Return Water Temperature Research Articles

New multi-energy sources coupling a low-temperature sustainable central heating system with a multifunctional relay energy station

Due to short cost-effective heat transportation distance, the existing geothermal heating technologies cannot be used to develop the deep hydrothermal type geothermal fields situated far away from urban area. To solve the problem, a new multi-energy source coupling a low-temperature sustainable central heating system with a multifunctional relay energy station is put forward. As for the proposed central heating system, a compression heat pump integrated with a heat exchanger in the heating substation and a gas-fired water/lithium bromide single-effect absorption heat pump in the multifunctional relay energy station are used to lower return temperature of the primary network step by step. The proposed central heating system is analyzed by using thermodynamics and economics, and matching relationships between design temperature of return water and main line length of the primary network are discussed. The studied results indicate that, as for the proposed central heating system, the cost-effective main line length of the primary network can approach 33.8 km, and the optimal design return temperature of the primary network is 23 ℃. Besides, annual coefficient of performance and annual exergy efficiency of the proposed central heating system are about 3.01 and 42.7%, respectively.

Topology optimization of thermally activated building system in high-rise building

The building sector accounts for nearly 1/3 of global energy end-use and associated carbon emissions. Fulfilling buildings' thermal demand in an efficient way is critical for decarbonizing the building sector. Therefore, this paper introduces the thermally activated building system (TABS) as an energy retrofit solution. A TABS floor-by-floor return water temperature control approach is proposed to reduce buildings' energy use and carbon emissions. The approach includes the topology optimization of TABS and flow control. The bypass pipe connections of the TABS to each floor, namely, the floor-by-floor pipe network topology, have been optimized by using genetic algorithms to achieve the lowest total cost. A case study of a high-rise building located in Beijing demonstrates that the proposed approach can improve the system's energy efficiency compared to the conventional TABS without optimization. The reduction of energy cost can be up to 11.21% with a 13.53% annual emission reduction. In general, the proposed TABS floor-by-floor return water temperature control approach can significantly improve the building efficiency comprehensively, therefore can be considered as a promising energy retrofit solution for the decarbonization of high-rise buildings.

In situ model fusion for building digital twinning

Building digital twins are crucial in holistic, synthetic, and supervisory digital environments for intelligent and energy-efficient building operations. As buildings are designed, constructed, and operated for long-term periods with uncertainties, it is essential to model, verify, extend, and calibrate the digital twin models in situ over the building life cycle, unlike digital twin modeling in the manufacturing industries. Therefore, this study proposes in situ model fusion techniques for building digital twinning, which include (1) model coupling and (2) model assembly. The first model coupling technique enables nonintrusive in situ verification and calibration for prediction models without the model observations (Y). The second model assembly enables in situ modeling or a more accurate model construction by connecting the verified models through the model coupling technique to the input layer of the target model. In the case studies conducted in a central heating system, the prediction model for the secondary-side return water temperature was modeled and calibrated without relying on the model observation data (Y), thanks to the model coupling technique. The nonintrusive model showed an accuracy with a root mean square error (RMSE) of 0.53 °C, which was comparable to the intrusive gray-box models (around 0.5 °C RMSE) calibrated using intrusive datasets (Y). Additionally, the prediction model could improve the benchmark model's performance from 0.44 °C to 0.26 °C (RMSE) using the model assembly technique. The in situ model fusion would enable and enhance nonintrusive modeling approaches, providing a reliable and extensive model environment for digital twins in the building sector.

Field studies on indoor thermal environment and start-stop characteristics of the radiant air conditioning system during intermittent heating

