Data Analysis and Machine Learning Algorithms Evaluation for Bioliq AI-based Predictive Tool
In synthesis gas generation, there is a challenge in dealing with the strong variations in the chemical and physical properties of the input materials, optimal plant settings can be heavily influenced by input material. The processes in which biomass is processed must be designed and operated...
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| Main Author: | |
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| Format: | Final Year Project |
| Language: | English |
| Published: |
IRC
2019
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| Subjects: | |
| Online Access: | http://utpedia.utp.edu.my/20895/1/Augusto%20Simbine_22341.pdf http://utpedia.utp.edu.my/20895/ |
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| Summary: | In synthesis gas generation, there is a challenge in dealing with the strong variations in the
chemical and physical properties of the input materials, optimal plant settings can be heavily
influenced by input material. The processes in which biomass is processed must be designed
and operated much more flexibly, due to the special features and the type of biomass with
highly fluctuating compositions. The mass production hasn’t initiated due to the inefficiency
of the process and the dependency on experts. EDI GmbH in collaboration with Karlsruhe
Institute of Technology (KIT) are working on the solution (AI-based tool for predictive process
optimization for chemical plants) for the above-mentioned problem. This final year project
identified relevant parameters through literature research, analysis and expert interview, and
evaluated different machine learning algorithms and identified linear regression as the most
applicable and efficient with its R-square of 0.8015, qualifying it to be used for the
development of a hybrid model for the AI-based tool for predictive process optimization for
chemical plants. |
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