Chiller Model Research Articles

Chilling and Forcing Requirements of Wintersweet (Chimonanthus praecox L.) Flowering in China

Numerous studies have reported phenological changes and their driving mechanisms in spring flowering plants. However, there is little research on the shifts of winter flowering phenology and its response to forcing and chilling requirements. Based on the China Phenological Observation Network (CPON) ground observation data from nine sites over the past 20 years, we explored the spatial and temporal variation patterns of flowering plants and their response to chilling and forcing in wintersweet (Chimonanthus praecox L.), a common winter flowering plant species in temperate and subtropical zones of China. We used three chilling models (chilling hour, Utah, and dynamic models) and the growing degree hours (GDHs) model to calculate each site’s daily chilling and forcing. Using the partial least squares (PLSs) regression approach, we established the relationship between the first flowering date (FFD) and pre-season chilling and forcing in wintersweet, based on which we identified chilling and forcing periods and calculated chilling and forcing requirements. This study found that the FFD of wintersweet in China showed an overall advancement trend during the last 20 years. Still, there were temporal and spatial differences in the FFD of wintersweet among different sites. The PLS results showed that wintersweet also had periods of chilling and forcing, both of which co-regulated wintersweet flowering. We found the forcing and chilling requirements of wintersweet varied significantly from site to site. The higher the latitude is, the more chilling requirements are needed. The chilling requirements for wintersweet were about 6.9–34.9 Chill Portions (CPs) and 1.4–21.6 CP in the temperate and subtropical zones, respectively, with corresponding forcing requirements of 3.2–1922.9 GDH and 965.3–8482.6 GDH, respectively. In addition, we found that the temperature requirements of wintersweet were correlated by a negative exponential relationship, suggesting that chilling and forcing requirements have an antagonistic effect on initiating flowering phenology. Our results could help us understand how flowering dates of winter flowering plants respond to climate change.

A novel load allocation strategy based on the adaptive chiller model with data augmentation

Model-based load allocation strategy is an impactful solution to enhance energy efficiency of multiple-chiller system. Its performance is heavily dependent on the accuracy of chiller model. Data-driven model is a pretty-good solution. However, in real multiple-chiller system, the range of operation condition in historical data is commonly narrow, so it is challenging to develop an accurate data-driven model of chiller throughout full range of operation condition. In this paper, data augmentation algorithm is presented to generate the data outside of historical data, which is based on conditional generative adversarial network (CGAN) and elastic weight consolidation algorithm (EWC). Combined historical data and generated data, augmented training dataset is set up and updated by online operation data. Trained by online updated augmented training dataset periodically, adaptive chiller model is set up. Based on adaptive chiller model, a novel load allocation strategy presented for multiple-chiller system. The proposed strategy is verified by field test in multiple-chiller system. The results show that adaptive chiller model, with the aid of data augmentation algorithm, is more accurate. The proposed strategy can achieve 5.03 % energy saving compared with fixed set-point strategy, and the EER of proposed strategy is 6.27 % higher than that of fixed set-point strategy.

The Influence of the Chiller Model on Achieving and Developing Proactive Motivation Among Female Students of the Institute of Fine Arts in the Theme of Aesthetics

The Influence of the Chiller Model on Achieving and Developing Proactive Motivation Among Female Students of the Institute of Fine Arts in the Theme of Aesthetics

Characterizing seed dormancy in Epimedium brevicornu Maxim.: Development of novel chill models and determination of dormancy release mechanisms by transcriptomics

