Microwave measurement techniques for industrial process monitoring and quality control

Process monitoring and quality control by sensor measurements are essential for the automatisation and optimisation of many industrial manufacturing processes. This thesis is concerned with microwave sensing, which is a measurement modality with potential to improve the in-line sensing capabilities in several industries. Two process-industrial measurement problems are considered that involve the estimation and detection of permittivity variations for granular media in a fluidised or flowing state. For these problems, we present microwave measurement techniques based on resonant cavity sensors, accounting for the electromagnetic design and modelling of the sensor, signal processing algorithms, and experimental evaluation in relevant industrial settings. These measurement techniques make simultaneous use of multiple resonant modes with spatial diversity to improve the measurement capabilities. Furthermore, we exploit model-based signal processing algorithms where knowledge of the underlying physics is utilised for improved estimation and detection.The first problem is to monitor the internal state of a pharmaceutical fluidised bed process used for film-coating and drying of particles. The metal vessel that confines the process is here treated as a cavity resonator and the complex resonant frequency of eight different cavity modes are measured using a network analyser. Based on the resonant frequencies, we estimate parameters in a low-order model for the spatial permittivity distribution inside the vessel, which can be related to process states such as the liquid and solid content of the particles in different regions.The second measurement problem is an aspect of quality control, namely the detection of undesirable objects in flowing granular materials. We present measurement techniques based on resonant cavity sensors that are capable to detect the presence of small dielectric objects embedded in a flowing granular material. Detection algorithms that exploit the statistics of the noise caused by material density fluctuations and the characteristic signatures caused by an object passage event, are evaluated based on experiments which lead to quantitative assessments of the detection performance

Global monitoring of fluidized-bed processes by means of microwave cavity resonances

We present an electromagnetic measurement system for monitoring of the effective permittivity in closed metal vessels, which are commonly used in the process industry. The measurement system exploits the process vessel as a microwave cavity resonator and the relative change in its complex resonance frequencies is related to the complex effective permittivity inside the vessel. Also, thermal expansion of the process vessel is taken into account and we compensate for its influence on the resonance frequencies by means of a priori information derived from a set of temperature measurements. The sensitivities, that relate the process state to the measured resonance frequencies, are computed by means of a detailed finite element model. The usefulness of the proposed measurement system is successfully demonstrated for a pharmaceutical fluidized-bed process, where the water and solid contents inside the process vessel is of interest

Resonant Microwave Sensors for Industrial Applications

This thesis presents developments in the area of microwave-based sensor systems for industrial applications. The work is motivated by a need for advanced measurement techniques in industry in general, and the process industry in particular.Microwaves have several advantages for many measurement problems encountered in process industries, including the possibilities for non-destructive and non-invasive measurements in-line. Furthermore, microwaves have good penetrability in many biological and chemical substances and interact strongly with water, which enables sensitive moisture measurements.The sensor systems developed in this thesis are based on microwave resonances, which is a well-suited technique for measurements inside hollow metallic enclosures such as vessels and pipes. These structures are commonly used in process industries for storage, transportation and processing of materials and they form natural environments for hosting resonant electromagnetic modes at microwave frequencies. A main novelty is that multiple resonant modes are used simultaneously to improve the measurement performance.Detailed electromagnetic modelling based on the finite element method is employed, with emphasis on eigenvalue problem formulations for resonant systems.Two specific process-industrial applications are considered, namely the monitoring of pharmaceutical manufacturing processes and the detection of undesirable objects in different material mixtures.In the first application, a fluidised-bed process for coating and drying of pharmaceutical particles is monitored by measurements of the complex resonance frequencies of cavity-modes inside the process vessel. Experimental results from a real fluidised-bed process demonstrate the usefulness of this measurement technique.For the second application, a resonant microwave sensor is developed for measurements on low-permittivity materials, such as dilute powders, that are flowing in metal pipes. The problem of detecting undesired dielectric objects in the material flow is particularly studied. Two detection algorithms based on the likelihood-ratio test are investigated for this purpose, where the first is based on measured resonance frequencies and the second uses uncalibrated S-parameter data. The sensor and detection algorithms are evaluated based on simulated and measured data, and favourable detection properties are observed

