Structural Characteristic Integrated Computer-Aided Molecular Design for Phenols Removal Considering Synergistic Effect

Zero liquid discharge (ZLD) of coal chemical wastewater is a significant strategy for sustainable management of water resources. Efficient removal of phenols has high significance for the realization of ZLD. In this study, a method of structural characteristics integrated computer-aided molecular design (CAMD) is used for phenols removal considering synergistic effect. Solvent mixture for synergistic extraction of phenols by using methyl propyl ketone (MPK) in combination with n-pentanol is proposed with the volume ration of MPK to n-pentanol 8:2. Using solvent mixture (80% MPK, 20% n-pentanol), the phenols removal efficiencies are observably better than that using methyl isobutyl ketone (MIBK) or diisopropyl ether (DIPE). The total phenols concentration of coal gasification wastewater can be removed from 6273 mg/L to less than 300 mg/L after two-stage extraction. In addition, solvent mixture (MPK, n-pentanol) can also achieve the total phenols target if the volume fraction of n-pentanol was no more than 70%, the wide range volume fraction of which has potential application in industry

Normalization error trends at different mode numbers.

Normalization error trends at different mode numbers.</p

Percentage decrease in the error of discarding secondary residual components.

Percentage decrease in the error of discarding secondary residual components.</p

PE of each component.

Due to the ability of sidestepping mode aliasing and endpoint effects, variational mode decomposition (VMD) is usually used as the forecasting module of a hybrid model in time-series forecasting. However, the forecast accuracy of the hybrid model is sensitive to the manually set mode number of VMD; neither underdecomposition (the mode number is too small) nor over-decomposition (the mode number is too large) improves forecasting accuracy. To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. Experimental results validate the effectiveness of the proposed combination.</div

Trend of wind power generation in 2020 and 2021.

Due to the ability of sidestepping mode aliasing and endpoint effects, variational mode decomposition (VMD) is usually used as the forecasting module of a hybrid model in time-series forecasting. However, the forecast accuracy of the hybrid model is sensitive to the manually set mode number of VMD; neither underdecomposition (the mode number is too small) nor over-decomposition (the mode number is too large) improves forecasting accuracy. To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. Experimental results validate the effectiveness of the proposed combination.</div

Flow chart of BER to determine the mode number.

Due to the ability of sidestepping mode aliasing and endpoint effects, variational mode decomposition (VMD) is usually used as the forecasting module of a hybrid model in time-series forecasting. However, the forecast accuracy of the hybrid model is sensitive to the manually set mode number of VMD; neither underdecomposition (the mode number is too small) nor over-decomposition (the mode number is too large) improves forecasting accuracy. To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. Experimental results validate the effectiveness of the proposed combination.</div

Percentage decrease in the error sum.

Due to the ability of sidestepping mode aliasing and endpoint effects, variational mode decomposition (VMD) is usually used as the forecasting module of a hybrid model in time-series forecasting. However, the forecast accuracy of the hybrid model is sensitive to the manually set mode number of VMD; neither underdecomposition (the mode number is too small) nor over-decomposition (the mode number is too large) improves forecasting accuracy. To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. Experimental results validate the effectiveness of the proposed combination.</div

Decomposition results of daily power generation data in 2020 under different mode numbers.

Decomposition results of daily power generation data in 2020 under different mode numbers.</p

Comparison of experimental errors in three levels.

Comparison of experimental errors in three levels.</p

Normalization error trends at different mode numbers.

Normalization error trends at different mode numbers.</p