scholarly journals Experimental Investigation of the Performance and Energy Consumption of an Automated Ice-cube Making Machine

2018 ◽  
pp. 1-12
Author(s):  
Rasheed Ayofe Shittu ◽  
Isaac Femi Titiladunayo
Keyword(s):  
Energy Consumption ◽  
System Performance ◽  
Production Capacity ◽  
Production Cycle ◽  
Refrigeration System ◽  
Ambient Conditions ◽  
Harvesting Time ◽  
Ice Cube ◽  
Geographical Regions ◽  
Ice Production

Aims: This study investigates the performance of a developed automated ice-cube making machine under controlled ambient conditions, in which its energy usage, rate of ice cube production and refrigeration system performance was analysed. Study Design: Average ambient temperatures of 24°C and 32°C were considered for investigation in order to determine their influence on ice production capacity, rate of ice-cube making and energy consumption. The choice of the ambient temperature is based on the extreme ambient conditions under which the machine is designed to operate in a wide range of geographical regions. The refrigeration system performance was carried out under normal room temperature (average of 23°C). Place and Duration of Study: Department of Mechanical Engineering, Federal University of Technology Akure, Ondo State Central Workshop, Between December 2017 and January 2018. Methodology: The machine was set into operation for 5 consecutive ice production cycle during which the ice making time, harvest time, quantity of ice produced and energy consumption were recorded. Results: The ice production capacity, harvesting time and energy consumption show various dissimilarities at both temperatures. 0.618 kg of ice cubes were produced within an ice making cycle of 34.9 minutes, harvesting time of 1.28 minutes and total energy consumption of 0.14053 kWh at 24°C while at 32°C, the machine produced an average of 0.612 kg ice cubes within an ice making cycle of 38.5 minutes harvesting time 1.21 minutes and energy consumption of 0.15947 kWh respectively. Consequently, 13.5% more energy is consumed, with about 1% less quantity of ice produced at 32°C than at 24°C per ice production cycle. Conclusion: Therefore, the ice making capacity of the developed machine suggests that the temperature of the environment has a strong influence on the energy consumption, but little effect on the quantity of ice produced per cycle. The refrigeration system cycle performance analysis results showed a considerably high cooling capacity of 0.379 kW during the ice-making cycle with a corresponding coefficient of performance (COP) of 2.23, and a heating capacity of 2.24 kW during the harvest cycle with a corresponding COP of 8.21. The results obtained showed that the machine is reliable in operation with minimal energy consumption.

Experimental Analysis on Vapour Compression Refrigeration System Using Nanolubricant with HFC-134a Refrigerant

Nano Hybrids ◽  
2015 ◽  
Vol 9 ◽  
pp. 33-43 ◽  
Author(s):  
A. Manoj Babu ◽  
S. Nallusamy ◽  
K. Rajan
Keyword(s):  
Energy Consumption ◽  
System Performance ◽  
Mineral Oil ◽  
Coefficient Of Performance ◽  
Refrigeration System ◽  
Hfc 134A ◽  
Reduce Energy Consumption ◽  
And Performance ◽  
Vapour Compression ◽  
Better Than

This paper investigates the reliability and performance of a refrigeration system using nanolubricant with 1, 1, 1, 2-Tetrafluoroethane (HFC-134a) refrigerant. Mineral Oil (MO) is mixed with nanoparticles such as Titanium Dioxide (TiO2) and Aluminium Oxide (Al2O3). These mixtures were used as the lubricant instead of Polyolester (POE) oil in the HFC-134a refrigeration system as HFC-134a does not compatible with raw mineral oil. An investigation was done on compatibility of mineral oil and nanoparticles mixture at 0.1 and 0.2 grams / litre with HFC-134a refrigerant. To carry out this investigation, an experimental setup was designed and fabricated in the lab. The refrigeration system performance with the nanolubricant was investigated by using energy consumption test. The results indicate that HFC-134a and mineral oil with above mentioned nanoparticles works normally and safely in the refrigeration system. The refrigeration system performance was better than the HFC-134a and POE oil system. Thus nanolubricant (Mixture of Mineral Oil (MO) and nanoParticles) can be used in refrigeration system to considerably reduce energy consumption and better Coefficient of Performance (COP).

