Optimization of leucomalachite green degradation through electron beam and persulfate oxidation using box-behnken design and life cycle assessment.

This study explored the treatment of Leucomalachite Green (LMG) solutions using an electron beam and sodium persulfate (Na2S2O8), employing Box-Behnken design (BBD) to optimize operational variables such as absorbed dose, initial pH and Na2S2O8 concentration. The findings highlighted an optimal absorbed dose of 4.5 kGy, a Na2S2O8 concentration of 1.0 mM, and an initial pH of 6, leading to a remarkable 97.77% removal of LMG. The adjusted R2 for the model indicated a close match of 1.4% between predicted and actual outcomes under these optimized conditions, affirming the quadratic model's suitability for predicting the LMG removal process using combined EB and Na2S2O8. To assess the environmental impact of the LMG treatment, the study applied SimaPro 9.4 with the TRACI tool, examining ten distinct environmental impact categories. The results unveiled that deionized water and Na2S2O8 exhibited a notable impact on global warming (GW) and ecotoxicity (ET) in controlled laboratory settings. Furthermore, a comparative analysis of four scenarios shed light on the environmental implications of different energy sources. Notably, electricity generated from waste incineration demonstrated a substantial influence on all environmental indicators. In contrast, natural gas emerged as the cleanest source for electricity generation, offering a promising avenue for reducing environmental impacts. This study presents a practical method for addressing dye contaminants through the employment of EB in conjunction with Na2S2O8, with potential implications for broader applications.

Pesticide production wastewater treatment by Electro-Fenton using Taguchi experimental design.

In this study, the electro-Fenton (EF) method was applied to remove total organic carbon (TOC) from the pesticide production wastewater containing tricyclazole (TC). Statistical Taguchi method was used to optimize the treatment performance. Analysis of variance (ANOVA) indicated that the polynomial regression model fitted experimental data with R2 of 0.969. The optimal conditions for eliminating 75.4% TOC and 93.7% TC were 0.2 mM of Fe2+, 990 mg/L of Na2SO4, 180 min of reaction time at pH 3 with 2.22 mA/cm2 of current density. The removal of TC present in the wastewater followed the first-order reaction kinetic model (R2 = 0.993); while that was the second-order kinetic model in the case of the TOC removal (R2 = 0.903). In addition, the experimental results and theory approaches (density functional theory and natural bond orbital calculations) also showed the C-N bond breaking and nitrate ions cleavage to ammonia. Acute toxicity of the pesticide wastewater after treatment (PWAT) on microcrustaceans showed that the treated wastewater still exhibited high toxicity against D. magna, with LC50 values of 3.84%, 2.68%, 2.05%, and 1.78% at 24 h, 48 h, 72 h, and 96 h, respectively.

Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis.

Sentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. However, it will be challenging to build a corpus on new topics to train machine learning algorithms in sentiment classification with high confidence. This study proposes a method that processes embedding knowledge in the ontology of opinion datasets called knowledge processing and representation based on ontology (KPRO) to represent the significant features of the dataset into the word embedding layer of deep learning algorithms in sentiment classification. Unlike the methods that lexical encode or add information to the corpus, this method adds presentation of raw data based on the expert's knowledge in the ontology. Once the data has a rich knowledge of the topic, the efficiency of the machine learning algorithms is significantly enhanced. Thus, this method is appliable to embed knowledge in datasets in other languages. The test results show that deep learning methods achieved considerably higher accuracy when trained with the KPRO method's dataset than when trained with datasets not processed by this method. Therefore, this method is a novel approach to improve the accuracy of deep learning algorithms and increase the reliability of new datasets, thus making them ready for mining.