Comparing classic time series models and state-of-the-art time series neural networks for forecasting as-fired liquor properties

The properties of as-fired black liquor dictate kraft recovery boiler operation. If these properties could be forecasted, operations could be adjusted to optimize boiler performance. Here, we compare the performances of classic time series models and two state-of-the-art time series neural networks for forecasting as-fired liquor heating value, viscosity, and boiling point rise at a Canadian mill. Additionally, we show that, like classic time series models, autoregressive neural networks can be regarded as functions of unknown disturbances, which is useful in comparing model complexities. Our results show that classic time series models can accurately forecast as-fired liquor properties and that classic time series models perform comparably to state-of-the-art time series neural networks. We suspect this is due to the high autocorrelation of mill data that results from frequent measurements relative to long residence times. This autocorrelation is suspected to attenuate the cross-correlations between upstream disturbances and as-fired liquor properties. As a result, neural networks, which are useful for accommodating non-linear cross-correlations and dynamics, struggle to outperform classic time series models and may not always be appropriate for forecasting chemical process parameters.