Super-Wettable Macrocycle Polymer Membranes for Effective Oil-Water Separation.

ABSTRACTThe world’s highest energy consumer (HC) countries currently constitute around 62% of the world energy consumption. Therefore, it is highly important to model their energy consumption to obtain an estimated profile of future world energy consumption. In this study the HCs’ energy consumptions are modeled using artificial neural networks (ANNs). The models are developed based on economic and demographic variables, which are gross domestic product, population, import, and export of the countries selected. Performance of the derived models is assessed using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE) for the testing data. The contribution rate of each variable to the HCs’ energy consumption are also determined to demonstrate the governing variables on the energy consumption. The results show that the correlation coefficients between the ANN predictions and actual energy consumptions are higher than 90%. This indicates a high reliability of the models for forecasting future energy consumption of the HC. Additionally, MAPE, MAE and RMSE values indicate that the ANN models can give adequate forecasting for the HCs’ energy consumption. Furthermore, contribution rates of input variables on energy consumption also indicate that energy consumption of each country studied is governed by different variables. It is expected that this study will be helpful for developing highly applicable energy policies for the HC countries. Furthermore, the results of this study can also be used for determining future trends in the global energy demand.

Investigating novel thin-film nanocomposite membranes for sustainable water recovery using fertilizer-driven forward osmosis – Experimental and machine learning approaches

Human activity is an important contributor to local temperature change, especially in urban areas. Energy consumption is treated here as an index of the intensity of human induced local thermal forcing. The relationship between energy consumption and temperature change is analyzed in China by Observation Minus Reanalysis (OMR) method. Temperature trends for observation, reanalysis and OMR are estimated from meteorological records and 2 m‐temperature from NCEP/NCAR Reanalysis 1 for the period 1979–2007. A spatial mapping scheme based on the spatial and temporal relationship between energy consumption and Gross Domestic Production (GDP) is developed to derive the spatial distribution of energy consumption of China in 2003. A positive relationship between energy consumption and OMR trends is found in high and mid energy consumption region. OMR trends decline with the decreasing intensity of human activity from 0.20°C/decade in high energy consumption region to 0.13°C/decade in mid energy consumption region. Forty‐four stations in high energy consumption region that are exposed to the largest human impact are selected to investigate the impact of energy consumption spatial pattern on temperature change. Results show human impact on temperature trends is highly dependent on spatial pattern of energy consumption. OMR trends decline from energy consumption center to surrounding areas (0.26 to 0.04°C/decade) and get strengthened as the spatial extent of high energy consumption area expands (0.14 to 0.25°C/decade).

The air is polluted and the atmospheric pollution has becoming a great problem for human beings due to the high rate consumption of energy. Most of the scientists dealing with the air pollution state that the first reason of all environmental problems is the population increase. Depending on population, the high consumption of energy (including heating purposes, electricity production, transportation and industries) agricultural activities, waste disposal and the pressure for destroying forest areas create air pollution problem. The atmosphere is polluted and the concentrations of pollutants have increased tremendously. Karabuk Province is one of the famous provinces of heavy industries. There are 3 air quality measurement stations. In this study, the last 2 years hourly and daily air pollutants (PM10, SO2 , NO, NOx , NO2 , O3 and CO) were studied as a time series by using graphical and statistical approaches. The results were considered seasonally and annually. The meteorological condition is also very effective in atmospheric pollution. Moreover, the industrial production and consumption of energy are also high pressure on Karabuk province. The concentrations in each station are also showing different characteristics during two years’ period. The highest concentrations (such as PM10, SO2 , NO, NOx ) are, as usual, seen in winter season due to industrial usage of energy for production and household consumption of energy for heating. However, for O3 , the highest concentration is observed in summer season, because the atmospheric ozone trend is supposed to show an opposite trend compared with NO due to photochemical reaction with these gases. For CO, the maximum concentration is recorded as 4779 μg/m3 in august in 2015. The analyses of data have shown that, the atmosphere is polluted highly with PM10, SO2 , NO, NOx , NO2 , and CO during winter season. The households are very effective using high coal for heating purposes in Karabuk Province.

