unusual low frequency reverberating pulse that appeared systematically across the basin, and the unexpected depletion of the ground surface motions from high frequency components, especially away from the basin edges. In order to do that we study the response of a 2D cross section of Kathmandu Basin subjected to vertically propagating plane SV waves. Despite the scarcity of geotechnical information and of strong ground motion recordings, we show that an idealized plane-strain elastic model with a simplified layered velocity structure can capture surprisingly well the low frequency components of the basin ground response. We finally couple the 2D elastic simulation with a 1D nonlinear analysis of the shallow basin sediments. The 1D nonlinear approximation shows improved performance over a larger frequency range relative to the first order approximation of a 2D elastic layered basin response

Application of artificial intelligence to evaluate the fresh properties of self-consolidating concrete

This paper numerically investigates the required superplasticizer (SP) demand for self-consolidating concrete (SCC) as a valuable information source to obtain a durable SCC. In this regard, an adaptive neuro-fuzzy inference system (ANFIS) is integrated with three metaheuristic algorithms to evaluate a dataset from non-destructive tests. Hence, five different non-destructive testing methods, including J-ring test, V-funnel test, U-box test, 3 min slump value and 50 min slump (T50) value were performed. Then, three metaheuristic algorithms, namely particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution optimization (DEO), were considered to predict the SP demand of SCC mixtures. To compare the optimization algorithms, ANFIS parameters were kept constant (clusters = 10, train samples = 70% and test samples = 30%). The metaheuristic parameters were adjusted, and each algorithm was tuned to attain the best performance. In general, it was found that the ANFIS method is a good base to be combined with other optimization algorithms. The results indicated that hybrid algorithms (ANFIS-PSO, ANFIS-DEO and ANFIS-ACO) can be used as reliable prediction methods and considered as an alternative for experimental techniques. In order to perform a reliable analogy of the developed algorithms, three evaluation criteria were employed, including root mean square error (RMSE), Pearson correlation coefficient (r) and determination regression coefficient (R2). As a result, the ANFIS-PSO algorithm represented the most accurate prediction of SP demand with RMSE = 0.0633, r = 0.9387 and R2 = 0.9871 in the testing phase

Electrochemical determination of tramadol using modified screen-printed electrode

The detection of tramadol using a screen-printed electrode (SPE) modified with La3+/ZnO nano-flowers and multi-walled carbon nanotubes (La3+/ZnO NFs-MWCNTs/SPE) is reported in this work. To examine electrochemical oxidation of tramadol, the modified electrode was implemented with the utilization of differential pulse voltammetry, chronoamperometry and cyclic voltammetry as diagnostic techniques. The proposed electrode displays favorable electrocatalytic behavior concerning tramadol oxidation with an approximately 330 mV potential shift to less positive potential. In the range of tramadol concentrations of 0.5 to 800.0 μM, differential pulse voltammetry displays the linear response. Tramadol detection limit of 0.08 μM was achievedwithin optimized testing conditions for this sensorof simple construction. Lastly, thefabricated sensor was utilized for the determination oftramadol in urine samples

Utilizing artificial intelligence to predict the superplasticizer demand of self-consolidating concrete incorporating pumice, slag, and fly ash powders

Self-consolidating concrete (SCC) is a well-known type of concrete, which has been employed in different structural applications due to providing desirable properties. Different studies have been performed to obtain a sustainable mix design and enhance the fresh properties of SCC. In this study, an adaptive neuro-fuzzy inference system (ANFIS) algorithm is developed to predict the superplasticizer (SP) demand and select the most significant parameter of the fresh properties of optimum mix design. For this purpose, a comprehensive database consisting of verified test results of SCC incorporating cement replacement powders including pumice, slag, and fly ash (FA) has been employed. In this regard, at first, fresh properties tests including the J-ring, V-funnel, U-box, and different time interval slump values were considered to collect the datasets. At the second stage, five models of ANFIS were adjusted and the most precise method for predicting the SP demand was identified. The correlation coefficient (R2), Pearson’s correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), and Wilmot’s index of agreement (WI) were used as the measures of precision. Later, the most effective parameters on the prediction of SP demand were evaluated by the developed ANFIS. Based on the analytical results, the employed algorithm was successfully able to predict the SP demand of SCC with high accuracy. Finally, it was deduced that the V-funnel test is the most reliable method for estimating the SP demand value and a significant parameter for SCC mix design as it led to the lowest training root mean square error (RMSE) compared to other non-destructive testing methods

A sustainable approach for site selection of underground hydrogen storage facilities using fuzzy-delphi methodology

One of the consequences of rapid global population growth is the increase in the energy demand. Currently, the main source of energy for various applications is fossil fuels, which are not renewable and their utilization at large scales have caused a number of environmental issues such as global warming. Hydrogen is one of the main renewable energy sources; however, its utilization has not yet been sufficiently commercialized due to some existing technical issues. For large-scale underground Hydrogen storage facilities, selecting the most suitable set-up location is accounted to be a crucial factor in order to use Hydrogen as a promising and environmentally friendly energy carrier. This study aims to develop an expert judgment approach for the prioritization of criteria involving site selection of large-scale Hydrogen storage facilities to support development of modern cities and industries. In this regard, Fuzzy-Delphi methodology was used to prioritize the criteria and sub-criteria, which seemed to be most relevant for the underground Hydrogen storage site selection process. A comprehensive screening was performed in the literature and eighteen criteria from technical, economic, health, safety and environment (HST) and social points of view were extracted. A professional questionnaire was designed for the criteria prioritization and SPSS 25.0 was employed to analyse the achieved results. According to the gained results, the most important sub-criteria were identified as legal restrictions, reservoir permeability and porosity, and regional risks. Also, the findings demonstrated that HSE and technical issues of sustainability for the site selection of H2 underground storage were more underscored in comparison to economic and social criteria. It is concluded that more in-depth studies are still needed to cover more aspects of sustainability regarding site selection for underground gas storages with special focus on social dimensions.publishersversionpublishe

Effective permeability of an immiscible fluid in porous media determined from its geometric state

Based on the phenomenological extension of Darcy's law, two-fluid flow is dependent on a relative permeability function of saturation only that is process/path dependent with an underlying dependency on pore structure. For applications, fuel cells to underground $CO_2$ storage, it is imperative to determine the effective phase permeability relationships where the traditional approach is based on the inverse modelling of time-consuming experiments. The underlying reason is that the fundamental upscaling step from pore to Darcy scale, which links the pore structure of the porous medium to the continuum hydraulic conductivities, is not solved. Herein, we develop an Artificial Neural Network (ANN) that relies on fundamental geometrical relationships to determine the mechanical energy dissipation during creeping immiscible two-fluid flow. The developed ANN is based on a prescribed set of state variables based on physical insights that predicts the effective permeability of 4,500 unseen pore-scale geometrical states with $R^2 = 0.98$.Comment: 6 Pages, 2 Figures, and Supporting Materia