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Growing focus has been drawn to the continuous detection of high estrogens levels in the soil environment. Additionally, microplastics (MPs) are also of growing concern worldwide, which may affect the environmental behavior of estrogens. However, little is known about effects of MPs occurrence on estrogens degradation in soil. In this study, polyethylene microplastics (PE-MPs) were chosen to examine the influence on six common estrogens (estrone (E1), 17 α -estradiol (17 α -E2), 17 β -estradiol (17 β -E2), estriol (E3), diethylstilbestrol (DES), and 17 α -ethinylestradiol (17 α -EE2)) degradation. The results indicated that PE-MPs had little effect on the degradation of E3 and DES, and slightly affected the degradation of 17 α -E2, however, significantly inhibited the degradation of E1, 17 α -EE2, and 17β-E2. It was explained that (i) obvious oxidation reaction occurred on the surface of PE-MPs, indicating that PE-MPs might compete with estrogens for oxidation sites, such as redox and biological oxidation; (ii) PE-MPs significantly changed the bacterial community in soil, resulting in a decline in the abundance of some bacterial communities that biodegraded estrogens. Moreover, the rough surface of PE-MPs facilitated the estrogen-degrading bacterial species (especially for E1, E2, and EE2) to adhere, which decreased their reaction to estrogens. These findings are expected to deepen the understanding of the environmental behavior of typical estrogens in the coexisting system of MPs. [Display omitted] • Effects of PE-MPs on degradation of estrogens were investigated in soil. • PE-MPs inhibited the degradation process of E1 and 17 α -EE2. • The abundance of some bacterial communities that biodegraded estrogens decreased. • PE-MPs might lead to reduced reaction of estrogen-degrading bacteria to estrogens. [ABSTRACT FROM AUTHOR]

It is reported that oxidative stress homeostasis was involved in PM 2.5 -induced foam cell formation and progression of atherosclerosis, but the exact molecular mechanism is still unclear. Melatonin is an effective antioxidant that could reverse the cardiopulmonary injury. The main purpose of this study is to investigate the latent mechanism of PM 2.5 -triggered atherosclerosis development and the protective role of melatonin administration. Vascular Doppler ultrasound showed that PM 2.5 exposure reduced aortic elasticity in ApoE-/- mice. Meanwhile, blood biochemical and pathological analysis demonstrated that PM 2.5 exposure caused dyslipidemia, elicited oxidative damage of aorta and was accompanied by an increase in atherosclerotic plaque area; while the melatonin administration could effectively alleviate PM 2.5 -induced macrophage M1 polarization and atherosclerosis in mice. Further investigation verified that NADPH oxidase 2 (NOX2) and mitochondria are two prominent sources of PM 2.5 -induced ROS production in vascular macrophages. Whereas, the combined use of two ROS-specific inhibitors and adopted with melatonin markedly rescued PM 2.5 -triggered macrophage M1 polarization and foam cell formation by inhibiting NOX2-mediated crosstalk of Keap1/Nrf2/NF-κB and TLR4/TRAF6/NF-κB signaling pathways. Our results demonstrated that NOX2-mediated oxidative stress homeostasis is critical for PM 2.5 -induced atherosclerosis and melatonin might be a potential treatment for air pollution-related cardiovascular diseases. [Display omitted] • PM 2.5 exposure triggers macrophage M1 polarization and foam cell formation. • NOX2-mediated crosstalk of Keap1 and TLR4 pathway is critical for PM 2.5 -induced atherosclerosis. • Melatonin can alleviate PM 2.5 -triggered atherosclerosis via oxidative stress homeostasis. [ABSTRACT FROM AUTHOR]

