CTF-former: A novel simplified multi-task learning strategy for simultaneous multivariate chaotic time series prediction

Multivariate chaotic time series prediction is a challenging task, especially when multiple variables are predicted simultaneously. For multiple related prediction tasks typically require multiple models, however, multiple models are difficult to keep synchronization, making immediate communication between predicted values challenging. Although multi-task learning can be applied to this problem, the principles of allocation and layout options between shared and specific representations are ambiguous. To address this issue, a novel simplified multi-task learning method was proposed for the precise implementation of simultaneous multiple chaotic time series prediction tasks. The scheme proposed consists of a cross-convolution operator designed to capture variable correlations and sequence correlations, and an attention module proposed to capture the information embedded in the sequence structure. In the attention module, a non-linear transformation was implemented with convolution, and its local receptive field and the global dependency of the attention mechanism achieve complementarity. In addition, an attention weight calculation was devised that takes into account not only the synergy of time and frequency domain features, but also the fusion of series and channel information. Notably the scheme proposed a purely simplified design principle of multi-task learning by reducing the specific network to single neuron. The precision of the proposed solution and its potential for engineering applications were verified with the Lorenz system and power consumption. The mean absolute error of the proposed method was reduced by an average of 82.9% in the Lorenz system and 19.83% in power consumption compared to the Gated Recurrent Unit.

Fed-batch treatment attenuates diffusional limitation while preparing high solid microfibrillated cellulose from Gelidium amansii

This study investigates the effects of fed-batch treatment on the fibrillation degree and properties of Gelidium amansii-derived microfibrillated cellulose (MFC). Fed-batch milling was conducted with the initial solid loading of 1 % w/v followed by three stages of feeding to obtain a final solid concentration of 5 % w/v. This process provides a high-solid MFC of around 10 %, while batch milling only provides the maximum solid loading of 4 %. It also reduces approximately 83 % power consumption of batch milling at the same solid loading (4 %). The obtained MFC 5 % has lower fibrils length (14.9 µm) and width (16.46 nm), but higher consistency index (>250 Pa.s) than MFC 1 % (22 µm, 21 nm, 5.88 Pa.s). The crystallinity and maximum decomposition temperatures of both MFCs are comparable, varying at 49-53 % and 318 °C-320 °C. In summary, fed-batch treatment is promising for the techno-economic development of MFC production by lowering energy and maintaining product quality.

Power consumption prediction for electric vehicle charging stations and forecasting income

Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

Sodium titanate: A proton conduction material for ppb-level NO2 detection with near-zero power consumption

Constrained by the traditional charge transfer sensing mechanism, it is quite challenging to fabricate NO2 sensors that simultaneously exhibit high sensitivity, rapid response/recovery, and low power consumption. Herein, sodium titanate (NTO), a layered material with abundant surface-rooted OH groups (OHR), is demonstrated to be a promising NO2 sensing material. To understand the sensing behavior of NTO, the influences of operating temperature, applied voltage, and relative humidity are investigated, and a novel OHR-enabled proton conduction sensing mechanism is proposed. The sensing process mainly involves selective NO2 adsorption on OHR, thereby lowering the activation energy for proton transportation along the NTO surface. Meanwhile, the moderate intermolecular interaction makes NO2 both easily adsorbed and desorbed at room temperature. Hence, NTO exhibits a highly sensitive, rapid, and fully recoverable response (∼5.7-1 ppm NO2 within 3 s), wide detection range (1 ppb-20 ppm), good stability (>2 months), and near-zero power consumption (0.5 nW). Finally, we demonstrate that NTO has an excellent practical indoor/outdoor NO2 sensing ability. This work offers a new pathway to resolve the inherent conflicts in available NO2 sensors by using NTO via the OHR-enabled proton conduction sensing mechanism, which may also provide insight into designing high-performance sensors for other gases.

