The model of odour intensity analyzer in a form of two layers of neurons. was proposed. The first layer consists of specific receptors of odorants, which are randomly distributed. The odorant influence on odour intensity of mixtures is depends on the number of suitable receptors and number of their connections with the second layer neurons (the degree of divergence). The second layer neuron is stimulated with the same power after receiving the signal from any receptor or receptors. Il was assumed that the odour intensity is directly proportional to the number of stimulated neurons in the second layer. The investigated model contained 1000 neurons in the first layer and 1456 neurons in the second layer. The first layer contained receptors A and B (RA and RB) in quantities 15% RA, IS% RB and 30% RA, 10% RB. Receptors A characterized the divergence I :9, receptors B - 1 :25. The stimulus were mixtures containing 5-100 molecules A and B per hundred (x , = 0.0, 0.2, 0.5, 0.8 and 1.0). The general similarity of • the model reaction with that of the human smell reaction was found.

Evaluations of odour intensity of the air polluted with cyclohexane (A), cyclohexanone (B) and cyclohexane with admixtures of cyclohexanone (mixtures MI, M2 and M3; x8 = 0.14; 0.05 and 0.02 respectively) were made. Empirical parameters of psychophysical equations (Weber-Fechner' and Stevens') were determined. Total concentrations of the mixtures were within the range C = 1500-3500 ppm. The interaction constant av= 115-132° was estimated for the mixtures. Verification of known models of odour interaction led to the conclusion that possible application of the models for prediction of odour intensity of the air polluted with mixtures of cyclohexane and hexane is limited.

Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish's field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios.