scholarly journals Energetic Aspects of Turfgrass Mowing: Comparison of Different Rotary Mowing Systems

Agriculture ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 178
Author(s):  
Michel Pirchio ◽  
Marco Fontanelli ◽  
Fabio Labanca ◽  
Mino Sportelli ◽  
Christian Frasconi ◽  
...  
Keyword(s):  
Power Consumption ◽  
Tall Fescue ◽  
Energy Requirement ◽  
Electricity Consumption ◽  
Primary Energy ◽  
Electric Motors ◽  
Full Throttle ◽  
Nitrogen Rates ◽  
Over Time ◽  
Primary Energy Requirement

Turfgrass mowing is one of the most important operations concerning turfgrass maintenance. Over time, different mowing machines have been developed, such as reel mowers, rotary mowers, and flail mowers. Rotary mowers have become the most widespread mowers for their great versatility and easy maintenance. Modern rotary mowers can be equipped with battery-powered electric motors and precise settings, such as blade rpm. The aim of this trial was to evaluate the differences in power consumption of a gasoline-powered rotary mower and a battery-powered rotary mower. Each mower worked on two different turfgrass species (bermudagrass and tall fescue) fertilized with two different nitrogen rates (100 and 200 kg ha−1). The battery-powered mower was set at its lowest and highest blade rpm value, while the gasoline-powered mower was set at full throttle. From the data acquired, it was possible to see that the gasoline-powered mower had a much higher primary energy requirement, independent of the turf species. Moreover, comparing the electricity consumption of the battery-powered mower over time, it was possible to see that the power consumption varied according to the growth rate of both turf species. These results show that there is a partial waste of energy when using a gasoline-powered mower compared to a battery-powered mower.

scholarly journals Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Ning-Kang Pan ◽  
Chunwan Lv
Keyword(s):  
Neural Network ◽  
Policy Making ◽  
Energy Requirement ◽  
Moving Average ◽  
Network Models ◽  
Primary Energy ◽  
Backpropagation Neural Network ◽  
Neural Network Models ◽  
Autoregressive Integrated Moving Average ◽  
Primary Energy Requirement

Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

Exergy Analysis of a Novel Cryogenic Concept for the Liquefaction of Natural Gas Integrated Into an Air Separation Process

2016 ◽  
Author(s):  
S. Tesch ◽  
T. Morosuk ◽  
G. Tsatsaronis
Keyword(s):  
Natural Gas ◽  
Power Consumption ◽  
Exergy Analysis ◽  
Primary Energy ◽  
Separation Process ◽  
Air Separation ◽  
Total Power ◽  
Separation Unit ◽  
Total Power Consumption ◽  
Liquefaction Temperature

The increasing demand for primary energy leads to a growing market of natural gas and the associated market for liquefied natural gas (LNG) increases, too. The liquefaction of natural gas is an energy- and cost-intensive process. After exploration, natural gas, is pretreated and cooled to the liquefaction temperature of around −160°C. In this paper, a novel concept for the integration of the liquefaction of natural gas into an air separation process is introduced. The system is evaluated from the energetic and exergetic points of view. Additionally, an advanced exergy analysis is conducted. The analysis of the concepts shows the effect of important parameters regarding the maximum amount of liquefiable of natural gas and the total power consumption. Comparing the different cases, the amount of LNG production could be increased by two thirds, while the power consumption is doubled. The results of the exergy analysis show, that the introduction of the liquefaction of natural gas has a positive effect on the exergetic efficiency of a convetional air separation unit, which increases from 38% to 49%.

Exploration of an Electroactive Polymer as an Actuator for Microrobotics

2003 ◽  
Vol 785 ◽  
Author(s):  
C. Bielmeier ◽  
W. Walter
Keyword(s):  
Steady State ◽  
Power Consumption ◽  
Parallel Plate ◽  
Electroactive Polymers ◽  
Electroactive Polymer ◽  
Low Power Consumption ◽  
Parallel Plate Capacitor ◽  
Steady State Current ◽  
A Current ◽  
Over Time

ABSTRACTThe development of lightweight low power consumption actuators is critical to the development of micro-robotics. Electroactive Polymers (EAP), i.e. Nafion N-117, meet these requirements. In the actuation of an EAP, the current does not remain constant over time. The development of a circuit model of current draw over time to best predict a current dynamic has been explored. While the material mimics a parallel plate capacitor, it has been found that capacitance plays no role in achieving steady state current levels. This development is critical to understanding and developing the material as an actuator.

