DESIGN AND IMPLEMENTATION OF DATA-DRIVEN MODEL-BASED GENETIC ALGORITHM WITH CASCADE CONTROL FOR TEMPERATURE OPTIMIZATION IN A GRANULATE BATCH REACTOR

• Main Author: Arifin
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/86564
• Institution: Institut Teknologi Bandung

Today, the world entered a period of prolonged conflict, with many wars in various regions. Maintaining state sovereignty is a top priority for every nation, which requires support from both state-owned and private agencies. The independence of the defense industry was very important in realizing a sovereign and independent national defense. Therefore, mastery of the technology of bullet or rocket propulsion material known as propellant was crucial to reduce dependence on imports and ensure a safe supply, especially in the current crisis situation. This research discussed the control system of one of the chemical processes for the manufacture of Double Base Propellant (DBP) with raw materials that were abundantly available in this country, and the needs that continue to increase every year. A chemical process is a series of chemical reactions that convert raw materials into high-value products. chemical reactors are where these processes take place. The chemical process used to manufacture propellants involves several complex stages. One of the processes is the granulation process, which is the process of converting small and fine particles into large lumps known as granules, so that easier to compress and meet propellant grain size standards. The propellant manufacturing or production process is the key to producing quality propellant. The chemical reactor used is a batch reactor. This means all reagents are placed in the reactor at the beginning of the process, and there is no inflow or outflow of materials during the process. The controller used in this research was developed based on LabVIEW. Its purpose is to design and implement a data-driven model- based genetic algorithm with cascade control for temperature optimization in a granulation batch reactor. The focus of the research is on accurate temperature optimization, which can influence granulation quality, granule size, and production process efficiency. To achieve this goal, a Proportional, Integral, Derivative (PID) temperature controller optimized with a genetic algorithm was used. Implementation on Human-Machine Interface (HMI) integrated with industrial scale controllers such as variable speed drive model ATV320 and temperature controller model TK4S. Both controllers already support data communication, known as Modbus. iv The research methodology includes several main stages. First, control system design, equipment installation, HMI programming, integrating the controller with the HMI. The next step is system identification based on a black box approach. Next is the design of cascade control and optimization of PID parameters using genetic algorithms. Tests were conducted on a granulation batch reactor using water as the test medium, replacing the original chemicals for safety reasons. The test results show that the PID controller optimized with a data-driven genetic algorithm produces better performance compared to the conventional control method, characterized by a decrease in overshoot by 86%. The control system is able to achieve a high accuracy of 1.05 degrees Celsius in set point tracking. In addition, the use of genetic algorithm allows the system to adjust the control parameters. Comparison of cascade control performance with dynamic set point on-off control speeds up the process time by 2010 seconds or 37% compared to on-off control. In addition, integration with a LabVIEW-based HMI provides advantages in terms of ease of use and accessibility, as the operator can set and monitor the system interactively. The HMI can also be further developed to support additional functions, such as historical data logging and automatic system performance analysis.