Year: 2025
Type: article
Abstract: Heat exchangers are widely used process equipment in industrial sectors, making the study of their temperature dynamics particularly appealing due to the nonlinearities involved. Model-free approaches enable the use of input and output data to generate specific and accurate estimations for each proposed system. In this work, a model-free identification strategy is proposed using a convolutional neural network to estimate the system’s behavior. Notably, the model does not rely on direct temperature measurements; instead, temperature is inferred from other system signals such as reference, flow, and control inputs. This data-driven approach offers greater specificity and adaptability, often outperforming manufacturer-provided coefficients whose performance may vary from design expectations. The results yielded an R2 index of 0.9951 under nominal conditions and 0.9936 when the system was subjected to disturbances.
Authors: Mario C. Maya-Rodriguez, Ignacio Carvajal-Mariscal, Mario A. Lopez-Pacheco, Raúl López-Muñoz, René Tolentino-Eslava
Institution: Instituto Politécnico Nacional
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W4415169494