Epidemiological evidence showed that the particulate matter exposure is associated with atherosclerotic plaque progression, which may be related to foam cell formation, but the mechanism is still unknown. The study was aimed to investigate the toxic effects and possible mechanism of PM 2.5 on the formation of macrophage foam cells induced by oxidized low density lipoprotein (ox-LDL). Results showed that PM 2.5 induced cytotoxicity by decreasing the cell viability and increasing the LDH level in macrophage foam cells. PM 2.5 aggravated the lipid accumulation in ox-LDL-stimulated macrophage RAW264.7 within markedly increasing level of intracellular lipid by Oil red O staining. The level of ROS increased obivously after co-exposure to PM 2.5 and ox-LDL than single exposure group. In addition, serious mitochondrial damage such as the mitochondrial swelling, cristae rupturing and disappearance were observed in macrophage foam cells. The loss of the mitochondrial membrane potential (MMP) further exacerbated the mitochondrial damage in PM 2.5 -induced macrophage foam cells. The apoptotic rate increased more severely via up-regulated protein level of Bax, Cyt C, Caspase-9, Caspase-3, and down-regulated that of Bcl-2, indicating that PM 2.5 activated the mitochondrial-mediated apoptosis pathway. In summary, our results demonstrated that PM 2.5 aggravated the lipid accumulation, mitochondrial damage and apoptosis in macrophage foam cells, suggesting that PM 2.5 was a risk factor of atherosclerosis progression. Image 1 • PM 2.5 aggravated the content of intracellular TC and lipid accumulation in macrophage foam cells. • PM 2.5 triggered the ROS generation and loss of MMP, resulted in mitochondrial damage. • PM 2.5 could accelerate the apoptosis via the Cyt C/Caspase-9/Caspase-3 pathway. [ABSTRACT FROM AUTHOR]

The data-driven-based methods, which rely on history data, are the most common methods used in the fault diagnostics of building energy system because of their simplicity. However, a major problem with the application of data-driven methods is its interpretability due to the complicated algorithm theory and structure. This paper therefore proposes a methodology which is able to conduct both fault diagnosis and diagnostic knowledge discovery for building energy systems. A case study is implemented in an experimental variable refrigerant flow (VRF) system. The clustering of variable around latent variables (CLV) method is used for variable selection. Then, a classification-based-on-associations (CBA) classifier is set up for fault diagnosis based on the mined association rules. It achieves an overall diagnosis accuracy of 95.33%. In addition, the class association rules (CARs) of the classifier are visualized by grouped matrix-based method and graph-based method, respectively. Further, the CARs with high confidences and supports are interpreted by domain knowledge in the individual fault level. Results show that the diagnostic outcomes comply well with the expert knowledge. The underlying system operational characteristics at faulty conditions could be mined and understood. Moreover, the diagnostic outcomes provide a reasonable and reliable reference for further FDD researches. • We proposed a method for the fault diagnostic knowledge discovery in building energy systems. • The CBA classifier is set up for fault diagnosis based on the mined association rules. • The association rules are visualized in individual fault level and analyzed by domain knowledge. • The diagnostic outcome can provide reference for further FDD researches. • Case study was conducted under various faults of the VRF system. [ABSTRACT FROM AUTHOR]

The variable refrigerant flow (VRF) system has extremely different energy performance at various operation conditions. Its power consumption is inconsistent even under the steady operation condition. In order to accurately evaluate the VRF system’s dynamic energy performance, this study proposed a data-mining-based method to benchmark and assess its energy uses. The correlation analysis is used for key factors selection and the interquartile range rule is employed to remove outliers of the database. In addition, the power consumption patterns are classified using decision tree (DT) method. The classification results are validated by the ANOVA analysis and post hoc test. Nine energy benchmarks are established based on the classified power consumption patterns. Moreover, an energy consumption rating system is established to provide quantitative assessment on the power consumption of the VRF system. A case study is conducted by comparatively analyzing the energy performance of the VRF system at multiple refrigerant charge fault cases. Results show that both the PLR and OT significantly affected the power consumption of the VRF system. However, the degree to which the refrigerant charge fault affects system power consumption varies with the power consumption patterns. For different patterns, the power consumptions of the VRF system were either lower, higher or similar to each other at various RCLs. Results also suggest that the energy benchmarking process provide reasonable classification criteria, and the grading process provide quantitative assessment on the energy consumption. Therefore, the proposed dynamic energy benchmarks are reliable and reasonable to evaluate the dynamic energy performance of VRF systems. [ABSTRACT FROM AUTHOR]

