Towards real time monitoring of reacting species and pH coupling electrical resistance tomography and machine learning methodologies

Review of applications of electrical resistance tomography to chemical engineering

In spite of decades of study and investigation, the research on tomography and electrical resistance tomography (ERT) in particular, remains to be focus of immense scientific significance. ERT provides the ability to measure conductivity distribution inside a process plant and delivers time evolving multidimensional information. Such important and otherwise inaccessible information enhances critical process knowledge whilst improving the design and function of the process equipment. ERT has been employed in a variety of fields including chemical engineering. This paper reviews previous research carried out on the application of ERT within the chemical engineering arena. The applications are classified based on the objective of ERT measurements, the unit operations ERT has been utilized on, the media under examination, and also other technologies and data processing techniques used in combination with ERT. The objective of this taxonomy is to offer the reader with a broad insight into the current situation of ERT related research and developed applications in the chemical engineering field and to assist in the identification of research gaps for future investigation.

Real-Time Fault Detection and Diagnosis of CaCO3 Reactive Crystallization Process by Electrical Resistance Tomography Measurements

In the present research work, an electrical resistance tomography (ERT) system is utilized as a means for real-time fault detection and diagnosis (FDD) during a reactive crystallization process. The calcium carbonate crystallization is part of the carbon capture and utilization scheme where process monitoring and malfunction diagnostics strategies are presented. The graphical logic representation of the fault tree analysis methodology is used to develop the system failure states. The measurement consistency due to the use of a single electrode from a set of ERT electrodes for malfunction identification is experimentally and quantitatively investigated based on the sensor sensitivity and standard deviation criteria. Electrical current measurements are employed to develop a LabVIEW-based process automation program by using the process-specific knowledge and historical process data. Averaged electrical current is correlated to the mechanical failure of the stirrer through standard deviation evaluation, and slopes of the measured data are used to monitor the pump and concentrations status. The performance of the implemented methodology for detecting the induced faults and abnormalities is tested at different operating conditions, and a basic signal-based alarming technique is developed.