Abstract
With the serious pollution of the ecological environment, there are a large number of harmful gases in the chemical gases emitted by the industry. Relevant intelligent chemical algorithms control the emission of chemical gases, which can effectively reduce emissions and predict emissions more accurately. This paper proposes a gray wolf optimization algorithm based on chaotic search strategy combined with extreme learning machine to predict chemical emission gases, taking a 330 MW pulverized coal-fired boiler as a test object and establishing chemical emissions of CNGWO-ELM. The prediction model, by using the relevant data collected by DCS as training samples and test samples, trains and tests the model. Simulation experiments show that the chemical emission prediction model of CNGWO-ELM has better accuracy and stronger generalization ability, with higher practical value.
Highlights
In recent years, with the release of chemical gases, environmental pollution problems have become increasingly serious [1, 2]
Among the chemical emissions emitted from the ecological environment, circulating fluidized bed (CFB) combustion is one of the main coal combustion methods in China
E specific steps of CNGWO to optimize Extreme Learning Machine (ELM) model parameters are as follows: Step 1: population initialization: randomly generate a population consisting of N individuals, each consisting of input weights and thresholds, encoded according to xj (ω11, . . . , ω1M, ω21, ω22, . . . , ωm1, . . . , ωmM, b1, b2, . . . , bM)
Summary
With the release of chemical gases, environmental pollution problems have become increasingly serious [1, 2]. Erefore, the introduction of Kent chaos optimization strategy in the basic GWO algorithm to optimize the solution that falls into the local optimum will effectively help the algorithm find a better solution. The introduction of nonlinear dynamic weighting strategy in the GWO algorithm will effectively balance the development and exploration capabilities of the algorithm and further improve the global optimization performance of the GWO algorithm. In the GWO algorithm, it is assumed that the solution does not improve significantly after continuous limit iterative search, indicating that the solution falls into local optimum, so Kent is used to optimize chaos. In the standard GWO algorithm, the control parameter A decreases linearly from 2 to 0 as the number of iterations increases This linearly decreasing strategy cannot fully reflect the actual complex optimization process of the algorithm. CNGWO Algorithm Steps. e following are the basic steps of the CNGWO algorithm, as shown in Algorithm 1
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