A Python implementation of a Steady-state Real Time Optimization (SRTO) and Realtime optimization with persistent adaptation (ROPA)

• Published in: Computer Aided Chemical Engineering Vol. 53; pp. 1633 - 1638
• Main Authors: Sanza, Carlos C., Le Rouxa, Galo A.C.
• Format: Book Chapter
• Language: English
• Published: 2024
• Subjects: python; real-time optimization; real-time optimization persistente adptation; real-time optimization; python; real-time optimization persistente adptation
• ISBN: 9780443288241; 0443288240
• ISSN: 1570-7946
• DOI: 10.1016/B978-0-443-28824-1.50273-8

In recent years, the chemical/petrochemical industry has been under increasing pressure to optimize its processes. Economic, environmental, Industry 4.0, and circular economy considerations are some of the main drivers for this optimization. However, most companies rely on expensive software for process simulation and optimization algorithms. For leading chemical/petrochemical companies, this type of investment is not a problem. In some companies, an entire team is dedicated to this task. However, for medium-sized or small companies that are subject to the same constraints, such as environmental compliance or the most profitable operating point, the burden can be very high. Unfortunately, there are not many low-cost solutions available. In addition, in some cases, optimization should be performed in a small portion of the process, such as just a reactor or a distillation train. And, often, small companies tend to oversize the problem and the involved costs.