PurposeEpimedium brevicornu Maxim. is a perennial persistent C3 plant of the genus Epimedium Linn. in the family Berberaceae that exhibits severe physiological and morphological seed dormancy.We placed mature E. brevicornu seeds under nine stratification treatment conditions and explored the mechanisms of influence by combining seed embryo growth status assessment with related metabolic pathways and gene co-expression analysis.ResultsWe identified 3.9 °C as the optimum cold-stratification temperature of E. brevicornu seeds via a chilling unit (CU) model. The best treatment was variable-temperature stratification (10/20 °C, 12/12 h) for 4 months followed by low-temperature stratification (4 °C) for 3 months (4-3). A total of 63801 differentially expressed genes were annotated to 2587 transcription factors (TFs) in 17 clusters in nine treatments (0-0, 0-3, 1-3, 2-3, 3-3, 4-3, 4-2, 4-1, 4-0). Genes specifically highly expressed in the dormancy release treatment group were significantly enriched in embryo development ending in seed dormancy and fatty acid degradation, indicating the importance of these two processes. Coexpression analysis implied that the TF GRF had the most reciprocal relationships with genes, and multiple interactions centred on zf-HD and YABBY as well as on MYB, GRF, and TCP were observed.ConclusionIn this study, analyses of plant hormone signal pathways and fatty acid degradation pathways revealed changes in key genes during the dormancy release of E. brevicornu seeds, providing evidence for the filtering of E. brevicornu seed dormancy-related genes.

Cold hardiness-informed budbreak reveals role of freezing temperatures and daily fluctuation in a chill accumulation model.

Fundamental questions in bud dormancy remain, including what temperatures fulfill dormancy requirements (i.e. chill accumulation). Recent studies demonstrate freezing temperatures promote chill accumulation and cold hardiness influences time to budbreak-the phenotype used for dormancy evaluations. Here we evaluated bud cold hardiness and budbreak responses of grapevines (Vitis hybrids) throughout chill accumulation under three treatments: constant (5 °C), fluctuating (-3.5 to 6.5 °C daily), and field conditions (Madison, WI, USA). Chill treatments experiencing lower temperatures promoted greater gains in cold hardiness (field>fluctuating>constant). All treatments decreased observed time to budbreak with increased chill accumulation. However, perceived treatment effectiveness changed when time to budbreak was adjusted to remove cold acclimation effects. Among three classic chill models (North Carolina, Utah, and Dynamic), none was able to correctly describe adjusted time to budbreak responses to chill accumulation. Thus, a new model is proposed that expands the range of chill accumulation temperatures to include freezing temperatures and enhances chill accumulation under fluctuating temperature conditions. Most importantly, our analysis demonstrates that adjustments for uneven acclimation change the perceived effectiveness of chill treatments. Therefore, future work in bud dormancy would benefit from simultaneously evaluating cold hardiness.

Comparison models of the operation of absorption machines using the characteristic equation

The integration of absorption machines in micro-trigeneration systems based on micro gas turbines is one of the most viable options for the use of waste gases from micro-turbines, increasing the energy efficiency of the systems, for which the analytical model proposed by the authors is used (Malinowski & Lewandowska, 2013) and (Wang, Cai, & Zhang, 2004), which was reproduced, tested, modified and extended to gas micro-turbines with different power ratings but of the same type, this model is used to heat the activation fluids of the absorption chillers used in these systems, directly influencing the behavior of the chillers according to the load and operating conditions of the micro-turbines, This makes it necessary to develop and apply models of the absorption chillers, with the aim of analyzing the degree of influence that micro-turbines operating at partial load have on the absorption chillers. For this reason, the model selected to obtain the performance of the chillers must be a simple model capable of predicting the behavior of the chillers based on the information from the external currents of the chillers. In the literature, there are a large number of studies focused on the development of absorption chiller models with experimental validation, which take into account specific characteristics of the machine with a greater or lesser degree of detail; this type of model and the literature review.

HVAC early fault detection using a fuzzy logic based approach

The need for improving the energy efficiency of existing buildings has driven to the implementation of building energy management systems (BEMS) that can help facilities manager to discover and identify problems that may cause energy wastage or affect to occupants’ comfort. Modern data-driven fault detection and diagnosis (FDD) make use of the data collected by the building BEMS to provide high accuracy in the revelation of heating, ventilation and air-conditioning (HVAC) system faults. However, these methods need a large amount of faulty data samples during the training, which is an uncommon situation in the real world. The main focus of this paper is to present a methodology to detect faults when the number of faulty samples is low. For this purpose, a regression-based methodology based on an adaptative neuro-fuzzy inference system (ANFIS) chiller model is developed using the data collected from a real use case. The model presents good results, that can be used for benchmarking the machine operation and detect the abnormal operation states.