A rational and reliable energy benchmark is useful for understanding and enhancing building performance while most buildings cannot provide sufficient information for a detailed energy assessment. This work presents a systematic methodology of developing dynamic energy benchmarks for individual office building with very limited information. Simultaneously, an energy consumption rating (ECR) system is established to provide vertical energy assessment for individual office building in a short time span, i.e. hourly. Based on the data produced by DOE prototype large office building model performed in the EnergyPlus environment, this study is conducted in three steps: (1) Step 1: Data preparation; (2) Step 2: Development of the dynamic energy benchmarks; and (3) Step 3: Evaluation of the dynamic energy benchmarks and ECR system. Based on the decision tree analysis, the system energy consumption is classified into eight patterns by few commonly accessible weather and time variables, i.e. outdoor dry-bulb temperature, relative humidity, day type and time type. Then, four energy benchmarks are developed according to four energy consumption patterns on weekdays. To verify the effectiveness of the proposed dynamic energy benchmarks, it is used to evaluate the building energy performance on September, October and November, respectively. Besides, comparative analysis is conducted between the energy baseline (i.e. the same benchmark is used for all energy consumption patterns) and proposed dynamic energy benchmarks. Accordingly, the hourly ECRs were calculated using energy baseline and proposed dynamic energy benchmarks, respectively. Results showed that the energy baseline can be improved by using the proposed dynamic energy benchmarks. And the proposed method is capable of evaluating the energy performance of information poor office buildings. [ABSTRACT FROM AUTHOR]

The mechanism and kinetics for the O( 3 P) + CH 3 CH CHCN reaction has been investigated firstly. The BHandHLYP and M05-2X methods were employed to obtain the initial geometries. The triplet/singlet potential energy surfaces (PESs) were constructed with high-level BMC-CCSD method. The conventional transition-state theory (CTST) and multichannel RRKM theory were employed to calculate the total and individual rate constants over a wide range of temperatures under high-pressure limit. Our computed rates agree well with the available experimental results. The yield of IM1 is 0.9–0.5 from 200 to 1000 K, and the construction of h-P1(OH + CH 2 CHCHCN) is 0.42 at 2000 K under high-pressure limit. The yields of the predicted decomposition products P1(CH 3 + HCOHCCN), P2(HCCN + CH 3 CHO) and P3(H + CH 3 COHCCN) at 298 K and 1 atom are 0.81, 0.07, and 0.09, respectively. [ABSTRACT FROM AUTHOR]

Building energy prediction is a potential tool for benchmarking the future energy uses of individual buildings. However, inevitable gaps between predicted and actual energy uses and discrepancies between buildings make it challenging to quantify energy consumption. Therefore, this paper proposes a systematic methodology of quantitative building energy evaluation based on short-term energy prediction. First, the 24-h ahead building energy prediction model is developed based on a recurrent neural network via the Multi-Input Multi-Output strategy. Second, the quantitative energy evaluation strategy is proposed to quantify prediction gaps based on the 1-D k-means clustering. Third, case studies are conducted on five real buildings to verify the reliability of the proposed methodology. Results show that the energy prediction models achieved outstanding accuracies. Besides, it is necessary to analyze the absolute percentage error (APE) variation of each time step to deeply understand the building energy performance rather than the overall prediction performance evaluation index, such as CV-RMSE and MAPE. Further, customized energy quantification systems are established for buildings per their specific, individual energy performance. The building energy is quantified by labeling APE s into multiple levels. Moreover, building operation characteristics can be further understood by quantifying energy uses. • A systematic methodology is proposed to quantitatively evaluate the individual building energy. • Short-term energy prediction models are established for energy benchmarking based on LSTM algorithm. • Customized energy quantification systems are established for various buildings by clustering analysis. • The building operation characteristics can be understood by quantifying the energy uses. [ABSTRACT FROM AUTHOR]

• A systematic methodology is proposed to quantitative evaluate the energy of individual building. • The energy evaluation method comprises both dynamic energy benchmarking and dynamic energy grading. • The number of grades and their ranges of the dynamic energy grading system is determined by clustering analysis. • Energy penalty of various faults could be well assessed by the proposed energy quantitative evaluation method. The energy benchmarking has been recognized as an effective methodology for assessing the energy uses of buildings. The authors' previous study proposed both energy benchmarking and energy consumption grading (ECG) methods to quantify the energy of the individual office building. However, the previous energy evaluation method employed a fixed ECG system, which causing the energy uses of most situations included in the same grade. It is insufficient to reflect the individual differences of actual buildings. Therefore, this study proposes an improved dynamic energy grading system to overcome the limitations. First, the new ECG system is established based on the clustering analysis. Then, comparative analysis is conducted between the previous and improved ECG systems. Further, the building energy performance at various faulty conditions are evaluated by the improved ECG system to verify its reliability. Results show that the improved ECG system addressed the irrational evaluation process of the previous ECG system. Besides, the building energy penalties in various faulty cases could be quantitatively assessed by the improved ECG system. With the proposed method, the building owners can more accurately understand the real-time energy consumption level of the individual building and its energy conservation potential. [ABSTRACT FROM AUTHOR]

