Experimental Research On Gas Injection High Temperature Heat Pump With An Economizer

Gas injection technology is often used in cold regions to solve heat pump’s low heating capacity and high discharge temperature at low ambient temperature. Injecting gas into port opened at specific position of compressor could increase mass flow rate of compressor and total heating capacity of heat pump. Gas injection also changes compression ratio of compressor and decreases discharge temperature. An optimal gas injection pressure is got when the coefficient of performance reached to peak value at a certain working condition. It’s a feasible way to increase performance of heat pump at cold regions. High temperature heat pump could provide higher temperature water for industrial usage regions but there still existed some problems on its usage. Total heating capacity decreased and discharge temperature increased with the raise of condensation temperature. Refrigerant temperature before throttling valve was high and may exceed working temperature range of electrical expansion valve. Gas injection technology with an economizer was adopted to solve these problems. A new high temperature heat pump cycle was designed based on gas injection and outlets water temperature of the prototype manufactured was reached to 90?. Temperature before throttling valve was well controlled by the usage of economizer. Heating capacity, discharge temperature, compressor power consumption of the heat pump system at different amount of injected gas was conducted by theoretical and experimental research in this paper. This study showed the function of gas injection technology which used in high temperature heat pump

Development Of An Industrial High Temperature Heat Pump With Twin Screw Compressor

In industrial applications, 90-150? hot water needed during production processes is traditionally provided by fuel-fired boilers. The usage of fuel-fired boiler has a low efficiency of energy use and potential danger of explosion. 30-60?waste water with large amount of energy is rejected to environment directly, which causes a great energy loss and thermal pollution. Seeking for a much high efficiency and safe heating pattern is essential to replace the boilers used now. High temperature heat pump is an ideal solution to recover the heat in waste water and to produce hot water needed. It has a higher efficiency than boilers through the use of energy contained in waste water. Water temperature provided by high temperature heat pump reached to 90-120?. In this paper, an industrial high temperature heat pump with twin screw compressor was developed. Optimized heat pump cycle was made and heat exchangers were designed for using in specific workplace. For operating at high condensation and evaporation temperature safely, the twin screw compressor used in high temperature heat pump should be modified, like the changes of inner volume ratio and motor capacity. Discharge temperature was regulated by liquid injection and oil cooling system. Moreover, extensive experimental tests were carried out under several working scenarios and the results showed high temperature heat pump can fully meet the requirement in industrial field to supply 90-120? hot water with excellent system performance

Field Evaluation for Air-source Transcritical CO2 Heat Pump Water Heater with Optimal Pressure Control

Air-source transcritical CO2 heat pump water heater (ATHW) can supply hot water from 60 ? to 90 ? at high efficiency with environment-friendly refrigerant CO2 for commercial, residential and industrial applications. Several optimal discharge pressure correlations for transcritical CO2 heat pump have been proposed in the past few years, most of which are related to the ambient temperature, the evaporation temperature and the gas cooler outlet temperature. In an earlier study, the authors’ research group had presented a study on the dependency of the optimal discharge pressure on the ambient temperature and the hot water outlet temperature. In this study, a revised model for optimal discharge pressure is developed based on experimental results. In order to validate the optimal discharge pressure model developed, field tests are conducted to evaluate the performance of an air-source transcritical CO2 heat pump water heater in practical application. The system is comprised of a semi-hermetic reciprocating compressor, a counter-flow tube-in-tube gas cooler, a counter-flow internal heat exchanger, a fin-and-tube evaporator, and an electronic expansion valve (EEV) driven by electrically operated step motor. A Siemens SIMATIC S7-200 Programmable Logic Controller (PLC) was used to regulate the compressor discharge pressure by adjusting the EEV opening and the water flow rate by changing the frequency of the variable speed water pump. Field tests were conducted under three different operating scenarios: the nominal test condition, high water supply temperature condition and low ambient air temperature condition. The results show that the coefficient of performance (COP) can achieve 3.76 in the nominal test condition with 15? water inlet temperature and 80? hot water supply temperature. Even when the hot water temperature is higher than 90?, the COP remains at 3.21 with 20? dry-bulb temperature and 15? wet-bulb temperature. Under low ambient air temperature condition, the COP was 2.19 with the hot-water supply temperature of 60?. Comparison between the field test results and the model predictions show that the maximum relative error of discharge pressure control was 5.6% in the low temperature condition, while the maximum relative error of system COP was only 4.7%. With the reasonable agreement observed between the field test results and the model prediction. It is reasonable and effective to model the optimal discharge pressure as the function of the ambient temperature and the water outlet temperature

MOCA: A Network Intrusion Monitoring and Classification System

Optimizing the monitoring of network traffic features to detect abnormal traffic is critical. We propose a two-stage monitoring and classification (MOCA) system requiring fewer features to detect and classify malicious network attacks. The first stage monitors abnormal traffic, and the anomalous traffic is forwarded for processing in the second stage. A small subset of features trains both classifiers. We demonstrate MOCA’s effectiveness in identifying attacks in the CICIDS2017 dataset with an accuracy of 99.84% and in the CICDDOS2019 dataset with an accuracy of 93%, which significantly outperforms previous methods. We also found that MOCA can use a pre-trained classifier with one feature to distinguish DDoS and Botnet attacks from normal traffic in four different datasets. Our measurements show that MOCA can distinguish DDoS attacks from normal traffic in the CICDDOS2019 dataset with an accuracy of 96% and DDoS attacks in non-IoT and IoT traffic with an accuracy of 99.94%. The results emphasize the importance of using connection features to discriminate new DDoS and Bot attacks from benign traffic, especially with insufficient training samples