With the improvement of social economy, the heating demand of residents in Hot Summer and Cold Winter (HSCW) areas in China is increasing. The radiant air conditioning (A/C) system, is popular as a heating way due to its high comfort, low noise and high energy saving potential. There is still a lack of field research on the system under the intermittent heating mode in the HSCW area. To provide a guide for the operation mode of the radiant A/C system during intermittent heating in HSCW areas, this paper focused on the radiant A/C system with the fresh air system, and then studied system's practical operational parameters and dynamic characteristics in winter. The indoor and outdoor air temperatures and relative humidity, the water temperatures of inlet and outlet of the air source heat pump (ASHP) unit, the supply and return water temperatures of radiant terminals, as well as supply air temperature were analyzed in three stages, i.e., the initial start-up stage, the intermittent shutdown stage and the intermittent start-up stage. The results show that, it took 7 h and 50 min for the indoor air temperature to reach the set indoor temperature during the initial start-up stage. Compared to the initial start-up stage, the intermittent start-up stage reduced the time for the indoor air temperature to reach the set indoor temperature by 4 h and increased the indoor air temperature rise rate by 97%. The indoor air temperature rise rate was 0.46 oC/h and 0.91 oC/h, during the initial start-up and the intermittent start-up stage, respectively. The indoor air temperature decrease rate was 0.23 oC/h during the shutdown period. During the entire winter season, the ASHP unit switched on and off intermittently 10 times. The longest shutdown lasted for 111 h, and the indoor air temperature dropped to 17.2oC.

A field test of medium-depth geothermal heat pump system for heating in severely cold region

Due to the worldwide attention of fossil fuel consumption and environment pollution, geothermal is recognized to be amongst the prospering alternative energy. Previous researches mainly focused on the shallow-depth geothermal heat pump systems (SD-GHPs), while medium-depth geothermal heat pump system (MD-GHPs) lacked of the experimental support especially in severely cold region. A field test of MD-GHPs using deep borehole heat exchanger (DBHE) was conducted and verified in terms of temperature variation, heat exchange and COP. The indoor temperature remained 20 °C around even though the outdoor temperature was as low as −25.10 °C. The water supply and return water temperature on the customer side performed steadfast heating characteristics that the averages were 35.13 °C and 30.97 °C. Moreover, flow rate and borehole depth are the essential influencing factors on DBHE outlet temperatures. This suggests that lifting flow rates is a fantastic option when only considering the enhancement of the thermal extraction. The DBHE average heat exchange per meter is 1000/1200 m for 61.1/84.9 W/m and 2000 m is 152.6 W/m respectively. Besides, the average heat exchange of DBHEs for working days was 485.4 kW while during Chinese New Year Holidays was 388.6 kW. Correspondingly, the average COPs for MD-GHPs ground side were 4.5 and 5.2, respectively. Results show that MD-GHPs can be extended to severely cold regions and obtain superior performance.

Experimental study on the performance and control strategy of a water-cooled subcooler-assisted carbon dioxide heat pump for combined cooling and heating

Carbon dioxide heat pump is considered a potential low-carbon combined cooling and heating solution. However, there is a strong need for simple methods to improve the energy efficiency of the carbon dioxide heat pump due to the larger throttling losses that result in poor performance. Given this, a compact water-cooled subcooler-assisted carbon dioxide heat pump for combined cooling and heating is designed in this paper. An experimental test platform is built, where experimental investigations are performed to reveal the working performance. Furthermore, a matching control strategy is developed and verified as well. The experiments demonstrated that the adoption of the water-cooled subcooler has significantly improved the cooling performance. The cooling capacity (Q̇c) and cooling coefficient of performance (COPc) are increased by up to 20.2% and 31.5%, respectively. Moreover, the optimal discharge pressure is lowered by 0.5 MPa. However, the heating performance is somewhat degraded, for which the control strategy with the synergistic variation of subcooling degree and compressor rotation speed is developed. The deteriorated heating performance is thus mitigated. The developed control strategy has been verified in practice, and the carbon dioxide heat pump performance could be maintained at the highest possible level. Q̇c and COPc are further improved by a maximum of 40.7% and 37.7%, respectively. The heating and overall coefficient of performance could reach 2.55 and 5.10 at the return water and outlet water temperatures of 40 °C and 75 °C, respectively.

Heat exchange pipe spacing for optimal temperature uniformity on cold radiant ceiling surfaces

Radiant cooling systems have great potential for comfort and energy savings, but uneven surface temperature distributions and the risk of condensation restrict their use in engineering applications. In this study, a cold radiant panel heat transfer model was established and experimentally verified. Radiant panels with 3 heat transfer pipe spacings were designed and used in 7 tandem combination modules with variable pipe spacing (VPS) that were compared with 3 conventional cold radiant panel tandem combination modules with equal pipe spacing (EPS). Compared to the EPS modules, the overall heat transfer performance of the VPS modules was better, the surface temperature distribution was more uniform, and the average surface temperature was lower for different values of the operating parameters such as the water supply flow rate and supply and return water temperature difference. Additionally, the values of the temperature nonuniform coefficient (TNC) and surface average temperature were lower for the VPS than the EPS modules. Although the method of combining cold radiant panels with different heat transfer pipe spacings in series to form a cooling module improves the overall heat transfer performance, the balance of cost and heat transfer performance improvement should be considered in practical applications.

