@article{Ale2022,
Author = {Alexandra Pehlken, Henriette Garmatter, Lisa Dawel, Fabian Cyris, Hendrik Beck, Fenja Schwark, Roland Scharf, Astrid Nieße},
Title = {How can machine learning improve waste-to-energy plant operation},

Journal = {ICE IEEE},
Year = {2022},
type = {article},
Abstract = {This paper deals with the integration and the role of
waste-to-energy plants. The hygienic and safe disposal of waste
is a central aspect of human infrastructure. It is a prerequisite
for preventing the spread of disease in society and is a very
important issue today as it has been in the past. While serving as
waste disposal in the past, today, waste incineration has changed
to waste-to-energy plants, in addition to keeping the first goal
of waste reducing and sanitation. Therefore, the highest energy
harvesting is not the primary goal of the plant. The plant oper-
ation is difficult to control related to the stochastical variation
of the properties of municipal waste due to its heterogeneous
nature. Hence, it is expected that the optimization of waste-to-
energy plants will benefit significantly if any applied method
may handle the stochastic properties well. The work presented
here aims to provide insights into a novel approach to develop
a new method for enhancing the performance of the waste-
to-energy plant related to blast cleanings to prevent build-up
of particles. This also ensures that the overall performance of
the plant improves. Artificial Intelligence was applied to sensor
emission data directly from an operating real incinerator and
with machine learning the data shows the effects of the blast
cleanings related to typical plant properties. The results of this
paper give first indications that forecasting of the incineration
process is possible. With these results, the plant operator could
handle the plant efficiently with the least performance reduction.
Index Terms—waste-to-energy, machine learning, artificial in-
telligence, regression, XGBoost}
}
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