Monitoring of the primary drying of a lyophilization process in vials

An innovative and modular system (LyoMonitor) for monitoring the primary drying of a lyophilization process in vials is illustrated: it integrates some commercial devices (pressure gauges, moisture sensor and mass spectrometer), an innovative balance and a manometric temperature measurement system based on an improved algorithm (DPE) to estimate sublimating interface temperature and position, product temperature profile, heat and mass transfer coefficients. A soft-sensor using a multipoint wireless thermometer can also estimate the previous parameters in a large number of vials. The performances of the previous devices for the determination of the end of the primary drying are compared. Finally, all these sensors can be used for control purposes and for the optimization of the process recipe; the use of DPE in a control loop will be shown as an exampl

Trends in shuttle entry heating from the correction of flight test maneuvers

A new technique was developed to systematically expand the aerothermodynamic envelope of the Space Shuttle Protection System (TPS). The technique required transient flight test maneuvers which were performed on the second, fourth, and fifth Shuttle reentries. Kalman filtering and parameter estimation were used for the reduction of embedded thermocouple data to obtain best estimates of aerothermal parameters. Difficulties in reducing the data were overcome or minimized. Thermal parameters were estimated to minimize uncertainties, and heating rate parameters were estimated to correlate with angle of attack, sideslip, deflection angle, and Reynolds number changes. Heating trends from the maneuvers allow for rapid and safe envelope expansion needed for future missions, except for some local areas

Real-time implementation of an adaptive control system for a 3-zone rapid thermal processing station

In this thesis, the implementation details of a real time adaptive control system for a TI 3-zone RTP station, as well as the simulation and the experimental results are presented. Extensive simulation of the system is performed in order to ensure proper operation of the system. The experimental results are used to verify proper operation of the closed loop control system. Initial experiments were conducted using two thermocouples. Further experiments were conducted using one thermocouple for the purpose of testing the performance of the system using Extended Kalman Filter as the state estimator. The implementation of the control system is carried out on an IBM compatible PC hosting a Transputer parallel -processing system. The motivation for utilizing the parallel processing system is to ensure future extensibility of the system. The eventual incorporation of a remote temperature sensing method such as Multi-Wavelength Imaging Pyrometer (M-WIP) will require great deal of computing power from the system. The implementation of the software for the system is also carried out with goal of providing ease of maintenance and extensibility. The implementation of graphical user environment also provides to the user point and click operation of the system as well as real time plotting capability

Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations

The Community Land Model (CLM) includes a large variety of parameterizations, also for flow in the unsaturated zone and soil properties. Soil properties introduce uncertainties into land surface model predictions. In this paper, soil moisture and soil properties are updated for the coupled CLM and Community Microwave Emission Model (CMEM) by the Local Ensemble Transform Kalman Filter (LETKF) and the state augmentation method. Soil properties are estimated through the update of soil textural properties and soil organic matter density. These variables are used in CLM for predicting the soil moisture retention characteristic and the unsaturated hydraulic conductivity, and the soil texture is used in CMEM to calculate the soil dielectric constant. The following scenarios were evaluated for the joint state and parameter estimation with help of synthetic L-band brightness temperature data assimilation: (i) the impact of joint state and parameter estimation; (ii) updating of soil properties in CLM alone, CMEM alone or both CLM and CMEM; (iii) updating of soil properties without soil moisture update; (iv) the observation localization of LETKF. The results show that the characterization of soil properties through the update of textural properties and soil organic matter density can strongly improve with assimilation of brightness temperature data. The optimized soil properties also improve the characterization of soil moisture, soil temperature, actual evapotranspiration, sensible heat flux, and soil heat flux. The best results are obtained if the soil properties are updated only. The coupled CLM and CMEM model is helpful for the parameter estimation. If soil properties are biased, assimilation of soil moisture data with only state updates increases the root mean square error for evapotranspiration, sensible heat flux, and soil heat flux

Dynamic Modelling and Control of Grid-Level Energy Storage Systems

The focus of this work is on two energy storage technologies, namely pumped storage hydroelectricity (PHS) and secondary batteries. Under secondary battery technologies, two potential technologies for grid-scale storage, namely high-temperature sodium-sulfur (NaS) battery and vanadium redox flow battery (VRFB), are investigated. PHS is a largescale (\u3e100 MW) technology that stores and generates energy by transporting water between two reservoirs at different elevations. The goal is to develop a detailed dynamic model of PHS and then design the controllers to follow the desired load trajectory accurately with high efficiency. The NaS battery and VRFB are advanced secondary batteries which can be charged and discharged rapidly. Since temperature excursion of high temperature NaS batteries especially under fast cycling conditions is a safety hazard and the temperature excursion can take place at some location within the cell where measurement is not feasible, the focus is on a model-based approach for transient analysis and development of novel thermal management techniques. A detailed thermo-electrochemical dynamic model of a single NaS has been developed. As a detailed cell model is computationally intractable for simulating large number of cells in the battery, various strategies such as coordinate transformation, orthogonal collocation, and model reformulation have been developed to obtain a reduced order model that solves significantly faster than the full, high-dimensional model but provides an accurate estimate of the key variables such as transient voltage/current/temperature profile in the cell. Sodium sulfur batteries need to be maintained within a temperature range of 300-4000C. Therefore, the focus was on developing thermal management strategies that can not only maintain the cell temperature near the optimum, but can effectively utilize the heat, improving the overall efficiency of the battery system. VRFBs can provide large amount of storage as the electrolytes are stored in separate tanks. However, the self-discharge reactions (due to crossover) along with the undesired side reactions and the dissolved water in the membrane, can significantly reduce the capacity. A dynamic model-based approach is developed for detection, identification, and estimation of capacity fade and SOC as a function of time. A model-based prognostic capability has been developed for estimating the remaining useful cell life