Predicting uncertainty of a chiller plant power consumption using quantile random forest: A commercial building case study

Selecting an appropriate confidence level for chillers models could lead to better prediction of the peak electricity load, to more efficiently participate in demand response programs, and to manage the operation and performance of chiller plants. Therefore, this study aims to predict the power consumption of a chiller plant in a high-rise commercial building based on Quantile Random Forest (QRF). This study proposed a simplified data-driven approach to compute the lower and upper bounds of the prediction intervals (PIs) of power consumption for a chiller plant, consisting of four chillers. An evaluation of the proposed data-driven approach using measured datasets and computed PIs metrics demonstrates that the PI coverage probability for the samples in test datasets for all chiller models with the confidence level of 80% and 90%, are above 80% and 90%, respectively. Also, the results show that by increasing the confidence level from 80% to 90%, the upper and lower bounds of the demand charge differ from the actual value by a factor of 3.3 and 1.7 times greater, respectively. This approach has implications for developing sequences of operations for chiller plants in building automation systems for evaluating past performance and for achieving future optimization of these plants.

Reduced CO2 footprint of buildings in agro-industrial communities in Qatar with improved insulation standards and solar cooling

Food production in arid countries like Qatar is strongly linked to water and energy resources to grow plants or rear cattle in a protected and controlled environment. The high energy demand for air conditioning in agro-industrial communities leads to significant CO2 emissions. In a case-study on buildings that are typical in the agro-industrial sector, the reduction potential for these emissions is investigated. Therefore, greenhouses and community buildings with an insulation according to the Passivhaus standard and solar cooling with photovoltaic driven compression chillers are compared to conventional buildings.An innovative solar cooling system is developed: Power is generated in hybrid photovoltaic / thermal collectors to enhance the electric efficiency of the photovoltaic cells and to deliver heat for food processing or other purposes at a high temperature level. A compression chiller model is designed, that is based on the experimentally determined performance of the refrigeration cycle for the harsh operating conditions in Qatar. Different refrigerants are compared regarding their cycle performance and Global Warming Potential.A result of the study at hand is that greenhouses are very well suited for solar cooling since their cooling load is nearly congruent to photovoltaic power generation due to their low thermal inertia and radiative cooling during the night.The case study reveals that an insulation according to the Passivhaus standard combined with the proposed solar cooling system reduces the CO2 emissions caused by the air conditioning of greenhouses by 98% and of residential buildings by 94.7% compared to conventional buildings and air conditioning systems.

What Is Wrong with Winter Chill Models in Warm Climates?

Bud dormancy is an evolutionary strategy that protects temperate fruit trees from low temperatures during winter. With adequate winter chilling, it is a process consisting of a rapid entrance phase where growth inhibition increases to a maximum and is then released via an exit phase. This progression is, however, different in areas with insufficient winter chill. In such areas, the dormancy entrance phase is protracted, and the exit phase is often incomplete. Several chill models have been designed to describe the plants’ response to chill accumulation to match cultivar and site. Although it is known that these models are less accurate in warm winter areas, their accuracy during the two dormancy phases has always been assumed equal. The aim of this study was to investigate the accuracy of four chill models during the entrance and exit phases of bud dormancy of ‘Royal Gala’ buds from two contrasting climatic areas. One-year-old shoots were collected from commercial orchards and forced to determine their bud dormancy level. Before forcing, the shoots received varied amounts of chill additional to the field chill. The bud dormancy progression was represented as joint two- or three-line models to determine the entrance and exit phases. For each phase, the dormancy level of every sample was plotted against its chill accumulation based on four chill models (Chilling Hours, Utah, Positive Utah, and Dynamic Model). Results were compared to determine the ability of each model to predict the linearity of the dormancy progression. The results indicated that although all the models were able to describe the exit phase of bud dormancy successfully, none could describe a linear entrance phase. This suggests that during the onset of dormancy, buds respond to temperature differently to what is measured by the chill models making the models unreliable when winter chill is inadequate.

