Adaptive meta-learning extreme learning machine with golden eagle optimization and logistic map for forecasting the incomplete data of solar iradiance

Solar energy has become crucial in producing electrical energy because it is inexhaustible and sustainable.However,its uncertain generation causes problems in power system operation.Therefore,solar irradiance forecasting is significant for suitable controlling power system operation,organizing the transmission expansion planning,and dispatching power system generation.Nonetheless,the forecasting performance can be decreased due to the unfitted prediction model and lacked preprocessing.To deal with mentioned issues,this paper pro-poses Meta-Learning Extreme Learning Machine optimized with Golden Eagle Optimization and Logistic Map(MGEL-ELM)and the Same Datetime Interval Averaged Imputation algorithm(SAME)for improving the fore-casting performance of incomplete solar irradiance time series datasets.Thus,the proposed method is not only imputing incomplete forecasting data but also achieving forecasting accuracy.The experimental result of fore-casting solar irradiance dataset in Thailand indicates that the proposed method can achieve the highest coeffi-cient of determination value up to 0.9307 compared to state-of-the-art models.Furthermore,the proposed method consumes less forecasting time than the deep learning model.

Light biofuel production from waste cooking oil via pyrolytic catalysis cracking over modified Thai dolomite catalysts

Renewable biofuels have gained increasing attention as a potential alternative fuel to decrease CO_(2) emission from combustion of fossil fuels.The aims of the work were to modify Thai dolomite by adding magnesium carbonate(MgCO_(3))at various contents(0-30 wt%),and used as catalyst in pyrolytic catalysis cracking(PCC)process to produce light biofuels including gasoline and kerosene.All catalysts were calcined at 600℃ for 4 h prior to the characterization and experiments.The physicochemical properties were done by various techniques such as X-ray diffractometer(XRD),N2 adsorption-desorption,thermogravimetric analyzer and differential thermal analyzer(TGA-DTA),Field-emission scanning electron microscope(FE-SEM),and energy dispersive X-ray spectroscopy(EDX).The experiments of PCC process were carried out at different reaction temperatures of 450-550℃.The results from XRD and SEM-EDX confirmed that the Mg was successfully added in Thai dolomite.The Mg content in the catalysts increased with increasing MgCO_(3) loadings.The calcination temperature of 600℃ cannot completely convert CaCO3 to CaO form.The pyrolytic oil and distilled oil yields and quality were affected by both Mg content and reaction temperature.In addition,pyrolytic oil was completely distillated according to ASTM D86 to separate into gasoline,kerosene,and diesel.The light biofuel production was enhanced with increasing Mg content in the reaction temperatures of 500 and 550℃.The appropriate condition was suggested at reaction temperature of 500℃ with 20 wt%Mg/dolomite catalyst as it showed the highest production yield of about 84 vol%and light biofuel yield of about 65 vol%.

Predicting the true density of commercial biomass pellets using near-infrared hyperspectral imaging

The use of biomass is increasing because it is a form of renewable energy that provides high heating value.Rapid measurements could be used to check the quality of biomass pellets during production.This research aims to apply a near-infrared(NIR)hyperspectral imaging system for the evaluation of the true density of individual biomass pellets during the production process.Real-time measurement of the true density could be beneficial for the operation settings,such as the ratio of the binding agent to the raw material,the temperature of operation,the production rate,and the mixing ratio.The true density could also be used for rough measurement of the bulk density,which is a necessary parameter in commercial production.Therefore,knowledge of the true density is required during production in order to maintain the pellet quality as well as operation conditions.A prediction model was developed using partial least squares(PLS)regression across different wavelengths selected using different spectral pre-treatment methods and variable selection methods.After model development,the performance of the models was compared.The best model for predicting the true density of individual pellets was developed with first-derivative spectra(D1)and variables selected by the genetic algorithm(GA)method,and the number of variables was reduced from 256 to 53 wavelengths.The model gave R_(cal)^(2),R_(val)^(2),SEC,SEP,and RPD values of 0.88,0.89,0.08 g/cm^(3),0.07 g/cm^(3),and 3.04,respectively.The optimal prediction model was applied to construct distribution maps of the true density of individual biomass pellets,with the level of the predicted values displayed in colour bars.This imaging technique could be used to check visually the true density of biomass pellets during the production process for warnings to quality control equipment.