| Autor: |
Erick Y. Emori, Mauro A.S.S. Ravagnani, Caliane B.B. Costa |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Digital Chemical Engineering, Vol 5, Iss , Pp 100049- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2772-5081 |
| DOI: |
10.1016/j.dche.2022.100049 |
| Popis: |
With the recent development of machine learning, reinforcement learning is an interesting alternative to PID controllers. In this context, a discrete predictive Q-learning approach is applied in the control of a sugarcane biorefinery multiple-effect evaporation system. The algorithm is built using Scilab and learns to control the multiple-effect evaporator outlet concentration by manipulating its feed steam flow rate. Based on multiple episodes, the state-actions that consist of discrete changes in steam flow rate are chosen with a greedy algorithm. In order to increase the training efficiency and overcome the large dead time of the system, a neural network is applied to predict the outlet concentration of each control action after reaching the steady-state. The control policy was built and tested through simulations on a phenomenological model. The controller performance was evaluated in set-point tracking and disturbance rejection tests and compared with PID responses. The research showed that the Q-learning controller exhibited better performance than the PID controller. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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