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.

本研究通过机器学习模型对垃圾转燃料发电厂未来传感器读数进行预测，从而实现对厂址运营的主动控制。我们开发了三种不同类型的模型进行比较（仅考虑上一次预测值的naive预测，基于过去传感器数据的神经网络以及使用特征构建的梯度提升树回归器），并通过加拿大垃圾转燃料发电厂的真实案例进行了开发和测试。我们发现，采用方法(c)得到了最佳结果，而方法(b)的效果较为不稳定且无法始终超越naive。

废物-燃料工厂传感器数值预测：一项案例研究