Robust superhydrophilic and underwater superoleophobic membrane optimized by Cu doping modified metal-organic frameworks for oil-water separation and water purification

Engineers continue to be concerned about electrical discharge-machined components’ high energy consumption, machining debris, and poor dimensional precision. The aim of this research is to propose a hybrid neuro-genetic approach to improve the machinability of the electrical discharge machining (EDM) of the Nimonic C263 superalloy. This approach focuses on reducing the energy consumption and negative environmental impacts. The material removal rate (MRR), electrode wear ratio (EWR), specific energy consumption (SEC), surface roughness (Ra), machining debris (db), and circularity (C) are examined as a function of machining parameters such as the voltage (V), pulse on time (Ton), current (I), duty factor (τ), and electrode type. By employing the VIKOR method, all the responses are transformed into a distinctive VIKOR index (VI). Neuro-genetic methods (a hybrid VIKOR-based ANN-GA) can further enhance the best possible result from the VIKOR index. During this step, the hybrid technique (VIKOR-based ANN-GA) is used to estimate an overall improvement of 9.87% in the response, and an experiment is conducted to confirm this condition of optimal machining. This work is competent enough to provide aeroengineers with an energy-efficient, satisfying workplace by lowering the machining costs and increasing productivity.

An integrated strategy for improving the desalination performances of activated carbon-based capacitive deionization systems

Conventional sewage treatment based on biological and chemical methods have made historical contributions to humans. However, it breaks the biogeochemical cycles of carbon, nitrogen, and phosphorus and cannot remove hazardous materials including viruses and nano/microplastics. Therefore, we rethought the conceptual revolution of principles of sewage treatment in the 1890s, that is, “the replacement of a philosophy that saw sewage purification as the prevention of decomposition with one that tried to facilitate the biological processes that destroy sewage naturally”. We proposed a promising sewage treatment system based on physical separation, which mainly consists of the source separators and the insoluble-pollutants separators, soluble-pollutants separators, and the wastewater heat recovery devices in wastewater treatment plants. By using the promising system, the carbon in wastewater will be recovered by sending biosolids directly into the soil after removing the hazardous materials and organic toxicity. The nitrogen and phosphorus in wastewater will be sent back into the soil or be used for hydroponics rather than be mineralized. The thermal energy in wastewater will be recovered and reused, and the hazardous materials will be removed. As a result, the promising system will turn the wastewater treatment system with high resource and thermal energy waste and high energy consumption into a no-chemicals, green factory. At present, nonetheless, it is still urgent to develop more advanced insoluble-pollutants separators and soluble-pollutants separators with high separation efficiency and low energy consumption, especially volume separators. Because the volume separators (e.g., functionalized sand filters) have the potential for replacing the surface separators (e.g., membranes).

NH3 is not only an important component of agricultural and industrial production, but also an extremely promising energy carrier and storage intermediate. Currently, the Haber‐Bosch process used in industry for NH3 production has shortcomings such as high energy consumption and low output. The electrocatalytic nitrogen reduction reaction (NRR) can improve the route and conditions of NH3 synthesis through high‐efficient electrocatalyst, and realize the production mode of high efficiency and low energy consumption. Therefore, the design and synthesis of the NRR electrocatalysts with high catalytic performance are very important. Here, the first principles calculation based on density functional theory was used to form alloy catalysts by using Mn and Fe atoms instead of nine Ir atoms on the surface of Ir(100), and the electrocatalytic performance of the NRR was systematically studied. The results showed that N2 could be stably adsorbed on Mn9@Ir(100) and Fe9@Ir(100) in the side‐on configuration. The possible reaction pathways were analyzed and discussed, and the enzymatic pathway was determined to be the best. Through the simulation of the entire NRR process, it was found that the limit potential was only −0.659 and −0.647 V for Mn9@Ir(100) and Fe9@Ir(100). In addition, the electronic properties of Mn9@Ir(100) and Fe9@Ir(100) were analyzed utilizing charge density difference and density of states, and the reasons for their high activity were obtained. We hope this work can not only reduce the number of noble metals and develop highly active catalysts, but also provide theoretical support and guidance for the catalytic mechanism of alloy electrocatalysts.

