Chilled Water Outlet Temperature Research Articles

Automated data mining framework for building energy conservation aided by generative pre-trained transformers (GPT)

Data mining technologies have showed promising capabilities in extracting building operation patterns from massive amounts of building operational data for energy conservation. However, the number of the extracted operation patterns is always large. It is time-consuming and tedious for users to find valuable operation patterns among them. To overcome this barrier, this study proposes an automated data mining framework based on maximal frequent itemset mining and generative pre-trained transformers (GPT). An improved maximal frequent itemset mining-based data mining method is developed to extract non-redundant operation patterns from numerous building operational data for reducing the number of the extracted operation patterns. A template-based prompt generation method is proposed to transform the extracted operation patterns into prompts. The prompts are inputted into GPT to determine whether there are energy waste patterns hidden in the extracted operation patterns. It liberates humans from the tedious work on analyzing the extracted operation patterns. The framework is applied to analyze the one-year operational data from a real-world building chiller plant system for verifying its performance. Most of the energy waste patterns in this system are detected successfully, such as valve faults, low chilled water outlet temperature, small chilled and cooling water temperature differences, and improper coordinated control among devices. The detection accuracy of GPT is 89.17 % for energy waste patterns and 99.48 % for normal operation patterns. The response time and cost of GPT are 6747.60 s and $17.68, respectively.

Simulation and performance research of absorption heat pump unit based on R134a-DMF working fluid pair

R134a-DMF absorption heat pump unit is an energy-saving heat pump unit that can utilize renewable energy, and has great potential in the refrigeration and heating fields of urban and rural areas. The purpose of this article is to conduct in-depth research on the dynamic characteristics of absorption heat pump units based on R134a-DMF, a new working fluid pair. A mathematical model of the thermophysical properties of the R134a-DMF working fluid pair and the mathematical models of various components of the heat pump unit are constructed. This paper constructs a simulation program, and uses the Control variates to study the change trend of the Coefficient of performance of R134a-DMF absorption heat pump unit affected by the generator outlet concentrated solution temperature, condenser air volume and temperature rise, and chilled water outlet temperature. The results indicate that the established mathematical model for thermophysical properties and the unit model are both accurate models, which can provide guidance for the actual operation and optimization of R134a-DMF absorption heat pump units. Through simulation, it can be concluded that for the three combined forms of R134a DMF (3:2), R134a DMF (1:1), and R134a DMF (2:3), the average increase in COP and refrigeration capacity is 0.85 %, 0.39 %, and 0.42 % for each 1 °C increase in the outlet concentrated solution temperature of the generator, respectively, and the growth rate is relatively slow. The larger the proportion of refrigerant in the binary solution, the greater the COP of the unit under the same operating conditions.

A machine learning-based control strategy for improved performance of HVAC systems in providing large capacity of frequency regulation service

Heating, ventilation and air-conditioning systems (HVAC), at demand side, have been regarded increasingly as promising candidates to provide frequency regulation service to smart power grids. In many control systems, chilled water outlet temperature setpoint is reset to change the power use of HVAC systems after the regulation capacity is determined. However, the conflict between changed power use and unchanged cooling/heating demand could become a prominent problem when a large regulation capacity is provided. This problem can deteriorate the performance of frequency regulation service provided by HVAC systems. In this study, a machine learning-based control strategy is proposed to solve this problem for improved performance of HVAC systems in providing large capacity of frequency regulation service. It adjusts the power use of HVAC systems by simultaneously resetting chilled water outlet temperature setpoint and indoor temperature setpoint. The proposed control strategy is validated on a simulation platform. Results show that the strategy can significantly increase the performance of service when an HVAC system provides different regulation capacities. Moreover, the robustness of the strategy is studied. The results show that the strategy can still work effectively even the machine learning algorithms has a relatively low prediction performance in real application due to practical difficulties.

Application of machine learning and its improvement technology in modeling of total energy consumption of air conditioning water system.

