The Evaluation method of HVAC System’s operation performance based on Energy Flow Analysis and DEA

An energy flow model of an airport terminal’s HVAC system is established in this paper. Based on energy flow model, the exergy efficiency, exergy loss and exergy cost distribution ratio of each component are calculated and analyzed by the energy flow model. Optimization method and exergy balance equations are used to calculate the least exergy loss of HVAC system under certain operation condition, which is defined as the near-optimum operation level. DEA method is then applied to obtain the benchmarking frontier of near-optimum operation levels, and the frontier will illustrate the ideal operation level under overall operation conditions. In order to measure the gap between actual operation level and ideal operation level, a new index—control perfect index (CPI) is defined by the ratio between the actual exergy loss and exergy loss of ideal operation level of HVAC system, and it can reflect control influences on operation performance of HVAC system. Thus, a new evaluation method is presented which regards ideal operation level as the benchmark and uses CPI to evaluate actual operation performance of HVAC system. Two kinds of control strategies, optimal supply air temperature reset strategy (SAT Strategy) and optimal load allocation control strategy are implemented to validate this evaluation method. Test’s results show that optimal load allocation control strategy can improve the operation performance of the system more greatly than the SAT Strategy. This evaluation method not only can evaluate the operation performance of HVAC system, but also can indicate the direction of optimal control of HVAC system

An energy-saving control strategy for VRF and VAV combined air conditioning system in heating mode

Although variable refrigerant flow (VRF) systems have become attractive due to good energy performances in part load conditions, the shortcoming of no outdoor air intake has not been solved thoroughly. A VRF and VAV combined air conditioning system is proposed to solve this problem. VAV part of the combined system consists of an outdoor air processing (OAP) unit and VAV boxes. Generally the VRF unit operates to maintain indoor temperature and the OAP unit operates to process the outdoor air. A control strategy for the combined system aiming at reducing energy consumption is presented in this paper. When both VRF unit and OAP unit are operating, a load allocation optimization module is executed to find the best load allocation between them to minimize the energy consumption of the combined system. When the allocated load of the OAP unit is very small, the proposed control strategy stops the OAP unit, leaving only the VRF unit to operate to improve the overall energy efficiency of the combined system. When load requirements are met, the OAP unit is restarted and the load allocation optimization module is executed again. The proposed control strategy is evaluated based on the developed simulation platform. Results show that the proposed control strategy can effectively decrease the energy consumption of the combined system

Comparing the Self- and External Assessment Versions of the HCL-33 as Screening Instruments for Bipolar Disorder in Older Depressed Patients

Objectives: The misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD) is common in depressed older adults. The self-rated HCL-33 and its external assessment version (HCL-33-EA) have been developed to screen for hypomanic symptoms. This study compared the screening ability of these two instruments to discriminate BD from MDD. Methods: A total of 215 patients (107 with BD and 108 with MDD) and their carers were recruited. Patients and their carers completed the HCL-33 and HCL-33-EA, respectively. The consistency of the total score and the positive response to each item between the two scales was calculated with the intraclass correlation coefficient (ICC) and Cohen's kappa coefficient separately. Receiver operating characteristics (ROC) curves were drawn for both instruments. The optimal cut-off points were determined according to the maximum Youden's Index. The areas under the ROC curve (AUC) of the HCL-33 and HCL-33-EA were calculated separately and compared. The sensitivity and specificity at the optimal cut-off values were also calculated separately for the HCL-33 and HCL-33-EA. Results: The intraclass correlation coefficient (ICC) between the total scores of the HCL-33 and HCL-33-EA was 0.823 (95% CI = 0.774-0.862). The positive response rate on all items showed high agreement between the two instruments. ROC curve analysis demonstrated that the total scores of both HCL-33 and HCL-33-EA differentiated well between MDD and BD, while there was no significant difference in the AUCs between the two scales (Z = 0.422, P = 0.673). The optimal cutoff values for the HCL-33 and HCL-33-EA were 14 and 12, respectively. With the optimal cutoff value, the sensitivities of the HCL-33 and HCL-33-EA were 88.8% and 93.5%, and their specificities were 82.4% and 79.6%. Conclusion: Both the HCL-33 and HCL-33-EA had good screening ability for discriminating BD from MDD in depressed older adults

Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault.Wavelet analysis Neural network Fault diagnosis Sensor Variable air volume

Applying deep learning-based regional feature recognition from macro-scale image to assist energy saving and emission reduction in industrial energy systems