• We proposed a method for both fault diagnosis and action mechanism analysis in building chillers. • The method is developed based on the classification based on association (CBA) and association rule mining. • Seven common faults of chiller are well identified with an overall diagnosis accuracy of 90.15%. • The mined diagnostic rules match well with domain knowledge and the fault action mechanisms are concluded. • The discrepant rule analysis can provide proper reference for multiple faults decoupling. Developing advanced fault detection and diagnosis (FDD) techniques for building chillers is becoming increasingly essential for building energy saving. Previous FDD studies have mainly concentrated on the model performance, while fewer studies have examined the chiller fault action mechanism. This paper, therefore, proposes a method that can conduct both fault diagnosis and fault action mechanism explanation of building chillers. The method is data-driven-based and can be trained by system operational data based on the classification based on association (CBA) algorithm. Also, it is qualitatively based because system operational rules can be extracted from the diagnostic model by association rule mining. The fault diagnosis process and the fault action mechanism on the chiller system can be then understood by rule interpretation. The experimental chiller data of ASHRAE RP-1043 is used to validate the effectiveness of the proposed method, and the results show that the CBA-based fault diagnosis model can well identify seven common chiller faults with an overall diagnostic accuracy of 90.15%. In this work, the key rules of each fault are extracted and visualized. The mined rules can be well interpreted by domain knowledge, and the action mechanisms of seven faults are concluded. Moreover, the discrepant rule analysis can provide a proper reference for multiple fault decoupling. The knowledge discovered from the fault diagnosis process is valuable for the development of FDD researches and shortcuts for field application. [ABSTRACT FROM AUTHOR]

Timely and accurate fault diagnosis (FD) in building energy systems (BESs) can promote energy efficiency and sustainable development. Especially the heating, ventilating, and air-conditioning (HVAC) systems are diverse and operate under complex and variable operation conditions. System and operation differences lead to great differences in operational data which causes poor adaptability of data-driven FD models that are developed using data from a single HVAC system or limited operation condition. To improve diagnostic performance across different HVAC systems and operation conditions, this study proposes high-adaptability FD models using three deep transfer learning (DTL) strategies including network-based fine-tuning (FT), mapping-based domain-adaptive neural network (DaNN) and adversarial-based domain adversarial neural network (DANN). The effectiveness of DTL-based FD is validated by fault datasets of two typical BESs: one is a 703-kW screw chiller while the other is the 316-kW centrifugal chiller from ASHRAE RP-1043. Two types of TL scenarios (cross-system and cross-operation-condition fault diagnosis) are set up consisting of eight TL tasks. For DTL strategies, both FD performance and transferability are evaluated using metrics like accuracy and accuracy improvement degree (AID). Results indicate that FT obtains 93% FD accuracy averagely for all tasks of the two TL scenarios considered, which is an average 55% AID compared with the non-transfer benchmark model convolutional neural network (CNN). Further, the impacts of source and target data volumes, and TL tasks are analyzed. For cross-operation-condition scenario, DTL-based FD accuracy grows with the increase of target data volume. For cross-system scenario, FT still show high FD performance with less training data. The reason why FT outperforms DANN and DaNN is explained by visualizing classification scatterplots of the last NN layers. Practical application issues of the DTL-based FD strategy for building energy systems are discussed at last. [Display omitted] • Proposed 3 different DTL strategies to validate BES cross-system and cross-operation-condition FD. • The 3 DTL chiller FD strategies: FT outperforms DANN and DaNN with 93% FD accuracy. • FT gets 55% average FD accuracy improvement for 8 cross-system and cross-operation-condition tasks. • DTL gets higher FD accuracy with growing target data for cross-operation-conditions scenario. • FT learns general features in bottom NN layers while specific ones related to the FD task in top layers. [ABSTRACT FROM AUTHOR]