Accurate energy consumption model is the basis of energy saving optimal control of air conditioning system. The existing energy consumption model of air conditioning water system mainly focuses on a certain equipment or a part of the cycle. However, the coupling between water system equipment will affect the setting of optimal energy consumption of equipment. It is necessary to establish the energy consumption model of water system as a whole. However, air conditioning water system is a highly nonlinear complex system, and its precise physical model is difficult to establish. The main goal of this paper is to develop an accurate machine learning modeling and optimization technique to predict the total energy consumption of air conditioning water system by using the actual operation data collected. The main contributions of this work are as follows: (1) Three commonly used machine learning techniques, artificial neural network (ANN), support vector machine (SVM) and classification regression tree (CART), are used to build prediction models of air conditioning water system energy consumption. The results show that all the three models have fast training speed, but the ANN model has better performance in cross-validation. (2) The improved differential evolution algorithm was used to optimize the parameters (initial weights and thresholds) of the ANN, which solved the problem that the ANN is easy to fall into the local optimal solution. The simulation results show that the root mean square error (RMSE) of the improved model decreases by 20.5%, the mean absolute error (MAE) decreases by 30.2%, and the coefficient of determination (R2) increases from 0.9227 to 0.9512. (3) Sensitivity analysis of the established optimization model shows that chilled water flow, chilled water outlet temperature and air conditioning load are the main factors affecting the total energy consumption.

Systematic assessment of the dynamic behavior of ecofriendly refrigerants used in dual vapor compression chiller

This paper investigates the dynamic behavior of dual vapor compression Refrigeration (VCR) chillers, considering nine ecofriendly refrigerants such as R134a, R32, R1234yf, R1234ze(E), R717, R600, R600a, R290, R410a and compared to R22. Disturbances are introduced to the secondary loop of the VCR as sudden and ramp increases of the inlet cooling and chilled water temperatures and their impact on the dynamic responses of the condensing, evaporating and chilled water outlet temperatures, and Coefficient of Performance (COP) are discussed. The duration of the dynamic responses, the rise in temperatures and the change in COP are used as criteria to evaluate the fluids through a combination of rank-ordering and multi-criteria assessment approaches. R290 and R410a exhibit lower transient response time than the other options. Refrigerants R290 and R717 exhibit the lowest rise in the condensing temperature, R717 exhibits the minimum rise in the evaporating temperature, whereas R717 and R1234yf indicate the lowest rise in the chilled water temperature among others. R717 further exhibits very low change in COP under disturbances, whereas it exhibits the optimum tradeoffs among all fluids between robustness in dynamic operation and steady-state efficiency. It is recommended to introduce disturbances in a ramp manner instead of a sudden increase.

In-situ application of an ANN algorithm for optimized chilled and condenser water temperatures set-point during cooling operation

In this study, an artificial neural network (ANN) based real-time predictive control and optimization algorithm for a chiller based cooling system was developed and applied to an actual building to analyze its cooling energy saving effects through in-situ application and actual measurements. For this purpose, we set the cooling tower's condenser water outlet temperature and the chiller's chilled water outlet temperature as the system control variables. To evaluate the algorithm performance, we compared and analyzed the electric consumption and the COP when the chilled and condenser water temperatures were controlled conventionally and controlled based on the ANN. As a result, the ANN model's accuracy was high, with a Cv(RMSE) of 4.9%. In addition, the ANN based control algorithm's energy analysis showed that the average energy saving rate for the chiller was 24.7% and that the total average energy saving effect for the chiller and cooling towers was 7.4%. The results confirmed that the proposed MPC algorithm could contribute to improved HVAC energy efficiency in commercial buildings.

