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In this paper, a novel wood moisture content prediction model is established via SVR (support vector regression) for drying process with severe nonlinear and coupling. The particle position and velocity of particle swarm optimization (PSO) algorithm is used to optimize the model parameters, so as to realize wood moisture content prediction. Simulation results of Quercus mongolica show that the PSO...
The performance of a model, which is trained with offline data, is highly relied on the conditions in which the system is working. When the working conditions change, the prediction accuracy of the model will be reduced significantly. To solve this problem, we propose an adaptive SVR modeling method based on vector-field-smoothed (VFS) algorithm. This method can adapt the model quickly to new working...
A new heterogeneous catalysis modeling methodology, namely support vector regression (SVR) and chaotic particle swarm optimization algorithm (CPSO) was presented, for catalyst compositional models and catalytic reaction mechanism models, for reducing both high temporal costs and financial costs, and accelerating the process of industrialization synthesis of dimethyl ether (DME). In the SVR-CPSO approach,...
This paper presents a comparative study of two artificial intelligence based heterogeneous catalysis modeling strategies, namely ANN-CPSO and SVR-CPSO, for modeling dimethyl ether (DME) in direct synthesis from syngas process (called STD process), for reducing both high temporal costs and financial costs, and accelerating the process of industrialization synthesis of DME. In the two hybrid approaches,...
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