[pt] A indústria 4.0 impulsionou o desenvolvimento de novas tecnologias para atender as demandas atuais do mercado. Uma dessas novas tecnologias foi a incorporação de técnicas de inteligência computacional no cotidiano da indústria química. Neste âmbito, este trabalho avaliou o desempenho de controladores baseados em aprendizado por reforço em processos químicos industriais. A estratégia de controle interfere diretamente na segurança e no custo do processo. Quanto melhor for o desempenho dessa estrategia, menor será a produção de efluentes e o consumo de insumos e energia. Os algoritmos de aprendizado por reforço apresentaram excelentes resultados para o primeiro estudo de caso, o reator CSTR com a cinética de Van de Vusse. Entretanto, para implementação destes algoritmos na planta química do Tennessee Eastman Process mostrou-se que mais estudos são necessários. A fraca ou inexistente propriedade Markov, a alta dimensionalidade e as peculiaridades da planta foram fatores dificultadores para os controladores desenvolvidos obterem resultados satisfatórios. Foram avaliados para o estudo de caso 1, os algoritmos Q-Learning, Actor Critic TD, DQL, DDPG, SAC e TD3, e para o estudo de caso 2 foram avaliados os algoritmos CMA-ES, TRPO, PPO, DDPG, SAC e TD3. / [en] Industry 4.0 boosted the development of new technologies to meet current market demands. One of these new technologies was the incorporation of computational intelligence techniques into the daily life of the chemical industry. In this context, this present work evaluated the performance of controllers based on reinforcement learning in industrial chemical processes. The control strategy directly affects the safety and cost of the process. The better the performance of this strategy, the lower will be the production of effluents and the consumption of input and energy. The reinforcement learning algorithms showed excellent results for the first case study, the Van de Vusse s reactor. However, to implement these algorithms in the Tennessee Eastman Process chemical plant it was shown that more studies are needed. The weak Markov property, the high dimensionality and peculiarities of the plant were factors that made it difficult for the developed controllers to obtain satisfactory results. For case study 1, the algorithms Q-Learning, Actor Critic TD, DQL, DDPG, SAC and TD3 were evaluated, and for case study 2 the algorithms CMA-ES, TRPO, PPO, DDPG, SAC and TD3 were evaluated.

With increased penetration of renewable energy sources, maintaining equilibrium between production and consumption in the world’s electrical power systems (EPS) becomes more and more challenging. One way to increase stability and efficiency in an EPS is to use flexible alternating current transmission systems (FACTS). However, an EPS containing multiple FACTS-devices with overlapping areas of influence can lead to negative effects if the reference values they operate around are not updated with sufficient temporal resolution. The reference values are usually set manually by a system operator. The work in this master thesis has investigated how three different reinforcement learning (RL) algorithms can be used to set reference values automatically with higher temporal resolution than a system operator with the aim of increased voltage stability. The three RL algorithms – Q-learning, Deep Q-learning (DQN), and Twindelayed deep deterministic policy gradient (TD3) – were implemented in Python together with a 2-bus EPS test network acting as environment. The 2-bus EPS test network contain two FACTS devices: one for shunt compensation and one for series compensation. The results show that – with respect to reward – DQN was able to perform equally or better than non-RL cases 98.3 % of the time on the simulation test set, while corresponding values for TD3 and Q-learning were 87.3 % and 78.5 % respectively. DQN was able to achieve increased voltage stability on the test network while TD3 showed similar results except during lower loading levels. Q-learning decreased voltage stability on a substantial portion of the test set, even compared to a case without FACTS devices. To help with continued research and possible future real life implementation, a list of suggestions for future work has been established.