Resonant Microwave Sensors for Industrial Applications

This thesis presents developments in the area of microwave-based sensor systems for industrial applications. The work is motivated by a need for advanced measurement techniques in industry in general, and the process industry in particular.Microwaves have several advantages for many measurement problems encountered in process industries, including the possibilities for non-destructive and non-invasive measurements in-line. Furthermore, microwaves have good penetrability in many biological and chemical substances and interact strongly with water, which enables sensitive moisture measurements.The sensor systems developed in this thesis are based on microwave resonances, which is a well-suited technique for measurements inside hollow metallic enclosures such as vessels and pipes. These structures are commonly used in process industries for storage, transportation and processing of materials and they form natural environments for hosting resonant electromagnetic modes at microwave frequencies. A main novelty is that multiple resonant modes are used simultaneously to improve the measurement performance.Detailed electromagnetic modelling based on the finite element method is employed, with emphasis on eigenvalue problem formulations for resonant systems.Two specific process-industrial applications are considered, namely the monitoring of pharmaceutical manufacturing processes and the detection of undesirable objects in different material mixtures.In the first application, a fluidised-bed process for coating and drying of pharmaceutical particles is monitored by measurements of the complex resonance frequencies of cavity-modes inside the process vessel. Experimental results from a real fluidised-bed process demonstrate the usefulness of this measurement technique.For the second application, a resonant microwave sensor is developed for measurements on low-permittivity materials, such as dilute powders, that are flowing in metal pipes. The problem of detecting undesired dielectric objects in the material flow is particularly studied. Two detection algorithms based on the likelihood-ratio test are investigated for this purpose, where the first is based on measured resonance frequencies and the second uses uncalibrated S-parameter data. The sensor and detection algorithms are evaluated based on simulated and measured data, and favourable detection properties are observed

Microwave Measurement System for Detection of Dielectric Objects in Powders

Detection and removal of undesirable objects is an important issue in many material processing industries. This paper presents a microwave-based measurement technique for detection of dielectric objects in powder materials flowing through metal pipes. A nonintrusive microwave sensor is developed, which uses multiple resonant modes to obtain high sensitivity with respect to permittivity variations inside the pipe. Undesirable objects are detected based on the scattering parameters of the sensor, which are measured using a fast-sampling microwave transmitter and receiver unit. We present a detection algorithm derived from the likelihood-ratio test, which includes a parametric model of the local statistical distribution of the measured scattering parameter data. The model involves a set of Mobius transformations that map the measured scattering parameters to a domain where the data are Gaussian distributed. All unknown parameters in the model are estimated from data using maximum likelihood. Based on measurement results from a gravity-fall experimental setup, we conclude that small dielectric objects can be reliably detected in heterogeneous flows of dielectric powder

Matched filter for microwave-based detection of dielectric objects in powders

We present a microwave measurement system for detection of dielectric objects in inhomogeneous powder flows in metal pipes. The system includes a non-intrusive microwave sensor that uses multiple cavity modes to measure the permittivity inside the pipe, and a fast-sampling microwave transmitter and receiver instrument. In this paper, we study a matched filter detection algorithm that takes temporal and inter-channel correlation of the noise into account. The target waveforms used in the filter are constructed from measurements on single object passages. Based on repeated gravity-fall measurements with polyethylene powder at flow rates up to 109 kg/h, we find that wood and plastic objects of 5mm size can be detected with high accuracy, and that the matched filter detector yields minor improvements as compared to a power detector that ignores the temporal correlation

Microwave Resonator Sensor for Detection of Dielectric Objects in Metal Pipes

We present a microwave sensor for detection of dielectric objects in material mixtures that are transported in metal pipes. The sensor exploits several resonant modes that operate below the cut-off frequency of the dominant waveguide mode in the pipe. This non-invasive measurement technique is particularly attractive for detection of undesirable objects in certain industries. Initial measurements on a prototype sensor show good agreement with electromagnetic simulations and indicate favorable detection properties. A detection algorithm based on the likelihood-ratio test is evaluated based on synthetically generated data from a finite-element model that simulates inhomogeneous background materials. We conclude that the multi-mode feature of the sensor is advantageous for the ability to distinguish between undesirable objects and ordinary fluctuations in the background material