Coventional control schemes are developed under the assumption that the sensors and actuators are free from faults. However, the occurrence of faults will cause degradation in the closed-loop performance and also will have an impact on safety, productivity, and plant economy. In the present work, we have proposed a fault-tolerant control scheme (FTCS) by integrating a fault detection and identification (FDI) technique with conventional control. The principal component of our proposed FTCS is a compensation strategy (supervisory system) which uses the information provided by the FDI to appropriately modify the controller as well as the model used in FDI. This allows online application of the FTCS without causing significant degradation in the closed-loop performance due to the occurrences of biases in sensors and actuators or due to changes in unmeasured disturbance variables and due to moderate change in process parameters. Through stochastic simulation studies of a continuous stirred tank reactor process, we demonstrate that our proposed FTCS leads to significant improvement in the closed-loop performance in comparison to a conventional control scheme, especially as the fault magnitude increases. The proposed compensation scheme also allows identification of multiple faults that occur sequentially in time and is also found to be robust with respect to moderate plant−model mismatch.

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Model predictive control (MPC) schemes are typically developed under the assumption that the sensors and actuators are free from faults. Attempts to develop fault-tolerant MPC schemes have mainly focused on dealing with hard faults, such as sensor or actuator failures, process leaks, etc. However, soft faults such as biases or drifts in sensors or actuators are more frequently encountered in the process industry. Occurrences of such faults can lead to degradation in the closed loop performance of the MPC controller. Since MPC controllers are typically used to control key operations in a chemical plant, this can have an impact on safety and productivity of the entire plant. The conventional approach to dealing with such soft faults in MPC formulations is through the introduction of additional artificial states to the model. The main limitation of this approach is that number of artificial extra states introduced cannot exceed the number of measurements. This implies that it is necessary to have a priori knowledge of which subset of faults are most likely to occur. In this paper, an active on-line fault-tolerant model predictive control (FTMPC) scheme is proposed by integrating state space formulation of MPC with the fault detection and identification (FDI) method based on generalized likelihood ratios. The fact that both these schemes use a Kalman filter as their basis facilitates tight integration of these two components. The main difference between the conventional MPC formulation and FTMPC formulation is that the bias corrections to the model are made as and when necessary and at qualified locations identified by the FDI component. The FTMPC eliminates offset between the true values and set points of controlled variables in the presence of a variety of faults while conventional MPC does not. Also, the true values of state variables, manipulated inputs, and measured variables are maintained within their imposed bounds in FTMPC, while in conventional MPC, these may be violated when soft faults occur. These advantages of the proposed scheme are demonstrated using simulation studies on a CSTR process and experimental studies conducted on the temperature control of a coupled two tank heater system.

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In this study, we present an analytical study on blood flow analysis through with a tapered porous channel. The blood flow was driven by the peristaltic pumping. Thermal radiation effects were also taken into account. The convective and slip boundary conditions were also applied in this formulation. These conditions are very helpful to carry out the behavior of particle movement which may be utilized for cardiac surgery. The tapered porous channel had an unvarying wave speed with dissimilar amplitudes and phase. The non-dimensional analysis was utilized for some approximations such as the proposed mathematical modelling equations were modified by using a lubrication approach and the analytical solutions for stream function, nanoparticle temperature and volumetric concentration profiles were obtained. The impacts of various emerging parameters on the thermal characteristics and nanoparticles concentration were analyzed with the help of computational results. The trapping phenomenon was also examined for relevant parameters. It was also observed that the geometric parameters, like amplitudes, non-uniform parameters and phase difference, play an important role in controlling the nanofluids transport phenomena. The outcomes of the present model may be applicable in the smart nanofluid peristaltic pump which may be utilized in hemodialysis.

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