Methanol production and purification via membrane-based technology: Recent advancements, challenges and the way forward.

Industrial revolution on the back of fossil fuels has costed humanity higher temperatures on the planet due to ever-growing concentration of carbon dioxide emissions in Earth's atmosphere. To tackle global warming demand for renewable energy sources continues to increase. Along renewables, there has been a growing interest in converting carbon dioxide to methanol, which can be used as a fuel or a feedstock for producing chemicals. The current review study provides a comprehensive overview of the recent advancements, challenges and future prospects of methanol production and purification via membrane-based technology. Traditional downstream processes for methanol production such as distillation and absorption have several drawbacks, including high energy consumption and environmental concerns. In comparison to conventional technologies, membrane-based separation techniques have emerged as a promising alternative for producing and purifying methanol. The review highlights recent developments in membrane-based methanol production and purification technology, including using novel membrane materials such as ceramic, polymeric and mixed matrix membranes. Integrating photocatalytic processes with membrane separation has been investigated to improve the conversion of carbon dioxide to methanol. Despite the potential benefits of membrane-based systems, several challenges need to be addressed. Membrane fouling and scaling are significant issues that can reduce the efficiency and lifespan of the membranes. The cost-effectiveness of membrane-based systems compared to traditional methods is a critical consideration that must be evaluated. In conclusion, the review provides insights into the current state of membrane-based technology for methanol production and purification and identifies areas for future research. The development of high-performance membranes and the optimization of membrane-based processes are crucial for improving the efficiency and cost-effectiveness of this technology and for advancing the goal of sustainable energy production.

Developing machine learning models for relative humidity prediction in air-based energy systems and environmental management applications.

The prediction of relative humidity is a challenging task because of its nonlinear nature. The machine learning-based prediction strategies have attained significant attention in tackling a broad class of challenging nonlinear and complex problems. The random forest algorithm is a well-proven machine learning algorithm due to its ease of training and implementation, as it requires minimal preprocessing. The random forest algorithm has hitherto not been employed for estimating air quality parameters, such as relative humidity. In this study, the random forest approach is implemented to estimate the relative humidity as a function of dry- and wet-bulb temperatures. A well-known commercial process simulator called Aspen HYSYS® V10 is linked with MATLAB® version 2019a to establish a data mining environment. The robustness of the prediction model is evaluated against varying wet-bulb depressions. There is high absolute deviation that indicates a lower prediction performance of the model against the higher wet-bulb depression i.e., ~20.0 °C. The random forest model can predict relative humidity with a 1.1% mean absolute deviation compared to the values obtained through Aspen HYSYS. The performance of the RF estimation model is also compared with a well-known support vector regression model. The random forest model demonstrates 74.4% better performance than the support vector machine model for the problem of interest, i.e., relative humidity estimation. This study will significantly help the practitioners in efficient designing of air-dependent energy systems as well as in better environmental management through rigorous prediction of relative humidity.