Local Path Searching Based Map Matching Algorithm for Floating Car Data

AbstractThe information acquisition of road traffic flow is requisite for urban traffic control and management. Floating car data (FCD) is emerging technique for traffic flow collection of urban large-scale road network, and it can provide effective means to model and analyze road traffic conditions. Map-matching is one of the key techniques for FCD. The typical navigation map-matching algorithms are not suitable for handling FCD with large sample interval. Through analyzing FCD characteristics, we first propose FCD map-matching algorithm based on local path searching. The information of the previous matched GPS point is utilized to reduce the search space significantly. Square confidence area is constructed to decrease the number of candidate paths. This algorithm can not only achieve FCD location with high accuracy, but also determine vehicle moving trajectory. The experimental results show our method is robust for the different sample intervals of FCD

MOCA: A Network Intrusion Monitoring and Classification System

Optimizing the monitoring of network traffic features to detect abnormal traffic is critical. We propose a two-stage monitoring and classification (MOCA) system requiring fewer features to detect and classify malicious network attacks. The first stage monitors abnormal traffic, and the anomalous traffic is forwarded for processing in the second stage. A small subset of features trains both classifiers. We demonstrate MOCA’s effectiveness in identifying attacks in the CICIDS2017 dataset with an accuracy of 99.84% and in the CICDDOS2019 dataset with an accuracy of 93%, which significantly outperforms previous methods. We also found that MOCA can use a pre-trained classifier with one feature to distinguish DDoS and Botnet attacks from normal traffic in four different datasets. Our measurements show that MOCA can distinguish DDoS attacks from normal traffic in the CICDDOS2019 dataset with an accuracy of 96% and DDoS attacks in non-IoT and IoT traffic with an accuracy of 99.94%. The results emphasize the importance of using connection features to discriminate new DDoS and Bot attacks from benign traffic, especially with insufficient training samples

Mitigating IoT Privacy-Revealing Features by Time Series Data Transformation

As the Internet of Things (IoT) continues to expand, billions of IoT devices are now connected to the internet, producing vast quantities of data. Collecting and sharing this data has become crucial to improving IoT technologies and developing new applications. However, the publication of privacy-preserving IoT traffic data is exceedingly challenging due to the various privacy concerns surrounding users, IoT networks, and devices. In this paper, we propose a data transformation method aimed at safeguarding the privacy of IoT devices by transforming time series datasets. Based on our measurements, we have found that the transformed datasets retain the intrinsic value of the original IoT data and maintains data utility. This approach will enable non-expert data owners to better understand and evaluate the potential device-level privacy risks associated with their IoT data while simultaneously offering a reliable solution to mitigate their concerns about privacy violations

Evaluation of Empirical Equations and Machine Learning Models for Daily Reference Evapotranspiration Prediction Using Public Weather Forecasts

Although the studies on model prediction of daily ETo based on public weather forecasts have been widely used, these studies lack the comparative evaluation of different types of models and do not evaluate the seasonal variation in model prediction of daily ETo performance; this may result in the selected model not being the best model. In this study, to select the best daily ETo forecast model for the irrigation season at three stations (Yinchuan, Tongxin, and Guyuan) in different climatic regions in Ningxia, China, the daily ETos of the three sites calculated using FAO Penman–Monteith equations were used as the reference values. Three empirical equations (temperature Penman–Monteith (PMT) equation, Penman–Monteith forecast (PMF) equation, and Hargreaves–Samani (HS) equation) were calibrated and validated, and four machine learning models (multilayer perceptron (MLP), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and gradient boosting with categorical features support (CatBoost)) were trained and validated against daily observed meteorological data (1995–2015 and 2016–2019). Based on public weather forecasts and daily observed meteorological data (2020–2021), the three empirical equations (PMT, PMF, and HS) and four machine learning models (MLP, XGBoost, LightGBM, and CatBoost) were compared in terms of their daily ETo prediction performance. The results showed that the daily ETo performance of the seven models in the irrigation season with a lead time of 1–7 days predicted by the three research sites decreased in the order of spring, autumn, and summer. PMT was the best model for the irrigation seasons (spring, summer, and autumn) at station YC; PMT and CatBoost with C3 (Tmax, Tmin, and Wspd) as the inputs were the best models for the spring, autumn irrigation seasons, and summer irrigation seasons at station TX, respectively. PMF, CatBoost with C4 (Tmax, Tmin) as input, and PMT are the best models for the spring irrigation season, summer irrigation season, and autumn irrigation season at the GY station, respectively. In addition, wind speed (converted from the wind level of the public weather forecast) and sunshine hours (converted from the weather type of the public weather forecast) from the public weather forecast were the main sources of error in predicting the daily ETo by the models at stations YC and TX(GY), respectively. Empirical equations and machine learning models were used for the prediction of daily ETo in different climatic zones and evaluated according to the irrigation season to obtain the best ETo prediction model for the irrigation season at the study stations. This provides a new idea and theoretical basis for realizing water-saving irrigation during crop fertility in other arid and water-scarce climatic zones in China