scholarly journals Heat pump systems: A mini review

2021 ◽  
Vol 4 (2) ◽  
pp. 73-81
Author(s):  
Mohammad Omar Temori ◽  
František Vranay
Keyword(s):  
Heat Pump ◽  
Electricity Consumption ◽  
Cost Savings ◽  
Electrical Energy ◽  
Primary Energy ◽  
Heat Pumps ◽  
Coefficient Of Performance ◽  
Heating And Cooling ◽  
Ground Source ◽  
Reduce Electricity Consumption

In this work, a mini review of heat pumps is presented. The work is intended to introduce a technology that can be used to income energy from the natural environment and thus reduce electricity consumption for heating and cooling. A heat pump is a mechanical device that transfers heat from one environmental compartment to another, typically against a temperature gradient (i.e. from cool to hot). In order to do this, an energy input is required: this may be mechanical, electrical or thermal energy. In most modern heat pumps, electrical energy powers a compressor, which drives a compression - expansion cycle of refrigerant fluid between two heat exchanges: a cold evaporator and a warm condenser. The efficiency or coefficient of performance (COP), of a heat pump is defined as the thermal output divided by the primary energy (electricity) input. The COP decreases as the temperature difference between the cool heat source and the warm heat sink increases. An efficient ground source heat pump (GSHP) may achieve a COP of around 4. Heat pumps are ideal for exploiting low-temperature environmental heat sources: the air, surface waters or the ground. They can deliver significant environmental (CO2) and cost savings.

scholarly journals Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Jihyun Kim ◽  
Thi-Thu-Huong Le ◽  
Howon Kim
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Distribution System ◽  
Short Term Memory ◽  
Electrical Power ◽  
Electricity Consumption ◽  
Classification Performance ◽  
Robust Solution ◽  
Proposed Model ◽  
Load Monitoring

Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.

scholarly journals Application of PVStorage System in Typical Industrial Users

2019 ◽  
Vol 118 ◽  
pp. 02068
Author(s):  
Haiwen Wang ◽  
Daoyuan Wen ◽  
Qunyin Gu ◽  
Fangqin Li ◽  
Weijun Gao ◽  
...  
Keyword(s):  
Power Consumption ◽  
High Reliability ◽  
Electricity Consumption ◽  
Optical Storage ◽  
Large Power ◽  
Storage Model ◽  
Industrial Enterprises ◽  
Photovoltaic Power ◽  
Roof Area ◽  
The Cost

The power consumption of industrial enterprises is characterized by large power consumption and high reliability requirement, so the cost of electricity consumption is relatively high. Distributed photovoltaic power generation is clean and environmentally friendly, making full use of the roof area to generate electricity. Based on the characteristics of distributed photovoltaic and energy storage, this paper constructs the distributed optical storage model and operation strategy. In addition, this paper takes an industry as an example to carry out relevant verification and analysis.

scholarly journals 173 The effects of Fescue EMTTM Mineral Defense on stocker heifer growth while grazing tall fescue

2019 ◽  
Vol 97 (Supplement_1) ◽  
pp. 60-60
Author(s):  
Anna R Taylor ◽  
Randy Dew ◽  
Ken Bryan ◽  
J Nathan Pike ◽  
T Ryan Lock
Keyword(s):  
Weight Gain ◽  
Reproductive Performance ◽  
Tall Fescue ◽  
Repeated Measures ◽  
Animal Nutrition ◽  
Forage Availability ◽  
Randomized Design ◽  
Endophyte Infection ◽  
Completely Randomized Design ◽  
Over Time

