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| 1 | New Trends in Olefin Production显示文摘Most olefins (e.g., ethylene and propylene) will continue to be produced through steam cracking (SC) ofhydrocarbons in the coming decade. In an uncertain commodity market, the chemical industry is investingvery little in alternative technologies and feedstocks because of their current lack of economic viability,despite decreasing crude oil reserves and the recognition of global warming. In this perspective, some of themost promising alternatives are compared with the conventional SC process, and the major bottlenecks ofeach of the competing processes are highlighted. These technologies emerge especially from the abundanceof cheap propane, ethane, and methane from shale gas and stranded gas. From an economic point of view,methane is an interesting starting material, if chemicals can be produced from it. The huge availability ofcrude oil and the expected substantial decline in the demand for fuels imply that the future for proventechnologies such as Fischer-Tropsch synthesis (FFS) or methanol to gasoline is not bright. The abundance ofcheap ethane and the large availability of crude oil, on the other hand, have caused the SC industry to shiftto these two extremes, making room for the on-purpose production of light olefins, such as by the catalyticdehydrogenation of orooane. | Ismael Amghizar Laurien A. Vandewalle Kevin M. Van Geem Guy B. Matin | 2017 | Engineering2017，3，2： | 33 |
| 2 | Artificial Intelligence in Steam Cracking Modeling: A Deep Learning Algorithm for Detailed Effluent Prediction显示文摘Chemical processes can bene t tremendously from fast and accurate ef uent composition prediction for plant design, control, and optimization. The Industry 4.0 revolution claims that by introducing machine learning into these elds, substantial economic and environmental gains can be achieved. The bottleneck for high-frequency optimization and process control is often the time necessary to perform the required detailed analyses of, for example, feed and product. To resolve these issues, a framework of four deep learning arti cial neural networks (DL ANNs) has been developed for the largest chemicals production process steam cracking. The proposed methodology allows both a detailed characterization of a naphtha feedstock and a detailed composition of the steam cracker ef uent to be determined, based on a limited number of commercial naphtha indices and rapidly accessible process characteristics. The detailed char- acterization of a naphtha is predicted from three points on the boiling curve and paraf ns, iso-paraf ns, ole ns, naphthenes, and aronatics (PIONA) characterization. If unavailable, the boiling points are also estimated. Even with estimated boiling points, the developed DL ANN outperforms several established methods such as maximization of Shannon entropy and traditional ANNs. For feedstock reconstruction, a mean absolute error (MAE) of 0.3 wt% is achieved on the test set, while the MAE of the ef uent predic- tion is 0.1 wt%. When combining all networks using the output of the previous as input to the next the ef uent MAE increases to 0.19 wt%. In addition to the high accuracy of the networks, a major bene t is the negligible computational cost required to obtain the predictions. On a standard Intel i7 processor, predictions are made in the order of milliseconds. Commercial software such as COILSIM1D performs slightly better in terms of accuracy, but the required central processing unit time per reaction is in the order of seconds. This tremendous speed-up and minimal accuracy loss make the presented framework highly suitable for the continuous monitoring of dif cult-to-access process parameters and for the envi- sioned, high-frequency real-time optimization (RTO) strategy or process control. Nevertheless, the lack of a fundamental basis implies that fundamental understanding is almost completely lost, which is not always well-accepted by the engineering community. In addition, the performance of the developed net- works drops signi cantly for naphthas that are highly dissimilar to those in the training set. | Pieter PPlehiers Steffen HSymoens Ismaël Amghizar Guy B.Marin Christian V.Stevens Kevin M.Van Geem | 2019 | Engineering2019，5，6： | 9 |
