Multi-scale integrated management and operation of chemical/energy systems combining reinforcement learning and mathematical programming = 강화학습과 수학적계획법의 연계를 통한 화학/에너지 시스템의 계층적 통합 관리 및 운영 모델 제시

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Uncertainty on the information essential to decision-making and inconsistencies between different decision levels are the main contributors to interrupt sustainable operation of chemical manufacturing systems. Thus a new discipline is required beyond the existing deterministic and reactive approaches to introduce a globally optimal solution at each time. Therefore, the purpose of this study is to introduce a smart and sustainable management and operation strategy for energy/chemical manufacturing system through data and model based decision supporting tools representing (projecting) an uncertain real world. In particular, the study focuses on the linkage between the high-level (planning) and the low-level (operation) decision layers. The resulting expansion of the boundary of decision-making process can provide more robust and flexible management and operation strategies by resolving inconsistency between different levels. For this, I develop a multi-scale decision-making model that combines Markov decision process and mathematical programming in a complementary way. To support the integration of the decision hierarchy, a data-driven uncertainty prediction model is suggested which is valid across all time scales considered. To obtain a computationally feasible solution of the proposed model, reinforcement learning is introduced and knowledge necessary for decision-making is learned through optimization-embedded simulation. Specifically, function approximation techniques and Bayesian inference are performed to learn the value function that quantifies the long-term value of the current system state and to statistically improve the beliefs on the incomplete model parameters. The usefulness of the developed multi-scale decision-making model is proven through the following case studies that are motivated by real industrial problems: 1) integrated procurement planning and scheduling considering trade-offs between multiple suppliers and uncertainties in supply and demand, 2) operational planning and optimal sizing of microgrid considering multi-scale uncertainty in wind supply, and 3) crude procurement and refinery operational planning considering variations in crude/product price and randomness in crude quality.
Advisors
Lee, Jay Hyungresearcher이재형researcher
Description
한국과학기술원 :생명화학공학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 생명화학공학과, 2019.2,[viii, 137 p. :]

Keywords

Management and operation of chemical/energy systems▼aIntegration of decision hierarchy▼aUncertainty▼aMathematical programming▼aMarkov decision process▼aReinforcement learning; 에너지 및 화학 제조 시스템의 관리 및 운영▼a의사결정 계층구조의 통합▼a불확실성▼a수학적 계획법▼a마르코프 결정 과정▼a강화 학습

URI
http://hdl.handle.net/10203/264886
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=842129&flag=dissertation
Appears in Collection
CBE-Theses_Ph.D.(박사논문)
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