Optimization on integrated inverter-compressor CO2 heat pump with new operating model

Xu, Yingjie; Mao, Chengbin; Zhang, Xiaoyu; Shen, Xi; Huang, Yuangong; Chen, Guangming

doi:10.1016/j.applthermaleng.2021.117632

Cited by 8 publications

(5 citation statements)

References 47 publications

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“…The annual capital cost of the whole system can be obtained by evenly distributing the total capital cost into all operating years, according to the below equation

{\dot{Z}}_{\mathrm{inv}+\mathrm{main}}=\text{CRF}\cdot \varphi \cdot {Z}_{\mathrm{sum}},

where Z sum is the sum of investment costs of all components, which can converse to total capital cost by multiplying the maintenance factor φ (specified as 1.05). Capital recovery factor (CRF) can be determined with interest rate ( i = 3%) and system life time ( n = 10), according to literature 18 . Investment cost of condenser ( Z gc ), evaporator ( Z e ), fan‐coil unit ( Z fan ), compressor ( Z com ), pump ( Z pump ), water tank ( Z wt ), and expansion valve ( Z ev ) are determined by Equations ()–(), 19–21 respectively.…”

Section: System Description and Modelingmentioning

confidence: 99%

“…Capital recovery factor (CRF) can be determined with interest rate (i = 3%) and system life time (n = 10), according to literature. 18 Investment cost of condenser (Z gc ), evaporator (Z e ), fan-coil unit (Z fan ), compressor (Z com ), pump (Z pump ), water tank (Z wt ), and expansion valve (Z ev ) are determined by Equations ( 16)-( 22), [19][20][21] respectively. Additionally, the cost of accessories, such as check valves, and connecting pipes, is added to the total investment cost, which accounts for about 1% of the cost of the whole system.…”

Section: Economic Modelmentioning

confidence: 99%

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Thermoeconomic multiobjective optimization of tobacco drying heat pump recovering waste heat from monocrystal silicon furnace based on SVR ANN model in Southwest China

Hou

Mao

2023

Energy Science & Engineering

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Great quantities of energy are consumed by heat pump drying systems (HPDSs), such as tobacco drying. It can be reduced by increasing evaporating temperature using waste heat. In this paper, based on the case of two main industries in southwest China (monocrystalline silicon industry and tobacco industry), a novel integrated HPDS especially for the tobacco drying using waste heat from single crystal silicon furnaces is proposed. It is then analyzed from an energetic and economical perspective, investigating the effects of heat exchanger areas on system performance. To simultaneously minimize system CO 2 emissions and total annual cost (TAC), a multiobjective optimization using nondominated sorting genetic algorithm is conducted based on the support vector regression model, which has been rarely reported for integrated HPDS improved by waste heat. The final optimum solution on the Pareto optimal front is selected using different decisionmaking methods (LINMAP, TOPSIS, and fuzzy decision method), and are compared with the performance of the initial system. The results show that the TOPSIS method is most suitable in three decision-making methods with a great reduction in m CO 2 (−12.7%) and nearly unchanged in TAC (+0.5%) compared with the initial solution. And the decreases in m CO 2 and TAC of the optimized system is as large as 50% and 1/3, compared with traditional vapor compression heat pump dryer without heat recovery of the monocrystal silicon furnace.

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“…The annual capital cost of the whole system can be obtained by evenly distributing the total capital cost into all operating years, according to the below equation

{\dot{Z}}_{\mathrm{inv}+\mathrm{main}}=\text{CRF}\cdot \varphi \cdot {Z}_{\mathrm{sum}},

Section: System Description and Modelingmentioning

confidence: 99%

Section: Economic Modelmentioning

confidence: 99%

Thermoeconomic multiobjective optimization of tobacco drying heat pump recovering waste heat from monocrystal silicon furnace based on SVR ANN model in Southwest China

