Fault Diagnosis for Chemical Batch Reactors

Chapter 6 is focused on fault diagnosis methods for chemical batch processes. Consistent with the approach followed in Chap. 5, the focus of the chapter is on model-based techniques and, in particular, on techniques based on the use of state observers. 

Several kinds of failures may compromise safety and productivity of industrial processes. Indeed, faults may affect the efficiency of the process (e.g., lower product quality) or, in the worst scenarios, could lead to fatal accidents (e.g., temperature runaway) with injuries to personnel, environmental pollution, and equipments damage. In the chemical process fault diagnosis area, the term fault is generally defined as a departure from an acceptable range of an observed variable or a parameter. Fault diagnosis (FD) consists of three main tasks fault detection, i.e., the detection of the occurrence of a fault, fault isolation, i.e., the determination of the type and/or the location of the fault, and fault identification, i.e., the determination of the fault profile. After a fault has been detected, controller reconfiguration for the self-correction of the fault effects (fault accommodation) can be achieved in some cases. 

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In the fifth chapter, a general overview of temperature control for batch reactors is presented the focus is on model-based control approaches, with a special emphasis on adaptive control techniques. Finally, the sixth chapter provides the reader with an overview of the fundamental problems of fault diagnosis for dynamical systems, with a special emphasis on model-based techniques (i.e., based on the so-called analytical redundancy approach) for nonlinear systems then, a model-based approach to fault diagnosis for chemical batch reactors is derived in detail, where both sensors and actuators failures are taken into account. 

The literature focused on model-based FD presents a few applications of observers to chemical plants. In [10] an unknown input observer is adopted for a CSTR, while in [7] and [21] an Extended Kalman Filter is used in [9] and [28] Extended Kalman Filters are used for a distillation column and a CSTR, respectively in [45] a generalized Luenberger observer is presented in [24] a geometric approach for a class of nonlinear systems is presented and applied to a polymerization process in [38] a robust observer is used for sensor faults detection and isolation in chemical batch reactors, while in [37] the robust approach is compared with an adaptive observer for actuator fault diagnosis. 

In Chaps. 5 and 6 model-based control and early diagnosis of faults for ideal batch reactors have been considered. A detailed kinetic network and a correspondingly complex rate of heat production have been included in the mathematical model, in order to simulate a realistic application however, the reactor was described by simple ideal mathematical models, as developed in Chap. 2. In fact, real chemical reactors differ from ideal ones because of two main causes of nonideal behavior, namely the nonideal mixing of the reactor contents and the presence of multiphase systems. 

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