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A novel machine learning-based optimization approach for the molecular design of solvents
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Computer-aided molecular design of solvents for chemical separation processes
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Energy-efficient recovery of tetrahydrofuran and ethyl acetate by triple-column extractive distillation - entrainer design and process optimization
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Hybrid data-driven and mechanistic modeling approaches for multiscale material and process design
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Integrated ionic liquid and rate-based absorption process design for gas separation - global optimization using hybrid models
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Integrated metal-organic framework and pressure/vacuum swing adsorption process design - descriptor optimization
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Model-based thermodynamic analysis of supercritical water gasification of oil-containing wastewater
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Comparative economic analysis of physical, chemical, and hybrid absorption processes for carbon capture
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Extending the UNIFAC model for ionic liquid-solute systems by combining experimental and computational databases
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Investigation of energy-efficient and sustainable reactive/pressure-swing distillation processes to recover tetrahydrofuran and ethanol from the industrial effluent
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Model-based optimal design of phase change ionic liquids for efficient thermal energy storage
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Prediction of CO2 solubility in ionic liquids using machine learning methods
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Screening of functional solvent system for automatic aldehyde and ketone separation in aldol reaction - a combined COSMO-RS and experimental approach
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Systematic screening of deep eutectic solvents as sustainable separation media exemplified by the CO 2 capture process
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Big data creates new opportunities for materials research - a review on methods and applications of machine learning for materials design
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