Developing ANN based Virtual/Soft Sensors for Industrial Problems

This paper briefly reports experience of our research group in developing and deploying some promising approaches for virtual and soft sensing of crucial parameters for use in control systems of a process or plant. It briefs on the constraints and limitations of the measurement of crucial parameters by physical means and hence the need and viability to go for virtual/soft sensing‥ The approaches used are variants of Artificial Neural Network topologies and their supervised and partly supervised training algorithms. The paper provides a brief overview of the virtual sensor development based on these approaches for selected process situations and provides validation results to justify the approaches used. The industry sectors for which these solutions were developed are automotive, cement grinding process and kiln process, and chemical process for pH control. Some of these approaches have been implemented in the industry underlining the significant role the virtual/soft sensing mechanisms could play in process operations.