A statistical model of an ammonium nitrate fluidised bed granulator

Abstract

The inference modelling of a continuous industrial ammonium nitrate fluidised bed granulator was investigated. Fluidised bed granulators are difficult to model and control due to a large number of operating variables and long sample analysis time. This study aims at developing inference models using the multiple linear regression (MLR) modelling technique to predict the output variables. These MLR models are qualitatively compared to the artificial neural network (ANN) modelling technique. The granulation process starts with seed particles that are fluidised by air. These fluidised particles are sprayed with a solution of ammonium nitrate and water to induce granule growth. The final particles exit the granulator and are sieved to form the final product. Different literature sources obtained correlations between most of the operating variables concerning the output variables. Some authors obtained significant regression and ANN models that provided accurate predictions. A screening phase is conducted to determine the significance of the chosen operating variables. The complete randomise experimental design is used where each operating variable is varied randomly from each run to produce unbiased data and lower the number of data points required for model development. The input variables include the fluidising air flow rate, fluidising air temperature, spray liquid flow rate, spray liquid temperature, spray liquid concentration, seed particle size and the seed particle size distribution slope. The output variables consist of the production rate, recycle ratio, efficiency, product porosity, product circularity, product mean particle size, product particle size distribution slope, granulator mean particle size and granulator particle size distribution slope. The correlations between the operating and output variables are determined using the Spearman’s rho correlation technique. It is concluded that the spray liquid variables had the strongest correlations with the output variables and are therefore important variables for the MLR main effect models. Accurate main effects (MLR-M) and interaction effects (MLR-I) multiple linear regression models were developed with a fair performance. The addition of the two-way interactions increased the accuracy and performance of the MLR-I models. The adjusted coefficient of multiple determination was used to evaluate the addition of these variables. The MLR-I models were compared to the ANN models that included all the independent variables (ANN- I). The MLR-I models performed in some cases better than the ANN-I due to undertraining from the ANN-I models. The production rate, recycle ratio, efficiency and granulator particle slope models were accurate enough for prediction purposes. Future work can include the optimisation of the ANN models and the investigation of additional variables for inference modelling, e.g. the bed height, bed density and granulator humidity. A control system using the developed models can also be developed and evaluated on the plant.