Bioelectrochemical production of hydrogen in an innovative pressure-retarded osmosis/microbial electrolysis cell system: experiments and modeling

Background While microbial electrolysis cells (MECs) can simultaneously produce bioelectrochemical hydrogen and treat wastewater, they consume considerable energy to overcome the unfavorable thermodynamics, which is not sustainable and economically feasible in practical applications. This study presents a proof-of-concept system in which hydrogen can be produced in an MEC powered by theoretically predicated energy from pressure-retarded osmosis (PRO). The system consists of a PRO unit that extracts high-quality water and generates electricity from water osmosis, and an MEC for organic removal and hydrogen production. The feasibility of the system was demonstrated using simulated PRO performance (in terms of energy production and effluent quality) and experimental MEC results (e.g., hydrogen production and organic removal). Results The PRO and MEC models were proven to be valid. The model predicted that the PRO unit could produce 485 mL of clean water and 579 J of energy with 600 mL of draw solution (0.8 M of NaCl). The amount of the predicated energy was applied to the MEC by a power supply, which drove the MEC to remove 93.7 % of the organic compounds and produce 32.8 mL of H2 experimentally. Increasing the PRO influent volume and draw concentration could produce more energy for the MEC operation, and correspondingly increase the MEC hydraulic retention time (HRT) and total hydrogen production. The models predicted that at an external voltage of 0.9 V, the MEC energy consumption reached the maximum PRO energy production. With a higher external voltage, the MEC energy consumption would exceed the PRO energy production, leading to negative effects on both organic removal and hydrogen production. Conclusions The PRO-MEC system holds great promise in addressing water-energy nexus through organic removal, hydrogen production, and water recovery: (1) the PRO unit can reduce the volume of wastewater and extract clean water; (2) the PRO effluents can be further treated by the MEC; and (3) the osmotic energy harvested from the PRO unit can be applied to the MEC for sustainable bioelectrochemical hydrogen production.NPRP grant # 6-289-2-125 from the Qatar National Research Fund (a member of Qatar Foundation)

Mrdbscan: An efficient parallel density-based clustering algorithm using mapreduce

Abstract-Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. In this paper, we propose an efficient parallel density-based clustering algorithm and implement it by a 4-stages MapReduce paradigm. Furthermore, we adopt a quick partitioning strategy for large scale non-indexed data. We study the metric of merge among bordering partitions and make optimizations on it. At last, we evaluate our work on real large scale datasets using Hadoop platform. Results reveal that the speedup and scaleup of our work are very efficient

Strategies towards enabling lithium metal in batteries: interphases and electrodes

Despite the continuous increase in capacity, lithium-ion intercalation batteries are approaching their performance limits. As a result, research is intensifying on next-generation battery technologies. The use of a lithium metal anode promises the highest theoretical energy density and enables use of lithium-free or novel high-energy cathodes. However, the lithium metal anode suffers from poor morphological stability and Coulombic efficiency during cycling, especially in liquid electrolytes. In contrast to solid electrolytes, liquid electrolytes have the advantage of high ionic conductivity and good wetting of the anode, despite the lithium metal volume change during cycling. Rapid capacity fade due to inhomogeneous deposition and dissolution of lithium is the main hindrance to the successful utilization of the lithium metal anode in combination with liquid electrolytes. In this perspective, we discuss how experimental and theoretical insights can provide possible pathways for reversible cycling of twodimensional lithium metal. Therefore, we discuss improvements in the understanding of lithium metal nucleation, deposition, and stripping on the nanoscale. As the solid–electrolyte interphase (SEI) plays a key role in the lithium morphology, we discuss how the proper SEI design might allow stable cycling. We highlight recent advances in conventional and (localized) highly concentrated electrolytes in view of their respective SEIs. We also discuss artificial interphases and three-dimensional host frameworks, which show prospects of mitigating morphological instabilities and suppressing large shape change on the electrode level

Theoretically-Efficient and Practical Parallel DBSCAN

The DBSCAN method for spatial clustering has received significant attention due to its applicability in a variety of data analysis tasks. There are fast sequential algorithms for DBSCAN in Euclidean space that take $O(n\log n)$ work for two dimensions, sub-quadratic work for three or more dimensions, and can be computed approximately in linear work for any constant number of dimensions. However, existing parallel DBSCAN algorithms require quadratic work in the worst case, making them inefficient for large datasets. This paper bridges the gap between theory and practice of parallel DBSCAN by presenting new parallel algorithms for Euclidean exact DBSCAN and approximate DBSCAN that match the work bounds of their sequential counterparts, and are highly parallel (polylogarithmic depth). We present implementations of our algorithms along with optimizations that improve their practical performance. We perform a comprehensive experimental evaluation of our algorithms on a variety of datasets and parameter settings. Our experiments on a 36-core machine with hyper-threading show that we outperform existing parallel DBSCAN implementations by up to several orders of magnitude, and achieve speedups by up to 33x over the best sequential algorithms

Numerical Simulation of the Influence of Natural Fractures on Hydraulic Fracture Propagation

