Multi-agent systems provide a powerful platform for supervision and control of distributed and complex processes. This work focuses on controlling the product grade distribution in RAFT polymerization reactor networks for smooth transition between different product grades or maintaining the targeted product grade despite various disturbances using an agent based approach. The control algorithm is designed for control of the complete molecular weight distribution. The use of a distributed reactor network allows for flexibility in targeting almost any shape for the overall molecular weight distribution.

RAFT polymerization is one of the most popular living polymerization methods. Compared to the other living polymerization methods a wider range of monomers can be used for RAFT Polymerization with more flexible operating conditions. This system is studied in a network of interconnected continuous stirred tank reactors (CSTRs). Individual reactors produce polymer chains with different molecular weight distributions contributing to the overall molecular weight distribution of the network.

A number of centralized and decentralized agent-based control structures have been developed for controlling the distribution of autocatalytic species in CSTR networks [1, 2, 3, 4, 5]. These distributed structures consist of multiple layers of agents where in one layer, local controller agents work on the local objectives in parallel using local information in that region of the network together with heuristics. On the other hand, agents in higher layers coordinate local controller agents in order to achieve the global objective.

A similar agent-based structure is implemented to control the molecular weight distribution in RAFT polymerization in CSTR networks. Molecular weight distribution is one of the most important properties that characterize the application properties of polymeric materials which makes it a critical control issue. In this work two control problems will be studied. The first problem is to maintain the overall molecular weight distribution of the RAFT polymerization system in the CSTR network that is subject to a number of disturbances and the second one is to drive the system from one molecular weight distribution to another.

The overall distribution obtained for the network results from the blend of the individual reactor's distributions taking into account the material flow between the reactors. One can change the individual reactors' molecular weight distributions to control the overall network distribution. KR Method [6, 7, 8] is used for solving the corresponding population balance equations for obtaining the individual and overall molecular weight distributions. As a result, the distribution is represented by a number of pivot points that are used as the basis for controlling the system. A global agent will track the current overall molecular weight distribution and compare it with the desired one. In the case of a difference between the current and targeted distribution, local controller agents will calculate their individual contributions to the error at each pivot point and shift their reactor's molecular weight distribution to minimize the error, in accordance with their local objectives. The local controller agents have access to a number of manipulated variables such as the inlet monomer concentration, inlet initiator concentration, RAFT agent concentration and interconnection flow rates between reactors. Using local information available and heuristics, local controller agents decide on which manipulated variables to adjust to reach their local objectives.

The state equations of the individual CSTR's that are obtained by applying KR Method are solved by a DAE solver available in MATLAB. The agent based control framework is developed in REPAST Symphony which is an agent development platform based on Java. The connection between the control system and the process simulator is achieved by JMatLink library.

[1] E. Tatara, I. Birol, F. Teymour, and A. Cinar, “Agent-based Control of Autocatalytic Replicators in Networks of Reactors,” Computers & Chemical Engineering, vol. 29, pp. 807–815, 2005.

[2] E. Tatara, C. Hood, F. Teymour and A. Cinar, “Adaptive Agent-based Control of Product Grade Transitions in Reactor”, Prepr. IFAC Intl Symposium on Advanced Control of Chemical Processes (ADCHEM 03), Gramado, Brazil, April 2-5, 2006.

[3] E. Tatara, F. Teymour and A. Cinar, “Agent-based Control of Spatially Distributed Chemical Reactor Networks”, AIChE Annual Meeting, Cincinnati, OH, November, 2005.

[4] M. D. Tetiker, A. Artel, E. Tatara, F. Teymour, M. North, C. Hood, A. Cinar, “Agent-based System for Reconfiguration of Distributed Chemical Reactor Network Operation”, Proc. American Control Conf., June 14-16, 2006, Minneapolis, MN.

[5] M. D. Tetiker, A. Cinar, F. Teymour, M. North, “Decentralized Multi-Agent Control of Distributed Reactor Networks”, AIChE Annual Meeting, San Francisco CA, November, 2007.

[6] S. Kumar, D. Ramkrishna, “On the Solution of Population Balance Equations by Discretization-I. A Fixed Pivot Technique”, Chem. Eng. Sci. 1996, 51, 1311.

[7] S. Kumar, D. Ramkrishna, “On the Solution of Population Balance Equations by Discretization-I. A Fixed Pivot Technique”, Chem. Eng. Sci. 1996, 51, 1333.

[8] S. Kumar, D. Ramkrishna, “On the Solution of Population Balance Equations by Discretization-I. A Fixed Pivot Technique”, Chem. Eng. Sci. 1997, 52, 4659.