Oily wastewater treatment by a continuous flow electrocoagulation reactor with polarity switch: Assessment of the relation between process variables and the aluminum released to the environment

Electrocoagulation with electrical polarity inversion was used to treat oil in water emulsions (145 ± 5 mg dm-3) using a cylindrical 4.8 dm3 reactor in continuous mode. The effects of spatial time and time between polarity inversion were explored using a three-level full factorial design (32), followed by Spearman correlation (ps), which has shown that the aluminum concentration in the treated effluent is not directly dependent on the mass of aluminum released by the electrodes. Nonetheless, the loss of mass of the electrodes is correlated (ps = 0.6970) to oil removal and to less electric power consumption (ps = -0.6909). Surface response analysis revealed that increasing the number of inversion cycles reduces electrode degradation. The treatment reduced the effluent's chemical oxygen demand by over 92.8%. Regarding environmental impact, there is an inverse statistical correlation between aluminum in the treated effluent and oil removal (ps = -0.7426), indicating that removing more oil with less environmental impact is possible. The better condition, considering oil removal and lower electrode consumption, was obtained with a spatial time of 36 min and a polarity inversion time of 10 s; for this condition, oil removal reached 87.0% with an energy expenditure of about 7.21 kW h.m-3.

Dataset of 15-minute values of active and reactive power consumption of 1000 households during single year

This article presents a comprehensive dataset comprising average 15-minute values of active and reactive energy consumption in 1000 anonymized households located in the Slovak Republic, a central European country, throughout the year 2016. The dataset provides a valuable resource for researchers and practitioners interested in analysing energy consumption patterns at the individual household level within the unique context of Central Europe. Privacy concerns are addressed through anonymization techniques, ensuring the dataset's compliance with ethical considerations and privacy regulations. However, ZIP code information is included for each household. Researchers can confidently analyze the data without compromising the households' confidentiality. The dataset offers significant opportunities for researchers to explore energy consumption patterns, develop targeted energy management strategies, and contribute to the advancement of sustainable energy practices.

Energy simulation and CFD coupled analysis for the optimal operation of combined convection and radiant air conditioning considering dehumidification

The use of radiant panels in homes has increased recently because they do not cause a drafty feeling, unlike air conditioners. However, air conditioners are more power-efficient than radiant panels and have a higher coefficient of performance (COP). Therefore, combining radiant panels and air conditioning can provide an optimal solution for thermal control in residences. Energy simulation (ES) and computational fluid dynamics (CFD) can be used to simulate such environments. ES is suitable for non-steady state calculations, and combined with appropriate modeling, enables an accurate estimation of power consumption. Effective dehumidification becomes necessary, during summer as the relative humidity in a room increases. Both air conditioners and radiant panels can achieve this. This study developed a simulation tool that incorporates the effects of dehumidification. Based on a relative evaluation, a case was proposed where both energy efficiency and comfort were satisfied by jointly using air conditioners and radiant panels. The study found that a small number of panels could achieve the most balanced operation. The results of this study can serve as a reference for general housing, and the developed simulation tool can be applied to product development and building material design.

Study on the performance efficiency, mechanism, power consumption and biochemical properties of E/Ce(IV)/PMS on the enhanced removal of RB19

In this study, an advanced oxidation process with E/Ce(IV) synergistic PMS (E/Ce(IV)/PMS) was established for the efficient removal of Reactive Blue 19 (RB19). The catalytic oxidation performance of different coupling systems was examined and the synergistic effect of E/Ce(IV) with PMS in the system was substantiated. The oxidative removal of RB19 in E/Ce(IV)/PMS was excellent, achieving a removal efficiency of 94.47% and a reasonable power consumption (EE/O value was 3.27 kWh·m^-3). The effect of pH, current density, Ce(IV) concentration, PMS concentration, initial RB19 concentration and water matrix on the removal efficiency of RB19 were explored. Additionally, quenching and EPR experiments showed that the solution contains different radicals such as SO₄^·-, HO∙ and ¹O₂, where ¹O₂ and SO₄^·- played key roles, but HO∙ just acted a weaker role. Ce ion trapping experiment confirmed that Ce(IV) was involved in the reaction process and played a major role (29.91%). RB19 was subject to three possible degradation pathways, and the intermediate products displayed well biochemical properties. To conclude, the degradation mechanism of RB19 was explored and discussed. In the presence of current, E/Ce(IV)/PMS performed a rapid Ce(IV)/Ce(III) cycle, continuously generating strong catalytic oxidation Ce(IV), The reactive radicals derived from the decomposition of PMS, in conjunction with Ce(IV) and direct electro-oxidation, efficiently destroyed the molecular structure of RB19 and showed an efficient removal rate.