Enhancement technology of underground water flow field in coal mine to improve energy efficiency of heat pump system in geothermal energy development

In order to solve the problem that the cold/heat released from the underground pipe of coal mine into the soil layer is difficult to dissipate in a short time, the technology of enhancing the underground flow field of coal mine in geothermal energy development to improve the energy efficiency of the heat pump system is proposed. In this paper, based on the ground source heat pump project located in a coal mine, the energy efficiency enhancement technology of the artificial flow field ground source heat pump system is studied and tested. The test results show that when starting work at about 8:00, due to the increase of heating load, the accumulation of cooling capacity around the buried pipe increases gradually, and the return water temperature at the ground source side decreases gradually. At 14:05, the water was pumped for the operation of the artificial groundwater flow field. It can be seen that the temperature of the return water at the ground source side began to rise gradually, indicating that the accumulation of cold water around the buried pipe was relieved to a certain extent. In conclusion, the artificial groundwater flow field can alleviate the accumulation of cold water around the buried pipe heat exchanger to a certain extent, improve the return water temperature of the buried pipe heat exchanger, and improve the energy efficiency of the ground source heat pump system.

Thermal energy storage thermal data processing for heating systems

In order to solve the problem that the traditional industrial control methods cannot control the heating flow and water temperature in a timely and effective manner due to the high delay and complex coupling characteristics of the urban central heating system, the authors propose deep learning-based data processing and management for thermal heating systems. The author analyzes the non-ideality of district heating system and its influence on the application of deep learning technology, and gives solutions, respectively, finally, a primary side regulation scheme of district heating system based on deep learning and automatic control technology is proposed as a whole. The experimental results show that, by comparing the water supply temperature predicted by the equipment model of the primary side heat station with its actual measured value, the mean square error of the prediction results using the model directly is 1.30%, and the mean square error after model correction is 0.094%. The secondary return water temperature was controlled by adjusting the opening of the primary side electric valve, the expected secondary return water temperature in the scheme was compared with the actual secondary return water temperature, and the mean square error was 0.102%. It is proved that the scheme can achieve good control effect in the actual system, and the data result proves that the scheme is feasible.

In-situ observation virtual sensor in building systems toward virtual sensing-enabled digital twins

Sensing in building operations is essential to realize intelligent buildings and digital twin-enabled operations. However, buildings have a limited sensing environment with sensor absences, faulty sensors, low redundancy, and less dense deployment because of the inherent building characteristics (e.g., massive, heterogeneous, and long-term). It impedes the advanced building operations. To tackle these issues, this study proposes a novel sensing method for in-situ observation virtual sensors (OVS) in building operations. The proposed OVS is intended to predict the unmeasured variables in real time, without the target sensors. To do so, the OVS is modeled in-situ and then calibrated indirectly without the target observation (Y) so that the virtual sensing can be effectively applied in the building sector, where it is very difficult to establish a laboratory environment having Y for the OVS modeling. The OVS can be developed and operated in the building digital twins, thus extending the physical sensing coverages for digital twin-enabled intelligent operations. For real application, the proposed OVS was developed to observe the return water temperature of a real district heating system. The OVS was experimentally validated with the real measurement to discuss the in-situ OVS model and calibration performance. The OVS could be successfully modeled without using the target observation (Y), and indirect calibration improved the initial OVS performance by 32 %. The OVS demonstrates a significant performance with a root mean square error of 0.55 °C.

