Particles mass flow rate and concentration measurement using electrostatic sensor

In many industries where flow parameters measurement is essential to control manufacturing process, the use of a reliable, cost effective and high accuracy instrument is an important issue. Appropriate measurement method and design leads to improvement of pneumatic conveyors operation and process efficiency. This paper present an instrumentation design based on passive charge detection using a single electrostatic sensor. Two different sensor electrodes are applied to show the flexibility of electrostatic sensor application. A time domain signal processing algorithm is developed to measurement of mass flow rate and concentration profile from acquired electrical charge signal. The findings is led to a low cost and high accuracy design, the experimental test results of the design shows less than ±5% error between measured parameters and reference reading acquired from the manual weighing

ADAPTIVE METHOD TO PREDICT AND TRACK UNKNOWN SYSTEM BEHAVIORS USING RLS AND LMS ALGORITHMS

This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity

Private Computation of Polynomials over Networks

This study concentrates on preserving privacy in a network of agents where each agent seeks to evaluate a general polynomial function over the private values of her immediate neighbors. We provide an algorithm for the exact evaluation of such functions while preserving privacy of the involved agents. The solution is based on a reformulation of polynomials and adoption of two cryptographic primitives: Paillier as a Partially Homomorphic Encryption scheme and multiplicative-additive secret sharing. The provided algorithm is fully distributed, lightweight in communication, robust to dropout of agents, and can accommodate a wide class of functions. Moreover, system theoretic and secure multi-party conditions guaranteeing the privacy preservation of an agent's private values against a set of colluding agents are established. The theoretical developments are complemented by numerical investigations illustrating the accuracy of the algorithm and the resulting computational cost.Comment: 11 pages, 2 figure

Particle size measurement using electrostatic sensor through spatial filtering method

Particle size measurement is important in powder and particle industries in which the particle size affects the productivity and efficiency of the machine, for example, in coal-fired power plants. An electrostatic sensor detects the electric charge from dry particles moving in a pipeline. Analysis of the detected signal can provide useful information about the particle velocity, mass flow rate, concentration and size. Using electrostatic sensors, previous researches studied particle sizing using magnitude dependent analysis which is a highly conditional method where the results can be affected by other parameters such as particle mass flow rate, velocity and concentration. This research proposes a magnitude independent analysis for particle sizing in the frequency domain called spatial filtering method. The solution was started by modeling and analysis of the charge induced to the ring electrode using finite-element analysis to find the sensitivity of electrode. A mathematical model was provided to compute particle position on the radial axis of the electrode and then a new technique was proposed to extract a single particle size from the calculated particle radial position. To validate the proposed method experimentally, a sensor was designed and five test particles ranging from 4 mm to 14 mm were selected for measurement. The results show a 0.44 mm estimation error between the estimated and expected results. The results also show that the method is promising for the establishment of a reliable and cost-effective solid particle sizing system

Private Computation of Polynomials over Networks

This study concentrates on preserving privacy in a network of agents where each agent seeks to evaluate a general polynomial function over the private values of her immediate neighbors. We provide an algorithm for the exact evaluation of such functions while preserving privacy of the involved agents. The solution is based on a reformulation of polynomials and adoption of two cryptographic primitives: Paillier as a Partially Homomorphic Encryption scheme and multiplicative-additive secret sharing. The provided algorithm is fully distributed, lightweight in communication, robust to dropout of agents, and can accommodate a wide class of functions. Moreover, system theoretic and secure multi-party conditions guaranteeing the privacy preservation of an agent's private values against a set of colluding agents are established. The theoretical developments are complemented by numerical investigations illustrating the accuracy of the algorithm and the resulting computational cost.Comment: 12 pages, 4 figure