FASTory assembly line power consumption data

Machine learning (ML) techniques are widely adopted in manufacturing systems for discovering valuable patterns in shopfloor data. In this regard, machine learning models learn patterns in data to optimize process parameters, forecast equipment deterioration, and plan maintenance strategies among other uses. Thus, this article presents the dataset collected from an assembly line known as the FASTory assembly line. This data contains more than 4,000 data samples of conveyor belt motor driver's power consumption. The FASTory assembly line is equipped with web-based industrial controllers and smart 3-phase energy monitoring modules as an expansion to these controllers. For data collection, an application was developed in a timely manner. The application receives a new data sample as JavaScript Object Notation (JSON) every second. Afterwards, the application extracts the energy data for the relevant phase and persists it in a MySQL database for the purpose of processing at a later stage. This data is collected for two separate cases: static case and dynamic case. In the static case, the power consumption data is collected under different loads and belt tension values. This data is used by a prognostic model (Artificial Neural Network (ANN)) to learn the conveyor belt motor driver's power consumption pattern under different belt tension values and load conditions. The data collected during the dynamic case is used to investigate how the belt tension affects the movement of the pallet between conveyor zones. The knowledge obtained from the power consumption data of the conveyor belt motor driver is used to forecast the gradual behavioural deterioration of the conveyor belts used for the transportation of pallets between processing workstations of discrete manufacturing systems.

A neurodynamic approach for nonsmooth optimal power consumption of intelligent and connected vehicles

This paper investigates a class of power consumption minimization and equalization for intelligent and connected vehicles cooperative system. Accordingly, a distributed optimization problem model related to power consumption and data rate of intelligent and connected vehicles is presented, where the power consumption cost function of each intelligent and connected vehicle may be nonsmooth, and the corresponding control variable is subject to the constraints generated by data acquisition, compression coding, transmission and reception. We propose a distributed subgradient-based neurodynamic approach with projection operator to achieve the optimal power consumption of intelligent and connected vehicles. By differential inclusion and nonsmooth analysis, it is confirmed that the state solution of neurodynamic system converges to the optimal solution of the distributed optimization problem. With the help of the algorithm, all intelligent and connected vehicles asymptotically reach a consensus on an optimal power consumption. Simulation results show that the proposed neurodynamic approach is capable of effectively solving the problem of power consumption optimal control for intelligent and connected vehicles cooperative system.

Multi-objective operation optimization of spent coffee ground torrefaction for carbon-neutral biochar production

Many energy-intensive processes are employed to enhance biomass fuel properties to overcome the difficulties in utilizing biomass as fuel. Therefore, energy conservation during these processes is crucial for realizing a circular bioeconomy. This study develops a newly devised method to evaluate SCG biochars' higher heating value (HHV) and predict moisture content from power consumption. It is found that the increasing rates of HHV immediately follow decreases in power consumption, which could be used to determine the pretreatment time for energy conservation. The non-dominated sorting genetic algorithm II (NSGA-II) maximizes SCG biochar's HHV while minimizing energy consumption. The results show that producing SCG biochar with 23.98 MJ∙kg^-1 HHV requires 20.042 MJ∙kg^-1, using a torrefaction temperature of 244 °C and torrefaction time of 27 min and 43 sec. Every kilogram of biochar with an energy yield of 85.93 % is estimated to cost NT$ 12.21.

Upscale fermenter design for lactic acid production from cheese whey permeate focusing on impeller selection and energy optimization

This study focusses on the design and scale-up of industrial lactic acid production by fermentation of dairy cheese whey permeate based on standard methodological parameters. The aim was to address the shortcomings of standard scale-up methodologies and provide a framework for fermenter scale-up that enables the accurate estimation of energy consumption by suitable selection of turbine and speed for industrial deployment. Moreover, life cycle assessment (LCA) was carried out to identify the potential impacts and possibilities to reduce the operation associated emissions at an early stage. The findings showed that a 3000 times scale-up strategy assuming constant geometric dimensions and specific energy consumption (P/V_w) resulted in lower impeller speed and energy demand. The Rushton turbine blade (RTB) and LightninA315 four-blade hydrofoil (LA315) were found to have the highest and lowest torque output, respectively, at a similar P/V_w of 2.8 kWm⁻³, with agitation speeds of 1.33 and 2.5 s⁻¹, respectively. RTB demonstrating lower shear damage towards cells (up to 1.33 s⁻¹) was selected because it permits high torque, low-power and acceptable turbulence. The LCA results showed a strong relation between the number of impellers installed and associated emissions suggesting a trade-off between mixing performance and environmental impacts.

