A Closed Loop Wastewater Biorefinery Based on the Recently Introduced SHEFROL Reactor and Two Freely Available Macrophytes

With an objective to develop a closed loop wastewater biorefinery, the performance of two free floating aquatic weeds water hyacinth (Eichhornia crassipes, Mart, Solms) and salvinia (Salvinia molesta, Mitchell) is explored when either is used as the main bioagent for treating sewage in “sheet flow root level” (SHEFROL) bioreactors. Both the macrophytes prove to be easy to propagate and maintain while they are able to help in achieving significant primary, secondary, and tertiary treatment steps of greywater in a single step. Water hyacinth is able to remove suspended solids, chemical oxygen demand (COD), biological oxygen demand (BOD), total Kjeldahl nitrogen, soluble phosphorous, zinc, copper, nickel, and manganese to the average extents of 81%, 79%, 43%, 41%, 40%, 36%, 34%, and 29%, respectively. The reduction in COD and BOD levels achieved by salvinia is marginally lesser than by water hyacinth but in terms of the removal of other pollutants, the performance of salvinia is comparable to that of water hyacinth. It is possible to convert the dead or harvested plants to organic fertilizer thus making it a closed-loop system with no waste of its own.     

A new integration scheme for application to seismic hybrid simulation

Hybrid simulation is a powerful test method for evaluating the seismic performance of structural systems. This method makes it feasible that only critical components of a structure be experimentally tested. This paper presents a newly proposed integration algorithm for seismic hybrid simulation which is aimed to extend its capabilities to a wide range of systems where existing methods encounter some limitations. In the proposed method, which is termed the variable time step (VTS) integration method, an implicit scheme is employed for hybrid simulation by eliminating the iterative phase on experimental element, the phase which is necessary in regular implicit applications. In order to study the effectiveness of the VTS method, a series of numerical investigations are conducted which show the successfulness of the VTS method in obtaining accurate, stable and converged responses. Then, in a comparative approach, the improved accuracy of the VTS method over commonly used integration methods is demonstrated. The stability of the VTS method is also studied and the results show that it provides conditional stability; however, its stability limit is well beyond the accuracy limit. The effect of time delay on the VTS method results is also investigated and it is shown that the VTS method is quite successful in handling this experimental error.     

Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments

Performing a comprehensive risk analysis is primordial to ensure a reliable and sustainable water supply. Though the general framework of risk analysis is well established, specific adaptation seems needed for systems such as water distribution networks (WDN). Understanding of vulnerabilities of WDN against deliberate contamination and consumers’ sensitivity against contaminated water use is very vital to inform decision-maker. This paper presents an innovative step-by-step methodology for developing comprehensive indicators to perform sensitivity, vulnerability and criticality analyses in case of absence of early warning system (EWS). The assessment and the aggregation of these indicators with specific fuzzy operators allow identifying the most critical points in a WDN. Intentional intrusion of contaminants at these points can potentially harm both the consumers as well as water infrastructure. The implementation of the developed methodology has been demonstrated through a case study of a French WDN unequipped with sensors.     

Use of a trench adjacent to a shallow foundation as a mitigation measure for hazards associated with reverse faulting

Acta Geotechnica - Evidence from recent earthquakes reminds us that fault-induced permanent ground displacement has a devastating effect on structures in addition to damage caused by wave...     

Separation of magnetic anomalies into induced and remanent magnetization contributions

Inversion of magnetic data is complicated by the presence of remanent magnetization, and it provides limited information about the magnetic source because of the insufficiency of data and constraint information. We propose a Fourier domain transformation allowing the separation of magnetic anomalies into the components caused by induced and remanent magnetizations. The approach is based on the hypothesis that each isolated source is homogeneous with a uniform and specific Koenigsberger ratio. The distributions of susceptibility and remanent magnetization are subsequently recovered from the separated anomalies. Anomaly components, susceptibility distribution and distribution of the remanent and total magnetization vectors (direction and intensity) can be achieved through the processing of the anomaly components. The proposed method therefore provides a procedure to test the hypotheses about target source and magnetic field, by verifying these models based on available information or a priori information from geology. We test our methods using synthetic and real data acquired over the Zhangfushan iron-ore deposit and the Yeshan polymetallic deposit in eastern China. All the tests yield favourable results and the obtained models are helpful for the geological interpretation.     

