Industrial process data validation and reconciliation, or more briefly, process data reconciliation (PDR), is a technology that uses process information and mathematical methods in order to automatically ensure data validation and reconciliation by correcting measurements in industrial processes. The use of PDR allows for extracting accurate and reliable information about the state of industry processes from raw measurement data and produces a single consistent set of data representing the most likely process operation. Models, data and measurement errors Industrial processes, for example chemical or thermodynamic processes in chemical plants, refineries, oil or gas production sites, or power plants, are often represented by two fundamental means:

Models that express the general structure of the processes,

Data that reflects the state of the processes at a given point in time.

Models can have different levels of detail, for example one can incorporate simple mass or compo

Robust model development and enhancement techniques for improved on-line spectroscopic monitoring of bioprocesses

Michal Dabros

The recent phenomenal growth of the field of biotechnology has contributed to a mounting pressure to improve process efficiency and productivity and to increase the quality and safety of end-products. This demand gave rise to the discipline of on-line bioprocess monitoring encompassing tools that provide a live analytical window into the process and create extensive opportunities for process development, control and optimization. Among these tools, on-line spectroscopy has surfaced as one of the prominent techniques of monitoring the concentration of process metabolites and biomass. Unfortunately, one of the major obstacles currently impeding the industrial spread of the technology is the chronic lack of robustness and long-term stability of spectrometers in on-line monitoring conditions. The work presented in this dissertation aims to explore various ways of improving the reliability of spectroscopic bioprocess monitoring instruments without interfering with their real-time functionality. A Fourier-transform mid-infrared (FTIR) and a dielectric (capacitance) spectrometer are used as the model instruments in a series of experiments involving the cultivation of yeasts. A general review of methods that help maintain the on-line reliability of bioprocess spectrometers is presented first. A clear distinction is made between techniques that involve retrospective reprocessing of the obtained predictions using off-line measurements, and methods that perform the signal or calibration model correction in real-time. A case study, included in the review, demonstrates the effectiveness of some of the latter techniques in correcting mid-IR spectral drift comparable in magnitude of absorbance to a pure component spectrum of glucose at 10 g/l. It is shown that the drift can be significantly reduced using techniques such as spectrum derivation, spectral anchoring and Orthogonal Signal Correction (OSC). Proposed next is a technique to generate on-line reference standards for the FTIR without the need of sampling. The method involves the periodic injection of small amounts of the monitored metabolites into the culture medium. The corresponding measured differences in the spectra are used as reference measurements for recalibrating the model in real-time based on the technique of Dynamic Orthogonal Projection (DOP). Applying this approach leads to a decrease, ranging from 25 to 50 %, in the standard error of prediction of metabolite concentrations. The following study compares three distinct methods of calibrating a dielectric spectrometer: fitting capacitance data to the theoretical Cole-Cole equation, correlating capacitance measurements linearly to biomass concentration and the modeling of scanning capacitance spectra using multivariate (PLS) analysis. The performance and robustness of each calibration technique is assessed during a sequence of validation batches in two experimental settings differing in the level of signal noise. The linear and PLS models outperform the Cole-Cole model in terms of biomass concentration prediction error, particularly in the more noisy conditions. The PLS model proves to be the most robust in rejecting the signal variability. Estimates of the mean cell size are additionally done using the Cole-Cole and PLS models, the latter technique giving more precise results. Finally, in a study involving the simultaneous use of the FTIR and capacitance spectrometers, data reconciliation is shown to improve the on-line prediction of process analytes and biomass. The concentrations predicted by both spectrometers are reconciled in real-time based on mass and elemental balances involving off-gas analysis and measurements of base addition. A statistical test is used to confirm the integrity of the balances before the reconciliation. The technique leads to a significant reduction in the standard error of prediction for all the components involved.

EPFL2008

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

Diogo Filipe Mateus Rodrigues

The concept of reaction variants and invariants for lumped reaction systems has been known for several decades. Its applications encompass model identification, data reconciliation, state estimation and control using kinetic models. In this thesis, the concept of variants and invariants is extended to distributed reaction systems and used to develop new applications to estimation, control and optimization. The thesis starts by reviewing the material and heat balances and the concept of variants and invariants for several lumped reaction systems. Different definitions of variants and invariants, in particular the vessel extents, are presented for the case of homogeneous reaction systems, and transformations to variants and invariants are obtained. The extension to systems with heat balance and mass transfer is also reviewed. The concept of extents is generalized to distributed reaction systems, which include many processes involving reactions and described by partial differential equations. The concept of extents and the transformation to extents are detailed for various configurations of tubular reactors and reactive separation columns, as well as for a more generic framework that is independent of the configuration. New developments of the extent-based incremental approach for model identification are presented. The approach, which compares experimental and modeled extents, results in maximum-likelihood parameter estimation if the experimental extents are uncorrelated and the modeled extents are unbiased. Furthermore, the identification problem can be reformulated as a convex optimization problem that is solved efficiently to global optimality. The estimation of unknown rates without the knowledge or the identification of the rate models is described. This method exploits the fact that the variants computed from the available measurements allow isolating the different rates. Upon using a Savitzky-Golay filter for differentiation of variants, one can show that the resulting rate estimator is optimal and obtain the error and variance of the rate estimates. The use of variants and invariants for reactor control is also considered. Firstly, offset-free control via feedback linearization is implemented using kinetic models. Then, it is shown how rate estimation can be used for control via feedback linearization without kinetic models. By designing an outer-loop feedback controller, the expected values of the controlled variables converge exponentially to their setpoints. This thesis presents an approach to speed up steady-state optimization, which takes advantage of rate estimation without rate models to speed up the estimation of steady state for imperfectly known dynamic systems with fast and slow states. Since one can use feedback control to speed up convergence of the fast part, rate estimation allows estimating the steady state of the slow part during transient operation. The application to dynamic optimization is also shown. Adjoint-free optimal control laws are computed for all the types of arcs in the solution. In the case of reactors, the concept of extents allows the symbolic computation of optimal control laws in a systematic way. A parsimonious input parameterization is presented, which approximates the optimal inputs well with few parameters. For each arc sequence, the optimal parameter values are computed via numerical optimization. The theoretical results are illustrated by simulated examples of reaction systems.

EPFL2018

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

Diogo Filipe Mateus Rodrigues

EPFL2018

Robust model development and enhancement techniques for improved on-line spectroscopic monitoring of bioprocesses

Michal Dabros

EPFL2008

Data validation and reconciliation

Models that express the general structure of the processes,

Data that reflects the state of the processes at a given point in time.

Data Reconciliation and Sampling Protocol Design: The Case of a Paper Deinking Proces

Robust model development and enhancement techniques for improved on-line spectroscopic monitoring of bioprocesses

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

Robust model development and enhancement techniques for improved on-line spectroscopic monitoring of bioprocesses

Data Reconciliation and Sampling Protocol Design: The Case of a Paper Deinking Proces