Model-free optimal chiller loading method based on Q-learning

Chillers consume considerable energy in building HVAC systems, and the optimal operation of chillers is essential for energy conservation in buildings. This article proposes a model-free optimal chiller loading (OCL) method for optimizing chiller operation. Unlike model-based OCL methods, the proposed method does not require accurate chiller performance models as a priori knowledge. The proposed method is based on the Q-learning method, a classical reinforcement learning method. With the comprehensive coefficient of performance (COP) of chillers as the environmental feedback, the model-free loading controller can learn autonomously and optimize the chiller loading by adjusting the set points of the chilled water outlet temperature. A central chiller plant in an office building located in Shanghai is selected as a case system to investigate the energy conservation performance of the proposed method through simulations. The simulation results suggest that the proposed method can save 4.36% of chiller energy during the first cooling season compared to the baseline control, which is slightly inferior to the value for the model-based loading method (4.95%). Owing to its acceptable energy-saving capability, the proposed method can be applied to central chiller plants that lack a system model and historical data.

Economic optimization of parallel and series configurations of the double effect absorption refrigeration system

Utilization of waste or low grade energy can significantly contribute to the world’s energy demand and reduction of carbon dioxide emissions by employing absorption refrigeration systems. One of the major concerns linked with absorption refrigeration is its low efficiency and high capital cost. In present work, the objective was to develop an economic model for absorption refrigeration system adopting a previously validated thermodynamic model and optimize both the parallel and series configurations. The Thermodynamic and economic model was implemented in MATLAB®. The decision variables including temperatures of all the units of both configurations of absorption refrigeration system, outlet temperatures of cooling water, outlet temperature of chilled water, distribution ratio for parallel configuration and effectiveness of solution heat exchangers were optimized by cost minimization using Genetic Algorithm. All the required constraints, especially the crystallization enthalpy limit, were also included in the optimization problem to discard solutions which were not physically valid. The total annual cost was reduced by 33.48% and 26.26% for series and parallel configuration, respectively. Both solution heat exchangers were eliminated from the flow sheet of both configurations. Thermodynamic and economic performance at optimal operating conditions was found almost same for both configurations.

Experimental study of an adsorption chiller for extra low temperature waste heat utilization

Lots of low temperature waste heat exists in industrial fields which also requires cooling processes, i.e.: data center and glass and liquid crystal display panel manufacturing. Silica gel-water adsorption refrigeration can well match the operation temperature in waste heat utilization for these applications but its technical feasibility and experimental performance is not studied. In this paper, a silica gel-water adsorption chiller using mass recovery process is built and experimentally studied under the condition of lower than 80 °C driving hot water temperature. For the case of data center waste heat utilization, the adsorption chiller can be well operated under the condition of driving temperatures of 51.4–61.3 °C, with the COPs (coefficient of performance) of 0.285–0.477 and SCPs (specific cooling power) of 71.2–108.4 W∙kg−1 while producing 18.8–22.4 °C chilled water. For the cases of glass and liquid crystal display panel manufacturing waste heat utilization, COPs of 0.317–0.483 and SCPs of 95.2–146.7 W∙kg−1 are obtained under the conditions of driving temperatures of 56.2–74.9 °C and chilled water outlet temperatures of 14.2–16.8 °C. These experimental results indicate that feasibility in operation and good performance of silica gel-water adsorption chiller for extra low temperature waste heat utilization is confirmed.

Experimental assessment of solar absorption-subcooled compression hybrid cooling system

The solar absorption-subcooled compression hybrid cooling system (SASCHCS) is to be the economically feasible solution for the high-rise building. But the system has been seldom studied experimentally in the existing open literatures. Therefore, the prototype of SASCHCS is developed to measure the operational performance. The absorption subsystem is driven by the 27 m2 of stationary compound parabolic collector exclusively. And the test is based on the sunny day without and with fluctuation as well as cloudy day of subtropical Guangzhou. It is found that the outlet temperature of chilled water in the absorption chiller exceeds 22 °C since the cooling output of absorption subsystem serves as the subcooling power of compression subsystem. Consequently, the low grade solar energy of which temperature is higher than 60 °C can be used. The peak instantaneous cooling power and coefficient of performance (COP) of absorption subsystem is 4 kW and 0.69, respectively. Besides, the daily mean solar COP (SCOP) of absorption subsystem/daily average rise of COP in the compression subsystem on the sunny day, sunny day with fluctuation and cloudy day is 0.21/22.2%, 0.2/19.8% and 0.13/13.3%, respectively. The paper is helpful to adequately realize the real operation performance of SASCHCS and promote its further improvement.