Lithology-dependent seismic anisotropic amplitude variation with offset correction in transversely isotropic media

Analysis of amplitude variation with offset is an essential step for reservoir characterization. For an accurate reservoir characterization, the amplitude obtained with an isotropic assumption of the reservoir must be corrected for the anisotropic effects. The objective is seismic anisotropic amplitude correction in an effective medium, and, to this end, values and signs of anisotropic parameter differences (Δδ and Δε) across the reflection interfaces are needed. These parameters can be identified by seismic and well log data. A new technique for anisotropic amplitude correction was developed to modify amplitude changes in seismic data in transversely isotropic media with a vertical axis of symmetry. The results show that characteristics of pre-stack seismic data, that is, amplitude variation with offset gradient, can be potentially related to the sign of anisotropic parameter differences (Δδ and Δε) between two layers of the reflection boundary. The proposed methodology is designed to attain a proper fit between modelled and observed amplitude variation with offset responses, after anisotropic correction, for all possible lithofacies at the reservoir boundary. We first estimate anisotropic parameters, that is, δ and ε, away from the wells through Backus averaging of elastic properties resulted from the first pass of isotropic pre-stack seismic inversion, on input data with no amplitude correction. Next, we estimate the anisotropic parameter differences at reflection interfaces (values and signs of Δδ and Δε). We then generate seismic angle gather data after anisotropic amplitude correction using Rüger's equation for the P-P reflection coefficient. The second pass of isotropic pre-stack seismic inversion is then performed on the amplitude-corrected data, and elastic properties are estimated. Final outcome demonstrates how introduced methodology helps to reduce the uncertainty of elastic property prediction. Pre-stack seismic inversion on amplitude-corrected seismic data results in more accurate elastic property prediction than what can be obtained from non-corrected data. Moreover, a new anisotropy attribute (ν) is presented for improvement of lithology identification.     

Socio-hydrological framework for investigating farmers’ activities affecting the shrinkage of Urmia Lake; hybrid data mining and agent-based modelling

ABSTRACT

Developing a general framework to capture the complexities associated with the non-linear and adaptive nature of farmers facing water resources scarcity is a challenging problem. This paper integrates agent-based modelling (ABM) and a data mining method to develop a hybrid socio-hydrological framework to provide future insights for policy-makers. The data associated with the farmers’ main characteristics were collected through field surveys and interviews. Afterwards, the association rule was employed to discover the main patterns representing the farmers’ agricultural decisions. The discovered patterns were then used as the behavioural rules in ABM to simulate the agricultural activities. The proposed framework has been was applied to explore the interactions between agricultural activities and the main river feeding the Urmia-Lake, Iran. The outcomes indicate that farmers’ acquisitive traits and belongings have significant impacts on their socio-hydrological interactions. The reported values of the efficiency criteria may support the satisfactory performance of the proposed framework.     

Time-frequency decomposition of seismic signals via quantum swarm evolutionary matching pursuit

Matching pursuit belongs to the category of spectral decomposition approaches that use a pre-defined discrete wavelet dictionary in order to decompose a signal adaptively. Although disengaged from windowing issues, matching point demands high computational costs as extraction of all local structure of signal requires a large size dictionary. Thus in order to find the best match wavelet, it is required to search the whole space. To reduce the computational cost of greedy matching pursuit, two artificial intelligence methods, (1) quantum inspired evolutionary algorithm and (2) particle swarm optimization, are introduced for two successive steps: (a) initial estimation and (b) optimization of wavelet parameters. We call this algorithm quantum swarm evolutionary matching pursuit. Quantum swarm evolutionary matching pursuit starts with a small colony of population at which each individual, is potentially a transformed form of a time-frequency atom. To attain maximum pursuit of the potential candidate wavelets with the residual, the colony members are adjusted in an evolutionary way. In addition, the quantum computing concepts such as quantum bit, quantum gate, and superposition of states are introduced into the method. The algorithm parameters such as social and cognitive learning factors, population size and global migration period are optimized using seismic signals. In applying matching pursuit to geophysical data, typically complex trace attributes are used for initial estimation of wavelet parameters, however, in this study it was shown that using complex trace attributes are sensitive to noisy data and would have lower rate of convergence. The algorithm performance over noisy signals, using non-orthogonal dictionaries are investigated and compared with other methods such as orthogonal matching pursuit. The results illustrate that quantum swarm evolutionary matching pursuit has the least sensitivity to noise and higher rate of convergence. Finally, the algorithm is applied to both modelled seismograms and real data for detection of low frequency anomalies to validate the findings.     

Assessment of geomorphological bank evolution of the alluvial threshold rivers based on entropy concept parameters

The complex stream bank profiles in alluvial channels and rivers that are formed after reaching equilibrium has been a popular topic of research for many geomorphologists and river engineers. The entropy theory has recently been successfully applied to this problem. However, the existing methods restrict the further application of the entropy parameter to determine the cross-section slope of the river banks. To solve this limitation, we introduce a novel approach in the extraction of the equation based on the calculation of the entropy parameter (λ) and the transverse slope of the bank profile at threshold channel conditions. The effects of different hydraulic and geometric parameters are evaluated on a variation of the entropy parameter. Sensitivity analysis on the parameters affecting the entropy parameter shows that the most effective parameter on the λ-slope multiplier is the maximum slope of the bank profile and the dimensionless lateral distance of the river banks.