  In this research, we develop machine learning models to predict future sensor
readings of a waste-to-fuel plant, which would enable proactive control of the
plant's operations. We developed models that predict sensor readings for 30 and
60 minutes into the future. The models were trained using historical data, and
predictions were made based on sensor readings taken at a specific time. We
compare three types of models: (a) a n\"aive prediction that considers only the
last predicted value, (b) neural networks that make predictions based on past
sensor data (we consider different time window sizes for making a prediction),
and (c) a gradient boosted tree regressor created with a set of features that
we developed. We developed and tested our models on a real-world use case at a
waste-to-fuel plant in Canada. We found that approach (c) provided the best
results, while approach (b) provided mixed results and was not able to
outperform the n\"aive consistently.