Boron neutron capture therapy (BNCT) combines neutron irradiation with boron compounds that are selectively uptaken by tumor cells. Boronophenylalanine (BPA) is a boron compound used to treat malignant brain tumors. The determination of boron concentration in cells is of great relevance to the field of BNCT. This study was designed to develop a novel method for simultaneously measuring the uptake of BPA by U87 and U251 cells (two brain tumor cell lines) and number of cells using inductively coupled plasma atomic emission spectroscopy (ICP-AES). The results revealed a linear correlation between phosphorus intensity and the numbers of U87 and U251 cells, with correlation coefficients (R2) of 0.9995 and 0.9994, respectively. High accuracy and reliability of phosphorus concentration standard curve were also found. Using this new method, we found that BPA had no significant effect on phosphorus concentration in either U87 or U251 cells. However, BPA increased the boron concentration in U87 and U251 cells in a concentration-dependent manner, with the boron concentration in U87 cells being higher than that in U251 cells. In both U87 and U251 cells, boron was mainly distributed in the cytoplasm and nucleus, accounting for 85% and 13% of the total boron uptake by U87 cells and 86% and 11% of the total boron uptake by U251 cells, respectively. In the U87 and U251 cell-derived xenograft (CDX) animal model, tumor exhibited higher boron concentration values than blood, heart, liver, lung, and brain, with a tumor/blood ratio of 2.87 for U87 cells and 3.11 for U251 cells, respectively. These results suggest that the phosphorus concentration in U87 and U251 cells can represent the number of cells and BPA is easily uptaken by tumor cells as well as in tumor tissue. • Phosphorus can be used for representing cell numbers of U87 and U251. • U87 and U251 cells concentration-dependently uptakes boronophenylalanine. • In U87 and U251 cells, boron was mainly located in cytoplasm and nucleus. • In U87 and U251 CDX model, boron is preferentially distributed in tumor tissue. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive multistage constant current–constant voltage (MCCCV) strategy for charging electric vehicles in different situations. First, a high-fidelity thermoelectric-aging coupling model based on a resistor–capacitor pair electrical model, a thermal network model, and a semiempirical aging model is constructed. Second, an adaptive MCCCV charging strategy involving optimization of the charging current using particle swarm optimization is developed. It can satisfy the preference of users for reducing the charging time or the battery degradation. Finally, three charging strategies based on the Pareto boundary curve of the battery charging time–state of health are developed: a fast-charging strategy for motorway driving, a minimum-aging charging strategy for family use, and a balanced charging strategy for daily use. Additionally, according to the Pareto boundary, the effects of key factors on the optimization of the charging strategy are analyzed and compared. The results show that the balanced charging strategy is 3.60% better than the 0.5C constant current–constant voltage (CCCV) charging strategy recommended by the battery manufacturer with regard to aging loss. Moreover, the charging time is reduced by 37%. Compared with the traditional CCCV charging strategy, the proposed adaptive MCCCV charging strategy has good application prospects with regard to both the charging time and the battery degradation. • A Coupled Thermal-electric-aging model is established. • Adaptive multistage constant current-constant voltage (MCCCV) charging strategy is proposed. • A charging strategy that is easy to integrate into commercial charging circuits. • A charging strategy that is satisfying user's demand in different situations. [ABSTRACT FROM AUTHOR]

Abstract In this study, mechanically-dewatered sludge was used to investigate the effect of electro-dewatering (EDW) under two electrical modes, which are constant current mode followed by constant voltage mode (CV-EDW), and constant voltage mode followed by constant current mode (VC-EDW). The effect of current and voltage changes on dewatering efficiency and energy consumption of sludge electroosmosis under CV-EDW and VC-EDWs was evaluated The results show that compared with constant current mode (C-EDW), CV-EDW can improve the final dry solids content and reduce the heating rate, and the final dry solids content and unit energy consumption increase with the decrease of current and the increase of voltage. Under CV-EDW, when the dry solids content is 32%, the energy consumption can be reduced by changing to the constant voltage stage, and the energy consumption is 0.093–0.113 kWh/kg water. Compared with constant voltage mode (V-EDW), VC-EDW significantly improves sludge dewatering rate. Under VC-EDW, the final dry solids content of sludge increases with the decrease of current and voltage. When the voltage is decreased by 10 V, the unit energy consumption is reduced by 27.15 ± 1.77% on average, and the energy consumption is 0.132–0.163 kWh/kg water. Compared with CV-EDW, the dehydration rate of VC-EDW is increased by 72.9% on average. However, the unit energy consumption required for dehydration increases by 43.09% when the dry solids content is less than 45%. Graphical abstract Unlabelled Image Highlights • Constant current and voltage modes are combined for sludge electro-dewatering. • The combination shows advantage over constant current or voltage mode. • Constant current-to-voltage mode yields higher dry solids content and saves energy. • Best point for constant current-to-voltage mode is a dry solids content of 32%. • Constant voltage-to-current mode yields higher dehydration rate. [ABSTRACT FROM AUTHOR]

