An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model's predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products.

Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization

Aiming at the problems of single planning technology and relatively few resource types in the process of regional comprehensive energy system planning at the current stage, this paper proposes a method based on the construction of regional comprehensive energy system planning and the operation model. Through the integration of the particle swarm optimization algorithm, this method can better realize the optimization and economic operation of the regional comprehensive energy system and build a system optimization mode based on two stages of planning and operation to pursue the optimal configuration of system equipment. Through the simulation algorithm, it is found that the solution time of the traditional basic particle swarm optimization algorithm is 10.49s, while the average solution time of the particle swarm optimization system proposed in this study is 7.93s; the efficiency is increased by 24.4%, and the system operation efficiency is significantly improved, providing theoretical and technical guidance for the economic operation of the regional comprehensive energy system.