Reduced Order Machine Learning Models for Accurate Prediction of CO2 Capture in Physical Solvents.

CO2 sorption in physical solvents is one of the promising approaches for carbon capture from highly concentrated CO2 streams at high pressures. Identifying an efficient solvent and evaluating its solubility data at different operating conditions are highly essential for effective capture, which generally involves expensive and time-consuming experimental procedures. This work presents a machine learning based ultrafast alternative for accurate prediction of CO2 solubility in physical solvents using their physical, thermodynamic, and structural properties data. First, a database is established with which several linear, nonlinear, and ensemble models were trained through a systematic cross-validation and grid search method and found that kernel ridge regression (KRR) is the optimum model. Second, the descriptors are ranked based on their complete decomposition contributions derived using principal component analysis. Further, optimum key descriptors (KDs) are evaluated through an iterative sequential addition method with the objective of maximizing the prediction accuracy of the reduced order KRR (r-KRR) model. Finally, the study resulted in the r-KRR model with nine KDs exhibiting the highest prediction accuracy with a minimum root-mean-square error (0.0023), mean absolute error (0.0016), and maximum R2 (0.999). Also, the validity of the database created and ML models developed is ensured through detailed statistical analysis.

Simultaneous determination of phthalates and bisphenols from plastic bottled water samples by dispersive solid-phase extraction with multiwalled carbon nanotubes and liquid chromatography/atmospheric pressure photoionization/high-resolution mass spectrometry.

RATIONALE: Phthalates and bisphenols were reported as endocrine disrupting chemicals and hence a potential threat to human health. Polyethylene terephthalate bottles are being used to store drinking water and the probability of migration of phthalates and bisphenols from the bottles into the water is high. The migration of analytes with respect to different storage conditions need to be studied. METHOD: A sensitive analytical method for simultaneous determination of seven phthalates and three bisphenols from packaged drinking water was developed using liquid chromatography/atmospheric pressure photoionization/high-resolution mass spectrometry. The analytes were extracted by dispersive solid-phase extraction by multiwalled carbon nanotubes. RESULTS: The developed method showed linearity from 0.5 to 5000 µg/L with the limit of detection and limit of quantification ranging from 0.5 to 1 µg/L and 1 to 2 µg/L, respectively, for phthalates and bisphenols. The inter- and intraday variations were below 10%. The recoveries were in the range of 79.5% to 112%. The migration of phthalates and bisphenols increased with storage time and temperature. Maximum migration was observed for diisobutyl phthalate of 1209.7 ng/L followed by dibutyl phthalate at 777.8 ng/L on 180 days of analysis at room temperature. Migration of bis(2-ethylhexyl) phthalate was observed to be higher at elevated temperatures, increasing from 14.9 to 514 ng/L. Similarly, migration of bisphenol-A was increased at 45°C. The results were subjected to analysis of variance (ANOVA) studies and the results showed significant variations of phthalates and bisphenols with respect to storage temperature and time. CONCLUSION: The use of atmospheric pressure photoionization facilitated simultaneous determination of phthalates and bisphenols. The migration of phthalates and bisphenols increased with increasing temperature and storage time. Maximum migration was observed for diethyl, diisobutyl, dibutyl and bis(2-ethylhexyl) phthalates. This may be attributed to the type of plastic, the processing parameters and recycling.

Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production.

A hybrid machine learning (ML) aided experimental approach was proposed in this study to evaluate the growth kinetics of Candida antarctica for lipase production. Different ML models were trained and optimized to predict the growth curves at various substrate concentrations. Results on comparison demonstrate the superior performance of the Gradient boosting regression (GBR) model in growth curves prediction. GBR-based growth kinetics was found to be matching well with the results of the conventional experimental approach while significantly reducing the experimental effort, time, and resources by âˆ¼ 50%. Further, the activity and enzyme kinetics of lipase produced in this study was investigated on hydrolysis of p-nitrophenyl butyrate resulting in a maximum lipase activity of 24.07 U at 44 h. The robustness and significance of developed kinetic models were ensured through detailed statistical analysis. The application of the proposed hybrid approach can be extended to any other microbial process.

Climate drivers on malaria transmission in Arunachal Pradesh, India.

The present study was conducted during the years 2006 to 2012 and provides information on prevalence of malaria and its regulation with effect to various climatic factors in East Siang district of Arunachal Pradesh, India. Correlation analysis, Principal Component Analysis and Hotelling's T² statistics models are adopted to understand the effect of weather variables on malaria transmission. The epidemiological study shows that the prevalence of malaria is mostly caused by the parasite Plasmodium vivax followed by Plasmodium falciparum. It is noted that, the intensity of malaria cases declined gradually from the year 2006 to 2012. The transmission of malaria observed was more during the rainy season, as compared to summer and winter seasons. Further, the data analysis study with Principal Component Analysis and Hotelling's T² statistic has revealed that the climatic variables such as temperature and rainfall are the most influencing factors for the high rate of malaria transmission in East Siang district of Arunachal Pradesh.