As an important indicator of soil characteristics, soil temperature has a great impact on agricultural production, building energy savings and for shallow geothermal applications. Data-driven models have been developed and achieved good accuracy in monthly soil temperatures or daily soil temperatures predictions of a single site taking air temperature, solar radiant and time as inputs. Models’ accuracy obviously dropped if they are applied for predicting daily soil temperatures for various climates on a continental scale. We proposed a new data-driven model based on the support vector machine (SVM). The new model considers daily soil temperature variations as superposition of annual average ground temperatures predictions (long-term climates impact) and daily ground temperature amplitude predictions (short-term climates impact). Annual average soil temperature are determined by air temperature, solar radiant, wind speed and relative humidity; daily soil temperature amplitudes by air temperature amplitudes, solar radiant and day of year. For daily soil temperature predictions at 16 sites located in arid or dry summer climates, warm climates and snow climates in United States, the new model’s mean absolute error is 1.26 °C and root mean square error is 1.66 °C. Meanwhile, traditional SVM model’s mean absolute error is 2.20 °C and root mean square error is 2.91 °C. [ABSTRACT FROM AUTHOR]

The fault diagnosis of air-conditioning systems is of great significance to the energy saving of buildings. This study proposes a novel fault diagnosis approach for building energy saving based on the deep learning method which is deep belief network, and its application potential in the air conditioning fault diagnosis field is investigated. Then, a parameter optimization selection strategy is developed for model optimization. Four kinds of faults of the variable flow refrigerant system under heating mode are used to evaluate the performance of the models. The fault diagnosis results show that the deep belief network model with initial parameters can be used to diagnose the faults of the variable flow refrigerant system. Through the parameter optimization selection strategy, the fault diagnosis correct rate of the optimized model is 97.7%, which is improved by 5.05% compared with the model with initial parameters. The number of hidden layers of the deep belief network model is selected to be 2 layers. This result indicates that the fault diagnosis for variable flow refrigerant systems may not require a very deep model. Additionally, the performance of the optimized deep belief network model is compared with that of the traditional back propagation neural network, and the former is better. This finding also shows that the unsupervised restricted Boltzmann machine layer for data feature reconstruction can improve the fault diagnosis performance. [ABSTRACT FROM AUTHOR]

Energy demand prediction of building heating is conducive to optimal control, fault detection and diagnosis and building intelligentization. In this study, energy demand prediction models are developed through machine learning methods, including extreme learning machine, multiple linear regression, support vector regression and backpropagation neural network. Seven different meteorological parameters, operating parameters, time and indoor temperature parameters are used as feature variables of the model. Correlation analysis method is utilized to optimize the feature sets. Moreover, this paper proposes a strategy for obtaining the thermal response time of building, which is used as the time ahead of prediction models. The prediction performances of extreme learning machine models with various hidden layer nodes are analyzed and contrasted. Actual data of building heating using a ground source heat pump system are collected and used to test the performances of the models. Results show that the thermal response time of the building is approximately 40 min. Four feature sets are obtained, and the performances of the models with feature set 4 are better. For different machine learning methods, the performances of extreme learning machine models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the extreme learning machine model with feature set 4. [ABSTRACT FROM AUTHOR]