Refrigeration equipment model construction based in data center cooling station

ABSTRACT The energy consumption of data centers (DCs) is rising year by year, and cooling station accounts for more than 40% of the total DCs’ energy consumption, which has a huge energy-saving potential. Building models for whole DCs’ cooling station can help predict total power to improve the energy efficiency of the system, before this, establishing a single model for each component is a basic work. This paper mainly studies the chiller and cooling tower models and compares the predictive performance of the empirical model, hybrid model, and neural network model of chillers and cooling towers. Giving the model selection scheme of the chiller and cooling tower for the establishment of the whole system of the refrigeration station. For the chiller model, the Yoshida function model in empirical models has the highest accuracy with a mean square error of 0.0592, followed by the neural network model with a mean square error of 0.2, and the hybrid model has a lower accuracy than the former two models. For the cooling tower model, the empirical models and the neural network model have similar accuracy, and both are higher than the hybrid model.

A novel energy consumption prediction method for chillers based on an improved support vector machine

The energy consumption prediction of the chiller is an important means to reduce the energy consumption of buildings. Therefore, a novel energy consumption prediction model for chillers based on an improved support vector machine (ICA-DE-SVM) is proposed. The imperialist competitive algorithm (ICA) is used to optimize the penalty coefficient and kernel function width of SVM, greatly improving the generalization ability and prediction accuracy of the SVM model. The assimilation process is very important in ICA. Colonies of empires move randomly toward imperialists during the assimilation process in ICA, which decreases population diversity and can lead to premature convergence. Therefore, to create more new locations for colonies and increase population diversity, the idea of differential mutation proposed by differential evolution (DE) was applied to ICA. The established model was experimentally verified in an actual multi-chiller system in a building, and the results showed that the ICA-DE-SVM model could obtain good prediction results. Finally, the proposed model was compared with SVM model, PSO-SVM model, GA-SVM model, WOA-SVM model, and ICA-SVM model. With an MAPE of 0.6%, an MSE of 2.3, and an R2 of 0.9998, the findings demonstrate that the ICA-DE-SVM model has a greater prediction accuracy than the other models.

Agroclimatic requirements and adaptation potential to global warming of Spanish cultivars of sweet cherry (Prunus avium L.)

Traditional fruit tree cultivars are an important source of agricultural biodiversity. These genotypes are well adapted to the regions where they grow, and their fruits offer distinctive features compared to the commercial cultivars that are frequently grown. We analyzed the adaptation prospects of seven sweet cherry cultivars grown in Zaragoza (Spain) to future climate conditions. We first delineated chilling and forcing phases using Partial Least Squares (PLS) regression to correlate phenology records with daily accumulations of chill and heat during the months preceding flowering. We then calculated chill requirements using three chill models (Chilling Hours, Utah and Dynamic) and forcing requirements using one heat model (Growing Degree Hours, GDH). Results indicated that the chilling and forcing requirements ranged between 26.1 and 60.2 CP (chill portions) and from 5473 to 8030 GDH, respectively. We then assessed the cultivars’ potential to adapt to a warmer future using climate projections and comparing the chill requirements with the expected chill accumulation under two global warming scenarios, RCP4.5 (effective reduction of greenhouse gas emissions) and RCP8.5 (very high greenhouse gas emissions), by two time horizons, 2050 and 2085, with temperature projections from fifteen Global Climate Models (GCM). These projections established that chill accumulation has consistently decreased over the last 30 years, but temperate trees have shown regular breaking of dormancy, bud burst and blooming. Future chill levels are expected to continue decreasing given the sustained warming trend, so there is no guarantee that sufficient winter chill will be observed in the medium to long term if the warming trend continues. For three out of the seven cultivars we analyzed, most global climate models predict medium or low risks by 2050 and 2085 under the RCP4.5 scenario. Under the RCP8.5 scenario, particularly by the end of the 21st century (i.e. 2085), four of the cultivars with high chill needs are expected to not to meet their chill requirements very often.

