دانلود مقاله انگلیسی رایگان:مدیریت بهینه ذخیره مخزن کربن از طریق یادگیری تقویتی عمیق - 2020
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  • Optimal carbon storage reservoir management through deep reinforcement learning Optimal carbon storage reservoir management through deep reinforcement learning
    Optimal carbon storage reservoir management through deep reinforcement learning

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Optimal carbon storage reservoir management through deep reinforcement learning


    ترجمه فارسی عنوان مقاله:

    مدیریت بهینه ذخیره مخزن کربن از طریق یادگیری تقویتی عمیق


    منبع:

    Sciencedirect - Elsevier - Applied Energy, 278 (2020) 115660. doi:10.1016/j.apenergy.2020.115660


    نویسنده:

    Alexander Y. Sun


    چکیده انگلیسی:

    Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decisionmaking problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.
    Keywords: Reinforcement learning | Multistage decision-making | Deep autoregressive model | Deep Q network | Surrogate modeling | Markov decision process | Geological carbon sequestration


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 15
    حجم فایل: 1380 کیلوبایت

    قیمت: رایگان


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