This paper presents a new approach for sensor location for state and parameter estimation for stable nonlinear systems. The unique feature of this technique is that sensor locations for state estimation and measurement locations for parameter estimation can be determined within the same framework. To compute optimal sensor locations, information derived from observability covariance matrices is combined with already existing measures, which were proposed either for state or parameter estimation, to compute the degree of observability of a nonlinear system over an operating region. The optimal sensor locations then correspond to the configuration that returns the highest value of the measure for the degree of observability of a system. The proposed method is illustrated in case studies where optimal sensor locations for state and parameter estimation for a binary distillation column and a fixed-bed reactor are computed. The results obtained from the presented approach are compared with a technique based upon a linearized system.

Cytokines like interleukin-6 (IL-6) play an important role in triggering the acute phase response of the body to injury or inflammation. Signaling by IL-6 involves two pathways: Janus-associated kinases (JAK) and signal transducers and activators of transcription (STAT 3) are activated in the first pathway while the second pathway involves the activation of mitogen-activated protein kinases (MAPK). While it is recognized that both pathways play a major role in IL-6 signal transduction, a majority of studies have focused on signaling through either one of the pathways. However, simultaneous signaling through both JAK/STAT and MAPK pathways is still poorly understood. In this work, a mathematical model has been developed that integrates signaling through both the JAK/STAT and the MAPK pathway. The presented model is used to analyze the effect of three molecules that are involved in the regulation of IL-6 signaling-SHP-2 (domain containing tyrosine phosphatase 2), SOCS3 (suppressor of cytokine signaling 3), and a STAT3 nuclear phosphatase (PP2)-on the dynamics of IL-6 signal transduction in hepatocytes. The obtained results suggest that interactions between SHP-2 and SOCS3 influence signaling through the JAK/STAT and the MAPK pathways. It is shown that SHP-2 and SOCS3 do not just regulate the pathway that they are known to be associated with, (SHP-2 with MAPK and SOCS3 with JAK/STAT), but also have a strong effect on the other pathway. Several simulations with SOCS3, SHP-2, and PP2 knockout cells, that is, where the signaling pathway is unable to produce these proteins, have been performed to characterize the effect of these regulatory proteins on IL-6 signal transduction in hepatocytes.

This paper presents a new technique for placing sensors on processes described by stable nonlinear dynamic systems. The methodology can compute locations for individual sensors as well as networks of sensors where a tradeoff between process information, sensor cost, and information redundancy is taken into account. The novel features of the approach are (1) that the nonlinear behavior that a process can exhibit over its operating region can be taken into account, (2) that the technique reduces to already established methods, if the system is linear and only some of the objectives are looked at, (3) that the results obtained from the procedure can be easily interpreted, and (4) that the resulting optimization problem can be decomposed resulting in a significant reduction of the computational effort required for its solution. The tradeoff between the different objectives is achieved by formulating a mixed-integer nonlinear programming problem. While computation of process information and information redundancy are computationally the most expensive parts of the procedure, it is shown that most of this analysis can be performed outside of the optimization, resulting in a significant reduction of the computational effort for evaluating the objective function. The resulting optimization problem is ideally suited for solution by a genetic algorithm, due to its structure, the presence of multiple local optima, and the low effort required for evaluating the objective function. The presented technique has been applied to a nonlinear binary distillation column where up to six sensors are placed along the height of the column.

This paper presents a fiber Bragg gating (FBG) sensor that can be surface mounted for simultaneous strain and temperature measurements. By embedding a conventional FBG sensor in a composite laminate, local birefringence is introduced, which causes the bandwidth of the FBG spectrum to vary with strain as well as temperature. As such, temperature and strain can be simultaneously determined from two FBG spectral parameters, i.e. the spectral bandwidth and the Bragg wavelength. Techniques for improving the spectrum of the FBG-composite sensor and for inversely determining the strain and temperature from the measured FBG spectral parameters are discussed. Thermal–mechanical testing of the FBG-composite sensor was carried out to validate the sensor performance. The measurement errors, within one standard deviation, for the strain and temperature measurements were found to be ±62 με and ±1.94 °C, respectively.

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