Optimized deep learning-based prediction model for chiller performance prediction

With the development of primary energy consumption and global climate change are encouraged air conditioning chillers to conventional air-conditioning methods. In the last previous couple of decades, several absorption technologies are introduced for absorption cooling systems and solar cooling to achieve higher chiller performance. In this paper, an efficient and optimal chiller performance prediction mechanism, named Spider monkey Bat Algorithm (SMBat)-based Generative adversarial network (SMBat-based GAN), is proposed. It is exploited to predict the chiller performance from time-series data. The proposed SMBat algorithm integrates the Spider Monkey Optimization (SMO) and the Bat algorithm. The GAN classifier is used to predict the chiller performance using the time series data based on the fitness function. In addition, extraction of the feature is achieved using the chilled water supply temperature, chilled water return temperature, condenser water return temperature, chiller water flow, cooling capacity, and power utilization. The features are selected by the wrapper method for selecting the appropriate features for better chiller performance prediction. Then these features are forwarded to the prediction module, where the prediction is done based on the proposed SMBat-based GAN. Finally, the experimental analysis exhibits that the proposed model offered better performance based on the metrics, such as Mean Square Error (MSE), R2-score, and Mean Absolute Error (MAE) when considering four datasets. The developed SMBat-based GAN attained better results for dataset-3 with a minimal MAE of 0.008, a minimal MSE of 0.0001, and a maximal R2-score of 0.999. The R2-score of the proposed method is 5.905%, 0.4%, 0.2%, and 1% higher when compared to the existing approaches, namely, regression model, ANN+ Bat, ANN, and Multi obj GA+NN.

A numerical assessment of a latent heat storage system for district heating substations

This work deals with the development and sizing of a latent heat storage system for an urban district heating system (UDHS). The investigated system is a cylindrical tank containing spherical phase change material (PCM) capsules. A physical model based on the apparent specific capacity method was adopted and validated to describe the phase transition within the capsules. The effects of various designs and operating factors on storage performance are detailed and addressed, including the thermal energy provided by the system during the discharge period, the melting temperature, tank volume, and water return temperature. The results show that the energy efficiency of such a device can be enhanced only through a judicious choice of the mentioned parameters. For example, it was found that increasing the UDHN thermal power demand influences the system's performance and leads to reduce the discharging duration. Based on the presented analyses, it can be concluded that the proposed numerical tool can be used by thermal designers of urban heating networks to size, optimise, and predict the actual dynamic behaviour of the storage system under real exploitation conditions.

In-situ observation and calibration in building digitalization: Comparison of intrusive and nonintrusive approaches

Under digitalization-driven carbon neutrality in the building energy sector, in-situ virtual sensors could play a role in informative sensing environments and information modeling in building operations. This study proposes methodologies for the in-situ observation of virtual sensors and their in-situ calibration in building operations and digitalization. In the context of the building life cycle, in-situ observation virtual sensors are developed based on building related physics principles and design information, without physical observations, and then they are calibrated using two calibration approaches: (1) nonintrusive-indirect and (2) intrusive-direct calibrations. A comparative study is conducted to investigate the effectiveness of physics-based in-situ virtual sensors in real operation, compare the two calibration performances, and suggest recommendations for each calibration approach for real applications. According to a field study in a target district substation serving residential buildings, the in-situ observation virtual sensor for the demand-side return water temperature showed a root mean squared error (RMSE) of 0.81 °C before calibration in the heating season (112 days). The RMSEs of 0.61 and 0.55 °C were found for the nonintrusive-indirect and intrusive-direct calibrations in the representative case, respectively. These results showed the effectiveness of nonintrusive-indirect calibration, even though the target observations were unknown in the calibration. This study recommends obtaining informative datasets for improved intrusive calibration. In the case study, calibration with the informative dataset for 1 day (RMSE of 0.59 °C) was superior to that of the average RMSE for nonintrusive calibration. The current energy patterns could be a basis for deciding whether the next few days are suitable for obtaining informative intrusive datasets, resulting in better calibration accuracy or shortening of the required datasets.

A New Approach for Failure Modes, Effects, and Criticality Analysis Using ExJ-PSI Model—A Case Study on Boiler System