Alternative technology for the recovery of butyl acetate with low concentration: high capacity adsorbent and high efficiency adsorption

Water-energy-greenhouse gas nexus of a novel high-rate activated sludge-two-stage vertical up-flow constructed wetland system for low-carbon wastewater treatment

Apache Hadoop has evolved significantly over the last years, with more than 60 releases bringing new features. By implementing the MapReduce programming paradigm and leveraging HDFS, its distributed file system, Hadoop has become a reliable and fault tolerant middleware for parallel and distributed computing over large datasets. Nevertheless, Hadoop may struggle under certain workloads, resulting in poor performance and high energy consumption. Users increasingly demand that high performance computing solutions being to address sustainability and limit power consumption. In this paper, we introduce HDFSH, a hybrid storage mechanism for HDFS, which uses a combination of Hard Disks and Solid-State Disks to achieve higher performance while saving power in Hadoop computations. HDFSH brings to middleware the best from HDs (affordable cost per GB and high storage capacity) and SSDs (high throughput and low energy consumption) in a configurable fashion, using dedicated storage zones for each storage device type. We implemented our mechanism as a block placement policy for HDFS, and assessed it over six recent releases of the Hadoop project, representing different designs of the Hadoop middleware. Results indicate that our approach increases overall job performance while decreasing the energy consumption under most hybrid configurations evaluated. Our results also showed that in many cases storing only part of the data in SSDs results in significant energy savings and execution speedups.

Resource scheduling plays a crucial role in improving resource utilization rate and user service quality of cloud datacenter. An efficient resource scheduling algorithm enables the datacenter to achieve load balancing, becoming the core of enterprise development. However, at present, the scheduling algorithm of cloud datacenter is usually lack of dynamics, and the calculation is relatively complex. When searching for the optimal scheme, it is easy to fall into the local optimal value, resulting in a large amount of calculation, high energy consumption, low QoS (Quality of Service) and low resource utilization. In this paper, we focus on the prevalent problems of lacking of dynamics, the high makespan and energy consumption in cloud datacenter and design a dynamic load balancing schedule framework. In this framework, we propose an improved random walk algorithm which searches the global optimal scheme with simpler computing. We compare our proposed improved random walk algorithm with Round Rabin algorithm and Particle Swarm Optimization (PSO) algorithm. The experimental results prove that our proposed algorithm improves the utilization rate of resources. Particularly, the makespan of our proposed random walk algorithm is 7% lower than PSO's and the overall energy consumption of ours algorithm is about 15% lower than PSO's.

Nanoscale optical writing using far-field super-resolution methods provides an unprecedented approach for high-capacity data storage. However, current nanoscale optical writing methods typically rely on photoinitiation and photoinhibition with high beam intensity, high energy consumption, and short device life span. We demonstrate a simple and broadly applicable method based on resonance energy transfer from lanthanide-doped upconversion nanoparticles to graphene oxide for nanoscale optical writing. The transfer of high-energy quanta from upconversion nanoparticles induces a localized chemical reduction in graphene oxide flakes for optical writing, with a lateral feature size of ~50 nm (1/20th of the wavelength) under an inhibition intensity of 11.25 MW cm-2 Upconversion resonance energy transfer may enable next-generation optical data storage with high capacity and low energy consumption, while offering a powerful tool for energy-efficient nanofabrication of flexible electronic devices.

Study on the flow and heat transfer characteristics of gaseous hydrogen in heat exchange tubes coupled with ortho-para hydrogen conversion