Data Reconciliation Procedure Research Articles

Modelling and parameter estimation of trans‐β‐farnesene coordination polymerization

Abstracttrans‐β‐Farnesene is a bio‐derived terpene monomer that can polymerize, generating polymers with properties that can be similar to the properties of conventional petroleum‐derived polymers. For this reason, in the present study, several coordination polymerizations of trans‐β‐farnesene are carried out using the Ziegler–Natta catalyst system composed by neodymium versatate (), diisobutylaluminum hidride (DIBAH), and dimethyldichlorosilane (DMDCS) in order to evaluate the influence of key operation variables on the control of average molar masses and monomer conversion. A phenomenological model is proposed to describe the coordination polymerization of trans‐β‐farnesene, and the kinetic parameters required to simulate the reactions are estimated. The initial concentration of DIBAH used as a chain transfer agent (CTA) is calculated by a data reconciliation procedure since this very active compound can participate in undesired side reactions. It is shown that the initial monomer, DIBAH, and concentrations exert strong influences on the monomer conversion and average molar masses of (poly)farnese while the temperature effect is not so pronounced. The proposed kinetic mechanism was able to predict well the experimental data collected during the reactions, with the successful reconciliation of CTA concentrations and estimation of model parameters.

Virtual flow metering of production flow rates of individual wells in oil and gas platforms through data reconciliation

In the present work, virtual flow metering (VFM) tools are developed to provide estimates for the individual flow rate measurements of the wells operated simultaneously in typical oil and gas platforms. Data reconciliation (DR) procedures are used to calibrate phenomenological models using available measured profiles of well pressure and temperature, and overall flow rates of gas, oil and water. In order to evaluate the relative importance of key hypotheses, three different phenomenological models were used to describe the platform operation: (i) a simple single phase fluid flow model, to evaluate whether the DR task can be successfully accomplished with the available data; (ii) a simple multiphase fluid flow model, in order to evaluate whether the explicit consideration of the multiphase nature of the problem can affect the obtained DR results; and (iii) a detailed phenomenological multiphase flow model, to represent accurately the behavior of an oil and gas production platform. Obtained results show that the proposed DR scheme can be indeed implemented successfully in the form of a VFM sensor to assign online and in real time the individual production of wells operated simultaneously in typical oil and gas platforms. The DR-based VFM was able to reconcile total oil and total water flow rates with average relative deviations of 0.87% and 17% respectively and maximum deviation of 2.3% for oil flow rates. Besides, the DR-based VFM tool provided insights about the system behavior and sensoring management, being able to indicate the oil lines and sensors that were subject to the highest variability.

Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation.

For an industrial process, the estimation of feeding composition is important for analyzing production status and making control decisions. However, random errors or even gross ones inevitably contaminate the actual measurements. Feeding composition is conventionally obtained via discrete and low-rate artificial testing. To address these problems, a feeding composition estimation approach based on data reconciliation procedure is developed. To improve the variable accuracy, a novel robust M-estimator is first proposed. Then, an iterative robust hierarchical data reconciliation and estimation strategy is applied to estimate the feeding composition. The feasibility and effectiveness of the estimation approach are verified on a fluidized bed roaster. The proposed M-estimator showed better overall performance.

A rigorous simulation model of geothermal power plants for emission control

Geothermal power plant (GTPP) operating conditions and associated emissions mainly depend on the endogenic fluid used to generate power, and the case of GTPPs located in Tuscany (Italy) is considered in this study. Since measuring on-line the quantity of emitted pollutants is a difficult task, a process simulation model featuring all unit operations of the GTTP is developed using UniSim Design® to forecast and control pollutant emissions. An accurate identification of the thermodynamic correlation parameters for the solubility of the considered pollutants (mercury and hydrogen sulfide) in water has been performed to match literature values. A data reconciliation procedure is used to match the simulation model outcome with real measurements of two (20 MWe and 40 MWe) GTTPs. Results are fully satisfactory as the mercury emission forecasted is always slightly above the measured data, evidencing the model is conservative, and hence reliable in ensuring satisfaction of emission limits established by law. For the 20 MWe plant, the simulated total mercury emissions are 3.31 g/h exceeding the measured ones by 27%, while for the 40 MWe plant, they are 1.38 g/h above the measured ones by 4%. In addition, for the 20MWe plant, pollutants emission and net power production are both considered in a performance analysis. The worst case scenario for power generation (18.7 MW) is in summer conditions, while for pollutant emission is in winter conditions with 5.22 g/h of total mercury and 20.46 kg/h of hydrogen sulfide. Finally, energetic performances result to be independent from the environmental measures adopted.

