A Literature Review on the Sensor Network Design Problem Applied to Chemical Processes

In the present paper, a comprehensive review of methodologies for the optimal placement of measurement devices in chemical processes, commonly referred to as the sensor network design problem (SNDP), is presented. The SNDP is a combinatorial optimization problem of paramount importance in process design, monitoring, control, and data reconciliation, as sensor placement directly affects estimability, precision, reliability, and fault detectability. In addition to surveying solution strategies for SNDP, this review examines the closely related variable classification problem, which constitutes a fundamental building block in the systematic development of sensor networks. The evolution of the field is analyzed, highlighting early structural search approaches, subsequent optimization-based formulations relying on relaxation and decomposition techniques, and heuristic and metaheuristic strategies. The strengths and limitations of these methodological classes are discussed with respect to problem size and computational complexity. Moreover, this review identified a novel strategy to solve SNDPs based on machine learning algorithms. Finally, future research directions are outlined, emphasizing the growing need for scalable frameworks capable of addressing increasingly large and automated industrial systems. As computational capabilities continue to advance and process plants transition toward highly digitalized environments, research efforts devoted to efficient and robust SNDP methodologies are expected to intensify. In this context, Large Language Models (LLMs) can play a transformative role.