Forecasting a Short-Term Photovoltaic Power Model Based on Improved Snake Optimization, Convolutional Neural Network, and Bidirectional Long Short-Term Memory Network.

The precision of short-term photovoltaic power forecasts is of utmost importance for the planning and operation of the electrical grid system. To enhance the precision of short-term output power prediction in photovoltaic systems, this paper proposes a method integrating K-means clustering: an improved snake optimization algorithm with a convolutional neural network-bidirectional long short-term memory network to predict short-term photovoltaic power. Firstly, K-means clustering is utilized to categorize weather scenarios into three categories: sunny, cloudy, and rainy. The Pearson correlation coefficient method is then utilized to determine the inputs of the model. Secondly, the snake optimization algorithm is improved by introducing Tent chaotic mapping, lens imaging backward learning, and an optimal individual adaptive perturbation strategy to enhance its optimization ability. Then, the multi-strategy improved snake optimization algorithm is employed to optimize the parameters of the convolutional neural network-bidirectional long short-term memory network model, thereby augmenting the predictive precision of the model. Finally, the model established in this paper is utilized to forecast photovoltaic power in diverse weather scenarios. The simulation findings indicate that the regression coefficients of this method can reach 0.99216, 0.95772, and 0.93163 on sunny, cloudy, and rainy days, which has better prediction precision and adaptability under various weather conditions.

Chlorine removal from MSWI fly ash by thermal treatment: Effects of iron/aluminum additives.

The high content of alkali chlorides in municipal solid waste incineration (MSWI) fly ash limit its resource reuse due to the potential environmental risks. In this paper, with superheated steam as the gasifying agent and inducer, chlorides in fly ash were removed by thermal treatment within a moderate temperature range. Thermal treatment experiments were performed under different conditions: temperature (500-800°C), steam addition (mass ratio of steam to fly ash = 0.25-1) and residence time (0.5-3 hr). Iron and aluminum powders were added to fly ash to improve the chlorine removal efficiency. Water-soluble chlorides included NaCl and KCl, and insoluble chlorides mainly included Ca(OH)Cl. The heating process with the addition of water steam was more efficient than that without steam in terms of the removal performance of water-soluble chlorides. The removal efficiency of soluble chlorides reached 75.25% for a mass ratio of 1:1 after 1-hr thermal treatment at 700°C. When the residence time was increased above 1 hr, the total dechlorination efficiency was not increased dramatically. Moreover, adding iron and aluminum powder into the fly ash improved the removal of water-insoluble chlorides, and the total dechlorination efficiency was increased by 11.41%-16.64%.