Traditional static anaerobic digestion technology presents the disadvantages of a low gas production rate and long digestion cycle, which is not conducive to the treatment of livestock manure. A 12 m³ multiphase flow anaerobic digester (MFD) was developed in this study to improve the biogas production rate and maintain constant temperature digestion during winters. Full-scale field experiments were conducted on the biogas production rate at different temperatures, the dynamic digestion effects, and the dynamic heating digestion effects of the system at Sichuan, China. A comparison of the dynamic and static digestion results of 50 days indicated that the biogas production for the dynamic digestion (DD) group was 115.22 m³ or 127.1% higher than that of the static digestion (SD) group with the same digestion temperature. The results of the heat transfer performance experiment revealed that the heat transfer rate of the system increased significantly, and the temperature of the biogas slurry increased rapidly. The optimization analysis of the system was based on the experimental results of the relationship between the slurry temperature and biogas production rate, and the economical digestion temperature of the system was proposed and calculated. Different insulation materials or insulation thicknesses have an influence on the economical digestion temperature. Additionally, the economical digestion temperature of the system in which the polystyrene insulation layer with a thickness of 90 mm was used, was found to be 27.2 °C. When digestion temperature was 22.3 °C, the energy efficiency ratio (EER) of dynamic anaerobic digestion system is 1. The advantages of MFD are low biogas production unit cost and high heat and mass transfer rate. However, the disadvantage of high operation energy consumption needs further improvement. And additional energy was required when system digestion temperature below 22.3 °C. The proposed MFD and dynamic anaerobic digestion system can play a significant role in using biomass resources and promoting the development of biogas projects.

Smoke control is a crucial issue in a long-distance subway tunnel fire, and a two-point extraction ventilation system is an effective way to solve this problem, due to the characteristics of controlling the smoke in a limited area and removing high-temperature and toxic smoke in time. In this study, the ceiling temperature distribution and the critical exhaust volumetric flow rate to control the smoke in the zone between two extraction vents were investigated in a long-distance subway tunnel fire with a two-point extraction ventilation system. Experiments were carried out in a 1/20 reduced-scale tunnel model based on Froude modeling. Factors, including the heat release rate (HRR), the extraction vent length, the internal distance between two extraction vents and exhaust volumetric flow rate, were studied. Smoke temperature below the ceiling, exhaust volumetric flow rate and smoke spreading configurations were measured. The ceiling temperature distribution was analyzed. Meanwhile, an empirical equation was developed to predict the critical exhaust volumetric flow rate based on the one-dimensional theory, experimental phenomenon and the analysis of forces acting at the smoke underneath the extraction vent. The coefficients in the empirical equation were determined by experimental data. Compared with the experimental results, the developed empirical equation can predict the critical exhaust volumetric flow rate well. Research outcomes in this study will be beneficial to the design and application of two-point extraction ventilation system for a long-distance subway tunnel fire.

A chilled water system transports cooling functionality from refrigerators to users via heating, ventilation, and air conditioning (HVAC) systems. This paper investigated an optimal control strategy to regulate the volume flow rate of each user branch in a chilled water system, considering the minimum resistance operation to reduce energy consumption. An artificial neural network (ANN) was adopted to establish the nonlinear relationship between the volume flow rate of each user branch, pump frequency, and valve opening of each user branch. An optimal control strategy for a chilled water HVAC system is proposed in this article, according to the pump-valve combined control (PVCC) principle and an ANN model, i.e., pump-valve combined control using an artificial neural network model (PVCC-ANN). A series of tests were conducted to collect data to train the ANN model and analyze the performance of the PVCC-ANN in an experimental chilled water system. The results show that the trained ANN model has good prediction performance. A minimum resistance operation can be achieved to control the volume flow rate of each user branch independently by using the PVCC-ANN model. Moreover, the proposed PVCC-ANN method shows good energy-saving performance in chilled water systems, which can be attributed to the minimum resistance operation. Taking the energy consumption of the pump’s constant frequency operation as the reference, the energy saving rate using the PVCC-ANN is between 14.3% and 58.6% under 10 operating conditions, as reported in this paper.

Tunnels with natural ventilation and extraction have become the focus of ventilation research in recent years. It is significant to study the characteristics of smoke in tunnel fires to ensure the safety of people and the tunnel structure. Previous research has mainly focused on natural ventilation in horizontal tunnels, and there are few studies on sloped tunnels. In this paper, we studied the smoke characteristics of natural ventilation extraction in slope tunnel fires both experimentally and theoretically. The small-scale experimental results showed that the position of the fire source, heat release rate (HRR), and the size of the shaft had little effect on the deflection angle of the fire plume. The deflection angle of fire plume was only related to the tunnel slope and increased with the tunnel slope. The slope had no effect on the smoke temperature distribution on the downside of the tunnel, while the smoke temperature on the upside decreased with the increase in the slope. The calculation models of the maximum smoke temperature rise and the smoke temperature distribution were obtained based on the experimental results and theoretical analysis. Compared with the experimental data, the developed semi-empirical models could provide a reliable prediction of smoke temperature.

Artificial neural network has been widely used in air conditioning systems as an effective method for predicting parameters, and the accuracy of ANN model relies on training data and network structure. In order to increase the quality of chilled water loops model, this paper develops an optimal data processing algorithm combining Kalman filtering with particle swarm optimization to compensate for uncertain factors and disturbances of collected data from the case building and establishes the nonlinear variation trend database. Based on Elman and BP neural networks, this paper proposes the improved network structures to avoid the local optimum predicted value of chilled water loops and increase data training speed. Simulation results show that this algorithm improves the data accuracy of current percentage (CP) of chillers and chilled water temperatures 12% and 9%. Compared with Elman and BP models, mean absolute errors of CP improved models are improved 24.1% and 10.3%, and mean squared errors of water temperature improved models are improved 5.2% and 4.8%. For the purpose of energy conservation control in air conditioning systems, this work has an application value and can be used for predicting other parameters of buildings.

Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditional OTI method, the data-mining-based method can provide a more comprehensive analysis of energy-saving potential with less time cost, although it strongly relies on data quality in all steps and lacks flexibility for diagnosing specific equipment for energy-saving potential analysis. The results can deepen the understanding of the operating data characteristics of air-conditioning systems.