ON-LINE PROCESS MONITORING USING A ROBUST STATISTICS BASED METHODOLOGY

Robust Data Reconciliation strategies provide unbiased variable estimates in the presence of a moderate quantity of measurement gross errors. Systematic errors which persist in time, as biases or drifts, overcome this quantity causing the deterioration of the estimates. This also occurs due to the presence of process leaks. The fast detection of those faults avoids the use of biased solutions of the data reconciliation procedure, and allows to perform quick corrective actions. In this work, a methodology for leak detection is incorporated into a robust data reconciliation procedure that detects and classifies systematic observation errors. The strategy makes use of the Robust Measurement Test, to detect outliers and leaks, and the Robust Linear Regression of the data contained in a moving window to distinguish between biases and drifts. The methodology is applied for two benchmarks extracted from the literature. Results highlight the performance of the proposed strategy.

Dynamic System State Estimation and Outlier Detection Using Robust Data Reconciliation

State estimation and detection of measurement systematic errors are critical components of plant monitoring and control procedures. Reliable estimations of the process variables are attained by Classic Dynamic Data Reconciliation procedures when measurements follow exactly a known distribution. However, if this assumption happens approximately due to the presence of systematic errors, as outliers, classic dynamic data reconciliation provides biased results. In this work, a two-step methodology of Robust Dynamic Data Reconciliation and Systematic Error Detection is proposed. It takes advantages of a moving measurement window of fixed dimension and the features of the M-estimators. Furthermore, the presence of outliers is detected using a Robust Measurement Test. Two case studies are proposed, which work with the Huber and Biweigth M-estimators. A nonlinear benchmark extracted from the literature is considered, and performance measures are reported. The results obtained demonstrate the effectiveness of the proposed methodology.

An environment for topology analysis and data reconciliation of the pre-heat train in an industrial refinery

Monitoring the operating performance of the pre-heat train in an industrial refinery is of primary concern due to its effect on the production costs as well as on emissions and safety issues. The estimation of the true state conditions of a preheat train from the raw measurements is essential to achieve optimal process monitoring, control, and optimization. An integrated approach for data reconciliation involves a set of procedures applied on the process measurements. The case of a pre-heat train typically leads to the resolution of a large-scale problem and the manipulation of countless process data. In this paper, an environment is proposed to expedite the creation of the matrix systems and automatically execute the data reconciliation (DR) procedures. The implementation of the environment for the plant topology analysis, variable classification, bias detection/estimation, data reconciliation, and prediction of unmeasured process variables is discussed in terms of an application to a pre-heat train in an industrial refinery. The data used consists of a snapshot of the actual operating data and, in this specific application, an average during three hours of operation.

On-Line Dynamic Data Reconciliation in Batch Suspension Polymerizations of Methyl Methacrylate

A phenomenological model was developed to describe the dynamic evolution of the batch suspension polymerization of methyl methacrylate in terms of reactor temperature, pressure, concentrations and molecular properties of the final polymer. Then, the phenomenological model was used as a process constraint in dynamic data reconciliation procedures, which allowed for the successful monitoring of reaction variables in real-time and on-line. The obtained results indicate that heat transfer coefficients change significantly during the reaction time and from batch to batch, exerting a tremendous impact on the process operation. Obtained results also indicate that it can be difficult to attain thermodynamic equilibrium conditions in this system, because of the continuous condensation of evaporated monomer and the large mass transfer resistance offered by the viscous suspended droplets.

Concurrent reconciliation of chemical and mineral assays for mineral processing circuits

The steady-state data reconciliation of mineral processing plants has traditionally depended on chemical assays of samples collected from various streams in the plant. In some cases, additional information in the form of size assays and size-wise chemical assays have been employed to improve the reconciliation problem. Reconciliation algorithms, in addition to making the measured data consistent with the basic conservation principles, also help in predicting the material flow rates in different streams. This information helps the process engineer to identify and troubleshoot problem areas, and further use it to optimize the plant's performance. If all the minerals present in the ore system are known (established through X-Ray Diffraction), the chemical formulae of the minerals provide the relationship between the chemical assays and mineral composition. In this communication, first we propose an approach to estimate the mineral composition from the measured chemical assays for a given sample or stream. It is then extended to a plant-wide concurrent data reconciliation methodology, involving both the chemical assays and mineral composition. This concurrent data reconciliation procedure provides estimates of mineral composition around the plant even when mineral composition measurements are not available. This additional information will help the plant engineers to track and improve their plant's behavior more intuitively. When mineral compositions are measured (for example, through QEMSCAN) they can be reconciled along with chemical assays. The methodology is demonstrated with data from batch and semi-batch laboratory studies on selective dispersion-flocculation of iron ore slimes.