Variable refrigerant flow systems account for a considerable portion of energy consumption in buildings. In order to improve the energy efficiency and estimate the energy saving potentials of variable refrigerant flow systems this study proposes a data mining based method to identify and interpret the power consumption patterns and associations. Two descriptive data mining algorithms, clustering analysis and association rules mining, are used for data partitioning and association mining. The proposed method consists of four phases: data pre-processing, data partitioning, data association mining and knowledge interpretation. Experimental data collected from a tested variable refrigerant flow system in the standard psychrometer testing room are pre-processed and prepared to examine the proposed method. Three time independent influential factors: part load ratio, refrigerant charge level and cooling condition are analyzed. Results show that the method is able to help identify energy consumption patterns and extract energy consumption rules in variable refrigerant flow systems. Three distinct energy consumption patterns are identified: undercharge fault, low and high part load ratio conditions. For compressor operation frequency switch control and refrigerant undercharge patterns, the energy saving potentials could be estimated by making comparisons between energy patterns and rules in a top-down way. [ABSTRACT FROM AUTHOR]

Traditional building energy prediction(BEP) methods usually solve time-series prediction problems using either recursive strategy or direct strategy, which may ignore time-dependence between continuous building energy data in building energy systems. To overcome this issue, a sequence-to-sequence(Seq2seq) model combined with attention mechanism(Seq2seq-Att) is developed to realize multi-step ahead BEP. Compared with the original Seq2seq, both parameter-tuning and attention mechanism in the Seq2seq-Att model have great impacts on BEP performance improvement. To obtain quantitative analyses of performance improvement of these two aspects, this study conducted a comprehensive performance evaluation of four Seq2seq models (i.e., before and after parameter-tuning, adding attention and without attention). In this study, the length of sliding window is 24-h and prediction time steps ranges from 1-h to 12-h ahead. From the open-source Building Data Genome Project 2 , 36 buildings are selected. Results indicate that adding attention to Seq2seq together with parameter-tuning, the multi-step ahead prediction performance can be increased by 8%(parameter-tuning around 6% while adding attention about 2%) on average. For prediction time step less than 3-h, parameter-tuning is a convenient way to improve the Seq2seq-based multi-step ahead BEP model. But for cases of prediction time step over 3-h, combining attention to the Seq2seq after parameter-tuning is recommended. [Display omitted] • Evaluate Seq2seq and Attention on 36 buildings for multi-step short-term energy predictions. • Enhance Seq2seq multi-step prediction R2 averagely by 8% (attention 2%, parameter-tuning 6%). • arameter-tuning is enough to enhance Seq2seq multi-step prediction for time-step<3-h ahead. • Recommend adding attention to Seq2seq after parameter-tuning for time-step>3-h ahead. [ABSTRACT FROM AUTHOR]

This paper presents an algebraic solution of erroneous sensor's undetectable boundary to evaluate the sensitivity of chiller sensor fault detection based on principal component analysis. Q -statistic of PCA is normally applied as a collective statistical index to detect sensor fault by comparing its value with the threshold. However, Q -statistic has no specific physical meaning and cannot evaluate the sensitivity of the provided method for sensor fault detection. We analyzed the definition of Q -statistic and derived the numerical value of the minimum range not to detect sensor fault. Bias sensor fault of a fielded screw chiller was studied for each sensor in PCA model by introducing different severity levels. Results showed that each sensor has different fault detection sensitivity using the same PCA model. The undetectable boundary can be a criterion used to evaluate the detection sensitivity of PCA-based method easily. [ABSTRACT FROM AUTHOR]

Drying-induced hornification is an inevitable phenomenon of cellulosic fibers, which is used to describe internal aggregation structure changes of cellulosic fibers upon drying or water removal. To investigate the hornification process, never-dried cellulosic fibers with different components were thermally dried to different moisture contents. The results indicated that the hornification process could be divided into four stages, including the first crystallization period (>70% moisture), the cocrystallization period (70–31% moisture), the hemicellulose control period (31–11% moisture), and the second crystallization period (11–0% moisture). The decrease of water retention value (WRV) occurred in the cocrystallization period and the second crystallization period, which meant hornification happened in these two periods. Besides, hemicellulose and lignin inhibited hornification by reducing cellulose cocrystallization. The work elucidates the hornification process and mechanism of cellulosic fibers,which will be helpful to control the properties of cellulosic materials for extended utilization. Hornification process of cellulosic fibers. The hornification process is divided into four stages. Cellulose cocrystallization and crystallization is the mechanism of the first and second hornification, respectively. Lignin and hemicellulose control the strength and length of the cocrystallization period, respectively. [Display omitted] [ABSTRACT FROM AUTHOR]