Optimal setting strategy of electrocoagulation process in heavy metal wastewater treatment plant

With the increasingly stringent environmental protection policies of various countries, the contradiction between the treatment cost and the purification degree of environmental pollutants has become increasingly significant, which has become a major factor restricting the efficient operation of wastewater treatment plants. Hence, keeping the ion concentration at the outlet as low as possible while reducing the cost are the main objectives of treating heavy metal wastewater by electrocoagulation (EC) process. However, due to the complicated mechanism and uncertain production conditions, it is difficult to achieve those goals by manually setting the current through operators' experience. In this paper, we develop a dynamic multi-objective optimization strategy for EC process to balance these two conflicting production targets. First, we define the removal efficiency (RE) to measure the effectiveness of the EC process. Due to the anodic passivation and cathodic polarization in the EC process, the current reversing period (CRP) is proposed and optimized to ensure the stable performance of the electrodes. Then the current setting problem is formulated as a constrained multi-objective optimization problem with competing objectives of RE and cost. An interval-adjustable control parameterization (CP) approach is developed to reduce the complexity of this optimization problem. To compute this optimization problem, a heuristic method named multi-objective state transition algorithm (MOSTA) with evaluation value is investigated. The effectiveness of our model and optimization strategy is demonstrated by a successful implementation in an EC process of a wastewater treatment plant in Chenzhou, China.

Data on the hydrodynamics and power consumption induced by modified anchor impellers in cylindrical tanks

In the present paper, the data assembled concerning the stirring of a viscous Newtonian fluid in cylindrical and unbaffled tanks is disclosed. The stirring is ensured by close-clearance impellers rotating at low speeds. This is a comparative study between three modified geometries of anchor impellers aiming to enhance the fluid circulation in the whole vessel volume, and especially at the lower part of the vessel. The suggested geometrical configurations aim also to keep the energy consumption at its minimum. The data summarized here provides an additional knowledge for the best selection of stirrers for a specified industrial application.

Multi-level phase-change memory with ultralow power consumption and resistance drift

By controlling the amorphous-to-crystalline relative volume, chalcogenide phase-change memory materials can provide multi-level data storage (MLS), which offers great potential for high-density storage-class memory and neuro-inspired computing. However, this type of MLS system suffers from high power consumption and a severe time-dependent resistance increase ("drift") in the amorphous phase, which limits the number of attainable storage levels. Here, we report a new type of MLS system in yttrium-doped antimony telluride, utilizing reversible multi-level phase transitions between three states, i.e., amorphous, metastable cubic and stable hexagonal crystalline phases, with ultralow power consumption (0.6-4.3 pJ) and ultralow resistance drift for the lower two states (power-law exponent < 0.007). The metastable cubic phase is stabilized by yttrium, while the evident reversible cubic-to-hexagonal transition is attributed to the sequential and directional migration of Sb atoms. Finally, the decreased heat dissipation of the material and the increase in crystallinity contribute to the overall high performance. This study opens a new way to achieve advanced multi-level phase-change memory without the need for complicated manufacturing procedures or iterative programming operations.

Simultaneous evaluation of the effect of mixing efficiency on power consumption and methane production in an anaerobic digester with different wastewaters

This study simultaneously examines the effect of mixing rate on power consumption and methane production in a stirrer anaerobic reactor. The numerical simulation is carried out using the finite volume approach and validated against available experimental data. The methane production rate is determined using governing equations in the anaerobic digestion (AD) process. The results showed that a 60% increase in the mixing rate of the system (from 50% to 80%) in the wastewater with concentration of 14,549 (mgl^-1) increased the methane production rate by about 35% and increased the power consumption of the system by about 13 times. Among the 144 cases studied, the best stirrer model is figured out as the optimal model by applying an index of performance coefficient and also this model is investigated with scale-up criteria in larger sizes. A novel equation for evaluating the power production value is suggested in real digesters.

Uncovering the impact of the COVID-19 pandemic on energy consumption: New insight from difference between pandemic-free scenario and actual electricity consumption in China

