A Tool for Modelling of One- or Two-Step Microalgae Harvesting Processes

This paper discusses finite/fixed-time (FNT/FXT) synchronization of multi-layer networks based on energy consumption estimation. Firstly, a novel FXT stability theorem of dynamical system is presented. Secondly, sufficient criteria for FNT/FXT synchronization in multi-layer networks are given, and the estimation of the energy consumption are also gained. Especially, the range of the number of layers of network is gained based on the least convergence time and the estimation of the least energy consumption. The results show that shorter synchronization time and less energy consumption does not mean less network layers. Finally, the validity of the theoretical method is verified by numerical simulation.

An alga technology system has been developed, built and operated for the absorption of carbon dioxide and the production of biodiesel raw material. The main critical point in the technology is the separation of micro algae from the breeding medium (densification of the alga suspension). This is the most cost- and time-intensive part of the technology. The success of options in the literature, as well as further applicable phenomena and operations have also been studied. Along with flocculation, clarification and autoflocculation phenomena, special attention has been paid to membrane separation operations. Our aim is to devise a densification and separation process that has a low energy need and an advantageous operation time. The ultrafilter membrane splits the original mass flow (which consists of homogenized suspensions of different density) into two parts. One part passes the membrane, this is the permeate (deposited feed medium), the other is the concentrate, also called ‘retentate’ (in this case the concentrated alga suspension). This retentate can be easily and quickly cleaned of the accumulated metabolic products and remaining salt which can later cause a significant disturbance (at processing or in the following examinations). According to the assessment of the experimental results, the optimal solution seems to be membrane separation, on different grounds. It has a low energy need and an advantageous operation time and results in an appropriately clean suspension (i.e. void of metabolic products and salt). A PLC-controlled device equipped with a ZW-10 module was used for the densification experiments. The latter is the property of the Department of Chemical Engineering at the University of Pannonia.

Isotope separation methods for self-consistent nuclear energy system

The estimation of energy consumption is an important prerequisite for planning the required infrastructure for charging and optimising the schedules of battery electric buses used in public urban transport. This paper proposes a model using a reduced number of readily acquired bus trip parameters: arrival times at the bus stops, map positions of the bus stops and a parameter indicating the trip conditions. A deep learning network is developed for deriving the estimates of energy consumption stop by stop of bus lines. Deep learning networks belong to the important group of methods capable of the analysis of large datasets—“big data”. This property allows for the scaling of the method and application to different sized transport networks. Validation of the network is done using real-world data provided by bus authorities of the town of Jaworzno in Poland. The estimates of energy consumption are compared with the results obtained using a regression model that is based on the collected data. Estimation errors do not exceed 7.1% for the set of several thousand bus trips. The study results indicate spots in the public transport network of potential power deficiency which can be alleviated by introducing a charging station or correcting the bus trip schedules.

The article summarizes modern approaches to estimating energy consumption by households. In particular, international experience in determining the amount of final energy consumption by households by purpose using data from special household surveys and modeling methods. Possibilities of adaptation and modification of the specified approaches to an estimation of households’ energy consumption are defined. The article aims to highlight the results of practical application in Ukraine of the developed methodology of modeling and appropriate tools for estimation of the energy consumption by households. The study used methods of statistical indicators estimation based on the special household sample survey microdata, developed statistical models, in particular on dummy variables, graphical and tabular methods of data analysis, etc. The peculiarities of the use of different types of energy by different categories of households are assessed: it depends on the geographical location, area of residence, type of housing, and socio-economic characteristics of households. The method of estimation of energy consumption by Ukrainian households is offered to take into account features of the accessible information base, sources of power supply, and financial possibilities of households.

Oil-water separation is an important method for treating oily wastewater and recovering oil resources. Based on the different affinities of superhydrophobic surfaces to water and oil, long-term oil-water separation devices with low-energy and high efficiency can be developed through the optimization of structure and process parameters. Superhydrophobic coatings were prepared on stainless-steel mesh surfaces using a spray method to construct single-channel oil-water separation equipment with superhydrophobic/oleophilic meshes, and the effects of structural and process parameters on separation efficiency were systematically investigated. Additionally, a multi-channel oil-water separation device was designed and fabricated to evaluate the feasibility and stability of long-term continuous operations. The optimized single V-shaped channel should be horizontally placed and made from 150-mesh stainless-steel mesh folded at an angle of 38.9°. For the oil-water mixtures containing 20 wt.% oil, the oil-water separation efficiencies for single and two-stage separation were 92.79% and 98.96%, respectively. After 36 h of continuous operation, the multi-channel separation device achieved single-stage and two-stage separation efficiencies of 94.60% and 98.76%, respectively. The maximum processing capacity of the multi-channel device reached 168 L/h. The modified stainless mesh can remain stable with a contact angle (CA) higher than 150° to water for 34 days. The average residence time and contact area during the oil-water separation process significantly affect separation efficiency. By optimizing oil-water separation structures and process parameters, and using a superhydrophobic spray modification method, separation efficiency can be improved while avoiding the generation of secondary pollutants.

