Process robustness approach

A multivariate normal distribution data set was generated by the Monte Carlo method using the values of variances and true flowrates in order to simulate the process sampling data. The data, of sample size 1000, were used to investigate the performance of the robust approach in the two cases, with and without outliers. 

Let us consider the same chemical reactor as in Example 11.1 (Chen et al., 1998). Monte Carlo data for y were generated according to in order to simulate process sampling data. A window size of 25 was used here, and to demonstrate the performance of the robust approach two cases were considered, with and without outliers. 

Xr from the robust approach, as expected, still gives the correct answer however, the conventional approach fails to provide a good estimate of the process variables. Although the main part of the data distribution is Gaussian, the conventional approach fails in the task because of the presence of just one outlier. In a strict sense, the presence of this outlier results in the invalidation of the statistical basis of data reconciliation,... 

Remark 1. From Proposition 4, the existence of an input-output linearizing control law capable to regulate exponentially the total concentration of organic substrate St in a desired value S was demonstrated. However, in order to implement this controller in practice, a perfect knowledge of the process dynamics is required. In other words, this implies that either the influent composition St,in or the process kinetics k, /j,. ) must be perfectly known. Nevertheless, this condition is difficult to satisfy in practice limiting its application. But what about if the uncertain terms can be estimated from available measurements and a control scheme with a similar structure to that of the input-output linearizing controller (6) is used. In the next section, a robust approach is proposed based in this fact. 

In regard dynamics and control scopes, the contributions address analysis of open and closed-loop systems, fault detection and the dynamical behavior of controlled processes. Concerning control design, the contributors have exploited fuzzy and neuro-fuzzy techniques for control design and fault detection. Moreover, robust approaches to dynamical output feedback from geometric control are also included. In addition, the contributors have also enclosed results concerning the dynamics of controlled processes, such as the study of homoclinic orbits in controlled CSTR and the experimental evidence of how feedback interconnection in a recycling bioreactor can induce unpredictable (possibly chaotic) oscillations. 

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. 

To that end, it is needed a robust approach to generate this final integrated set of weights, and in this methodology, it is proposed to apply the Analytic Hierarchic Process (AHP). 

The LCI was computed using process simulation as a support tool. This approach is appropriate for both, the design of new processes and the optimisation of existing ones. The use of process simulators to obtain the LCI guarantees a robust approach that... 

When this synthesis was completed, the authors attempted to reduce the number of isolated intermediates, most of them are sensitive to the workup and purification procedures. This resulted in a 9-step process and 38% overall yield of sertraline. This approach, though declared by the authors as expedient total synthesis [28], is hardly competitive on a large-scale, with the robust approaches described in the former sections. Still, the many elegant synthetic solutions to issues encountered in individual steps makes this approach fascinating and inspiring for synthetic and medicinal chemists. 

Various methods have been presented in the literature to fabricate microfluidic devices. One of the most widely used are those made from poly(dimethylsiloxane) (PDMS) gels [8,10]. The materials for these devices are relatively inexpensive and they can be made with established soft lithography processes. This approach also has the potential to form complicated and intricate flow patterns. The soft lithography process is robust and reproducible, which allows replicating flows in different devices that have the same design. 

MFFDs provide a robust approach to form monodisperse emulsion drops [8]. It has been demonstrated that microfluidic-generated drops can function as both morphological tanplates and chemical reactors for the synthesis of monodisperse polymer [44,45,63] and biomolecular microparticles [48]. Below, we summarize our recent results on a novel procedure for fabrication of weU-defined monodisperse, mesoporous silica particles. It is based on MFFD ulsification of an aqueous-based sol [64] with subsequent EISA processing utilizing the aforementioned ESE method [55]. 

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