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In order to improve the quality of reconstructed images in electrical capacitance tomography (ECT), the image reconstruction method based on compressed sensing for ECT is studied. First, the traditional discrete Fourier transform and discrete cosine transform are used as a sparsity basis to make the grey vectors of the typical two-phase flow distributions sparse. The energy loss of the sparse signals under different sparsity degrees is calculated, and the effect of energy loss on the quality of reconstructed images is studied. Then, using the natural sparsity of the original signal, an improved orthogonal matching pursuit algorithm for ECT image reconstruction is proposed. There are two main improvements in the proposed algorithm. First, multiple columns instead of one column in each iteration are selected for improving the reconstruction speed. Second, a regularisation solution instead of the least-squares solution is used for improving the adaptability to ill-posed inverse problems. Simulation and experimental tests are carried out and the results show that the proposed method can effectively improve the reconstructed images quality, and on the whole, obtain better reconstruction results than the Landweber iteration algorithm, the Tikhonov regularisation algorithm, and the gradient projection for sparse reconstruction algorithm.

A hotspot in a grain bulk is a localized high temperature zone and normally spoilage begins in this location. The reconstruction strategies of a 3D space are investigated to detect hotspots in grain bulk by acoustic tomography. Since the size of a grain silo is usually large, the whole space to be reconstructed is divided into three blocks and two reconstruction ways: whole reconstruction and block reconstruction, are considered. Three sound ray path selection rules including intra-block, generalized intra-block and unrestricted paths are investigated. These reconstruction ways and path selection rules are combined into four reconstruction strategies. To fully compare the abilities of these strategies in detecting hotspots in grain bulk, 2304 single-hotspot temperature fields and a three-hotspot temperature field are reconstructed by using noise-free and noisy travel-time data. Based on reconstruction results and difficulty in travel-time measurement, the strategy using whole reconstruction way and generalized intra-block paths is the optimal choice.

Image reconstruction of electrical capacitance tomography (ECT) belongs to solving an inverse problem. The first step to solve the inverse problem is to establish a forward problem model which determines the capacitance values for known permittivity distribution. There are three main methods for ECT forward problem modelling. The first is based on the sensitivity definition. The second is based on the multiple linear regression (MLR) method. And the third is based on the electric field strength (EFS). In this study, comparisons of these three modelling methods for the forward problem in three-dimensional ECT are given in terms of modelling error, computational time and reconstruction quality. Comparison results show that the MLR method generates smallest modelling error and better reconstruction quality, especially for the very complex geometry with objects in the shape of letters H and U. However, the EFS method is significantly better than the other two methods in computational time.

The image reconstruction for electrical capacitance tomography is a non-linear, underdetermined and ill-posed inverse problem. It is difficult to obtain a reconstructed image with high quality, especially in the case of three-dimensional (3D) reconstruction. An iterative image reconstruction algorithm with fuzzy thresholding is proposed in this study. The threshold value in each iteration is generated by minimising the measure of fuzziness of current reconstructed image. The algorithm proposed is tested by the noise-free and the noise-contaminated capacitance data. Extensive computer simulations demonstrate that the fuzzy thresholding can reduce low grey-level artefacts effectively. As a result, the spatial error, volume error, permittivity error and scattered artefacts of the reconstructed image are reduced obviously; not only that, the number of iterations needed to obtain a good reconstruction result is decreased greatly. The result of 3D reconstruction of a H-shape object verifies the effectiveness of the fuzzy thresholding further.

Acoustic temperature measurement method uses the dependence of sound speed in materials on temperature along the sound propagation path. The measurement of acoustic wave travel-time in grain bulk has been researched in this paper for monitoring grain temperature by acoustic method. A measurement system of sound travel-time based on the virtual instrument is described. Transmitted signals include chirp and maximum length sequence (MLS). Travel-time of sound waves is calculated by cross-correlating the signals received by two microphones. Experiment results show that stable measurement values of sound travel-times in grain can be obtained by MLS combined with cross-correlation. Compared with chirp, MLS can provide much higher measurement accuracy.