In the cloud computing environment, the source-level energy consumption (EC) estimation is employed to approximately measure the EC of a cloud computing task before it is executed. The EC estimation on tasks is critical to task scheduling and source-code improvement in the aspect of EC optimization. The existing studies treat a task as a program, and EC of the task as the simple summation of each statement’s EC. However, EC of two tasks consisting of the same statements with different structures is unequal; therefore, the code structure should be highlighted in source-level EC estimation. In this paper, an abstract energy consumption (AEC) model, which is static and runtime-independent, is proposed. For the model, the two quantitative measurements, “cross-degree” and “reuse-degree,” are proposed as the code structure features, and the relationship between EC and the measurements is formulated. Although AEC is not a precise EC measurement, it can properly represent the EC of a task, compare with other tasks, and verify the optimization effect. Experimental results show that the ratios between the EC and AEC with 50 test cases are stable; the standard deviation is 0.0002; and the mean value is 0.005. The regularities of EC and code structures, represented as “cross-degree” and “reuse-degree,” are also validated. Though AEC, it is easier to schedule the cloud computing tasks properly and further reduce the consumed energy.

This paper applies a fuzzy method for the estimation of the daily electrical energy consumption of the fresh water hydrophore stations of a water distribution system. Based on the uncertainties of the water distribution system, the fuzzy logic was used to simulate a correlation between water and electrical energy quantities. Thus, the proposed method is proven as a useful tool for estimation of the daily electrical energy consumption considering the flow and the pressure growth of the water as fuzzy numbers. The results reveal that it is feasible in theory and reliable on calculation applying trapezoidal fuzzy numbers to the estimation of the daily electric energy consumption of the hydrophore stations. The estimation error is less than 6 %.

Estimation of city energy consumption in China based on downscaling energy balance tables

Four indirect estimations of energy expenditure were examined, (i) O(2) debt, (ii) O(2) deficit, (iii) blood lactate concentration, and (iv) excess CO(2) production during and after 6 exercise durations (2, 4, 10, 15, 30, and 75 s) performed at 3 different intensities (50%, 100%, and 200% of VO(2) max). Analysis of variance (ANOVA) was used to determine if significant differences existed among these 4 estimations of anaerobic energy expenditure and among 4 estimations of total energy expenditure (that included exercise O(2) uptake and excess post-exercise oxygen consumption, or EPOC, measurements). The data indicate that estimations of anaerobic energy expenditure often differed for brief (2, 4, and 10 s) bouts of exercise, but this did not extend to total energy expenditure. At the higher exercise intensities with the longest durations O(2) deficit, blood lactate concentration, and excess CO(2) estimates of anaerobic and total energy expenditure revealed high variability; however, they were not statistically different. Moreover, they all differed significantly from the O(2) debt interpretation (p < 0.05). It is concluded that as the contribution of rapid substrate-level ATP turnover with lactate production becomes larger, the greatest error in quantifying total energy expenditure is suggested to occur not with the method of estimation, but with the omission of a reasonable estimate of anaerobic energy expenditure.

We have to decrease electric energy consumption of information systems, especially servers to reduce carbon dioxide emission. In this paper, we discuss how to select a server to perform a new application process issued by a client so as to reduce the total energy consumption of servers in a cluster. Here, we have to estimate the execution time of application processes on a server and the energy consumption of the server to perform the new process and current active processes. In this paper, we newly propose an ESEC (EStimation of Energy Consumption) algorithm to estimate the execution time of current active processes and the energy consumption of a server by considering not only current active processes but also possible processes to be issued after the current time. By using the ESEC model, we also propose an ESECS (ESEC Selection) algorithm to select a server to perform an application process. In the evaluation, we show the total energy consumption of servers and the average execution time of processes can be reduced in the ESEC algorithm in compared with the previous estimation algorithm.

ABSTRACTIn this article, new estimates of energy consumption in the Swedish manufacturing industry during 1800–1913 are used for interpreting the Swedish industrialisation process from an energy economic perspective. For one we conclude that the revision of previous estimates is substantial when it comes to manufacturing. Furthermore, we demonstrate that the increase of coal consumption, the expansion of the fossil or mineral energy system, to a high degree can be explained by the increased use of steam engines in manufacturing and the transport sector. Finally, we conclude that overall energy intensity patterns is largely determined by assumptions on household firewood consumption. A narrative interpretation of the interplay between energy system transformation and the industrialisation in Sweden concludes the article.

