22 papers · ranked by Valyu relevance
Chao Li, Jinjin Tang, Jun Zhang, Qingqing Zhao + 3 more
'Jian Li' 'Yong-Hong Kuo'] Urban rail transit train operation plan is a comprehensive production plan encompassing line planning, timetabling, and rolling stock scheduling. In order to solve the problem of infeasibility of the line plan and timetable because the number of rolling stocks could be only precisely…
David A. Liñán, Luis A. Ricardez-Sandoval
Mixed integer nonlinear programming (MINLP) in chemical engineering originated as a tool for solving optimal process synthesis and design problems. Since then, the application of MINLP has expanded to encompass control and operational decisions that are in line with the arising challenges faced by the industry, e.g.…
Damien van de Berg, Nilay Shah, Ehecatl Antonio del Río-Chanona
Planning, scheduling, and control typically constitute separate decision-making units within chemical companies. Traditionally, their integration is modelled sequentially, but recent efforts prioritize lower-level feasibility and optimality, leading to large-scale, potentially multi-level, hierarchical formulations.…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Xiaobao Yu, Wenjing Zhao, Sani Isah Abba
Output instability is one of the important constraints limiting the large-scale application of renewable energy. The development of comprehensive energy systems can effectively improve energy utilization efficiency, but there is still a problem of randomness in renewable energy output. The paper conducts research on…
Liming Wei, Guoqing An
Renewable energy generation has become the general trend with increasing environmental problems. However, the instability of renewable energy generation and the diversification of user demand are highlighted and the optimization of energy scheduling has become the key to solve the problem. This study introduces an…
Timo R. Maarleveld, Bennett K. Ng, Herbert M. Sauro, Kyung Hyuk Kim
Biological organisms acclimatize to varying environmental conditions via active self-regulation of internal gene regulatory networks, metabolic networks, and protein signaling networks. While much work has been done to elucidate the topologies of individual networks in isolation, understanding of inter-network…
Leonard Schmiester, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer + 1 more
Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for…
Zixiang Xu
Gene knockout has been used to improve the conversion ratio of strains for some chemical products. Based on mixed integer bi-level linear programming (MIBLP) and cell network models, there have been several algorithms to predict the target for deletion to improve the productivity of chemicals. At present, the cell…
Florian Gisperg, Robert Klausser, Mohamed Elshazly, Julian Kopp + 2 more
'Eva Přáda Brichtová' 'Oliver Spadiut'] Title: ABSTRACT Bayesian optimization is a stochastic, global black-box optimization algorithm. By combining Machine Learning with decision-making, the algorithm can optimally utilize information gained during experimentation to plan further experiments-while balancing…
Authors not listed
This paper introduces Optical Fiber Chemistry (OFC) as a fourth-generation catalytic paradigm, distinguished not by incremental improvements in catalyst materials but by a fundamental reconfiguration of the catalytic reaction platform. By employing optical fibers as active photonic control elements, OFC achieves gen-…
Shijie Deng, Brandon Jolly, James Wilkes, Yu Mu + 5 more
Integrated catalysis is an emerging methodology that can streamline the multistep synthesis of complicated products in a single reaction vessel, achieving a high degree of control and reducing the waste and cost of the overall process. Integrated catalysis utilizes spatial and temporal control to couple different…
Zoran Jakšić, Swagata Devi, Olga Jakšić, Koushik Guha + 3 more
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at…
Peter Scarfe
Sensory cue integration is one of the primary areas in which a normative mathematical framework has been used to define the “optimal” way in which to make decisions based upon ambiguous sensory information and compare these predictions to behaviour. The conclusion from such studies is that sensory cues are integrated…
Eneko Osaba, Josu Díaz-de-Arcaya, Juncal Alonso, Jesús L. Lobo + 2 more
'Gorka Benguria' 'Iñaki Etxaniz'] Multiobjective optimization is a hot topic in the artificial intelligence and operations research communities. The design and development of multiobjective methods is a frequent task for researchers and practitioners. As a result of this vibrant activity, a myriad of techniques have…
M. Hanefi Calp
Artificial Intelligence has an important place in the scientific community as a result of its successful outputs in terms of different fields. In time, the field of Artificial Intelligence has been divided into many sub-fields because of increasing number of different solution approaches, methods, and techniques.…
Rémy Priem, Hugo Gagnon, Ian R. Chittick, Stéphane Dufresne + 2 more
'Youssef Diouane' 'Nathalie Bartoli'] The multi-level, multi-disciplinary and multi-fidelity optimization framework developed at Bombardier Aviation has shown great results to explore efficient and competitive aircraft configurations. This optimization framework has been developed within the Isight software, the latter…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Richa Verma, Dinesh Kumar, Kazuma Kobayashi, Syed Bahauddin Alam
Robust optimization is a method for optimization under uncertainties in engineering systems and designs for applications ranging from aeronautics to nuclear. In a robust design process, parameter variability (or uncertainty) is incorporated into the engineering systems' optimization process to assure the systems'…
Authors not listed
Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
Wen-Hao Zhang, He Wang, Aihua Chen, Yong Gu + 3 more
Our brain perceives the world by exploiting multiple sensory modalities to extract information about various aspects of external stimuli. If these sensory cues are from the same stimulus of interest, they should be integrated to improve perception; otherwise, they should be segregated to distinguish different stimuli.…
Giannis Poulopoulos, Hercules Avramopoulos, Cosimo Trono
Highlights What are the main findings?1. Optical sensing in industry and smart manufacturing is not a single platform choice. Distributed fiber sensing, FBG sensors, integrated photonic sensors, and nanophotonic/plasmonic devices address different combinations of spatial coverage, sensing volume, measurand type, and…