Chemical product design – recent advances and perspectives

Current Opinion in Chemical Engineering - Tập 27 - Trang 22-34 - 2020
Lei Zhang1, Haitao Mao1, Qilei Liu1, Rafiqul Gani2,3
1Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian, 116024, China
2PSE for SPEED, Skyttemosen 6, DK-3450 Allerød, Denmark
3State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Tài liệu tham khảo

Zhang, 2018, Advances in chemical product design, Int Rev Chem Eng, 34, 319, 10.1515/revce-2016-0067 Hill, 2004, Product and process design for structured products, AIChE J, 50, 1656, 10.1002/aic.10293 Zhang, 2016, New vistas in chemical product and process design, Annu Rev Chem Biomol, 7, 557, 10.1146/annurev-chembioeng-080615-034439 Uhlemann, 2019, Product experiments design and engineering in chemical engineering: past, present state and future, Chem Eng Technol, 42, 2258, 10.1002/ceat.201900236 Gani, 2004, Chemical product design: challenges and opportunities, Comput Chem Eng, 28, 2441, 10.1016/j.compchemeng.2004.08.010 Grossmann, 2004, Challenges in the new millennium: product discovery and design, enterprise and supply chain optimization, global life cycle assessment, Comput Chem Eng, 29, 29, 10.1016/j.compchemeng.2004.07.016 Ng, 2015, Challenges and opportunities in computer-aided molecular design, Comput Chem Eng, 81, 115, 10.1016/j.compchemeng.2015.03.009 Gani, 2015, Product design – molecules, devices, functional products and formulated products, Comput Chem Eng, 81, 71, 10.1016/j.compchemeng.2015.04.013 Austin, 2016, Computer-aided molecular design: an introduction and review of tools, applications, and solution techniques, Chem Eng Res Des, 116, 2, 10.1016/j.cherd.2016.10.014 Butler, 2018, Machine learning for molecular and materials science, Nature, 559, 547, 10.1038/s41586-018-0337-2 Gani, 2019, Group contribution-based property estimation methods: advances and perspectives, Curr Opin Chem Eng, 23, 184, 10.1016/j.coche.2019.04.007 Ng, 2019, Chemical product design: advances in and proposed directions for research and teaching, Comput Chem Eng, 126, 147, 10.1016/j.compchemeng.2019.04.008 Seider, 2017 Portehault, 2018, Beyond the compositional threshold of nanoparticle-based materials, Acc Chem Res, 51, 930, 10.1021/acs.accounts.7b00429 Zhou, 2015, Robust design of optimal solvents for chemical reactions – a combined experimental and computational strategy, Chem Eng Sci, 137, 613, 10.1016/j.ces.2015.07.010 Cignitti, 2018, Systematic optimization-based integrated chemical product-process design framework, Ind Eng Chem Res, 57, 677, 10.1021/acs.iecr.7b04216 Yang, 2018, Natural products-based pesticides: design, synthesis and pesticidal activities of novel fraxinellone derivatives containing N-phenylpyrazole moiety, Ind Crop Prod, 117, 50, 10.1016/j.indcrop.2018.02.088 Yu, 2018, Design of experiments and regression modelling in food flavor and sensory analysis: a review, Trends Food Sci Technol, 71, 202, 10.1016/j.tifs.2017.11.013 Lavecchia, 2015, Machine-learning approaches in drug discovery: methods and applications, Drug Discov Today, 20, 318, 10.1016/j.drudis.2014.10.012 Conte, 2010, Design of formulated products: a systematic methodology, AIChE J, 57, 2431, 10.1002/aic.12458 Bricks, 2015, Molecular design of near infrared polymethine dyes: a review, Dye Pigment, 121, 238, 10.1016/j.dyepig.2015.05.016 Teixeira, 2013, Chapter 1 – a product engineering approach in the perfume industry, 1 Zhang, 2018, A machine learning based computer-aided molecular design/screening methodology for fragrance molecules, Comput Chem Eng, 115, 295, 10.1016/j.compchemeng.2018.04.018 Kupgan, 2018, Modeling amorphous microporous polymers for CO2 capture and separations, Chem Rev, 118, 5488, 10.1021/acs.chemrev.7b00691 