Kalman Filtering and its Application to On-Line State Estimation of a Once-Through Boiler

This thesis contributes to non-linear continuous-discrete Kalman filtering of multiplex systems through the development of two main ideas, namely, integration of the unscented transforms with linearly implicit methods and incorporation of simulation errors in the state estimation problem. The newly developed techniques are then applied to the technically relevant problem of state estimation on the main components of a utility boiler. State estimators in industrial systems are used as soft-sensors in monitoring and control applications as the most cost effective and practical alternative to telemetering all variables of interest. One such example is in utility boilers where reliable and real-time data characterising its behaviour is used to detect faults and optimise performance. With respect to the state-of-the-art, state estimators display limitations in real-time applications to large-scale systems. This motivates theoretical developments in state estimation as a first part in this thesis. These developments are aimed at producing more practical and efficient algorithms in non-linear continuous discrete Kalman filtering for stiff large-scale industrial systems. This is achieved using two novel ideas. The first is to exploit the similarities between the extended and unscented Kalman filter in order to estimate the Jacobian required for linearly implicit schemes, thereby tightly coupling state propagation and continuous-time simulation. The second is to account for numerical integration error by appending a stochastic local error model to the system's stochastic differential equation. This allows for coarser integration time steps in systems that are otherwise only suited to relatively small step sizes, making the filter more computationally efficient without lowering its potential to construct accurate estimates. The second part of this thesis uses these algorithms to demonstrate the feasibility of on-line state estimation on the main components of a once-through utility power boiler that require in excess of a hundred state variables to capture its behaviour with adequate fidelity. Two separate models of the boiler are developed, a MATLAB® and a Flownex® model, comprising the economiser, evaporators, reheaters, superheaters and furnace. The mathematical MATLAB® model is better suited to real-time execution and is used in the filter. The more sophisticated model is based on a commercial thermal-hydraulic simulation environment, Flownex® , and is used to validate the mathematical modelling philosophies and construct filter observation data. After validating the performance of the filter against ground-truth data provided by the Flownex® model, the filter is demonstrated on historical plant data to illustrate its utility

To improve model soil moisture estimation in arid/semi-arid region using in situ and remote sensing information

Soil moisture plays a key role in water and energy exchange in the land hydrologic process. Effective soil moisture information can be used for many applications in weather and hydrological forecasting, water resources, and irrigation system management and planning. However, to accurate modeling of soil moisture variation in the soil layer is still very challenging. In this study, in situ and remote sensing information of near-surface soil moisture is assimilated into the Noah land surface model (LSM) to estimate deep-layer soil moisture variation. The sequential Monte Carlo-Particle Filter technique, being well known for capability of modeling high nonlinear and non-Gaussian processes, is applied to assimilate surface soil moisture measurement to the deep layers. The experiments were carried out over several locations over the semi-arid region of the US. Comparing with in situ observations, the assimilation runs show much improved from the control (non-assimilation) runs for estimating both soil moisture and temperature at 5-, 20-, and 50-cm soil depths in the Noah LSM. © 2012 Springer-Verlag

Feedback control of gas metal arc braze-welding using thermal signals

textIn serial manufacturing processes, localized energy sources (e.g. plasma cutters, arc welders or water jets) induce material geometry transformations that yield a desired product. Simple parameter control of these energy sources does not necessarily ensure an optimal or successful part because of disturbances in the manufacturing process (material and temperature variations, etc). Currently, control in manufacturing is based on statistical process control where large databases for the manufacturing of a fixed process are available and have been compiled over several manufacturing runs. In the absence of a statistical database, and with the increased need for improved monitoring and throughput, there is need for active process control in manufacturing. In this work, Gas Metal Arc Braze-Welding (GMABW) will serve as a test-bed for the implementation of model predictive control (MPC) for a serial manufacturing process. This dissertation investigates the integration of real time modeling of the temperature field with control algorithms to control the evolving temperature field in the ix braze-welded base metal. Fundamental problems involving MPC that are addressed are modeling techniques to calculate temperature fields with reduced computational requirements and control algorithms that utilize the thermal models directly to inform the controller. The dissertation first outlines and compares analytical and computational thermal models and comparison with experimental data are obtained. A thermal model based on a metamodeling approach is used as the plant model for a classical control system and control parameters are found. Various techniques for dealing with signal noise encountered during experimentation are investigated. A proportional controller is implemented in the experimental setup that applies feedback control of the braze –welding process using thermal signals. A novel approach to MPC is explored by using a metamodel as the plant model for the braze-welding process and having the temperature trajectory dictated by the metamodel in the steady state region of the weld. Lastly, future work and extensions of this research are outlined.Mechanical Engineerin