Performance analysis and optimization of a trigeneration process consisting of a proton-conducting solid oxide fuel cell and a LiBr absorption chiller

In this work, the trigeneration system, consisting of a proton-conducting solid oxide fuel cell (SOFC–H+) and a single-stage LiBr absorption chiller, was proposed. The SOFC–H+ and single-stage LiBr absorption chiller models were developed through Aspen Plus V10. From the sensitivity analysis, the results show that increases in temperature and fuel utilization can improve the performance of the SOFC–H+. Conversely, the air to fuel (A/F) molar ratio and pressure negatively affect the electrical efficiency and overall system efficiency. In the case of the absorption chiller, the coefficient of performance was increased and made stable according to a constant value when the generator temperature was increased from 90 to 100 °C. When the optimization was performed, it was found that the SOFC–H+ should be operated at 700 °C and 10 bar with fuel utilization of 0.8 and A/F molar ratio of 2 to achieve a maximum overall efficiency of 93.34%. For the energy and exergy analysis, a combined heat and power SOFC–H+ was found to have the highest energy and exergy efficiencies, followed by the trigeneration process. This indicates that the integration of the SOFC–H+ and LiBr absorption chiller is possible to efficiently produce electricity, heating and cooling.

Digital twins model and its updating method for heating, ventilation and air conditioning system using broad learning system algorithm

Digital Twins (DT) can be used for the energy efficiency management of entire life cycle of HVAC systems. The existing chiller models usually can not update in real-time, so they are not suitable for real-time interactions between DT models and real physical systems. In this paper, an intelligent DT framework is proposed for HVAC systems, which includes the equipment, data, simulation, and application layers. Broad learning system (BLS) is presented to build the simulation layer of the chiller and its DT platform. The basic BLS model is optimized to reach the best performance by choosing linear rectification function as activation function and setting batch size to 64 by enumeration method. The real HVAC system located in Zhejiang province is selected to verify the proposed method. For the first half year operation, the average mean absolute error, root mean square error and coefficient of determination (R2) of Multi-BLS model for nine chillers can reach 9.04, 15.20 and 0.98 respectively. For the second half year operation, the proposed method can be updated in 4.63s and its R2 is 0.95. Compared with conventional models, the proposed Multi-BLS model has better prediction precision and can be updated in real-time within a shorter time.

Efficient modeling of adsorption chillers: Avoiding discretization by operator splitting

Reliable and computationally efficient models are crucial to improve the performance of adsorption chillers. However, modeling of adsorption chillers is challenging due to the intrinsic process dynamics. Currently, adsorption chillers are most often represented by 1-d, lumped-parameter models that use lumped models for all components but resolve the heat exchangers in one spatial dimension. Still, accurate simulations require fine discretization leading to poor computational efficiency. To increase computational efficiency, here, an alternative modeling approach is proposed that avoids discretization by applying operator splitting.The benefits of the proposed modeling approach are demonstrated in two real-world case studies. First, the resulting adsorption chiller model is calibrated and validated with experimental data of a lab-scale one-bed adsorption chiller: The proposed model retains the accuracy of a state-of-the-art model while increasing computational efficiency by up to 70%. Second, the proposed model is applied to a commercial two-bed adsorption chiller with excellent accuracy. Finally, the validity of model is tested by varying the ratio between the overall heat transfer coefficient and the heat capacity rate of the fluid in the heat exchangers. The proposed model is shown to be well suited for the conditions present in most adsorption chillers.

Chilling requirement of four peach cultivars estimated by changes in flower bud weights

The adaptation of temperate fruit crops is a challenge being increased by the global warming. Chilling requirement is a key factor for adaptation. The objective of this study was to estimate the chilling requirement of peach cultivars BRS Bonão, Esmeralda, Granada and Eragil, using the Tabuenca test. Chilling accumulation was computed using four different chilling hour (≤ 7.2 ºC and ≤ 11 ºC) models; and chill units using the Low Chill model and the Taiwan model. The fresh bud weight and bud water contents were also evaluated. The Tabuenca test (based on differences in bud´s dry weight) showed a fairly good efficiency for estimating the end of dormancy in peach. However, under mild winter conditions, it is better to use fresh bud weights. Either one of three chilling accumulation computation models (temperature ≤ 7.2 °C, ≤ 11°C, or Taiwan model) is suitable to classify comparatively different cultivars, but none is accurate enough to conclude on the adaptation of a given cultivar to a specific site. Using hours of temperatures ≤ 11 ºC: ‘BRS Bonão’ needed around 180 hours for dormancy release; ‘Esmeralda’ around 250 hours; ‘Granada’ between 300 and 400 hours, and ‘Eragil’ more than 500 hours.

