Research and application of a novel selective stacking ensemble model based on error compensation and parameter optimization for AQI prediction.

With the ongoing process of industrialization, the issue of declining air quality is increasingly becoming a critical concern. Accurate prediction of the Air Quality Index (AQI), considered as an all-inclusive measure representing the extent of pollutants present in the atmosphere, is of paramount importance. This study introduces a novel methodology that combines stacking ensemble and error correction to improve AQI prediction. Additionally, the reptile search algorithm (RSA) is employed for optimizing model parameters. In this study, four distinct regional AQI data containing a collection of 34864 data samples are collected. Initially, we perform cross-validation on ten commonly used single models to obtain prediction results. Then, based on evaluation indices, five models are selected for ensemble. The results of the study show that the model proposed in this paper achieves an improvement of around 10% in terms of accuracy when compared to the conventional model. Thus, the model introduced in this study offers a more scientifically grounded approach in tackling air pollution.

Phase Field Simulation of the Effect of Second Phase Particles with Different Orientations on the Microstructure of Magnesium Alloys.

In this study, the phase field method has been used to study the effect of second phase particles with different shapes and different orientations on the grain growth of AZ31 magnesium alloy, after annealing at 350 °C for 100 min. The results show that the shape of the second phase particles would have an effect on the grain growth; the refinement effect of elliptical particles and rod-shaped particles was similar, and better than the spherical particles; the spatial arrangement direction of the second phase particles had no significant effect on the grain growth. On the other hand, when the microstructure of AZ31 magnesium alloy contained second phase particles with different shapes, the effect of mixing different shapes of second phase particles on the grain refinement was enhanced gradually with the decrease im the volume fraction of spherical particles.

An evolutionary deep learning soft sensor model based on random forest feature selection technique for penicillin fermentation process.

Accurate and reliable measurement of key biological parameters during penicillin fermentation is of great significance for improving penicillin production. In this research context, a new hybrid soft sensor model method based on RF-IHHO-LSTM (random forest-improved​ Harris hawks optimization-long short-term memory) is proposed for penicillin fermentation processes. Firstly, random forest (RF) is used for feature selection of the auxiliary variables for penicillin. Next, improvements are made for the Harris hawks optimization (HHO) algorithm, including using elite opposition-based learning strategy (EOBL) in initialization to enhance the population diversity, and using golden sine algorithm (Gold-SA) in the search strategy to make the algorithm accelerate convergence. Then the long short-term memory (LSTM) network is constructed to build a soft sensor model of penicillin fermentation processes. Finally, the hybrid soft sensor model is used to the Pensim platform in simulation experimental research. The simulation test results show that the established soft sensor model, with high accuracy of measurement and good effect, can meet the actual requirements of engineering.