The existing measurement of the impact of the COVID-19 pandemic on energy consumption is based on changes between the years, which demonstrates the changes in energy consumption over the years without fully reflecting the impact of the pandemic on energy consumption. To better uncover the impact of the COVID-19 pandemic on energy consumption, this research compared pandemic-free scenarios with actual (with COVID-19) energy consumption in 2020, rather than comparing energy consumption between 2020 and 2019 in the existing studies. The simulation approach used for scenario simulation was developed by combing the autoregressive integrated moving average (ARIMA) and back propagation neural network (BP). In the proposed ARIMAR-BP approach, BP was used to correct the error of ARMIA simulation, so as to reduce the error of simulation. The results of the model testing indicate that the simulation error of the developed approach is much lower than that of the BP or ARIMA simulation. The proposed simulation approach was run based on China's electricity consumption from 2015 to 2019 to produce the simulated value of China's electricity consumption from January to August of 2020 in the pandemic-free scenario. The actual electricity consumption was on average 29% lower than the electricity consumption in the pandemic-free scenario. which is much larger than the decline rate derived from year-to-year comparison. In addition, the results of the correlation analysis show the simulated decline in electricity consumption is only positively correlated with the number of new cases of COVID-19 in January-March, when the COVID-19 outbreak in China. This research provides a novel research structure for a more comprehensive understanding of the impact of the pandemic on energy consumption.

Certain Investigation on Healthcare Monitoring for Enhancing Data Transmission in WSN

Wireless Sensor Networks are often to perform autonomous sensing and controlling the real world objects through the sensor nodes across the globe. Since these sensor nodes are operated by the energy of the battery that has been performed a vital role in deploying a sensor network. Hence, the battery power needs to be minimized to prolong network lifetime for healthcare applications. The monitored data transmission is very important to process in building wireless sensor networks. In order to provide efficient data transmission wireless technology standards are followed as IEEE 802.15.4 standards that provide desirable communication between end to end with optimal routes using the proposed Energy Optimization Algorithm. The proposed algorithm has been improved the data packet transmission efficiency up to 25% and also helps to prolong the life time of Wireless Sensor Networks in order to achieve the efficient data transmission for health care monitoring.

Simultaneous implementation of sludge dewatering and solid biofuel production by microwave torrefaction

Sludge dewatering is a matter of great concern to reduce the volume of sludge, stabilize its organic components, and achieve resource utilization. This study investigates sludge dewatering by microwave torrefaction along with the production of sludge solid biofuel at 480-800 W combined with durations of 5-25 min. Proximate analysis, calorific value analysis, thermogravimetric analysis, and scanning electron microscopy observations are employed to evaluate the dewatering degree, fuel properties, and energy efficiency of the torrefaction process. The independent parallel reaction (IPR) model and particle swarm optimization (PSO) analysis are also adopted for sludge pyrolysis kinetics calculation. The results show that microwave torrefaction is efficient for sludge dewatering with a short duration. The produced sludge solid biofuel is similar to stone-like coal, and can be used for civil or industrial boilers after flotation or just co-firing with briquette. The ash content of sludge solid biofuel shows a declining trend and the surface characteristics change from smooth to rough and fluffy with increasing the torrefaction severity. The bio-oil is mainly composed of phenols, siloxanes, and cholesterol. In addition, hydrogen is detected in the torgas. Furthermore, it is found that lower torrefaction power with a shorter duration yields a higher energy efficiency of the torrefaction process.

Dataset of experimental and adaptive neuro-fuzzy inference system (ANFIS) model prediction of R600a/MWCNT nanolubricant in a vapour compression system

This research paper assessed the performance of R600a with the base lubricant and Multi-walled Carbon Nanotube (MWCNT) nanolubricant at steady state. It describes the instruments required for measurement of the data parameter and its uncertainties, steps involved in preparing and replacing the MWCNT nanolubricant concentration with base lubricant in vapour compression refrigeration. The system's temperature data was collected at the components inlets and outlets. Pressure data was also registered at the compressor outlet and inlet. The data was captured at 27 °C ambient temperature at an interval of 30 min for 300 min. The experiment includes the experimental data collection, Adaptive Neuro-Fuzzy Inference System (ANFIS) training and testing dataset. The use of ANFIS model is explained in predicting the efficiency of MWCNT nanolubricant in a vapour compression refrigerator system. The ANFIS model also provides statistical output measures such as Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and determination coefficient (R ²). The data is useful and important for replacing MWCNT nanolubricant with base lubricant in a vapour compression refrigeration system for researchers in the specialisation of energy-efficient materials in refrigeration. The data present can be reused for vapour compression refrigeration systems simulation and modelling.

Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence

Machine learning is yielding unprecedented interest in research and industry, due to recent success in many applied contexts such as image classification and object recognition. However, the deployment of these systems requires huge computing capabilities, thus making them unsuitable for embedded systems. To deal with this limitation, many researchers are investigating brain-inspired computing, which would be a perfect alternative to the conventional Von Neumann architecture based computers (CPU/GPU) that meet the requirements for computing performance, but not for energy-efficiency. Therefore, neuromorphic hardware circuits that are adaptable for both parallel and distributed computations need to be designed. In this paper, we focus on Spiking Neural Networks (SNNs) with a comprehensive study of neural coding methods and hardware exploration. In this context, we propose a framework for neuromorphic hardware design space exploration, which allows to define a suitable architecture based on application-specific constraints and starting from a wide variety of possible architectural choices. For this framework, we have developed a behavioral level simulator for neuromorphic hardware architectural exploration named NAXT. Moreover, we propose modified versions of the standard Rate Coding technique to make trade-offs with the Time Coding paradigm, which is characterized by the low number of spikes propagating in the network. Thus, we are able to reduce the number of spikes while keeping the same neuron's model, which results in an SNN with fewer events to process. By doing so, we seek to reduce the amount of power consumed by the hardware. Furthermore, we present three neuromorphic hardware architectures in order to quantitatively study the implementation of SNNs. One of these architectures integrates a novel hybrid structure: a highly-parallel computation core for most solicited layers, and time-multiplexed computation units for deeper layers. These architectures are derived from a novel funnel-like Design Space Exploration framework for neuromorphic hardware.

Optimizing UAV-based radiation sensor systems for aerial surveys

In this study, we qualitatively and quantitatively analyzed unmanned aerial vehicle (UAV)-based radiation sensor systems suitable for specific survey missions. We examined a variety of UAVs, radiation sensors, and radiological survey missions; after reviewing previous studies that developed and conducted field tests, we categorized them by mission and suggested suitable types of UAVs and radiation sensors for each mission. To quantitatively analyze various system designs previously suggested, we proposed a new figure of merit (FOM) formula that can explain the mutual effects of parameters of both radiation sensors and UAVs on system performance. After targeting a radiological survey mission, we selected UAV and radiation sensor types qualitatively. Then, the quantitative FOM formula can be employed to efficiently assess whether the system achieves the required minimum detectable activity (MDA) without field test, while keeping the error of the MDA values in the order of 10⁰. After defining the constraints including the proposed FOM formula, we replicated a severe nuclear power plant accident scenario. We found that one fixed-wing UAV and multiple rotary-wing UAVs are the minimum number of UAVs, and thus the optimal system for timely achievement of a satisfactory MDA and estimation of the three-dimensional radiation distribution contributed by both ground radiation and an atmospheric radioactive plume.

Design and analysis of phase change material based floor heating system for thermal energy storage

Pleasant interior space is essential for modern people who spend considerably more time in the buildings than they did in the past. To achieve this, one aspect includes an ambient temperature that maintains the thermal equilibrium of the human body. The construction of wood framed buildings is becoming increasingly popular worldwide, and there have been recent trends toward constructing high-rise wooden houses. In this respect, heating methods appropriate for use in wooden buildings are being studied. Dry floor heating systems are predominantly used in wooden houses, but they provide a poor heat storage performance, which is not conducive to saving energy. In this study, the effects of thermal comfort and energy savings were analyzed after applying a phase change material (PCM) to floor heating, which can be used to reduce the peak temperature and contribute to energy savings. To enable shape stabilization, this study used Macro-Packed PCM (MPPCM), as shape stabilization is necessary when applying PCM. The heat storage performance was improved by applying MPPCM to a dry floor heating system. Paraffin-based PCMs, such as n-octadecane, n-eicosane, and n-docosane, were used to obtain a comfortable floor temperature range. Experimental temperatures ranged from 28 °C to 35 °C, with an entire temperature range of 7 °C. Experimental results showed that the heat storage performance of MPPCM reduced the amount of energy used for heating by 43%, and n-eicosane was the most effective PCM for use in floor heating with respect to obtaining a comfortable floor temperature.

Electrochemical production of perchlorate as an alternative for the valorization of brines