An optimization scheme for chiller selection in cooling plants

Providing effective cooling for buildings and industrial facilities at minimum cost is one of the main challenges in the HVAC industry. Considerable effort has been put into the optimization of existing cooling plants, but the chiller selection procedure has been relegated to a second place. This paper introduces two alternative formulations to add the chiller selection into the overall optimization problem: i) a mathematical programming approach with a single cost-based objective function and ii) a multi-objective optimization of capital cost and energy consumption. It was analyzed the case of a cooling plant with a known load profile and 13 air-cooled screw chiller models ranging from 140 to 500 TR available for purchase. The minimum objective value in the mathematical programming approach is obtained by selecting three 500 TR chillers. On the other hand, the multi-objective optimization approach produced a set of nine nondominated solutions (including the three 500 TR chiller selection). Under the second approach it is not necessary to translate the energy consumption into monetary terms. The results of both alternatives are considerable different from the straightforward approach of selecting chillers with total nominal capacity closer to the peak load (900 TR). This reveals the importance of a formal selection procedure.

A survey of eight chilling models for apple trees in Southern Brazil under mild winters

ABSTRACT The main models used for estimating chill accumulation throughout the dormancy period of temperate fruit trees in Brazil were developed in temperate regions in the northern hemisphere. Thus, they exhibit low precision when they are used in regions with mild winters. However, there are several little-known models that are more precise. This study aimed at evaluating the precision of eight different estimation models of chill accumulation applied to five apple cultivars which were grafted on two distinct rootstocks in a region with mild winters. Based on the coefficient of variation, the Microsoft Excel® program was used for evaluating the precision of the following models: Utah, modified Utah, CH < 7.2°C, CH < 10°C, CH < 15°C, Low chill, Taiwan low chill and Daily positive Utah chill unit (PCU). The lowest precision was attributed to both models Utah and CH < 7.2°C (the most common model in Brazil). Models CH < 15°C, Taiwan low chill, Low chill and modified Utah were considered precise (coefficient of variation < 15% in annual chill accumulation and to evaluate requirement to overcome dormancy in cultivars). No important differences were observed in models precision between ‘M-26’ and ‘M-9’ rootstocks.

Effects of chilling on heat requirement of spring phenology vary between years

The controlled experiments are increasingly used to examine the effect of chilling on the spring phenology of temperate woody plants. Previous studies demonstrated a delaying effect of lack of chilling on spring events due to a negative relationship between chilling accumulation (CA) and heat requirement (HR). However, most experiments only lasted for one year, and limited studies examined the consistency of the CA-HR relationship between years. Here, we conducted a twig experiment with 14 temperate woody species in two winter seasons (2018/2019 and 2019/2020) to assess the between-year difference in the impact of chilling on HR of flowering or leaf-out. The CA and HR for each species in each year were estimated based on the common-used chilling and forcing model. The results showed that 8 of 14 species exhibited a significantly different slope or intercept of the linear function of HR against CA between two winter seasons. Using the first-year CA-HR function to simulate the second-year spring events would result in large uncertainties with root-mean-square errors ranging from 1.3 to 9.9 days. In addition to the original chilling model, we further selected an optimal one for each species from another 8 chilling models. The optimal chilling models varied among species and could reduce the RMSE of phenological simulation of 10 species by 0.1–3.7 days. Our results suggest that using one-year experimental data to develop the phenological model or estimate the chilling and heat requirement of spring events is unsuitable. Further studies are still needed to better quantify the rate of chilling under different temperature conditions for various species.