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Several recent studies on autocatalytic reactions in single and coupled continuous stirred-tank reactor (CSTR) networks have demonstrated a rich spectrum of complex behavior. From a control systems perspective, the operating regime of a CSTR network can be manipulated by changing the flow rates between the reactors. Systems of more than one CSTR require multiple controllers and may need transients through several operating regimes to achieve the desired operation. This may require a hierarchical control structure whereby local control objectives can change dynamically in order to achieve the global control objective of the system. An agent-based control system is used to observe and control various aspects of a CSTR network. The primary focus of analyzing complex emergent behavior is demonstrating methods or combinations of methods that influence the behavior and capabilities of the agent-based control system. Simulation studies illustrate scenarios of interest, especially conditions that lead to emergent behavior.

On-line real-time monitoring of fermentation processes is a crucial task. Conventional on-line process monitoring techniques and an adaptive hierarchical Principal Component Analysis technique were applied. A hybrid supervisory knowledge-based system with a heuristic rule-base was also developed and integrated with on-line monitoring techniques for providing real-time monitoring and supervision.

An intelligent sensor monitoring and auditing environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring sensor status in multivariable processes. The real-time KBS development environment G2 is integrated with an MSPM method that can monitor multivariable dynamic processes with autocorrelated data. The MSPM Inethod is based on a canonical variate state space (CVSS) process model of a dynamic process and it is used for auditing sensor status for bias, drift and excessive noise affecting the sensors of multivariable continuous processes. Changes in the magnitudes of means and variances of residuaLs between measured and predicted process variables are used to detect and discriminate sensor abnormalities. The statistical model that describes the in-control variability of sensor readings is based on a CVSS model. The CV state variables obtained from the state space model are linear combinations of the past process measurements which explain the variability of the future measurements the most, and they are regarded as the principal dynamic dimensions. The method can detect and discriminate between bias change, drift, and variations in noise levels of process sensors based on the analysis of data batches. The MSPM modules are developed in Matlab, cOnverted to C, and linked with G2. The presentation will focus on the structure and performance of the integrated system and illustration of the methodology by simulation studies.