This study establishes a passenger cabin-coupled air conditioning model that the heat exchangers adopted in the air conditioning model, are calibrated by testing, during which the parameters for establishing the passenger cabin model are measured. The cabin temperature simulation results of the air conditioning model proposed in this paper fit well with the test results, with a maximum difference of 3 °C. A genetic algorithm (GA) optimization-based multistage constant-compressor speed (MCCS) air conditioning system control strategy is proposed. This control strategy sets the cabin temperature as the input control factor and the compressor speed as the output factor, and different cabin temperature ranges correspond to the MCCSs, which are optimized by the GA. The presented strategy is contrasted with the most commonly used on/off controllers and the proportional integral derivative (PID) controller, and an engineering-applied (EA) air conditioning control strategy. The proposed controller can maintain passenger cabin thermal comfort and save energy simultaneously, and it can be easily applied in engineering. Based on the simulation results, the MCCS controller can save 17.5, 7.5, and 5.8% more energy consumption than the on/off, PID, and EA controllers. Moreover, it can improve the coefficient of performance of the air conditioning system by 5.3 and 3.9% more than the PID and EA controllers. Therefore, the proposed MCCS controller can increase the operation efficiency of electric vehicles AC system. [Display omitted] • A genetic algorithm optimization based adaptive multistage constant compressor speed controller for AC system is built. • Thermal model of the AC system coupled with passenger cabin is validated by experimental test. • The proposed controller is compared with an engineering applied controller and it is easy to apply in engineering. • The proposed controller can keep the passenger cabin thermal comfort and save energy simultaneously. [ABSTRACT FROM AUTHOR]

Abstract: This research studies the low carbon transition of the electric power sector in China using a multi-level perspective (MLP) of niches, socio-technical regime, and landscape, as well as literature on innovation systems. Three lines of thought on transition process are integrated in the paper to probe the possible transition pathways in China. A MLP analysis is presented to understand the current niches, regime, and landscape of China’s power sector. A brief analysis on the future macroscopic socio-economic transition in the process of industrialization, urbanization, and modernization of Chinese society and its implication on power landscape are depicted to prove the urgency and magnitude of transition in China and why systematic transition management is needed. Five transition pathways, namely reproduction, transformation, substitution, reconfiguration, de-alignment/re-alignment, and reconfiguration, with their possible technology options are presented. The paper goes further to propose an interactive framework for managing the transition to a low carbon energy system in China. Representative technology options are appraised by employing innovation theory to indicate the logic of policymaking within the framework. Institutional gaps in realizing the transition are also addressed. The work presented in the paper will be useful in informing policy-makers and other stakeholders and may provide references for power sector transition management in other countries. [Copyright &y& Elsevier]

• A novel organic flash combined power and refrigeration cycle is proposed. • The proposed cycle, a separated cycle and basic cycle are compared. • Partial exergies in the throttling losses are recycled by ejectors. • The proposed cycle can produce higher net power output and extra cooling. This proposes a novel dual-ejector based organic flash combined power and refrigeration cycle, which replaces the two throttle valves with two ejectors for the basic flash cycle, to provide power and cooling simultaneously for users. Detailed mathematical models of the proposed system are built and validated. The preliminary analysis results show that the exergy efficiency reaches 45.59% under geothermal water at 150℃. Then a parametric analysis is conducted to investigate the effects of five key parameters on system performance. The results show that both an optimal flash pressure and an extraction pressure exist to maximize the exergy efficiency. Finally, the proposed cycle, the separated power and refrigeration cycle consisting of a basic organic flash cycle and an ejector refrigeration cycle, and the basic organic flash cycle are optimized and compared by examining seven different organic fluids such as R245fa, R141b, R123, R245ca, R601, R365mfc, and R600. The results show that the R245fa brings the highest exergy efficiency to both the proposed cycle and organic flash cycle among seven involved organic fluids. Compared with the basic organic flash cycle, the proposed cycle with R245fa has a 4.91% percentage point higher exergy efficiency, 10.44% higher net power output, and an extra 172.6 kW of refrigeration output. Meanwhile, the optimal exergy efficiency and net power output of the proposed cycle are 5.82–6.80% percentage point higher and 15.06–23.87% higher than those of the separated power and refrigeration cycle. An exergy analysis suggests that the throttling losses are indeed significantly reduced by replacing the throttle valves with the ejectors for the organic flash cycle. [ABSTRACT FROM AUTHOR]