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- Kyprianidis, Konstantinos, et al.
(författare)
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On-line Powerplant Control using Near-InfraRed Spectroscopy : OPtiC-NIRS, REPORT 2021:746
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Near InfraRed Spectroscopy (NIRS) offers rapid on-line analysis of biomass feedstocks and can be utilized for process control of biomass- based combined heat and power plants. Within the OPtiC-NIRS project we have carried out a full-scale on-site testing of different NIRS for online powerplant control at the facilities of Mälarenergi and Eskilstuna Strängnäs Energi och Miljö. The project has been focused on developing and testing robust NIRS soft-sensors for fuel higher heating value and composition (incl. moisture, components such as recycle wood and glass, different type of plastics and ash) and combining them with dynamic models for on-line feed-forward process monitoring and control. Expected benefits include reduced risk of agglomeration and pollutant emissions formation as well as improved production control. A longer-term potential and ambition is to be able to identify the fossil content in municipal waste fuel, which can hopefully be addressed in a follow-up study.
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| 2. |
- Sevcik, Martin, et al.
(författare)
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Applications of hyperspectral imaging and machine learning methods for real-time classification of waste stream components
- 2019
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Konferensbidrag (refereegranskat)abstract
- Near-infrared (NIR) hyperspectral imaging (HSI) was applied together with machine learning methods to enable classification of typical municipal solid waste (MSW) components such as paper, biomass, food residues, plastics, textile and incombustibles. Classification models were developed using partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and radial-basis neural network (RBNN). The overall accuracy of SVM model calculated from classification sensitivity was 85% in prediction pixel by pixel for external sample set. The model outperformed other models in identifying incombustible material but it had higher computational time requirements. The accuracy of RBNN model reached 85% in prediction while being approx. 10 times faster. Minimum computational time was required by PLS-DA model reaching lower accuracy of 81% in prediction. The result indicate that developed models can be successfully used for real-time MSW component classification. NIR hyperspectral imaging coupled with machine learning methods has a large potential to be used for on-line material identification at waste sorting facilities or for pre-sorting at waste-to-energy powerplants.
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