Experimental evaluation of a 3 kW absorption chiller prototype

Thermally driven absorption chillers have the advantage of being powered by renewable or waste heat as well as using environmentally friendly refrigerants. However, micro scale capacities suitable for residential and office applications are still lacking. This study presents the experimental evaluation of a developed 3 kW capacity, single effect, hot water fired absorption chiller prototype. The chiller operates on the lithium bromide – water pair. To enhance wettability in the absorber, a double – tube configuration was employed to aid the falling film mode. Experimental test on the chiller showed the chilled water outlet temperatures from the evaporator ranged between 16.3 to 20.5oC. The chiller response time to cooling was about 15 minutes from the commencement of evaporation process at the evaporator. The chiller pressures stayed below 9 kPa throughout the experiment. Maximum cooling power of 2.01 kW was recorded, representing 67% of nominal cooling capacity. Maximum coefficient of performance (COP) of 0.47 was recorded, while an average COP of 0.43 was recorded. Keywords: absorption chiller, double – tube, experiment, cooling power, COP

Investigation on the performance of the chilled water system with the key sensor fault

Sensors for the role of control ensure the HVAC system operates reliably and optimally. Among them, the readings of some sensors are the key for the whole system since they are the representation of the controlled objects, such as the chilled water outlet temperature, or the room temperature and so on. Unfortunately, sensor fault is unavoidable due to the system error. The system will be misled to the unexpected or even very severe situation by the controller which got the erroneous input. In this paper, a real building model, which was developed in the simulation environment of the EnergyPlus and its external interface, BCVTB, was employed to investigate the performance under the key sensor fault condition. The chilled water outlet temperature sensor, which is in the supply side of the chilled water loop, and the room temperature sensors, which are in the demand side, were introduced different bias fault from negative levels to the positive levels to reveal the performance changing. Results show that the variation of energy consumption with the sensor fault in the demand side is higher than that in the supply side. Some performance parameters, such as COP of the chiller, and the SCOP of the plant, are quite different, while the indoor thermal comfort is stable under the chilled water outlet temperature sensor fault but different under the fault of the room temperature sensors. This simulation model will be combined with the strategy of the sensor fault detection, diagnosis and reconstruction to accomplish the fault-tolerant control in the future.

Solar absorption chiller performance prediction based on the selection of principal component analysis

In this paper, a method to predict the performance of an absorption chiller using solar thermal collectors as the energy input is analyzed rigorously. Artificial Neural Network (ANN) is developed based on experimental data to predict the performance of the solar absorption chiller system at Universitas Indonesia. In order to perform ANN accurately, some parameters such as chilled water inlet and outlet temperatures, cooling water inlet and outlet temperatures, solar hot water inlet and outlet temperatures, hot water inlet and outlet temperatures, ambient temperature and fuel consumption flow rate are chosen as the input variables. In addition, a Principle Component Analysis (PCA) is used to reduce the number of input variables for performance prediction. Without sacrificing the ANN's prediction accuracy, PCA identified the sensitive variables from all input variables. The developed ANN model combined with PCA (ANN + PCA) shows good performance which has a comparable error with ANN model, specifically the configuration 9–6-2 (9 neurons, 6 inputs, 2 outputs) of the ANN + PCA model leads to a COP root-mean-square error of 0.0145.

