Metalworking fluids (MWFs) used in milling generate oil particles through impaction, action of centrifugal forces and evaporation/condensation mechanisms. The oil particles suspended in the factory atmosphere can affect the health of the labor force. In order to study the emission properties of these oil particles, this work investigates the oil particle emission rate and size distribution during milling using an environmental chamber method. Two commonly used operating modes for MWFs were selected, the minimum quantity lubrication (MQL) mode (40 ml/h) and the cooling mode (1 m³/h). The cooling mode without cutting was studied separately for comparison with the cooling mode with cutting. The results show that the oil particle emission rate in milling ranges from 7.2 to 641 mg/h, and the size distribution ranges from 0.265 to 12.5 µm. Evaporation/condensation is the main mechanism in the MQL mode. The majority of oil particles formed by evaporation/condensation are in the range of 0.265 to 1.8 µm. As the tool rotation speed increases, the particle emission rate increases, while the mass mean diameter (MMD) and the sauter mean diameter (SMD) decrease. Oil particles are mainly generated by the action of centrifugal force in the cooling mode, and mainly distributed in the range of 1.8 to 12.5 µm. The particle emission rate increases with the tool rotation speed, and the particle MMD and SMD increase with the tool rotation speed only in the cooling mode without cutting. The particle emission rate ranging from 1.8 to 12.5 µm, as well as PM5 and PM10, are a polynomial function of the square of tool rotation speed during the cooling mode. The coefficient of determination (R²) is above 0.99. © 2018 American Association for Aerosol Research

To conserve building energy, optimal operation of a building’s energy systems, especially heating, ventilation and air-conditioning (HVAC) systems, is important. This study focuses on the optimization of the central chiller plant, which accounts for a large portion of the HVAC system’s energy consumption. Classic optimal control methods for central chiller plants are mostly based on system performance models which takes much effort and cost to establish. In addition, inevitable model error could cause control risk to the applied system. To mitigate the model dependency of HVAC optimal control, reinforcement learning (RL) algorithms have been drawing attention in the HVAC control domain due to its model-free feature. Currently, the RL-based optimization of central chiller plants faces several challenges: (1) existing model-free control methods based on RL typically adopt single-agent scheme, which brings high training cost and long training period when optimizing multiple controllable variables for large-scaled systems; (2) multi-agent scheme could overcome the former problem, but it also requires a proper coordination mechanism to harmonize the potential conflicts among all involved RL agents; (3) previous agent coordination frameworks (identified by distributed control or decentralized control) are mainly designed for model-based control methods instead of model-free controllers. To tackle the problems above, this article proposes a multi-agent, model-free optimal control approach for central chiller plants. This approach utilizes game theory and the RL algorithm SARSA for agent coordination and learning, respectively. A data-driven system model is set up using measured field data of a real HVAC system for simulation. The simulation case study results suggest that the energy saving performance (both short- and long-term) of the proposed approach (over 10% in a cooling season compared to the rule-based baseline controller) is close to the classic multi-agent reinforcement learning (MARL) algorithm WoLF-PHC; moreover, the proposed approach’s nature of few pending parameters makes it more feasible and robust for engineering practices than the WoLF-PHC algorithm.

Although the toxicity of high-dose formaldehyde (FA) inhalation has been extensively analyzed in animals, the effect of continuous long-term exposure to low-dose FA has not been well documented. This study aims to evaluate the toxicity of continuous long-term low-dose FA inhalation in mice. Forty-eight Kunming male mice were equally randomized to three groups according to the dose of FA inhalation exposure: a control (0 mg/m³) group, a low-dose (0.08 mg/m³) group and a high-dose (0.8 mg/m³) group. The mice have been selected to expose to FA for different consecutive days at 24 h/day. The learning and memory functions, pathological changes in the lung and liver, and the percentage of CD4 ⁺ T and CD8 ⁺ T cells were observed and analyzed. It was found that continuous long-term inhalation of FA at relatively low doses could impair the learning and memory functions and induce pathological changes in the lung and liver, but did not seem to significantly affect the number of immune (CD4 ⁺ T and CD8 ⁺ T) cells.

The use of metalworking fluids (MWFs) in the milling process can cause emissions of a large amount of oil particles, which can induce respiratory and immune system diseases of workers. This work examined the emission characteristics of oil particles caused by the milling process. A closed test chamber containing a real milling machine was built. The emission rates of oil particles in various sizes using five kinds of MWFs were measured, and the influence of the rotation speed of the spindle in the milling machine and the physical properties of MWFs on the oil particle emission characteristics was analysed. In addition, a prediction model of emission rate and size distribution of oil particles was introduced. Based on the measured data of oil particle emission rates in each size, the morphological particle size, distribution index and atomization coefficient in the prediction model are fitted. The deviation between the predicted and measured values of the emission rates is about 5%, which confirms the accuracy of the model. The prediction model is helpful for pollution control and ventilation strategy formulation in typical machining plants.