In this work, the valorization of brines, with concentrations similar to those produced by reverse osmosis or electrodialysis processes, by electrolysis with diamond anodes is evaluated. To do this, synthetic brines made from solutions of NaCl (with target concentrations ranging from 1.0 to 2.0 M and an additional test at 5.0 M) were used as the raw material for the electrochemical production of perchlorate using commercial electrochemical cells equipped with boron-doped diamond (BDD) anodes. The effect of key parameters on the rate and efficiency of perchlorate production was evaluated. The results show that it is possible to transform more than 80% of the initial chloride concentration into perchlorate, with current efficiencies higher than 70% regardless of the initial concentration of sodium chloride contained in the brine. Moreover, it was observed that both hypochlorite and chlorate were produced almost simultaneously at the beginning of electrolysis, while perchlorate was only produced when a certain value of applied electric charge was passed through the system. The results obtained were essentially independent of the concentration of NaCl, as the high concentrations used in this study avoided mass transfer limitations. Moreover, the specific energy cost of perchlorate production was estimated to range from 26.14 kWh kg^-1 (for 2.0 M and 1000 A m^-2) to 56.10 kWh kg^-1 (for 1.0 M and 2000 A m^-2). According to the results obtained, the electrochemical production of perchlorate by BDD electrochemical oxidation stands out as a promising novel technology for the valorization of the brine produced in reverse osmosis or electrodialysis processes.

Treatment of paper-recycling wastewater by electrocoagulation using aluminum and iron electrodes

Treatment of industrial wastewater by electrocoagulation (EC) is one of the most efficient methods to remove pollutants. Paper-recycling wastewater is a complex mixture containing toxic and recalcitrant substances, indicating complexity and difficulty of its treatment. The aim of the present study was to assess the effectiveness of paper-recycling wastewater treatment by EC process using aluminum (Al) and iron (Fe) plate electrodes. Removal of chemical oxygen demand (COD), total suspended solids (TSS), color and ammonia from paper-recycling mill effluent was evaluated at various electrolysis times (10-60 min), voltage (4-13 V) and pH (3.5-11). The optimum process conditions for the maximum removal of COD, TSS, color and ammonia from paper-recycling industry wastewater have been found to be pH value of 7, treatment time of 60 min and voltage of 10 V. Under optimum operating conditions, the removal capacities of COD, TSS, color and ammonia were 79.5%, 83.4%, 98.5% and 85.3%, respectively. It can be concluded that EC could be considered as an effective alternative for treatment of paper-recycling wastewater.

Energy-efficient transmission strategies for CoMP downlink-overview, extension, and numerical comparison

This paper focuses on energy-efficient coordinated multi-point (CoMP) downlink in multi-antenna multi-cell wireless communications systems. We provide an overview of transmit beamforming designs for various energy efficiency (EE) metrics including maximizing the overall network EE, sum weighted EE, and fairness EE. Generally, an EE optimization problem is a nonconvex program for which finding the globally optimal solutions requires high computational effort. Consequently, several low-complexity suboptimal approaches have been proposed. Here, we sum up the main concepts of the recently proposed algorithms based on the state-of-the-art successive convex approximation (SCA) framework. Moreover, we discuss the application to the newly posted EE problems including new EE metrics and power consumption models. Furthermore, distributed implementation developed based on alternating direction method of multipliers (ADMM) for the provided solutions is also discussed. For the sake of completeness, we provide numerical comparison of the SCA based approaches and the conventional solutions developed based on parametric transformations (PTs). We also demonstrate the differences and roles of different EE objectives and power consumption models.

An effect of the tubular baffles configuration in an agitated vessel with a high-speed impeller on the power consumption

The results of the power consumption for an agitated vessel equipped with vertical tubular baffles and high-speed impeller are presented in the paper. Aqueous solutions of CMC were agitated within transitional range of the non-Newtonian liquid flow in the agitated vessel of inner diameter equal to 0.6 m. Eight different types of the impellers were tested: Rushton or Smith turbines, turbine with straight blades, pitched blade turbines and propeller. The J tubular baffles of outer diameter B were located in the position e from the vessel wall. Different configurations of baffles, arranged around the vessel circumference singularly or blocked in the modules, were considered in the study. In total, 180 different tubular baffles-impeller systems were tested. The measurements of the torque were conducted by means of the strain gauges method. Based on the power characteristics obtained for each impeller type, the effect of the geometrical parameters of the vertical tubular baffles on the power number was determined and discussed. The results show that geometry of the tubular baffles mostly affects the power number for the system with radial flow Rushton turbine. Moreover, power numbers decrease with the increase of the clearance between baffle and vessel wall for the systems, in which the radially axial circulation of the liquid is promoted. The dependences of the power number on the geometrical parameters of the vertical tubular baffles arranged singularly around the vessel circumference were described by means of the Eqs. (5)-(16). These equations can be useful in the project computations.