Experimental investigation of a mechanical vapour compression chiller at elevated chilled water temperatures

The performance of a Mechanical Vapour Compression (MVC) chiller is experimentally investigated under operating conditions suitable for sensible cooling. With the emergence of the energy efficient dehumidification systems, it is possible to decouple the latent load from the MVC chillers which can be operated at higher chilled water temperatures for handling sensible cooling load. In this article, the performance of the chiller is evaluated at the elevated chilled water outlet temperatures (7–17°C) at various coolant temperatures (28–32°C) and flow rates (ΔT=4 and 5°C) for both full- and part-load conditions. Keeping the performance at the AHRI standard as the baseline condition, the efficacy of the chiller in terms of compression ratio, cooling capacity and COP at aforementioned conditions is quantified experimentally. It is observed that for each one-degree Celsius increase in the chilled water temperature, the COP of the chiller improves by about 3.5% whilst the cooling capacity improvement is about 4%. For operation at 17°C chilled water outlet temperature, the improvements in COP and cooling capacity are between 37–40% and 40–45%, respectively, compared to the performance at the AHRI standards. The performance of the MVC chiller at the abovementioned operation conditions is mapped on the chiller performance characteristic chart.

Investigation on an innovative cascading cycle for power and refrigeration cogeneration

In order to further realize efficient utilization of low grade heat, an innovative cascading cycle for power and refrigeration cogeneration is proposed. Pumpless Organic Rankine Cycle (ORC) acts as the first stage, and the refrigerant R245fa is selected as the working fluid. Sorption refrigeration cycle serves as the second stage in which silica-gel/LiCl composite sorbent is developed for the improved sorption characteristic. The concerning experimental system is established, and different hot water inlet temperatures from 75°C to 95°C are adopted to investigate the cogeneration performance. It is indicated that the highest power and refrigeration output are able to reach 232W and 4.94kW, respectively under the condition of 95°C hot water inlet temperature, 25°C cooling water temperature and 10°C chilled water outlet temperature. For different working conditions, the total energy and exergy efficiency of the cascading system range from 0.236 to 0.277 and 0.101 to 0.132, respectively. For cascading system the exergy efficiency of heat utilization ranges from 30.1% to 41.8%, which is 144% and 60% higher than that of pumpless ORC and sorption chiller when the hot water inlet temperature is 95°C.

An optimal multivariable controller for transcritical CO2 refrigeration cycle with an adjustable ejector

The fixed ejector has to work under a restricted operating condition to keep its positive effectiveness on the transcritical CO2 refrigeration cycle, and a controllable ejector will be helpful. In this paper, an optimal multivariable controller based on the dynamic model is proposed to improve transcritical CO2 refrigeration cycle with an adjustable ejector (TCRAE). A nonlinear dynamic model is first developed to model the dynamic characteristic of TCRAE. The corresponding model linearization is carried out and the simulation results reproduce transient behavior of the nonlinear model very well. Based on the developed model, an optimal multivariable controller with a tracker based linear quadratic state feedback algorithm and a predictor using steepest descent method is designed. The controller is finally applied on the experimental apparatus and the performance is verified. Using the tracker only, the gas cooler pressure and chilled water outlet temperature (cooling capacity) are well tracked rejecting the disturbances from each other. Furthermore, by the predictor, the optimal gas cooler pressure for a constant cooling capacity is actually approached on the experimental apparatus with a settling time about 700s.

Modeling the dynamic simulation and control of a single effect LiBr–H2O absorption chiller

In this study, a dynamic model of a single-effect LiBr–H2O absorption chiller with improved accuracy is built to explore the dynamic performance and the control strategy. Holman correlation is applied to the calculation of heat transfer coefficients of the evaporator, condenser, solution heat exchanger, and generator. The heat and mass transfer in the absorber, chiefly controlled by the mass transfer resistance on the liquid side, are described according to a lumped but accurate physical model based on Nusselt’s film theory. The dynamic performance of the absorption chiller is evaluated under the disturbance of the cooling water inlet temperature and heat source temperature. The effect of the thermal mass of the main components on the dynamic performance is explored. Two control strategies are implemented in the model of the absorption chiller: one is setting the chilled water outlet temperature as the manipulated variable, and the other is setting the generator solution temperature as the manipulated variable. The control performances of the two strategies are compared in detail. Results show that either increasing the heat source temperature or decreasing the cooling water inlet temperature increases the risk of crystallization in the dynamic process. The time to reach steady state is highly dependent on the thermal mass of the generator, rather than the thermal masses of the condenser, evaporator, and absorber. The single-effect chiller has better control performance in off-design condition when the generator solution temperature rather than the chilled water outlet temperature is selected as the manipulated variable.