scholarly journals Backstepping Methodology to Troubleshoot Plant-Wide Batch Processes in Data-Rich Industrial Environments

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1074
Author(s):  
Federico Zuecco ◽  
Matteo Cicciotti ◽  
Pierantonio Facco ◽  
Fabrizio Bezzo ◽  
Massimiliano Barolo
Keyword(s):  
Production Capacity ◽  
Batch Process ◽  
Batch Processes ◽  
Flow Diagram ◽  
Production Cycle ◽  
Multivariate Statistical ◽  
Process Flow Diagram ◽  
Root Cause ◽  
Industrial Batch ◽  
Specialty Chemical

Troubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far to the former, which is complex for several reasons: the processing units are often operated in a non-sequential way, with unusual series-parallel arrangements; holding vessels may be required to compensate for lack of production capacity, and reacting phenomena can occur in these vessels; and the evidence of batch abnormality may be available only from the end unit and at the end of the production cycle. We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments where multivariate statistical techniques can be exploited. Namely, we first analyze the last unit wherein the fault manifests itself, and we then step back across the units through the process flow diagram (according to the manufacturing recipe) until the fault cannot be detected by the available field sensors any more. That enables us to isolate the unit wherefrom the fault originates. Interrogation of multivariate statistical models for that unit coupled to engineering judgement allow identifying the most likely root cause of the fault. We apply the proposed methodology to troubleshoot a complex industrial batch process that manufactures a specialty chemical, where productivity was originally limited by unexplained variability of the final product quality. Correction of the fault allowed for a significant increase in productivity.

scholarly journals A Thermodynamic Transient Model for Performance Analysis of a Twin Shaft Industrial Gas Turbine

2017 ◽  
Author(s):  
Samuel Cruz-Manzo ◽  
Vili Panov ◽  
Yu Zhang ◽  
Anthony Latimer ◽  
Festus Agbonzikilo
Keyword(s):  
Gas Turbine ◽  
System Performance ◽  
Computational Tool ◽  
Ambient Conditions ◽  
Transient Model ◽  
Simulink Model ◽  
Artificial Neural Network Ann ◽  
Industrial Gas Turbine ◽  
Scientific Engineering ◽  
Steady State Performance

In this study, a Simulink model based on fundamental thermodynamic principles to predict the dynamic and steady state performance in a twin shaft Industrial Gas Turbine (IGT) has been developed. The components comprising the IGT have been implemented in the modelling architecture using a thermodynamic commercial toolbox (Thermolib, EUtech Scientific Engineering GmbH) and Simulink environment. Measured air pressure and air temperature discharged by compressor allowed the validation of the modelling architecture. The model assisted the development of a computational tool based on Artificial Neural Network (ANN) for compressor fault diagnostics in IGTs. It has been demonstrated that modelling plays an important role to predict and monitor gas turbine system performance at different operating and ambient conditions.

Design and Build of Teaching and Experimental Marine Cold Store

2014 ◽  
Vol 556-562 ◽  
pp. 907-911
Author(s):  
Chang Wei He ◽  
Meng Zhang ◽  
Xiao Ping Jia ◽  
Yuan Liu
Keyword(s):  
Thermal Performance ◽  
System Performance ◽  
Heat Load ◽  
Refrigeration System ◽  
Electrical System ◽  
Technology Application ◽  
Design Scheme ◽  
Expansion Valve ◽  
Cold Store ◽  
Practical Teaching

In the paper, the design scheme of the new cold store is proposed firstly, with the consideration of the latest technology application and the convenience and maneuverability of practical teaching. Then the refrigeration system is designed based on the calculation of the heat load of the cold store. The suited components such as compressor, evaporator, condenser and expansion valve are selected and the electrical system is designed. After that the whole unit is installed and adjusted to make sure that the installation is propitious to improve the system performance and convenient for training. Finally the thermal performance of the new cold store system is tested and compared with the old system test. The result shows that the matching of the new refrigeration system is reasonable and the new cold store is up to the mustard. With the help of training on the cold store, the students will meet the essential requirements of STCW 78/95 convention on application and management of the marine cold store.

scholarly journals A Bit String Content Aware Chunking Strategy for Reduced CPU Energy on Cloud Storage

