Recurrent neural networks, such as LSTMs, are well suited to time-series problems and other sequential tasks such as natural language processing and have been successfully applied in other areas of chemistry. The LSTM model constructed uses a sliding window approach, whereby a window of fixed size is formed over the data, and this window slides over the data to capture different portions of it. This method ensures the volume of data used in the model remains consistent. The method was employed to use the previous 20 minutes of sensor data.