Kalakul, 2018, Computer aided chemical product design – ProCAPD and tailor-made blended products, Comput Chem Eng, 116, 37, 10.1016/j.compchemeng.2018.03.029 Goldsmith, 2018, Machine learning for heterogeneous catalyst design and discovery, AIChE J, 64, 2311, 10.1002/aic.16198 Franco, 2019, Boosting rechargeable batteries R&D by multiscale modeling: myth or reality?, Chem Rev, 119, 4569, 10.1021/acs.chemrev.8b00239 Mitsos, 2005, Product engineering for man-portable power generation based on fuel cells, AIChE J, 51, 2199, 10.1002/aic.10456 Kontogeorgis, 2019, An integrated approach for the design of emulsified products, AIChE J, 65, 75, 10.1002/aic.16363 Fung, 2007, Chapter 8 product-centered process synthesis and development: detergents, Comput Aided Chem Eng, 23, 239, 10.1016/S1570-7946(07)80011-3 Yeo, 2019, Multiscale design of graphyne-based materials for high-performance separation membranes, Adv Mater, 31, 10.1002/adma.201805665 Li, 2018, From multiscale to mesoscience: addressing mesoscales in mesoregimes of different levels, Annu Rev Chem Biomol, 9, 3.1, 10.1146/annurev-chembioeng-060817-084249 Ten, 2017, A molecular design methodology by the simultaneous optimization of performance, safety and health aspects, Chem Eng Sci, 159, 140, 10.1016/j.ces.2016.03.026 Jhamb, 2019, Systematic model-based methodology for substitution of hazardous chemicals, ACS Sustain Chem Eng, 7, 7652, 10.1021/acssuschemeng.8b06064 Coley, 2018, Machine learning in computer-aided synthesis planning, Acc Chem Res, 51, 1281, 10.1021/acs.accounts.8b00087 Srai, 2015, Future supply chains enabled by continuous processing—opportunities and challenges. May 20–21, 2014 continuous manufacturing symposium, J Pharm Sci, 104, 840, 10.1002/jps.24343 Babi, 2015, Sustainable process synthesis-intensification, Comput Chem Eng, 81, 218, 10.1016/j.compchemeng.2015.04.030 Cussler, 2011 Lazić, 2004 Dionisio, 2018, The chemical and products database, a resource for exposure-relevant data on chemicals in consumer products, Sci Data, 5, 10.1038/sdata.2018.125 Tam, 2016, Product design: metal nanoparticle-based conductive inkjet inks, AIChE J, 62, 2740, 10.1002/aic.15271 Boone, 2018, Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries, BMC Bioinformatics, 19, 469, 10.1186/s12859-018-2514-6 Lee, 2014, A knowledge-based ingredient formulation system for chemical product development in the personal care industry, Comput Chem Eng, 65, 40, 10.1016/j.compchemeng.2014.03.004 Liu, 2019, OptCAMD: an optimization-based framework and tool for molecular and mixture product design, Comput Chem Eng, 124, 285, 10.1016/j.compchemeng.2019.01.006 Liu, 2019, Computer-aided reaction solvent design based on transition state theory and COSMO-SAC, Chem Eng Sci, 202, 300, 10.1016/j.ces.2019.03.023 Chao, 2018, Computer-aided design and process evaluation of ionic liquids for n-hexane-methylcyclopentane extractive distillation, Sep Purif Technol, 196, 157, 10.1016/j.seppur.2017.06.054 Shirahata, 2019, Alternative generation and multiobjective evaluation using a design framework: case study on sterile filling processes of biopharmaceuticals, Comput Chem Eng, 123, 286, 10.1016/j.compchemeng.2018.12.019 Austin, 2018, COSMO-based computer-aided molecular/mixture design: a focus on reaction solvents, AIChE J, 64, 104, 10.1002/aic.15871 Al-Qattan, 2018, Molecular dynamics simulation strategies for designing carbon-nanotube-based targeted drug delivery, Drug Discov Today, 23, 235, 10.1016/j.drudis.2017.10.002 Hernández, 2018, Computational Fluid Dynamics (CFD) modeling of swirling flows in industrial counter-current spray-drying towers fouling conditions, Ind Eng Chem Res, 57, 11988, 10.1021/acs.iecr.8b02202 Yunus, 2014, A systematic