Predicting biomass comminution: Physical experiment, population balance model, and deep learning.

An extended population balance model (PBM) and a deep learning-based enhanced deep neural operator (DNO+) model are introduced for predicting particle size distribution (PSD) of comminuted biomass through a large knife mill. Experimental tests using corn stalks with varied moisture contents, mill blade speeds, and discharge screen sizes are conducted to support model development. A novel mechanism in the extended PBM allows for including additional input parameters such as moisture content, which is not possible in the original PBM. The DNO+ model can include influencing factors of different data types such as moisture content and discharge screen size, which significantly extends the engineering applicability of the standard DNO model that only admits feed PSD and outcome PSD. Test results show that both models are remarkably accurate in the calibration or training parameter space and can be used as surrogate models to provide effective guidance for biomass preprocessing design. [Display omitted] • An extended population balance model (PBM) is developed for biomass comminution. • Biomass feed moisture is added in the PBM as a new input parameter. • An enhanced deep neutral operator (DNO+) model is developed for biomass comminution. • DNO+ allows for influencing factors such as moisture and screen size as extra inputs. • Both models are remarkably accurate in the calibration or training parameter space. [ABSTRACT FROM AUTHOR]