| Safety and optimality are crucial requirements for every industrial process. Process fault diagnosis is the problem of identifying the causal origins of faults in a process, given process measurements and a priori knowledge. Modern day chemical plants require comprehensive fault diagnosis procedures to function smoothly. Fault diagnosis strategies may be broadly classified as qualitative, quantitative and data-based approaches.; Relying on analytical redundancy, quantitative model based methods for fault diagnosis check the actual system behaviour against a parallel system model for consistency. Any inconsistencies, also called residuals are artificial signals reflecting the potential faults in the system. A technique for detecting and isolating faults based on residual generation for nonlinear systems will be discussed. The procedure is validated by numerical simulations of typical chemical engineering systems: an exothermic CSTR and counter-current heat exchanger.; The success of any fault diagnostic technique depends critically on the sensors measuring the important process variables. With thousands of possible measurements in a typical plant, the selection of variables for sensor placement is not an easy task. The diagnosability properties of a sensor network are defined in terms of fault observability, resolvability etc. However, as these indices are inherently incommensurate, it is difficult to compare different sensor networks. Hence, the problem of sensor selection for fault diagnosis is addressed by quantifying the diagnostic information in terms of process economics and operating profit. This procedure, is illustrated by a simulated example of a CSTR subject to parametric faults.; Mathematical systems biology is an emerging field of research at the interface of systems theory and biology. Of particular interest is the determination of unknown higher level regulatory signals by using gene expression data from DNA microarray experiments. Determining which regulatory signals are active (and their strengths, if possible) is an important problem in the analysis of Gene Regulatory Networks (GRN). This problem is described in a systems theoretic framework. In particular, qualitative modelling and diagnostic techniques are used to solve this problem. This procedure is described through an example and then extended to publicly available microarray gene expression data from yeast cell cycle experiments. |
