| Adaptive sequential sampling for surrogate model generation with artificial neural networks J Eason, S Cremaschi Computers & Chemical Engineering 68, 220-232, 2014 | 304 | 2014 |
| Optimization of CO2 capture process with aqueous amines using response surface methodology A Nuchitprasittichai, S Cremaschi Computers & chemical engineering 35 (8), 1521-1531, 2011 | 151 | 2011 |
| Process synthesis of biodiesel production plant using artificial neural networks as the surrogate models I Fahmi, S Cremaschi Computers & Chemical Engineering 46, 105-123, 2012 | 133 | 2012 |
| A perspective on process synthesis: Challenges and prospects S Cremaschi Computers & Chemical Engineering 81, 130-137, 2015 | 84 | 2015 |
| An algorithm to determine sample sizes for optimization with artificial neural networks A Nuchitprasittichai, S Cremaschi AIChE Journal 59 (3), 805-812, 2013 | 75 | 2013 |
| Data-driven model development for cardiomyocyte production experimental failure prediction B Williams, C Halloin, W Löbel, F Finklea, E Lipke, R Zweigerdt, ... Computer aided chemical engineering 48, 1639-1644, 2020 | 63 | 2020 |
| Optimization of CO2 Capture Process with Aqueous AminesA Comparison of Two Simulation–Optimization Approaches A Nuchitprasittichai, S Cremaschi Industrial & Engineering Chemistry Research 52 (30), 10236-10243, 2013 | 61 | 2013 |
| Efficient surrogate model development: impact of sample size and underlying model dimensions SE Davis, S Cremaschi, MR Eden Computer aided chemical engineering 44, 979-984, 2018 | 59 | 2018 |
| Design and optimization of artificial cultivation units for algae production S Yadala, S Cremaschi Energy 78, 23-39, 2014 | 57 | 2014 |
| Heuristic solution approaches to the pharmaceutical R&D pipeline management problem B Christian, S Cremaschi Computers & Chemical Engineering 74, 34-47, 2015 | 49 | 2015 |
| Solids transport models comparison and fine‐tuning for horizontal, low concentration flow in single‐phase carrier fluid FB Soepyan, S Cremaschi, C Sarica, HJ Subramani, GE Kouba AIChE Journal 60 (1), 76-122, 2014 | 49 | 2014 |
| Surrogate model selection for design space approximation and surrogatebased optimization BA Williams, S Cremaschi Computer aided chemical engineering 47, 353-358, 2019 | 46 | 2019 |
| Selection of surrogate modeling techniques for surface approximation and surrogate-based optimization B Williams, S Cremaschi Chemical Engineering Research and Design 170, 76-89, 2021 | 45 | 2021 |
| Sensitivity of amine-based CO2 capture cost: The influences of CO2 concentration in flue gas and utility cost fluctuations A Nuchitprasittichai, S Cremaschi International Journal of Greenhouse Gas Control 13, 34-43, 2013 | 42 | 2013 |
| Experimental Study of Low Concentration Sand Transport in Multiphase Air–Water Horizontal Pipelines K Najmi, AL Hill, BS McLaury, SA Shirazi, S Cremaschi Journal of Energy Resources Technology 137 (032908), 1 - 10, 2015 | 40 | 2015 |
| CFD-based optimization of a flooded bed algae bioreactor JD Smith, AA Neto, S Cremaschi, DW Crunkleton Industrial & Engineering Chemistry Research 52 (22), 7181-7188, 2013 | 33 | 2013 |
| Efficient surrogate model development: optimum model form based on input function characteristics SE Davis, S Cremaschi, MR Eden Computer Aided Chemical Engineering 40, 457-462, 2017 | 31 | 2017 |
| A dynamic optimization model for designing open-channel raceway ponds for batch production of algal biomass S Yadala, S Cremaschi Processes 4 (2), 10, 2016 | 27 | 2016 |
| Prediction of human induced pluripotent stem cell cardiac differentiation outcome by multifactorial process modeling B Williams, W Löbel, F Finklea, C Halloin, K Ritzenhoff, F Manstein, ... Frontiers in bioengineering and biotechnology 8, 851, 2020 | 26 | 2020 |
| Threshold velocity to initiate particle motion in horizontal and near-horizontal conduits FB Soepyan, S Cremaschi, BS McLaury, C Sarica, HJ Subramani, ... Powder Technology 292, 272-289, 2016 | 25 | 2016 |