According to the theory of plane mechanics involving the interaction of hydraulic and natural fractures, the law of hydraulic fracture propagation under the influence of natural fractures is verified using theoretical analysis and RFPA2D-Flow numerical simulation approaches. The shear and tensile failure mechanisms of rock are simultaneously considered. Furthermore, the effects of the approach angle, principal stress difference, tensile strength and length of the natural fracture, and elastic modulus and Poisson’s ratio of the reservoir on the propagation law of a hydraulic fracture are investigated. The following results are obtained: (1) The numerical results agree with the experimental data, indicating that the RFPA2D-Flow software can be used to examine the hydraulic fracture propagation process under the action of natural fractures. (2) In the case of a low principal stress difference and low approach angle, the hydraulic fracture likely causes shear failure along the tip of the natural fracture. However, under a high stress difference and high approach angle, the hydraulic fracture spreads directly through the natural fracture along the original direction. (3) When natural fractures with a low tensile strength encounter hydraulic fractures, the hydraulic fractures likely deviate and expand along the natural fractures. However, in the case of natural fractures with a high tensile strength, the natural fracture surface is closed, and the hydraulic fracture directly passes through the natural fracture, propagating along the direction of the maximum principal stress. (4) Under the same principal stress difference, a longer natural fracture corresponds to the easier initiation and expansion of a hydraulic fracture from the tip of the natural fracture. However, when the size of the natural fracture is small, the hydraulic fracture tends to propagate directly through the natural fracture. (5) A smaller elastic modulus and larger Poisson’s ratio of the reservoir result in a larger fracture initiation pressure. The presented findings can provide theoretical guidance regarding the hydraulic fracturing of reservoirs with natural fractures

Treatment and desalination of domestic wastewater for water reuse in a four-chamber microbial desalination cell

Microbial desalination cells (MDCs) have been studied for contaminant removal from wastewater and salinity reduction in saline water. However, in an MDC wastewater treatment and desalination occurs in different streams, and high salinity of the treated wastewater creates challenges for wastewater reuse. Herein, a single-stream MDC (SMDC) with four chambers was developed for simultaneous organic removal and desalination in the same synthetic wastewater. This SMDC could achieve a desalination rate of 12.2–31.5 mg L−1 h−1 and remove more than 90 % of the organics and 75 % of NH4 +-N; the pH imbalance between the anode and cathode chambers was also reduced. Several strategies such as controlling catholyte pH, increasing influent COD concentration, adopting the batch mode, applying external voltage, and increasing the alkalinity of wastewater were investigated for improving the SMDC performance. Under a condition of 0.4 V external voltage, anolyte pH adjustment, and a batch mode, the SMDC decreased the wastewater salinity from 1.45 to below 0.75 mS cm−1, which met the salinity standard of wastewater for irrigation. Those results encourage further development of the SMDC technology for sustainable wastewater treatment and reuse.Scopu

MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data

DBSCAN (density-based spatial clustering of applications with noise) is an important spatial clustering technique that is widely adopted in numerous applications. As the size of datasets is extremely large nowadays, parallel processing of complex data analysis such as DBSCAN becomes indispensable. However, there are three major drawbacks in the existing parallel DBSCAN algorithms. First, they fail to properly balance the load among parallel tasks, especially when data are heavily skewed. Second, the scalability of these algorithms is limited because not all the critical sub-procedures are parallelized. Third, most of them are not primarily designed for shared-nothing environments, which makes them less portable to emerging parallel processing paradigms. In this paper, we present MR-DBSCAN, a scalable DBSCAN algorithm using MapReduce. In our algorithm, all the critical sub-procedures are fully parallelized. As such, there is no performance bottleneck caused by sequential processing. Most importantly, we propose a novel data partitioning method based on computation cost estimation. The objective is to achieve desirable load balancing even in the context of heavily skewed data. Besides, We conduct our evaluation using real large datasets with up to 1.2 billion points. The experiment results well confirm the efficiency and scalability of MR-DBSCAN

Linking User Online Behavior across Domains with Internet Traffic

We are facing an era of Online With Offline (OWO) in the smart city - almost everyone is using various online services to connect friends, watch videos, listen to the music, download resources, and so on. Our online behaviors are separated by different domains, which may cause serious problem in the area of cross-domain recommendation, advertising, and criminal tracking in online and offline world, since it is a very challenging task to link user online behaviors belonging to the same natural person. Existing methods usually tackle user online behavior linkage problem by estimating the profile content similarity between two different online services. However, the profile contents in heterogeneous online services are unreliable or misaligned, and the proposed methods are always limited to several services in a specific domain. In order to link individual's online behavior across domains, in this paper, we propose user Online Behavior Linkage across Domains (OBLD), a novel hybrid model, to link user online behavior across domains with Internet traffic. It derives several signifficant attributes from users' online behaviors, such as user digital identity, various fingerprints of terminals and browsers, spatio-temporal behavior of users, and leverages a supervised classi_cation method to discover the relationship between users' online behaviors. Also, the proposed model has unsupervised setting for dataset with non or few label data if a certain percentage of user digital identities can be extracted from original dataset. By using real-world network traffic collected from two large provinces in China, we evaluate the OBLD model and the linkage precision achieves 89% and 97.9% for two datasets respectively. Especially, the inputs of OBLD, i.e., network traffic flows, cover all online behavior of users who connect with Internet through monitored networks, which makes it possible to link online behaviors of users in whole online world

Understanding electricity generation in osmotic microbial fuel cells through integrated experimental investigation and mathematical modeling

(Graph Presented). Osmotic microbial fuel cells (OsMFCs) are a new type of MFCs with integrating forward osmosis (FO). However, it is not well understood why electricity generation is improved in OsMFCs compared to regular MFCs. Herein, an approach integrating experimental investigation and mathematical model was adopted to address the question. Both an OsMFC and an MFC achieved similar organic removal efficiency, but the OsMFC generated higher current than the MFC with or without water flux, resulting from the lower resistance of FO membrane. Combining NaCl and glucose as a catholyte demonstrated that the catholyte conductivity affected the electricity generation in the OsMFC. A mathematical model of OsMFCs was developed and validated with the experimental data. The model predicated the variation of internal resistance with increasing water flux, and confirmed the importance of membrane resistance. Increasing water flux with higher catholyte conductivity could decrease the membrane resistance.This work was supported by NPRP grant #6-289-2-125 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The authors would like to thank Peppers Ferry Regional Wastewater Treatment Plant for providing inocula