Failure Modes, Effects, and Criticality Analysis are widely used to assess the failure modes of a system, along with their causes and effects. Several Multi-Criteria Decision-Making approaches have been developed to overcome the limitations of the traditional FMECA. In these approaches, several multiple criteria are considered to determine the criticality values and assign criticality ranks. In these developed approaches, only one expert can involve in criticality analysis. By incorporating several experts from design, manufacturing, and maintenance domains in the criticality analysis accuracy of the results can be improved. This paper proposes a new integrated Expert Judgment-based Preference Section Index (ExJ-PSI) model that combines MCDM approaches and integrates expert opinions. The proposed model is applied to a boiler system used in the textile industry. The results are compared with those obtained from the conventional FMECA and normalized median method. In the present study, the opinions of seven experts from various domains and organizations are discussed. To normalize the collected data, one scale is developed by considering four expert criteria, such as the experience of the expert, the number of boilers he is handling, the number of employees under his supervision, and the proficiency in the field. The proposed model is more effective and flexible in handling and analyzing data of complex configured systems consisting of many subsystems, components, and failure modes. The analysis reveals that the feed water pump, feed water pump motor, supply water temperature sensor, return water temperature sensor, header, and coal feed motor are some of the most critical components of the boiler system.

Advances on a free-piston Stirling engine-based micro-combined heat and power system

As a promising supplement to traditional central electric generation technologies, a distributed combined heat and power (CHP) system is conducive to renewable energy deployment and mitigation of carbon emissions. Among the candidate technologies, the free-piston Stirling engine (FPSE)-based CHP technology is competitive in micro- or small-scale applications. In this work, the performance of a FPSE-based micro-CHP system is improved through a combination of numerical optimization and experimental validation. First, by using a third-order model and thermoacoustic analysis, the impact of regenerator length, displacer moving mass, and force factor of the alternator on the free-piston Stirling generator (FPSG) inside acoustic field, supplied thermal power and exergy loss is studied numerically. The numerical results indicate a rational increase of the force factor, the regenerator length as well as the displacer moving mass is positive for improving the CHP performance. Based on the numerical optimization, the FPSG prototype is updated accordingly, and adequate measurements of the internal acoustic field of the FPSG have been deployed. Then, a series of experiments are conducted to evaluate the CHP performance of the refurbished FPSG prototype. The experimental results show that the highest CHP efficiency reaches 95.7%, the output electric power is 4.03 kW, and the corresponding thermal-to-electric efficiency is 32.2% when the average temperature of supply and return water is as high as 75 °C. Compared with the work before upgrade, the output electric power and CHP efficiency are improved significantly by 39% and 9.4%, respectively. An insight into the impact of the temperature difference between supply and return water indicates that it has a slight impact on electric power generation, especially under higher charge pressure. In contrast, thermal production exhibits an apparent negative correlation with the temperature difference.

A Novel Virtual Sensor Modeling Method Based on Deep Learning and Its Application in Heating, Ventilation, and Air-Conditioning System

Realizing the dynamic redundancy of sensors is of great significance to ensure the energy saving and normal operation of the heating, ventilation, and air-conditioning (HVAC) system. Building a virtual sensor model is an effective method of redundancy and fault tolerance for hardware sensors. In this paper, a virtual sensor modeling method combining the maximum information coefficient (MIC) and the spatial–temporal attention long short-term memory (STA-LSTM) is proposed, which is named MIC-STALSTM, to achieve the dynamic and nonlinear modeling of the supply and return water temperature at both ends of the chiller. First, MIC can extract the influencing factors highly related to the target variables. Then, the extracted impact factors via MIC are used as the input variables of the STA-LSTM algorithm in order to construct an accurate virtual sensor model. The STA-LSTM algorithm not only makes full use of the LSTM algorithm’s advantages in handling historical data series information, but also achieves adaptive estimation of different input variable feature weights and different hidden layer temporal correlations through the attention mechanism. Finally, the effectiveness and feasibility of the proposed method are verified by establishing two virtual sensors for different temperature variables in the HVAC system.

A specially-designed test platform and method to study the operation performance of medium-depth geothermal heat pump systems (MD-GHPs) in newly-constructed project

Medium-depth geothermal heat pump systems (MD-GHPs) use deep borehole ground heat exchangers (DBHEs) to extract heat from the medium-depth geothermal energy with depth of 2–3 km. To study the local geothermal condition and operation performance of MD-GHPs, so as to guide the system design and operation control, a test platform and method are raised and applied in a newly-constructed project, where a DBHE with depth of 2750 m is constructed. Results show that the maximum heat extraction rate of DBHE reaches 450 kW with inlet and outlet water temperature of 5.0 ℃ and 18.9 ℃, and the maximum heat supply rate of heat pump reaches 600 kW with supply and return water temperature of 51.8 ℃ and 40.6 ℃ under continuous operation mode. Then the energy efficiency of heat pump (COP) can still remain higher than 3.60 with renewable energy utilization rate higher than 72.2 % in this maximum load condition. Besides, the DBHE still extracts heat from outside ground to store heat when the heat pump system shuts off, thus the transient maximum heat extraction rate could reach 600 kW or even higher under intermittent operation mode. Finally, the optimization direction of DBHE and heat pump systems are analyzed, so as to improve the overall operation performance of MD-GHPs and achieve better energy conservation effect.