The purpose of this study was to verify the validity of wearable devices for estimating energy consumption during physical activity for sports club members in their 20s. In this study, absolute error and relative error were calculated to verify the error in the estimation of energy consumption between measurement tools, and repeated measured ANOVA analysis of variance was performed to verify the difference in the estimation of energy consumption. If there was a statistically significant difference in the repeated measured ANOVA analysis of variance, the dependent t test was performed to verify the difference in the mean value. Pearson correlation analysis was performed to verify the relative relationship of energy consumption estimates between measurement tools, and Bland-Altman plot was applied to confirm the error size between the measurement tool and the reference tool. As a result of the study, it was found that SenseWear Pro3(SWP) overestimates energy consumption during low-intensity physical activity and underestimates energy consumption during moderate-intensity and high-intensity physical activity. However, there was no statistically significant difference between the energy expenditure estimates of SWP and the energy expenditure estimates of the reference tool during moderate-intensity physical activity. Also, as a result of Pearson

M fuel cells (MFCs) have been thoroughly studied in the past decade and much understanding of their microbiology, electrochemistry, and reactor architecture/ operation has been obtained through intensive research and thousands of journal publications. MFCs are attractive as a new platform (e.g., micro-MFCs) to study microbial interaction with an electron acceptor/donor and the electron-transfer mechanisms involved in this interaction, power sources for remote sensors (sediment MFCs), or more popularly as an alternative approach for wastewater treatment and bioenergy production from wastes. Direct electricity generation in MFCs is an obvious advantage over other energy recovery methods, such as anaerobic digesters that require biogas collection, and conversion. Therefore, it is not unexpected that almost every paper about MFCs treating wastewater starts its introduction with the energy benefits of MFCs. However, are we really showing any energy performance of the MFCs that we have developed? “Yes” in very few studies, but “No” in the majority. When we describe the electricity-producing performance of an MFC, the most commonly used parameters include voltage, current, and power (or power density), none of which is an energy parameter. Energy is expressed in either joule (J) or kilowatt hour (kWh), which can be calculated by multiplying power by time. The factor of time is always important to our data expression, especially for batch operation. For a wastewater treatment process, it will be beneficial to express energy density in kWh/m (treated wastewater), or more accurately in kWh/ kg COD removed due to the fluctuation of organic contents in different wastewaters. Energy density makes it possible to compare the performance of different MFCs and it serves as a better link between organic contents and electricity generation than Coulombic efficiency. It is just a simple step beyond the power calculation, but will allow us to really start thinking and talking about energy in MFCs. With energy data we can construct an energy balance, which is an effective approach for understanding energy in MFCs. Interestingly, we have intensely focused on mass balances such as carbon and nitrogen, but ignored energy balances. To build an energy balance, we need to know the energy production and consumption in an MFC. The energy production is relatively easier to calculate from power, operating time, and the volume of the treated wastewater or the removed COD within that operating time. This is based on the assumption that electric energy is the only energy produced in an MFC; in fact, when MFCs treat high-strength wastes, methane is produced and should be included in the energy production, and if other energy contents such as algal biomass are coproduced, they should be included, as well. It requires additional efforts to study how much of the produced energy can actually be useful (energy loss will occur during transfer and use), but for an initial energy balance, it will be acceptable to assume 100% energy efficiency (from an MFC to an end-user such as a pump). The tough part seems to be the estimation of energy consumption due to the generally small sizes of MFC reactors. For an MFC that is run by pumps, the estimate of energy consumption is not “mission impossible”: the energy consumption is mainly due to the pumping system, and thus we can estimate this consumption by theoretic calculation involving flow rates and hydraulic pressure head, as described previously. A precise estimate of energy consumption in smallscale MFCs could be difficult, but at least it provides us with a good starting point to work with. For example, with this approach, we have built an energy balance in an MFC containing hollow-fiber membranes and found that it produced more energy than it consumed when treating a synthetic solution, but it had a negative energy balance when fed with actual domestic wastewater. That gives us a sense how far we are from achieving an energy-neutral treatment process by using MFC technology. Building an energy balance enables us to better understand the energy benefits of MFC technology. Previously, we interpreted the energy advantage of an MFC by focusing on how much energy it can produce (though not showing the related energy data). Now, with an energy balance, we can see that an MFC cannot produce much energy (<0.04 kWh/m, or <0.1 kWh/kg COD from our current research), so it may not

Energy autonomy is an important aspect that needs to be improved in order to increase efficiency in mobile robotic tasks. Having accurate power models allows the estimation of energy consumption along different trajectories. This article proposes a power model for two-wheel differential drive mobile robots. The proposed model takes into account the dynamic parameters of the robot and its motors, and predicts the energy consumption for trajectories with variable accelerations and variable payloads. The experimental validation of the proposed model was performed with a Nomad Super Scout II mobile robot which was driven along straight and curved trajectories, with different payloads and accelerations. The experiments using the proposed model showed accuracies of 96.67% along straight trajectories and 81.25% along curved trajectories in the estimation of energy consumption.