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With the development of the electric market, electric load forecasting has been increasingly pursued by many scholars. Because the electric load is affected by many factors, it is characterized by volatility and uncertainty, and it cannot be forecasted accurately only by a single model. In the research, a short‐term load forecasting integrated model is proposed to solve the problem of inaccurate forecasting of a single model. The key point of using the integrated model to forecast is to optimize the decomposed sequence to improve the accuracy of the forecast. empirical wavelet decomposition (EWT) is used to decompose the sequence into stationary sequences and avoid modal aliasing; the sparrow search algorithm (SSA) simulates the forecasting and anti‐forecasting behavior of the sparrow population, which is very similar to the electricity consumption behavior of various industries and has good optimization effect; generalized regression neural network (GRNN) is used for forecast and reconstruction; This is the EWT‐SSA‐GRNN model. This paper studies and analyzes the power load of a city in southern Australia. The results show that the integrated model reduces volatility through decomposition and optimization, and can improve forecast accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we present improvements to postcombustion capture ( PCC) processes based on aqueous monoethanolamine ( MEA). First, a rigorous, rate-based model of the carbon dioxide (CO2) capture process from flue gas by aqueous MEA was developed using Aspen Plus, and validated against results from the PCC pilot plant trials located at the coal-fired Tarong power station in Queensland, Australia. The model satisfactorily predicted the comprehensive experimental results from CO2 absorption and CO2 stripping process. The model was then employed to guide the systematic study of the MEA-based CO2 capture process for the reduction in regeneration energy penalty through parameter optimization and process modification. Important process parameters such as MEA concentration, lean CO2 loading, lean temperature, and stripper pressure were optimized. The process modifications were investigated, which included the absorber intercooling, rich-split, and stripper interheating processes. The minimum regeneration energy obtained from the combined parameter optimization and process modification was 3.1 MJ/kg CO2. This study suggests that the combination of a validated rate-based model and process simulation can be used as an effective tool to guide sophisticated process plant, equipment design and process improvement. [ABSTRACT FROM AUTHOR]