Sensor Placement Algorithms for Process Efficiency Maximization

Even though the senor placement problem has been studied for process plants, it has been done for minimizing the number of sensors, minimizing the cost of the sensor network, maximizing the reliability, or minimizing the estimation errors. In the existing literature, no work has been reported on the development of a sensor network design (SND) algorithm for maximizing efficiency of the process. The SND problem for maximizing efficiency requires consideration of the closed-loop system, which is unlike the open-loop systems that have been considered in previous works. In addition, work on the SND problem for a large fossil energy plant such as an integrated gasification combined cycle (IGCC) power plant with CO2 capture is rare.;The objective of this research is to develop a SND algorithm for maximizing the plant performance using criteria such as efficiency in the case of an estimator-based control system. The developed algorithm will be particularly useful for sensor placement in IGCC plants at the grassroots level where the number, type, and location of sensors are yet to be identified. In addition, the same algorithm can be further enhanced for use in retrofits, where the objectives could be to upgrade (addition of more sensors) and relocate existing sensors to different locations. The algorithms are developed by considering the presence of an optimal Kalman Filter (KF) that is used to estimate the unmeasured and noisy measurements given the process model and a set of measured variables. The designed algorithms are able to determine the location and type of the sensors under constraints on budget and estimation accuracy. In this work, three SND algorithms are developed: (a) steady-state SND algorithm, (b) dynamic model-based SND algorithm, and (c) nonlinear model-based SND algorithm. These algorithms are implemented in an acid gas removal (AGR) unit as part of an IGCC power plant with CO2 capture. The AGR process involves extensive heat and mass integration and therefore, is very suitable for the study of the proposed algorithm in the presence of complex interactions between process variables

Estimations of Gasifier Wall Temperature and Extent of Slag Penetration Using a Refractory Brick with Embedded Sensors

The short service life of refractory lining in a slagging gasifier in the integrated gasification combined cycle results in low availability and high operating cost. For longer life of the refractory lining, estimation of slag penetration length and monitoring of wall temperature are important. In this paper, we have investigated two types of embedded sensors in the refractory lining of gasifier, namely, thermistor and interdigital capacitor, to estimate the wall temperature profile and extent of slag penetration. Conventional correlation-based approaches are not satisfactory for estimating outputs of interest from the raw sensor data for these systems because of high temperature gradient along the sensor as well as temporal change in the refractory properties due to slag penetration. Therefore, a thermal model of refractory brick, slag penetration model, and models of the embedded sensors are developed and used to estimate temperature and slag penetration profile by using linear and nonlinear estimators

Population pharmacokinetics of cabotegravir following administration of oral tablet and long‐acting intramuscular injection in adult HIV‐1‐infected and uninfected subjects

AimTo characterize cabotegravir population pharmacokinetics using data from phase 1, 2 and 3 studies and evaluate the association of intrinsic and extrinsic factors with pharmacokinetic variability.MethodsAnalyses were implemented in NONMEM and R. Concentrations below the quantitation limit were modelled with likelihood-based approaches. Covariate relationships were evaluated using forward addition (P < .01) and backward elimination (P < .001) approaches. The impact of each covariate on trough and peak concentrations was evaluated through simulations. External validation was performed using prediction-corrected visual predictive checks.ResultsThe model-building dataset included 23 926 plasma concentrations from 1647 adult HIV-1-infected (72%) and uninfected (28%) subjects in 16 studies at seven dose levels (oral 10-60 mg, long-acting [LA] intramuscular injection 200-800 mg). A two-compartment model with first-order oral and LA absorption and elimination adequately described the data. Clearances and volumes were scaled to body weight. Estimated relative bioavailability of oral to LA was 75.6%. Race and age were not significant covariates. LA absorption rate constant (KALA ) was 50.9% lower in females and 47.8% higher if the LA dose was given as two split injections. KALA decreased with increasing BMI and decreasing needle length. Clearance was 17.4% higher in current smokers. The impact of any covariate was ≤32% on trough and peak concentrations following LA administration. The final model adequately predicted 5097 plasma concentrations from 647 subjects who were not included in the model-building dataset.ConclusionsA cabotegravir population pharmacokinetic model was developed that can be used to inform dosing strategies and future study design. No dose adjustment based on subject covariates is recommended