8 citations · 23 across the 7 of their papers we have counts for
7 papers
MORSE: Multi-Objective Reinforcement Learning via Strategy Evolution for Supply Chain Optimization
Niki Kotecha, Ehecatl Antonio del Rio Chanona
In supply chain management, decision-making often involves balancing multiple conflicting objectives, such as cost reduction, service level improvement, and environmental sustainab…
Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses
Sebastián Espinel-Ríos, José L. Avalos, Ehecatl Antonio del Rio Chanona +1
Efficient and robust bioprocess control is essential for maximizing performance and adaptability in advanced biotechnological systems. In this work, we present a reinforcement-lear…
Enhancing reinforcement learning for population setpoint tracking in co-cultures
Sebastián Espinel-Ríos, Joyce Qiaoxi Mo, Dongda Zhang +2
Efficient multiple setpoint tracking can enable advanced biotechnological applications, such as maintaining desired population levels in co-cultures for optimal metabolic division…
Simplest Mechanism Builder Algorithm (SiMBA): An Automated Microkinetic Model Discovery Tool
Miguel Ángel de Carvalho Servia, King Kuok, Hii +3
Microkinetic models are key for evaluating industrial processes' efficiency and chemicals' environmental impact. Manual construction of these models is difficult and time-consuming…
Discovery of mixing characteristics for enhancing coiled reactor performance through a Bayesian Optimisation-CFD approach
Nausheen Basha, Thomas Savage, Jonathan McDonough +2
Processes involving the manufacture of fine/bulk chemicals, pharmaceuticals, biofuels, and waste treatment require plug flow characteristics to minimise their energy consumption an…
The Automated Discovery of Kinetic Rate Models -- Methodological Frameworks
Miguel Ángel de Carvalho Servia, Ilya Orson Sandoval, Klaus Hellgardt +4
The industrialization of catalytic processes requires reliable kinetic models for their design, optimization and control. Mechanistic models require significant domain knowledge, w…
Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes
E. Bradford, L. Imsland, M. Reble +1
Nonlinear model predictive control (NMPC) is an efficient approach for the control of nonlinear multivariable dynamic systems with constraints, which however requires an accurate p…