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In this paper, modeling and machine learning with experimental data and a novel calibration function for a gas-solid flow sensor fusion are presented. Sensor fusion is the use of software that intelligently combines data from multiple sensors in order to improve overall system performance. This technique can be applied to measurement of mass flow rate of solids in a pipeline with non-intrusive electrostatic techniques. Data fusion from multiple heterogeneous/homogenous sensors can overcome the limitations of an individual sensor and measured variable. It is shown that the output voltage of a ring-shaped electrode is predominantly a function of solids mass flow rate and velocity for a flow of bulk solids in a pipeline, when particle size, properties and ambient conditions remain constant. By incorporating solids flow velocity in a proposed mathematical model (obtained via machine learning), meter output voltage could be predicted with superior accuracy, for wide range of different flow parameters from numerous experiments with a pneumatic conveying system. Transposing the model yields a new calibration function which, when deployed in signal processing software, enables accurate mass flow measurement with velocity compensation. The described method also de-necessitates determination of air solids ratio or solids volumetric concentration, thereby enabling simplification of the overall measurement system whilst yielding higher accuracy than calibration methods from previous studies. Accurate flow measurement facilitates enhanced monitoring and controllability of blast furnaces, power stations, chemical reactors, process plants etc. where there are bulk solids flows in pipelines. Optimization of such highly materially consumptive and energy intensive processes can yield significant reductions in waste and emissions (CO2, NOx) and increased efficiencies in global production of energy and materials.

A low-error calibration function for an electrostatic gas-solid flow meter obtained via machine learning techniques with experimental data