| 3 | Machine Learning in Chemical Engineering:Strengths,Weaknesses,Opportunities,and Threats显示文摘Chemical engineers rely on models for design,research,and daily decision-making,often with potentially large financial and safety implications.Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations.In the last five years,the increasing availability of data and computational resources has led to a resurgence in machine learning-based research.Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases,benchmarks,and representations for chemical applications and new machine learning frameworks.Machine learning has significant advantages over traditional modeling techniques,including flexibility,accuracy,and execution speed.These strengths also come with weaknesses,such as the lack of interpretability of these black-box models.The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns,learn from data,and become more intelligent over time.The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis.Nevertheless,machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers. | Maarten R.Dobbelaere Pieter P.Plehiers Ruben Van de Vijver Christian V.Stevens Kevin M.Van Geem | 2021 | Engineering2021，7，9： | 4 |
| 4 | Fab’-bearing siRNA TNFα-loaded nanoparticles targeted to colonic macrophages offer an effective therapy for experimental colitis显示文摘 | Hamed Laroui Emilie Viennois Bo Xiao Brandon S.B. Canup Duke Geem Timothy L. Denning Didier Merlin | 2014 | Journal of Controlled Release2014，，： | 2 |
| 5 | CX3CR1 regulates intestinal macrophage homeostasis, bacterial translocation, and colitogenic Th17 responses in mice显示文摘 | Medina-Contreras Oscar Geem Duke Laur Oskar Williams Ifor R Lira Sergio A Nusrat Asma Parkos Charles A Denning Timothy L | 2011 | Journal of Clinical Investigation2011，，12： | 2 |
| 6 | Optimal cost design of water distribution networks using harmony search 显示文摘 | Geem Z W | 2006 | Engineering Optimization2006，38，3： | 1 |
| 7 | A New Heuristic Optimization Algorithm:Harmony Search显示文摘 | Geem Z W Kim J H Loganathan G V | 2001 | Simulation2001，76，2： | 1 |
| 8 | Harmony search显示文摘 | Geem Z W Kim J H Loganathan G V | 2001 | Simulation2001，76，2： | 1 |
| 9 | Optimal cost design of water distribution net- works using harmony search显示文摘 | Geem ZW | 2006 | Eng Optimiz2006，38，3： | 1 |
| 10 | A new heuristic optimization algorithm:harmony search显示文摘 | Geem Z W Kim J H Loganathan G V | 2001 | Simulation2001，76，2： | 1 |
| 11 | Novel derivative of harmony search algorithm for discrete design variables 显示文摘 | GEEM Z W | 2008 | Applied Mathematics and Computation2008，199，1： | 1 |
| 12 | A new structural optimization method based on the harmony search algorithm显示文摘 | Kang S L Geem Z W | 2004 | Computer and Structures2004，82，910： | 1 |
| 13 | Harmony search显示文摘 | Geem Z W Kim J H Loganathan G V | 2001 | Simulation2001，76，2： | 1 |
| 14 | Harmony search显示文摘 | Kim J H Loganathan G V | 2001 | Simulation2001，76，2： | 1 |
| 15 | A new heuristic optimization algorithm: harmony search 显示文摘 | Geem Z W Kim J H Loganathan G V | 2001 | Simulation2001，76，2： | 1 |
| 16 | Harmony search opti- mization: application to pipe network design显示文摘 | Geem ZW Kimj H Logana TGV | 2002 | Interna- tional Journal of Model Simulation2002，22，2： | 1 |
| 17 | A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice显示文摘 | Kang S L Geem Z W | 2005 | Computer Methods in Applied Mechanics and Engineering2005，194，3638： | 1 |
| 18 | New Methodology, Harmony Search and Its Robustness显示文摘 | Geem ZW Tseng CL | 2002 | Late-Breaking Papers of Genetic and Evolutionary Computation Conference (GECCO-2002) New York City USA2002，2002，7： | 1 |
| 19 | A New Heu- ristic Optimization Algorithm: Harmony Search显示文摘 | GEEM Z W KIM J H LOGANATHAN G V | 2001 | Simula- tion2001，76，2： | 1 |
| 20 | A new heuristic optimization algorithm:harmony search显示文摘 | Geem Z W Kim J H Loganathan G V | 2001 | Simulation2001，76，2： | 1 |
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