Abstract Previous research demonstrates grazing tall fescue can decrease reproductive performance and weight gain in cattle. The objective of this study was to evaluate Fescue EMTTM Mineral Defense (Cargill Animal Nutrition, Minneapolis, MN) on summer weight gain in cattle grazing tall fescue pastures in SW Missouri. Heifers (n = 120; initial BW = 236 ± 2.5 kg) were stratified by weight to replicated tall fescue pastures to either a control mineral treatment or Fescue EMT™ Mineral Defense treatment. Forage availability was estimated weekly by ultrasonic sensor. Pasture samples were collected every 21 d and analyzed for ergovaline concentrations. Heifer weights and blood prolactin were measured throughout the trial. Average daily mineral consumption was calculated by mineral offered less residual. Data were analyzed on a pen-mean basis as a completely randomized design using JMP with 6 pens/ treatment and 10 heifers/pen. Prolactin was analyzed as Repeated Measures in JMP. Initial weights between treatments were not different (P > 0.05). Endophyte infection measured 75% or greater in all pastures. No differences were detected in pasture ergovaline (149 ± 19 µg/kg) or pasture availability (2,600 ± 150 kg/ha) between treatments (P > 0.20 at each sampling). Heifer ADG consuming Fescue EMT™ Mineral Defense compared to control mineral was greater at 0.28 kg versus 0.22 kg resulting in total gains of 21.8 kg versus 16.6 kg, respectively (P < 0.05). However, blood prolactin numerically decreased over time in both treatments. Results from this trial demonstrate a 31% improvement in weight gain for cattle consuming Fescue EMTTM Mineral Defense compared with cattle consuming a control mineral while grazing toxic tall fescue.

scholarly journals Comparison between Different Rotary Mowing Systems: Testing a New Method to Calculate Turfgrass Mowing Quality

Agriculture ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 152
Author(s):  
Michel Pirchio ◽  
Marco Fontanelli ◽  
Fabio Labanca ◽  
Christian Frasconi ◽  
Luisa Martelloni ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Poor Quality ◽  
New Method ◽  
Damage Level ◽  
Visual Rating ◽  
Turf Quality ◽  
Innovative Method ◽  
Full Throttle ◽  
Nitrogen Rates

Poor quality in turfgrass mowing is highlighted by the shredded leaf tips with necrotic tissues that give an unsightly brownish colour to the turf and may also lead to turf disease. Mowing quality is also typically assessed by visual rating, thus the score depends on the person doing the assessment. To make the evaluation of mowing quality not subjective, an innovative method was developed. The aim of the trial was to examine the effects of different mowing systems and two different nitrogen rates (100 and 200 kg ha−1) on two turfgrass species in order to test the new mowing quality calculation. Three different mowing systems were used: a battery-powered rotary mower set at 3000 rpm and 5000 rpm respectively and a gasoline-powered rotary mower set at full throttle. The battery-powered mower at low blade rpm produced a poorer mowing quality and turf quality than the gasoline-powered mower and battery-powered mower at high rpm, which produced a similar mowing quality and turf quality. Leaf tip damage level values showed a significant correlation with the results of the visual mowing quality assessment. Lower leaf tip damage level values (slightly above 1) corresponded to higher visual mowing quality scores (around 8).

scholarly journals Monthly Electricity Consumption Forecasting Method Based on X12 and STL Decomposition Model in an Integrated Energy System

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Tianhe Sun ◽  
Tieyan Zhang ◽  
Yun Teng ◽  
Zhe Chen ◽  
Jiakun Fang
Keyword(s):  
Power Consumption ◽  
Rapid Development ◽  
Electricity Consumption ◽  
Energy System ◽  
Small Scale ◽  
Power Demand ◽  
Distributed Energy ◽  
Strategic Competition ◽  
Integrated Energy System ◽  
Monthly Electricity Consumption

With the rapid development and wide application of distributed generation technology and new energy trading methods, the integrated energy system has developed rapidly in Europe in recent years and has become the focus of new strategic competition and cooperation among countries. As a key technology and decision-making approach for operation, optimization, and control of integrated energy systems, power consumption prediction faces new challenges. The user-side power demand and load characteristics change due to the influence of distributed energy. At the same time, in the open retail market of electricity sales, the forecast of electricity consumption faces the power demand of small-scale users, which is more easily disturbed by random factors than by a traditional load forecast. Therefore, this study proposes a model based on X12 and Seasonal and Trend decomposition using Loess (STL) decomposition of monthly electricity consumption forecasting methods. The first use of the STL model according to the properties of electricity each month is its power consumption time series decomposition individuation. It influences the factorization of monthly electricity consumption into season, trend, and random components. Then, the change in the characteristics of the three components over time is considered. Finally, the appropriate model is selected to predict the components in the reconfiguration of the monthly electricity consumption forecast. A forecasting program is developed based on R language and MATLAB, and a case study is conducted on the power consumption data of a university campus containing distributed energy. Results show that the proposed method is reasonable and effective.

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