Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Mercangöz, Mehmet"'
Autor:
Bloor, Maximilian, Torraca, José, Sandoval, Ilya Orson, Ahmed, Akhil, White, Martha, Mercangöz, Mehmet, Tsay, Calvin, Chanona, Ehecatl Antonio Del Rio, Mowbray, Max
PC-Gym is an open-source tool designed to facilitate the development and evaluation of reinforcement learning (RL) algorithms for chemical process control problems. It provides a suite of environments that model a range of chemical processes, incorpo
Externí odkaz:
http://arxiv.org/abs/2410.22093
Autor:
Bloor, Maximilian, Ahmed, Akhil, Kotecha, Niki, Mercangöz, Mehmet, Tsay, Calvin, Chanona, Ehecactl Antonio Del Rio
This work proposes a control-informed reinforcement learning (CIRL) framework that integrates proportional-integral-derivative (PID) control components into the architecture of deep reinforcement learning (RL) policies. The proposed approach augments
Externí odkaz:
http://arxiv.org/abs/2408.13566
Physics informed neural networks (PINNs) have recently been proposed as surrogate models for solving process optimization problems. However, in an active learning setting collecting enough data for reliably training PINNs poses a challenge. This stud
Externí odkaz:
http://arxiv.org/abs/2402.13588
Online Feedback Optimization (OFO) controllers steer a system to its optimal operating point by treating optimization algorithms as auxiliary dynamic systems. Implementation of OFO controllers requires setting the parameters of the optimization algor
Externí odkaz:
http://arxiv.org/abs/2312.01996
Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework
Many industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown process characteristics, real-time optimization becomes challenging, particularly for the satisfaction of safety constraints. In
Externí odkaz:
http://arxiv.org/abs/2211.13019
We present the development of a semi-supervised regression method using variational autoencoders (VAE), which is customized for use in soft sensing applications. We motivate the use of semi-supervised learning considering the fact that process qualit
Externí odkaz:
http://arxiv.org/abs/2211.05979
We consider the problem of decision-making under uncertainty in an environment with safety constraints. Many business and industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown characteris
Externí odkaz:
http://arxiv.org/abs/2211.05495
Publikováno v:
In Computers and Chemical Engineering October 2024 189
Devising optimal operating strategies for a compressor station relies on the knowledge of compressor characteristics. As the compressor characteristics change with time and use, it is necessary to provide accurate models of the characteristics that c
Externí odkaz:
http://arxiv.org/abs/2111.11890
Publikováno v:
In Computers in Industry August 2024 159-160