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Machine learning in process systems engineering: Challenges and opportunities
This “white paper” is a concise perspective of the potential of machine learning in the process systems engineering (PSE) domain, based …
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PO-SRPP: A Decentralized Pivoting Path Planning Method for Self-Reconfigurable Satellites
While there is ample research on hardware design and reconfiguration control for modular self-reconfigurable satellites, relatively few …
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The automated discovery of kinetic rate models – methodological frameworks
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Multi-fidelity data-driven design and analysis of reactor and tube simulations
Optimizing complex reactor geometries is vital to promote enhanced efficiency. We present a framework to solve this nonlinear, …
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Discovery of mixing characteristics for enhancing coiled reactor performance through a Bayesian optimisation-CFD approach
Plug flow characteristics are advantageous in various manufacturing processes for fine/bulk chemicals, pharmaceuticals, biofuels, and …
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Design and planning of flexible mobile Micro-Grids using Deep Reinforcement Learning
Ongoing risks from climate change have significantly impacted the livelihood of global nomadic communities and are likely to lead to …
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Integrated experimental and photo-mechanistic modelling of biomass and optical density production of fast versus slow growing model cyanobacteria
Biotechnological exploitation of fast-growing cyanobacterial species is hindered by unavailable mechanistic interpretations for the …
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Linearizing nonlinear dynamics using deep learning
The majority of systems of practical interest are characterized by nonlinear dynamics. This renders the control and optimization of …
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Data-driven coordination of subproblems in enterprise-wide optimization under organizational considerations
While decomposition techniques in mathematical programming are usually designed for numerical efficiency, coordination problems within …
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Distributional reinforcement learning for inventory management in multi-echelon supply chains
Reinforcement Learning (RL) is an effective method to solve stochastic sequential decision-making problems. This is a problem …
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