Power consumption for an agitated vessel equipped with pitched blade turbine and short baffles

Power characteristics for an agitated vessel equipped with planar short baffles of length L and pitched blade turbine of pitch β are presented in the paper. The studies were carried out in the vessel of inner diameter D = 0.6 m, where the baffles were located in the distance p from the vessel bottom (p + L = H). Torque was measured using strain gauge method within the turbulent regime of the flow of Newtonian liquid in the agitated vessel. The effects of the pitch β and geometrical parameter p/H on the power number Ne were determined mathematically. The results showed that, for the assumed value of the angle β, the function Ne = f (L/H) decreases with the decrease in the baffle length L (i.e. with the increase in the parameter p). Moreover, for the assumed value of the baffle length L, the function Ne = f (β) increases with the increase in the angle β of the inclination of the impeller blade.

Sono assisted electrocoagulation process for the removal of pollutant from pulp and paper industry effluent

In the present work, the efficiency of the sonication, electrocoagulation, and sono-electrocoagulation process for removal of pollutants from the industrial effluent of the pulp and paper industry was compared. The experimental results showed that the sono-electrocoagulation process yielded higher pollutant removal percentage compared to the sonication and electrocoagulation process alone. The effect of the operating parameters in the sono-electrocoagulation process such as electrolyte concentration (1-5 g/L), current density (1-5 A/dm²), effluent pH (3-11), COD concentration (1500-6000 mg/L), inter-electrode distance (1-3 cm), and electrode combination (Fe and Al) on the color removal, COD removal, and power consumption were studied. The maximum color and COD removal percentages of 100 and 95 %, respectively, were obtained at the current density of 4 A/dm², electrolyte concentration of 4 g/L, effluent pH of 7, COD concentration of 3000 mg/L, electrode combination of Fe/Fe, inter-electrode distance of 1 cm, and reaction time of 4 h, respectively. The color and COD removal percentages were analyzed by using an UV/Vis spectrophotometer and closed reflux method. The results showed that the sono-electrocoagulation process could be used as an efficient and environmental friendly technique for complete pollutant removal.

Data on mixing of viscous fluids by helical screw impellers in cylindrical vessels

In this article, the data assembled regarding the mixing of Newtonian and shear thinning fluids by screw impellers in a cylindrical tank is disclosed. The data summarizing some information on the efficiency of such impellers are obtained via 3D calculations of velocities and viscous dissipation in the whole vessel volume. The data presented herein may be useful for those who want to outline the mixing characteristics in terms of fluid circulation and power consumption for this kind of impellers, therefore, avoiding a great effort for achieving a high number of experiments.

Liberation characteristics after cryogenic modification and air table separation of discarded printed circuit boards

Liberating useful materials from printed circuit boards (PCBs) is challenging because PCBs are flexible and complex in terms of materials and components. In this study, the crushing of PCBs at low-temperature was investigated. The results indicated that when the temperature was decreased to approximately -20 °C, the strength of PCBs decreased and their brittleness increased, making them easier to crush. A double roll crusher was selected to crush the PCBs. The particle size distribution and power consumption were studied under different working conditions. The results showed that the particle size of most of the lumps was in the range 15×20-25×20 mm, and that power consumption was minimal when the frequency of the crusher was 40-50 Hz. A small shredder was used for cryogenic grinding, and it was found that its power consumption strongly depended on the cooling temperature. An orthogonal experiment was conducted, which revealed that a smaller cutter gap and higher rotational speed could achieve higher yield. Furthermore, the results indicated that the air table developed to liberate PCB materials could effectively separate 2.8-0.5mm grade materials. Overall, the results of this study provide an experimental foundation for more effectively recycling discarded PCBs.

Drying of chilli in a combined infrared and hot air rotary dryer

The investigation of an economical and efficient drying method for chilli is beneficial because it could provide a means of overcoming the drawbacks of traditional drying methods: high operating power and long drying time, which result in a decrease in the quality of the chilli. This study involved the design and development of a combined infrared and hot air laboratory-scale rotary dryer, which consists of three operating modes: hot air, infrared, and combined infrared and hot air. Drying experiments were conducted at five different temperatures (50, 55, 60, 65, and 70 °C). The drying behavior produced with the three operating modes was evaluated. The best mode was determined based on the parameters for evaluating the quality of chilli, the power consumption, and the retention time. The results indicate that the optimal overall drying performance for chilli was achieved at 70, 65, 50 °C drying temperatures in hot air, combined, and IR mode, respectively. A positive correlation was observed between retention time and power consumption with the hot air and the combined modes, while a negative correlation was identified in the IR mode.