As part of renewable energy revolution, a paradigm shift engendered by digitalisation is been experienced in the energy sector. This is even more interesting given the underlying significance of clean and renewable energy with the objectives of cost-effectiveness, operational efficiency and sustainability. Biomass is one of the renewable energy sources with high prospects in the clean energy strata. The diversity of its application has made digitalisation of its exploration presents an array of attractive opportunity to deploy different categories of biomass in a manner that mitigates climate change, advances economies and reduces power dependency. The enhancement of bioenergy utilization is hinged on modern and efficient technologies, which should be deployed on a commercial basis in order to guarantee high quality energy services. This study discusses various digitalisation opportunities and challenges along the bioenergy value chain. A case study based on the intelligent classification of biomass was discussed. The variation in properties and origin of biomass which are applied in biofuel production demand an intelligent classification of resources. Two classifiers which are Sparse Random Error-Correcting Output-based Support Vector Machine  (SRECO-SVM) and Euclidean distance-based *k*-Nearest Neighbour (ED-KNN) were elaborated while the various steps involved in the model development were discussed. The relevant performance indices were applied to evaluate the models which were developed. Most significantly, the accuracy, Sensitivity, Specificity, G-mean were 0.547, 0.570,0.933 0.730 respectively for SRECO-SVM at the computational time (CT) of 20.4 *secs* while 0.406, 0.277, 0.911, and 0.252 respectively were reported for ED-KNN at the computational time (CT) of 2.71 *secs.* Comparison of these intelligent classifiers confirms their simplicity in the classification of biomass feedstock and also holds promises in solving challenging problems related to decision making toward upscaling the bioenergy uptake and advancement of digitalisation of renewable energy exploration.


Digitalisation of Biomass Exploration: A Case Study of Biomass Feedstock Classification

Division / Sponsors: 
Power Division & Nuclear Engineering Division (NED)

Power Conference
Paper Proceedings: https://asmedigitalcollection.asme.org/POWER/POWER2020/volume/83747

International Conference on Nuclear Engineering
Paper Proceedings: https://asmedigitalcollection.asme.org/ICONE/ICONE2020/volume/83761

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