Aiming to resolve the problems of the electrodynamic sensor’s deficiency in absolute mass flow rate measurement and the effect of flow regime on the output of the sensor

a multi-modeling based non-linear soft sensor for particle mass flow rate is introduced.In the dense phase pneumatic conveying system under high pressure

abundant experimental data could be obtained by using the electrodynamic sensor and the signal characteristics of the experimental results could be extracted.The characteristic data space is partitioned into some local regions by the fuzzy clustering algorithm firstly

then a non-linear sub-model is established for each local region by using the radial basis function (RBF) neural network.Finally the whole soft measurement model could be accurately described by a set of fuzzy rules based sub-models.The soft model reduces the influence of flow regime on the measurement results and provides an effective solution to on-line mass flow rate measurement of pneumatically conveyed solid particles.