A techno-economic and life cycle assessment for the production of green methanol from CO_(2): catalyst and process bottlenecks

The success of catalytic schemes for the large-scale valorization of CO_(2) does not only depend on the development of active,selective and stable catalytic materials but also on the overall process design.Here we present a multidisciplinary study(from catalyst to plant and techno-economic/lifecycle analysis)for the production of green methanol from renewable H2 and CO_(2).We combine an in-depth kinetic analysis of one of the most promising recently reported methanol-synthesis catalysts(InCo)with a thorough process simulation and techno-economic assessment.We then perform a life cycle assessment of the simulated process to gauge the real environmental impact of green methanol production from CO_(2).Our results indicate that up to 1.75 ton of CO_(2) can be abated per ton of produced methanol only if renewable energy is used to run the process,while the sensitivity analysis suggest that either rock-bottom H2 prices(1.5$kg1)or severe CO_(2) taxation(300$per ton)are needed for a profitable methanol plant.Besides,we herein highlight and analyze some critical bottlenecks of the process.Especial attention has been paid to the contribution of H2 to the overall plant costs,CH4 trace formation,and purity and costs of raw gases.In addition to providing important information for policy makers and industrialists,directions for catalyst(and therefore process)improvements are outlined.

Predicting entropy and heat capacity of hydrocarbons using machine learning

Chemical substances are essential in all aspects of human life,and understanding their properties is essential for developing chemical systems.The properties of chemical species can be accurately obtained by experiments or ab initio computational calculations;however,these are time-consuming and costly.In this work,machine learning models(ML)for estimating entropy,S,and constant pressure heat capacity,Cp,at 298.15 K,are developed for alkanes,alkenes,and alkynes.The training data for entropy and heat capacity are collected from the literature.Molecular descriptors generated using alvaDesc software are used as input features for the ML models.Support vector regression(SVR),v-support vector regression(v-SVR),and random forest regression(RFR)algorithms were trained with K-fold cross-validation on two levels.The first level assessed the models’performance,and the second level generated the final models.Between the three ML models chosen,SVR shows better performance on the test dataset.The SVR model was then compared against traditional Benson’s group additivity to illustrate the advantages of using the ML model.Finally,a sensitivity analysis is performed to find the most critical descriptors in the property estimations.