The use of dual machine learning in industrial electrical tomography

Machine learning techniques are playing a key role in tomography. Process tomography, also known as industrial tomography, uses a variety of physical phenomena. Contrary to the commonly used computed tomography in medicine, electrical, ultrasound, radio and even optical tomography are used in industry. In electrical tomography we distinguish between impedance and capacitance tomography. This manuscript presents an algorithmic method to allow accurate measurements of reactors and industrial vessels using electrical impedance tomography. Reactors may contain liquids which undergo phase changes resulting in crystallization or gassing. The tomograph can detect gas crystals or bubbles. The innovative contribution of the authors is the development of an original algorithm that allows the conversion of input measurements to 2D images. First, the algorithm trains multiple single-output neural networks, each of which generates a single image pixel. Secondly, two models were used (support vector machines and artificial neural networks), which were assigned to individual pixels of the image. The image was reconstructed using two methods, not one, so the new method was called dual machine learning (DML). In order to assess the effectiveness of the new approach, both homogeneous methods (SVM and ANN) were compared with the new DML method. The results confirmed the higher effectiveness of the new approach.