Indicators are the basis for judging the working performance of exhaust hood and capture performance are usually used as the only indicator. An evaluation index system including three factors of cooking oil fumes (COF) instantaneous capture, health risk impact and thermal comfort was proposed to assess the comprehensive performance of exhaust hood in the present study. The primary capture efficiency (PCE) of formaldehyde, the PCE of particulate matter with the diameter less than or equal to 2.5 μm (PM_2.5), the incremental lifetime cancer risk (ILCR) of formaldehyde, the ILCR of PM_2.5 and the predicted mean vote (PMV), which can all be quantified with the aid of computational fluid dynamics (CFD), were selected as the indicators. And the analytic hierarchy process (AHP) method was introduced to perform the comprehensive performance evaluation of exhaust hood. The performance of two exhaust hood structures (grille and orifice type) with three exhaust rates (3000, 4000, and 5000 m³/h) in two cooking zones of a university canteen kitchen were evaluated. The result showed that the reduction of ILCR of COF exposure is the most important to the performance of exhaust hood. The comprehensive performance of orifice exhaust hood with exhaust rate of 4000 and 5000 m³/h are optimal; the orifice exhaust hood with exhaust of 3000 m³/h and grille exhaust hood with exhaust rate of 5000 m³/h are moderate; the grille exhaust hood with exhaust rate of 3000 and 4000 m³/h are low. Decision-making priorities based on comprehensive and individual performance are not exactly the same in the two cooking zones. It is necessary to use the index system to evaluate the comprehensive performance of exhaust hood that considers the impact on human health and thermal comfort.

Effective emission control of cooking oil fumes (COFs), particularly for grease particles, has always been a cause of great concern for catering industry. The review and evaluation of combinations of purification technology are urgently required. This work presents a literature review and combination strategy evaluation of purification technology of grease particles of commercial kitchens. A variety of mainstream purification technologies, such as mechanical separation (M), filtration (F), washing absorption (W) and electrostatic deposition (E) are discussed. In order to establish a complete and efficient fume purification system for commercial kitchen, this study proposes the four-point principles of combined purification technologies as: (1) from easy to difficult (for grease particle diameter); (2) fire prevention and noise reduction; (3) electrostatic deposition postposition; (4) Absorption and dissolution (by-product from electrostatic). Based on the above principles and separation characteristics, the recommended combinations of purification strategies are M-E, F-E, M-F-E and M-E-F. The combination strategy of M-F-E is adopted as an example to evaluate and optimize COFs purification system use life cycle assessment approach. The results indicate that the optimization of the M-F-E purification system using rotating mesh plate instead of baffle filter can reduce the environmental impact of global warming and eutrophication by about 35% which reduces the emissions of CO₂ and SO₂ from 92.533 kg and 0.110 kg to 60.214 kg and 0.072 kg, respectively. Besides the review of relevant purification technologies, the study also proposes the combination of principles of purification technologies and the evaluation and optimization of life cycle assessment for the optimal design of combined purification system.

When the indoor environment is designed by genetic algorithm (GA) and computational fluid dynamics (CFD), the artificial neural network (ANN) plays a role of surrogate model of CFD to reduce the computational cost. To improve the performance of ANN, a self-updating logarithm normalized method was proposed to enhance the local prediction of ANN in the inverse design based on GA and ANN. An MD-82 aircraft cabin was used to test the performance of the proposed method, and different environmental parameters were chosen to be the objectives of the cabin environment. The success rate (SR) was used to evaluate the local prediction ability of ANN. Instead of linear normalized ANN, SR was found to be increased by 10.5% with the logarithm normalized ANN and the computational cost was reduced by 23.2% for the same quality of solution.

Comfortable and healthy aircraft cabin environment is required as more and more people choose to travel by air. The cabin environment is optimized by searching the optimal control parameters such as air supply velocity, angle and temperature. The optimal solutions are obtained by combining a multi-objective particle swarm optimization (MOPSO) with the simulation of computational fluid dynamics (CFD). It is found that different combinations of optimal air supply parameters can build an optimal cabin environment and the locations of the obtained optimal solutions are isolated in their value spaces. To achieve a stable engineering control operation, the determination of a stable range of optimal air supply parameters is required. Therefore, a method by using cluster analysis is developed to obtain stable ranges of optimal air supply parameters. Results show that the proposed method can obtain the ranges of optimal air supply parameters successfully.