methodology for design of tailor-made blended products, Comput Chem Eng, 66, 201, 10.1016/j.compchemeng.2013.12.011 Tam, 2016, Product design: metal nanoparticle-based conductive inkjet inks, AIChE J, 62, 2740, 10.1002/aic.15271 Gu, 2013, Use of natural products as chemical library for drug discovery and network pharmacology, PLoS One, 8 Wibowo, 2001, Product-oriented process synthesis and development: creams and pastes, AIChE J, 47, 2746, 10.1002/aic.690471214 Marrero, 2001, Group-contribution based estimation of pure component properties, Fluid Phase Equilib, 183, 10.1016/S0378-3812(01)00431-9 Zhang, 2015, Generic mathematical programming formulation and solution for computer-aided molecular design, Comput Chem Eng, 78, 79, 10.1016/j.compchemeng.2015.04.022 Nørskov, 2011, Density functional theory in surface chemistry and catalysis, Proc Natl Acad Sci U S A, 108, 937, 10.1073/pnas.1006652108 Carter, 2008, Challenges in modeling materials properties without experimental input, Science, 321, 800, 10.1126/science.1158009 Śledź, 2018, Protein structure-based drug design: from docking to molecular dynamics, Curr Opin Struct Biol, 48, 93, 10.1016/j.sbi.2017.10.010 Liang, 2019, Computer-aided polymer design: Iintegrating group contribution and molecular dynamics, Ind Eng Chem Res, 58, 15542, 10.1021/acs.iecr.9b02769 Gmehling, 2015, Group contribution methods for phase equilibrium calculations, Annu Rev Chem Biomol Eng, 6, 267, 10.1146/annurev-chembioeng-061114-123424 Zhou, 2018, Prediction of acid dissociation constants of organic compounds using group contribution methods, Chem Eng Sci, 183, 95, 10.1016/j.ces.2018.03.005 Dong, 2019, COSMO-UNIFAC model for ionic liquids, AIChE J Scheffczyk, 2017, COSMO-CAMD: a framework for optimization-based computer-aided molecular design using COSMO-RS, Chem Eng Sci, 159, 84, 10.1016/j.ces.2016.05.038 Schilling, 2017, 1-stage CoMT-CAMD: An approach for integrated design of ORC process and working fluid using PC-SAFT, Chem Eng Sci, 159, 217, 10.1016/j.ces.2016.04.048 Nagy, 2008, A population balance model approach for crystallization product engineering via distribution shaping control, Comput Aided Chem Eng, 25, 139, 10.1016/S1570-7946(08)80028-4 Kiparissides, 2006, Challenges in particulate polymerization reactor modeling and optimization: a population balance perspective, J Process Control, 16, 205, 10.1016/j.jprocont.2005.06.004 Kougoulos, 2005, CFD modelling of mixing and heat transfer in batch cooling crystallizers: aiding the development of a hybrid predictive compartmental model, Chem Eng Res Des, 83, 30, 10.1205/cherd.04080 Chen, 2011, A fundamental CFD study of the gas–solid flow field in fluidized bed polymerization reactors, Powder Technol, 205, 276, 10.1016/j.powtec.2010.09.039 Jeldres, 2018, Population balance modelling to describe the particle aggregation process: a review, Powder Technol, 326, 190, 10.1016/j.powtec.2017.12.033 Fung, 2016, A grand model for chemical product design, Comput Chem Eng, 91, 15, 10.1016/j.compchemeng.2016.03.009 Smith, 2017, ANI-1: an extensible neural network potential with DFT accuracy at force field computation cost, Chem Sci, 8, 3192, 10.1039/C6SC05720A Mu, 2007, Group contribution prediction of surface charge density profiles for COSMO-RS(OI), AIChE J, 53, 3231, 10.1002/aic.11338 Amoozgar, 2012, Recent advances in stealth coating of nanoparticle drug delivery systems, Wiley Interdiscipl Rev Nanomed Nanobiotechnol, 4, 219, 10.1002/wnan.1157 Lutze, 2013, Phenomena based methodology for process synthesis incorporating process intensification, Ind Eng Chem Res, 52, 7127, 10.1021/ie302513y Fung, 2003, Product-centered processing: pharmaceutical tablets and capsules, AIChE J, 49, 1193, 10.1002/aic.690490512