Instrumentation And Control Systems By W Bolton Pdf 34

The selection of feedback control strategy depends on the complexity of system. If the system is just to be maintained at a static set point, then the use of computationally inexpensive proportional-integral (PI) control may be sufficient. More advanced control algorithms are needed for dynamic signal tracking or for control of complex systems. Irrespective of the computationally expensive nature of these algorithms, they have the potential to accurately capture the altercations of the desired metabolic pathways. These pathways contained complex regulation networks and thus show highly nonlinear behaviours. Recently, various digital approaches such as artificial intelligence (AI) for advanced monitoring and control and computational models have been implemented to study molecular or process-relevant behaviour [16,17,18].

Application of fuzzy logic-based controllers for control and decision making in bioprocess industry is well established. Bioprocesses such as fermentation operations are complex and laden with various uncertainty factors. Therefore, the setup of optimum process parameters is necessary for achieving higher growth rates and productivity. Multiple researchers have demonstrated application of fuzzy logic Takagi Sugeno fuzzy controller for tracking control of bioprocess [82,83]. Here, the process was modelled using the TS fuzzy model followed by the use of fuzzy observer for designing controller. Two different control approaches were employed for output tracking, i.e., parallel distributed compensation control and fuzzy optimal control. It was seen that fuzzy control had lower root mean square error while dealing with the non-linearity of the system. Similar results were published for the purification of secondary metabolite optimization [84]. In addition, implementation of fuzzy feedforward control strategy for temperature control in fermentation [85] or product concentration control in enzymatic reactor [86] have proved to be efficient control logics for improving load rejections in non-linear process. An interesting application of fuzzy logic controller in combination with ANN is seen for fed batch cultures. In this study, ANN was used as a soft sensor to estimate the glucose concentration whereas fuzzy logic controller was implemented for controlling substrate addition. An improvement in estimation and control was observed with an error of 6% [87]. Several researchers have demonstrated applications of fuzzy control systems for control and optimization of bioreactor operations [88].

An overview of the most recent developments in high-frequency high-field electron paramagnetic resonance (EPR) instrumentation is given. In particular, the practical choices concerning sources, detectors, resonators, propagation systems as well as magnet technology are discussed in the light of various possible applications. Examples of particular homodyne and heterodyne quasi-optic EPR systems illustrate the potential for future developments in EPR technology. 2b1af7f3a8