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bin Zhou ◽  
ShuDao Zhang ◽  
Ying Zhang ◽  
JiaHao Tan
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
System Performance ◽  
Cloud Storage ◽  
High Performance ◽  
Factors Affecting ◽  
Redundant Data ◽  
Energy Factors ◽  
The Cost ◽  
Content Aware

In order to achieve energy saving and reduce the total cost of ownership, green storage has become the first priority for data center. Detecting and deleting the redundant data are the key factors to the reduction of the energy consumption of CPU, while high performance stable chunking strategy provides the groundwork for detecting redundant data. The existing chunking algorithm greatly reduces the system performance when confronted with big data and it wastes a lot of energy. Factors affecting the chunking performance are analyzed and discussed in the paper and a new fingerprint signature calculation is implemented. Furthermore, a Bit String Content Aware Chunking Strategy (BCCS) is put forward. This strategy reduces the cost of signature computation in chunking process to improve the system performance and cuts down the energy consumption of the cloud storage data center. On the basis of relevant test scenarios and test data of this paper, the advantages of the chunking strategy are verified.

scholarly journals Superabundant design

2016 ◽  
Vol 5 (1) ◽  
pp. 60-69 ◽  
Author(s):  
Pablo R. Velasco González
Keyword(s):  
Energy Consumption ◽  
Production Rate ◽  
Electronic Devices ◽  
Production Cycle ◽  
Trace Back ◽  
The Creation ◽  
Definition Of ◽  
Time And Energy

Tiziana Terranova draws attention to the necessity of questioning how algorithmically enabled automation works “in terms of control and monetization” and “what kind of time and energy” is being subsumed by it (Terranova 387). Cryptocurrencies are payment technologies that automate the production of money-like tokens (Bergstra and Weijland) following algorithmic rules to maintain a fixed production rate. Different kinds of energy and residues, which are not always acknowledged, are involved in this process. Here I distinguish between two closely linked layers in the Bitcoin token production: first, an algorithmic layer, which contains the instructions and rules for the creation of bitcoins; second, a hardware layer, which performs and embodies the former. While these layers work together, I will argue that they enact their own kind of logics of energy and waste. I will begin at the more visible end of the production cycle, the hardware layer, where the definition of waste and energy consumption is shared with many electronic devices; then I will trace back its algorithmic layer, which as I argue, follows a different logic.

Migration-assisted, moisture gradient process for ultrafast, continuous CO2 capture from dilute sources at ambient conditions

2022 ◽  
Author(s):  
Aditya Prajapati ◽  
Rohan Sartape ◽  
Tomás Rojas ◽  
Naveen K. Dandu ◽  
Pratik Dhakal ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Electric Field ◽  
Co2 Capture ◽  
Moisture Gradient ◽  
Low Energy ◽  
Ambient Conditions ◽  
Capture Process ◽  
Low Energy Consumption

An ultrafast, continuous CO2 capture process driven by moisture gradient and electric field with low energy consumption to capture and concentrate CO2 from dilute sources.

Influence of Cryogenic Treatment on TiC Nanopowder in R600a and R290 Refrigerant Used in Vapor Compression Refrigeration System

2019 ◽  
Vol 27 (04) ◽  
pp. 1950040
Author(s):  
D. Senthilkumar
Keyword(s):  
Energy Consumption ◽  
Cryogenic Treatment ◽  
Vapor Compression ◽  
Refrigeration System ◽  
Vapor Compression Refrigeration

This paper studies the performance of vapor compression refrigeration system using hydrocarbon refrigerant (HCR) mixture (R600a and R290), hydrocarbon nanorefrigerant mixture (R600a and R290/TiC) and cryogenically treated hydrocarbon nanorefrigerant mixture (R600a and R290/Cryo TiC). The COP of HCR (R600a and R290) system is 1.2960, whereas, COP of R600a and R290/TiC nanorefrigerant system is 1.5223. The TiC nanopowder is cryogenically treated at [Formula: see text]C for 24[Formula: see text]h. The treated TiC is dispersed in HCR mixture. Hence, the COP of R600a and R290- Cryo TiC system is further increased to 1.5801. The energy consumption of R600a and R290-TiC is reduced by 10.3% when compared with HCR. Further, it is reduced by 12.69% with respect to cryogenically treated refrigerant (R600a and R290/Cryo TiC) system. The COP is enhanced due to deep cryogenic of TiC nanopowder.

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