Faulty scenarios in sour water treatment units: Simulation and AI-based diagnosis

• Published in: Process safety and environmental protection 2022-09, Vol.165, p.716-727
• Main Authors: do Nascimento Pereira Nogueira, Júlia, Melo, Príamo Albuquerque, de Souza Jr, Maurício B.
• Format: Article
• Language: English
• Subjects: Deep neural networks; Dynamic simulation; Faulty scenarios; Machine learning; Random forests; Sour water treatment units; Support vector machines
• ISSN: 0957-5820; 1744-3598
• DOI: 10.1016/j.psep.2022.07.043

Fault Detection and Diagnosis (FDD) is a Process System Engineering (PSE) area of great importance, especially with increased process automation. It is one of the chemical engineering fields considered promising to Artificial Intelligence (AI) application. FDD systems can be useful to supervise Sour Water Treatment Units (SWTU) behavior, as they are chemical processes that present operational difficulties when disturbances occur. SWTU remove contaminants from sour water (SW) streams generated through petroleum processing, consisting mainly of small amounts of H2S and NH3. They are considered one of the primary aqueous wastes of refineries and cannot be disposed of due to environmental regulations. However, no previous studies focused on the development of FDD systems for SWTU exist and works on its dynamics are scarce. Hence, the present work proposes to study the dynamic simulated behavior of an SWTU and develop an FDD system applying AI techniques with hyperparameters optimization. The simulation was performed in Aspen Plus Dynamics® and ran to create normal operation and six relevant faults, including occurrences in the process (e.g., inundation and fouling) and sensors. FDD was performed through data classification, and results were evaluated mainly by accuracy and confusion matrices. Even after variable reduction, FDD was satisfactory with over 87.50% accuracy in all AI techniques. RF and SVM with linear and Gaussian kernels presented the best results, with over 93% of accuracy in training and testing, and had the shortest computing times. The second column’s sump level proved to be the most relevant variable for fault identification. [Display omitted]