Cycle Optimization on Reheat Adsorption Cycle Applying Fixed Chilled Water Outlet Temperature

This study investigated the cycle optimization of four-bed, silica gel–water adsorption with reheat cycle, where the desorber (upper bed) and adsorber (lower bed) always interact with the condenser and evaporator, to exploit a low heat-source temperature. In a previous study, the performance of a reheat cycle with chilled water outlet temperature fixed at 9°C was observed without considering the cycle optimization. Maintaining a constant chilled water outlet temperature is also of equal importance to improve the conversion efficiency so that maximum cooling capacity can be derived. In this paper, a simulation model of reheat adsorption cycles is developed to analyze the optimization of the cycle time, including adsorption/desorption time, mass recovery time, and preheating/precooling time, with chilled water outlet temperature fixed. The reheat working principle is also introduced. The proposed cycle is compared with the four-bed versison without reheat cycle in terms of coefficient of performance (COP) and cooling capacity. The result shows that the performance of a reheat cycle is superior to that of four-bed version without reheat, especially for low heat-source temperature. For low heat-source temperature (55–65°C) both COP and cooling capacity of the reheat cycle with optimization were raised significantly compared to the high heat-source temperature (70–80°C).

An Investigation on the Thermal Characteristics of a Single-Effect Absorption Chiller Subjected to the Input Steam Fluctuation

This paper presents an investigation on the thermal characteristics of a single-effect absorption chiller subjected to the input steam fluctuation for an air-conditioning purpose. The absorption chiller driven by waste steam is utilized with the total capacity of 270 tons. The analyzed data are obtained by the field measurement. The results indicate that most measuring parameters appear to be nearly constant with time whereas the chilled water outlet temperature and the cooling water outlet temperature appear to have a similar wave characteristic to the mass flow rate of the input steam. The regression analysis is performed in order to obtain the sine function of these three parameters. The lagging behavior of the cooling load and the rejected heat rate behind the input heat rate from waste steam is observed due to the thermal inertia of the absorption chiller. The energy analysis shows that the average cooling load, rejected heat rate and input heat rate from waste steam is 345.78 kW, 1033.80 kW, 572.88 kW, respectively. The coefficient of performance of the absorption chiller has a high fluctuation from 0.296 to 1.524 with an average value of 0.754, resulted from the fluctuation of the mass flow rate of the input steam. The result indicates that the average value of the residual heat rate is 170.14 kW, which is approximately 10 percent of the total input heat.

Towards an optimal performance of adsorption chillers: Reallocation of adsorption/desorption cycle times

This paper presents a theoretical investigation of the effect of adsorption/desorption times allocation on the performance of adsorption chillers. The ratio between the duration of adsorption and desorption modes is varied to optimize the system performance. Herein, (f) stands for the ratio between duration of desorption and adsorption modes of the adsorption cooling cycle. Employing a two-bed silica gel/water based adsorption chiller, theoretical results show that the system performance in terms of cooling capacity and COP is improved by reducing the ratio (f). However, percentage of improvement depends on the system design and its operating conditions. It is also found that, for each cycle time, there is an optimal value of (f) that maximizes the system performance.Theoretical results show that for a half cycle time of 350s, switching time of 35s and f of 0.8, the system delivers continues cooling using only two sorption reactors and minimizes fluctuations in chilled water outlet temperature.