Introduction: Image recognition technology has immense potential to be applied in industrial energy systems for energy conservation. However, the low recognition accuracy and generalization ability under actual operation conditions limit its commercial application. Objectives: To improve the recognition accuracy and generalization ability, a novel image recognition method integrating deep learning and domain knowledge was applied to assist energy saving and emission reduction for industrial energy systems. Methods: As a typical industrial scenario, the defrosting control in the refrigeration system was selected as the specific optimization object. By combining deep learning algorithm with domain knowledge, a residual-based convolutional neural network model (RCNN) was proposed specifically for frosty state recognition, which features the residual input and average pooling output. Based on the real-time recognition of frosty levels, a defrosting control optimization method was proposed to initiate and terminate the defrosting operation on demand. Results: By combining the advanced image recognition technique with specific energy domain knowledge, the proposed RCNN enables both high recognition accuracy and strong generalization ability. The recognition accuracy of RCNN reached 95.06% for the trained objects and 93.67% for non-trained objects while that of only 75.86% for the conventional CNN. By adopting the presented system optimization method assisted by RCNN, the defrosting frequency, accumulated time and energy consumption were 53.8%, 57.02% and 34.5% less than the original control method. Furthermore, the environmental and cost analysis illustrated that the annual reduction in CO2 emissions is 2145.21 to 3412.84 kg and the payback time was less than 2.5 years which was far below the service life. Conclusion: The technical feasibility and significant energy-saving benefits of deep learning-based image recognition method were demonstrated through the field experiment. Our study shows the great application potential of image recognition technology and promotes carbon neutrality in industrial energy systems

Genetic Basis of Drought Resistance at Reproductive Stage in Rice: Separation of Drought Tolerance From Drought Avoidance

Drought tolerance (DT) and drought avoidance (DA) are two major mechanisms in drought resistance of higher plants. In this study, the genetic bases of DT and DA at reproductive stage in rice were analyzed using a recombinant inbred line population from a cross between an indica lowland and a tropical japonica upland cultivar. The plants were grown individually in PVC pipes and two cycles of drought stress were applied to individual plants with unstressed plants as the control. A total of 21 traits measuring fitness, yield, and the root system were investigated. Little correlation of relative yield traits with potential yield, plant size, and root traits was detected, suggesting that DT and DA were well separated in the experiment. A genetic linkage map consisting of 245 SSR markers was constructed for mapping QTL for these traits. A total of 27 QTL were resolved for 7 traits of relative performance of fitness and yield, 36 QTL for 5 root traits under control, and 38 for 7 root traits under drought stress conditions, suggesting the complexity of the genetic bases of both DT and DA. Only a small portion of QTL for fitness- and yield-related traits overlapped with QTL for root traits, indicating that DT and DA had distinct genetic bases

Comparing the Self- and External Assessment versions of the HCL-33 as screening instruments for bipolar disorder in older depressed patients

Objectives: The misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD) is common in depressed older adults. The self-rated HCL-33 and its external assessment version (HCL-33-EA) have been developed to screen for hypomanic symptoms. This study compared the screening ability of these two instruments to discriminate BD from MDD. Methods: A total of 215 patients (107 with BD and 108 with MDD) and their carers were recruited. Patients and their carers completed the HCL-33 and HCL-33-EA, respectively. The consistency of the total score and the positive response to each item between the two scales was calculated with the intraclass correlation coefficient (ICC) and Cohen’s kappa coefficient separately. Receiver operating characteristics (ROC) curves were drawn for both instruments. The optimal cut-off points were determined according to the maximum Youden’s Index. The areas under the ROC curve (AUC) of the HCL-33 and HCL-33-EA were calculated separately and compared. The sensitivity and specificity at the optimal cut-off values were also calculated separately for the HCL-33 and HCL-33-EA. Results: The intraclass correlation coefficient (ICC) between the total scores of the HCL-33 and HCL-33-EA was 0.823 (95% CI = 0.774–0.862). The positive response rate on all items showed high agreement between the two instruments. ROC curve analysis demonstrated that the total scores of both HCL-33 and HCL-33-EA differentiated well between MDD and BD, while there was no significant difference in the AUCs between the two scales (Z = 0.422, P = 0.673). The optimal cutoff values for the HCL33 and HCL-33-EA were 14 and 12, respectively. With the optimal cutoff value, the sensitivities of the HCL-33 and HCL-33-EA were 88.8% and 93.5%, and their specificities were 82.4% and 79.6%. Conclusion: Both the HCL-33 and HCL-33-EA had good screening ability for discriminating BD from MDD in depressed older adults