Experimental investigation on the performance of a boiler system with flue gas dehumidification and combustion air humidification

The temperature of the return water in heating networks is a key parameter for heat recovery from flue gas. At high the return water temperature, the conventional systems for boiler flue gas heat recovery do not perform well. This paper presents a more effective total heat recovery system by dehumidifying flue gas and humidifying combustion air. An experimental bench for flue gas waste heat recovery based on the combination of combustion air humidification and solution dehumidification was built to verify the feasibility of using potassium a formate aqueous solution (the concentration of the second dehumidifier was measured at 66%) to recover flue gas waste heat. The experimental results demonstrate that when the return water temperature of the heating network is as high as 52.5 °C, the boiler thermal efficiency increases by 9.1%. When the return water temperature of the heating network is 42–57 °C, the final relative humidity of the exhaust flue is 50–70% and the white plumes are remarkably eliminated. The equivalent thermal efficiency increases by >3% after the cooling tower is turned on. Compared with the existing waste heat recovery system, the experimental system developed in this study achieves a better waste heat recovery effect under the working conditions of a higher return water temperature of the heating network. Thus, this system holds promise for future applications as an energy-efficient, cost-effective, and environmentally-friendly system for waste heat recovery from district heating systems.

Experimental analysis of airflow uniformity and energy consumption in data centers

Temperature and airflow uniformity are experimentally investigated in this research study along with energy consumption analysis on the laboratory container size small data center. A raised floor plenum (RFP) data center system of size 6 (L) × 3.1 (W) × 2.3 (H) m with one Computer Room Air Handler (CRAH), two rows and each row contains five racks, five 8U simulator cabinets in each rack was constructed to test partial and closed cold aisle containment (CAC). Seven different cases were investigated with variables of perforated tiles (32%, 50 % & 70%), CRAH fan speed (60 Hz, 42 Hz), chiller return water temperature (CRWT) (15 °C & 17 °C) and heating load (full 50 kW, 75% load 37.5 kW). These cases were further analyzed using Rack Cooling Index (RCI) and Supply Heat Index (SHI) along with power consumption scenarios in terms of Coefficient of Performance (COP) and Power Usage Effectiveness (PUE). The uniqueness of this study is experimental investigations of system COP and PUE analysis for a wide range of important variables for partial and closed CAC. It was found that 50% porous tile provided the best airflow and temperature uniformity for the case of partial containment with full and 25% reduced heat load along with full and 30% reduced CRAH speed respectively. Slight recirculation was observed during the reduced CRAH fan speed case at the corner edge of data racks. However, due to additional supply airflow usage, these recirculation’s were within the acceptable SHI. Reduction of CRWT reduces the total Heating Ventilation and Air Conditioning (HVAC) power consumption and improves the overall system COP.

Simplified data-driven models for model predictive control of residential buildings

Owing to recent advancements in Internet of Things technologies, data-driven model predictive control (MPC) has received significant research interest as a promising strategy to optimize building operation. As the MPC performance relies on the model prediction accuracy, complex building prediction models have been used in MPC applications, despite their high computational cost for optimization. This study examines whether linear-form prediction models are reliable to support the MPC of residential buildings equipped with single types of heating systems. This study developed linear-form models, namely an autoregressive with exogenous inputs (ARX) for predicting the indoor temperature and threshold-piecewise models for the return and supply water temperatures. The MPC performance on the basis of the linear models was evaluated under varying prediction horizons and weights associated with objective attributes. A case study of a residential unit through the simulated virtual building showed that the proposed models achieved the high goodness-of fit values greater than 0.9. The resulting MPC framework achieved heating energy savings up to approximately 12% relative to a simple on/off thermostat or reduction of comfort violation magnitude less than 0.5 °C. The influences of weight and prediction horizon on MPC performance were also investigated.