The dynamic effect of Micro-MHD convection on bubble grown at a horizontal microelectrode

Relatively low efficiency is the biggest obstacle to the popularization of water electrolysis, which is a particularly feasible way to produce super-pure hydrogen. Imposing a magnetic field can increase the hydrogen production efficiency of water electrolysis. However, the enhancement's detailed mechanism still lacks an insightful understanding of the bubbles' micro vicinity. Our recent work aims to understand why the micro-magnetohydrodynamic (MHD) convection hinders single bubbles' detachment on the microelectrode. A water electrolysis experiment by microelectrode is performed under an electrode-normal magnetic field, and dynamic analysis of the single bubble growing on microelectrodes is performed. The variation of bubble diameter with time in the presence or absence of the magnetic field was measured, and the forces acting on the bubble were quantified. The result shows that the micro-MHD convection, induced by Lorentz force, can give rise to a downward hydrodynamic pressure force that will not appear in large-scale MHD convection. This force can be of the same magnitude as the surface tension, so it dramatically hinders bubbles' detachment. Besides, the Kelvin force provides a new potential way for further improving the efficiency of water electrolysis.     

Industrial sugar beets to biofuel: Field to fuel production system and cost estimates

Specialized varieties of sugar beets (Beta vulgaris L.) may be an eligible feedstock for advanced biofuel designation under the USA Energy Independence and Security Act of 2007. These non-food industrial beets could double ethanol production per hectare compared to alternative feedstocks. A mixed-integer mathematical programming model was constructed to determine the breakeven price of ethanol produced from industrial beets, and to determine the optimal size and biorefinery location. The model, based on limited field data, evaluates Southern Plains beet production in a 3-year crop rotation, and beet harvest, transportation, and processing. The optimal strategy depends critically on several assumptions including a just-in-time harvest and delivery system that remains to be tested in field trials. Based on a wet beet to ethanol conversion rate of 110 dm³ Mg⁻¹ and capital cost of 128 M$ for a 152 dam³ y⁻¹ biorefinery, the estimated breakeven ethanol price was 507 $ m⁻³. The average breakeven production cost of corn (Zea mays L.) grain ethanol ranged from 430 to 552 $ m⁻³ based on average net corn feedstock cost of 254 and 396 $ m⁻³ in 2014 and 2013, respectively. The estimated net beet ethanol delivered cost of 207 $ m⁻³ was lower than the average net corn feedstock cost of 254–396$ m⁻³ in 2013 and 2014. If for a mature industry, the cost to process beets was equal to the cost to process corn, the beet breakeven ethanol price would be $387 m^-3 (587 $ m⁻³ gasoline equivalent).     

Optimal siting and sizing of demand response in a transmission constrained system with high wind penetration

This paper describes algorithms that use demand-side management to address large-scale integration of wind power. In particular, demand response (DR) is used to manage wind power intermittency by shifting the time that electrical power system loads occur in response to real-time prices and wind availability. An economic dispatch with transmission, DR capacity and operational constraints is used to model the operation of a transmission constrained system with a high penetration of wind power. This optimization model is used to determine the optimal sizing and distribution of DR given a fixed budget for customer incentives and the installation of enabling technology. We demonstrate the effectiveness of the operational model based on a simple PJM 5-bus system and an IEEE 118-bus system. Simulation results show that transmission constraints have a greater effect on sizing of DR capacity than the location of wind power, which means that buses electrically close to congested lines tend to have higher incentives to deploy DR resources than other buses. The second part of the work examines optimal siting of technology that enables DR based on the frequency of DR based load changes, which are generally a function of the network location.     

Framework to manage multiple goals in community-based energy sharing network in smart grid

Smart grid has opened up a new role of “prosumer” in an energy value network, transforming many conventional energy consumers into prosumers, who not only generate green energy but also share the surplus with utilities and other consumers. The concept of a goal-oriented prosumer community group (PCG) has emerged recently as an effective way to fulfill sustainable energy exchange. Such community-based energy sharing networks comprise multiple irreconcilable objectives such as demand constraints, cost constraints, and income maximization. In many cases, one goal may be achievable only at the expense of other goals. This necessitates the development of an effective framework to manage the multiple goals and reduce the gap with their achievement levels. Therefore, in this research paper, an effective framework is developed to negotiate among the multiple goals and thus to define optimal mutual goals for each PCG in a more sustainable manner using multiple-criteria goal programming techniques. Simulation results are presented to illustrate how the methods work in practical situations, where each of the objective measure is given a target value and the unwanted deviations from this set are minimized in an achievement function.     

