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In this paper, a mathematical model of the GMA(Generalized Mass Action) system for a class of microbial continuous fermentation processes is proposed, and the mathematical properties of the GMA system are discussed. A two-stage method is proposed to determine the parameter values of the GMA system. First, the GMA system is rewritten. In the first stage, the logarithmic transformation and linear least square method are used to solve the parameter identification problem of the rewritten system. In the second stage, a system of linear equations is solved. The parameters in the original GMA system are then identified by the two-stage method. Compared with the error results of existing literature [5,6,10,20,22,25], smaller error values have been achieved. This shows that our proposed GMA system can better describe the microbial continuous fermentation process. To determine the optimal operating condition of the microbial continuous fermentation process, a steady-state optimization model is established by maximizing the volume yield of 1, 3-propanediol. Considering the power function structure of the steady-state optimization model, we further transform it into a problem with a linear objective and multiple linear constraints. The transformed problem can be easily solved by the presented interior point algorithm. The optimization results show that the maximum volume yield of 1, 3-propanediol has increased to 403.4028 mmol/$ (\mathrm{L} \cdot \mathrm{h}) $. This result can provide a theory guide for the practical production of 1, 3-propanediol. [ABSTRACT FROM AUTHOR]

Plastic has brought great convenience to people’s lives, but it has also brought severe environmental pollution to the world. It is almost impossible to break down entirely in nature, and plastic waste, whether in rivers, oceans, or on land, can persist in the environment for centuries. The problem of plastic waste disposal has caused worldwide concern. It is necessary to reduce the production of plastic products through global cooperation effectively. However, achieving this goal will also involve some unexpected issues, such as controlling the adverse economic impact of plastics, and the rationality and fairness of the distribution of responsibilities between different countries. This paper collects massive data and takes China as an example to study the impact of plastic output reduction on China’s Express industry and the plastics manufacturing industry using multivariate regression. This paper summarizes the positive and negative effects of plastic waste reduction. Given the distribution and management of national and regional plastic waste responsibilities, the global carbon emission rights distribution method is used for reference. The comprehensive index allocation method is used to deal with the problem of both fairness and efficiency relatively. What is more, based on the conclusions, this paper also provides suggestions for a global joint response to plastic waste.

The prediction of stock price has a great significance for the regulators to find out the operation of the financial market and for the investors to avoid the risk of stock market. This paper proposes a method based on gated recurrent unit (GRU) neural network and bagging, and applies it to the research of forecasts of stock indexes. The model uses Bagging method to process the training data set, introduces randomness in the model construction process, and combines the GRU model to predict stock prices, which can ultimately reduce prediction errors and improve prediction accuracy. Through the comparison of the experimental results of the fourdata sets, it is found that: (1) The GRU model can better predict stock index data. Compared with the other two single models, it has smaller forecast errors in most cases. (2) The Bagging-GRU model has a strong predictive ability. Compared with the three benchmark models (GRU, ELM, BP), it has smaller prediction errors and higher prediction stability. [ABSTRACT FROM AUTHOR]