Hou

Mao

2023

Energy Science & Engineering

Self Cite

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“…As mentioned by de Paula et al [63], in the literature, there are detailed models of compressors but these models required many parameters and geometrical details that are not provide by the manufactures of hermetic compressors [64,65,66]. Additionally, the compressor model used in the complete model of the refrigeration system is a simple one as those employed in other studies [49,67,68]. The mass flow rate (!)…”

Section: Compressor Modelmentioning

confidence: 99%

Experimental Validation Through a Parallel Computation Algorithm for Evaluation Uncertainty of the Mathematical Model of Direct Expansion Solar Assisted Heat Pump

Duarte

Paulino

Duarte

et al. 2023

J. Solar Eneg. Res. Updat.

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This paper presents the development of a mathematical model for a direct-expansion solar-assisted heat pump (DX-SAHP) operating in steady-state. The mathematical model was implemented using the scientific software EES and using a code written in Python. It was utilized a lumped parameter model for the heat exchangers and a semi-empirical model for the compressor. The mathematical model was validated using experimental data of a DX-SAHP running with R134a. Two hundred simulations were made combining different correlations for estimating the convective heat transfer coefficient in the evaporator/collector. The Mean Absolute Deviation (MAD) and the Mean Deviation (MD) between the theoretical and experimental values for the COP were 2.6±1.8 % and 0.9±1.8 %, respectively. The MAD and MD between the discharge temperature were 1.56±0.16 % and -1.45±0.16 %. The mean difference between the results using EES and Python were 1.4 %. The use of Python with parallel computing for uncertainty analyses, reduced the simulation time in 88 % if compared with EES. The model in Python is available as open-source through the platform Google Colaboratory.

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“…The model provided predictions of the heat pump performance with deviations below 7% for the tests in winter mode and below 10% during summer mode. Xu et al developed thermodynamic models for an air source CO 2 heat pump system for space heating and domestic hot water production [17]. The models predicted COP and heat production for two experimental datasets with mean absolute percentage errors (MAPE) of 5.5-6.1% and 2.9-3.1%, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…ANNs are easy to use, fast in response, and suitable for "off-line" and "on-line" applications [19]. The authors responsible for the CO 2 heat pump model referenced earlier used datasets generated from the physics-based model to train ANNs to increase the speed and precision when utilizing the model within an optimization framework [17].…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster

2023

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The use of heat pumps for heating and cooling of buildings is increasing, offering an efficient and eco-friendly thermal energy supply. However, their complexity and system integration require attention to detail, and minor design or operational errors can significantly impact a project’s success. Therefore, it is essential to have a thorough understanding of the system’s intricacies and demands, specifically detailed system knowledge and precise models. In this article, we propose a method using artificial neural networks to develop heat pump models from measured data. The investigation focuses on an operational heat pump plant for heating and cooling a cluster of municipal buildings in Stavanger, Norway. The work showcases that the network configurations can provide process insights and knowledge when detailed system information is unavailable. Model A predicts the heat pump response to temperature setpoint and inlet conditions. Except for some challenges during low-demand cooling mode, the model predicts outlet temperatures with Mean Absolute Percentage Error (MAPE) between 2 and 5% and energy production and consumption with MAPE below 10%. Summarizing the five-minute interval predictions, the model predicts the hourly energy production and consumption with MAPE at 3% or less. Model B predicts energy consumption and coefficient of performance (COP) from measured inlet and outlet conditions with MAPE below 5%. The model may serve as a tool to develop system-specific compressor maps for part-load conditions and for real-time performance monitoring.

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Optimization on integrated inverter-compressor CO2 heat pump with new operating model

Cited by 8 publications

References 47 publications

Thermoeconomic multiobjective optimization of tobacco drying heat pump recovering waste heat from monocrystal silicon furnace based on SVR ANN model in Southwest China

Thermoeconomic multiobjective optimization of tobacco drying heat pump recovering waste heat from monocrystal silicon furnace based on SVR ANN model in Southwest China

Experimental Validation Through a Parallel Computation Algorithm for Evaluation Uncertainty of the Mathematical Model of Direct Expansion Solar Assisted Heat Pump

Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster

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