Applicability and toxicity evaluation of an adsorbent based on jujube for the removal of toxic heavy metals

The removal of heavy metals from industrial wastewater is important, owing to its eco-toxicity in aqueous environment. In this study, the mechanism and efficiency of the removal of toxic heavy metals by an eco-friendly adsorbent was investigated. Various types of adsorbents made from jujube were synthesized by varying the drying temperature of gel-type beads and elution method for jujube constituents. The maximum adsorption capacity for lead and copper ions was determined using the Langmuir isotherm model, with DJB-A-S-F (freeze-dried jujube bead made from a solution of squeezed autoclaved jujube) having the highest values at 60.44 mg/g (lead) and 20.33 mg/g (copper). In addition, the characteristics of the various adsorbents were determined by the Brunauer–Emmett–Teller (BET) method, scanning electron microscopy (SEM), and energy dispersive X-ray analysis (EDX). Gas chromatography–mass spectrometry (GC–MS) was used to identify the constituents of DJB-A-S-F before and after adsorption of heavy metals (Pb- and Cu-DJB-A-S-F). Furthermore, in vitro cytotoxicity assay was performed to investigate whether the binding of heavy metals to DJB-A-S-F increases cellular toxicity. As a result, no differences in cell viability between DJB-A-S-F and the ones coupled to heavy metals were observed, indicating that the developed adsorbents are non-cytotoxic with good compatibility. Thus, DJB-A-S-F is a promising adsorbent for the removal of toxic heavy metal cations from wastewater.     

Mitigating Hydrological Risk with Energy Derivatives

The variability of river inflows affects the energy production of hydropower generators and may result in reductions in revenues that can be financially disruptive for these producers. Recent climatic changes have highlighted the risks involved in hydroelectriciy production in Brazil. In this paper, we propose a different approach to formulating a collar derivative, namely an Inverted Collar, to mitigate hydrological risk considering the particularities of Brazil's energy regulatory environment. In addition, we propose a customized collar-by-difference as a variation of the collar model. The effect of these derivatives is analyzed considering electricity market price and power generation uncertainty for a typical hydro generator. The results suggest that these derivatives are effective tools to manage hydrological risk during period of great climatic volatility, such as the height of the drought period experienced by Brazil in 2016. The results also indicate that our models outperform traditional commercial hedging commonly practiced by hydropower producers in the country.     

Digital image correlation strain measurement of thick adherend shear test specimen joined with an epoxy film adhesive

Structural bonding and bonded repairs of composite materials become more and more important. Understanding the strain within the bondline leads to suitable bonding design. For new design approaches the strain distribution within the bondline has to be analyzed. Thus, often finite element analysis (FE) are used. However, a huge challenge is the availability of reliable material properties for the adhesives and their validation. Previous work has shown that it is possible to measure the small displacements resulting within thin epoxy film adhesives using high resolution digital image correlation (DIC). In this work a 2D DIC setup with a high resolution consumer camera is used to visualize the strain distribution within the bondline over the length of the joint as well as over the adhesive thickness. Therefore, single lap joints with thick aluminum adherends according to ASTM D 5656 are manufactured and tested. Local 2D DIC strain measurements are performed and analyzed. Two different camera setups are used and compared. The evaluation provides reliable material data and enables a look insight the bondline. The results of the full field strain data measured with DIC are compared with numerical simulations. Thus, material models as well as chosen parameters for the adhesive are validated. Compared to extensometers, giving only point-wise information for fixed measuring points, the DIC allows a virtual point-wise inspection along the complete bondline. Furthermore, it allows measuring close to the bondline to reduce the influence of adherend deformation.     

Advances in multimetallic alloy-based anodes for alkali-ion and alkali-metal batteries

In order to meet the growing demand of portable electronic devices and electric vehicles, enhancements in battery performance metrics are required to provide higher energy/power densities and longer cycle lives, especially for anode materials. Alloying anodes, such as Group IVA elements-based materials, are attracting increasing interest as anodes for next-generation high-performance alkali-metal-ion batteries (AMIBs) owing to their extremely high specific capacities, low working voltages, and natural abundance. Nevertheless, alloying-type anodes usually display unsatisfactory cycle life due to their intrinsic violent volumetric and structural changes during the charge–discharge process, causing mechanical fracture and exacerbating side reactions. In order to overcome these challenges, efforts have been made in recent years to manufacture multimetallic anodes that can accommodate the induced strain, thus showing high Coulomb efficiency and long cycle life. Meanwhile, much work has been conducted to understand the details of structural changes and reaction mechanisms taking place by in-situ characterization methodologies. In this paper, we review the various recent developments in multimetallic anode materials for AMIBs and shed light on optimizing the anode materials. Finally, the perspectives and future challenges in achieving the practical applications of multimetallic alloy anodes in high-energy AMIB systems are proposed.     

ANN inverse analysis based on stochastic small-sample training set simulation

A new approach of inverse analysis is proposed to obtain parameters of a computational model in order to achieve the best agreement with experimental data. The inverse analysis is based on the coupling of a stochastic simulation and an artificial neural network (ANN). The identification parameters play the role of basic random variables with a scatter reflecting the physical range of potential values. A novelty of the approach is the utilization of the efficient small-sample simulation method Latin Hypercube Sampling (LHS) used for the stochastic preparation of the training set utilized in training the artificial neural network. Once the network has been trained, it represents an approximation consequently utilized to solve the key task: To provide the best possible set of model parameters for the given experimental data. The efficiency of the approach is shown using numerical examples from civil engineering computational mechanics.