Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A data-driven digital twin approach to optimize continuous production environment with deep reinforcement learning
Download
Ali Haydar Sivri_OpenMetu_Submission.pdf
Date
2023-9-6
Author
Sivri , Ali Haydar
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
219
views
112
downloads
Cite This
Today, the world mainly strives to minimize carbon emissions and maximize efficiency in energy production to lower energy costs and greenhouse effect. Therefore, optimizing hydrogen production processes becomes a crucial objective in organizations, especially for manufacturers that consume significant energy in production. Organizations should operate their production processes under the most optimum conditions to ensure their survival and attain competitive advantage. However, they face substantial challenges in optimizing their energy utilizations in their production processes. Those challenges consequently necessitate utilizing a data-driven approach and dynamic framework for managing and optimizing their process workflows. This thesis study consists of two main phases. The first phase focuses on developing a data-driven simulation environment (DDSE) of the hydrogen unit for Deep Reinforcement Learning (DRL) that can adapt to the complexity and variability of processes by utilizing the machine learning approaches. The DDSE comprises the intricacies of the hydrogen production process, including critical parameters such as temperature, pressure, and catalyst variables. At the second phase, advanced DRL algorithms are developed to support continuous learning and making decisions based on long-term outcomes rather than immediate rewards. The DRL model effectively interacts with the DDSE in adjusting controllable production parameters to optimize the hydrogen production yield. The proposed model's validation and efficacy are demonstrated through extensive simulations and real-world data. The test results reveal that we can improve energy efficiency by approximately 4%.
Subject Keywords
Machine learning
,
Deep reinforcement learning
,
Data driven simulation
,
Hydrogen production
,
Optimization
URI
https://hdl.handle.net/11511/105436
Collections
Graduate School of Informatics, Thesis
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
A. H. Sivri, “A data-driven digital twin approach to optimize continuous production environment with deep reinforcement learning,” M.S. - Master of Science, Middle East Technical University, 2023.