Applying Fuzzy and Probabilistic Uncertainty Concepts to the Material Flow Analysis of Palladium in Austria

AbstractMaterial flow analysis (MFA) is a widely applied tool to investigate resource and recycling systems of metals and minerals. Owing to data limitations and restricted system understanding, MFA results are inherently uncertain. To demonstrate the systematic implementation of uncertainty analysis in MFA, two mathematical concepts for the quantification of uncertainties were applied to Austrian palladium (Pd) resource flows and evaluated: (1) uncertainty ranges expressed by fuzzy sets and (2) uncertainty ranges defined by normal distributions given as mean values and standard deviations. Whereas normal distributions represent the traditional approach for quantifying uncertainties in MFA, fuzzy sets may offer additional benefits in relation to uncertainty quantification in cases of scarce information. With respect to the Pd case study, the fuzzy representation of uncertain quantities is more consistent with the actual data availability in cases of incomplete databases, and fuzzy sets serve to highlight the effect of uncertainty on resource efficiency indicators derived from the MFA results. For both approaches, data reconciliation procedures offer the potential to reduce uncertainty and evaluate the plausibility of the model results. With respect to Pd resource management, improved formal collection of end‐of‐life (EOL) consumer products is identified as a key factor in increasing the recycling efficiency. In particular, the partial export of EOL vehicles represents a substantial loss of Pd from the Austrian resource system, whereas approximately 70% of the Pd in the EOL consumer products is recovered in waste management. In conclusion, systematic uncertainty analysis is an integral part of MFA required to provide robust decision support in resource management.

Simultaneous allocation and data reconciliation procedure in life cycle inventory analysis using fuzzy mathematical programming

AbstractLife cycle assessment (LCA) is a methodology used in assessing the environmental impacts of products. Life cycle inventory analysis (LCI) is one of the four components of LCA which quantifies flows of materials and pollutants in the entire life cycle (cradle‐to‐grave) of the product. Data are commonly collected from various sources (e.g. publications, databases and site‐specific measurements) which may result in violations of mass and energy balances due to uncertainties. In such cases, the data can be adjusted using reconciliation methods to improve the reliability of the results. Another computational issue in LCI arises when two or more products are involved, known as the multi‐functionality or allocation problem. The problem is how to allocate resource and emission streams to each product. This work describes a model using fuzzy optimization for solving both allocation and data reconciliation problems simultaneously. The model is illustrated on an aluminum industry case study involving open‐loop recycling, and the approach is compared with two sequential allocation and data reconciliation methods. Copyright © 2010 Curtin University of Technology and John Wiley & Sons, Ltd.

Simultaneous robust data reconciliation and gross error detection through particle swarm optimization for an industrial polypropylene reactor

In a previous study, a nonlinear dynamic data reconciliation procedure (NDDR) based on the particle swarm optimization (PSO) method was developed and validated in line and in real time with actual industrial data obtained for an industrial polypropylene reactor ( Prata et al., 2009, 2008b). The procedure is modified to allow for robust implementation of the NDDR problem with simultaneous detection of gross errors and estimation of model parameters. The negative effects of the less frequent gross errors are eliminated with the implementation of the Welsch robust estimator, avoiding the computation of biased estimates and implementation of iterative procedures for detection and removal of gross errors. The performance of the proposed procedure was tested in line and in real time in an industrial bulk propylene polymerization process. A phenomenological model of the real process, based on the detailed mass and energy balances and constituted by a set of algebraic-differential equations, was implemented and used for interpretation of the actual plant behavior. The resulting nonlinear dynamic optimization problem was solved iteratively on a moving time window, in order to capture the current process behavior and allow for dynamic adaptation of model parameters. Results indicate that the proposed procedure, based on the combination of the PSO method and the robust Welsch estimator, can be implemented in real time in real industrial environments, allowing for the simultaneous detection of gross errors and estimation of process states and model parameters, leading to more robust and reproducible numerical performance.

Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: Application for an industrial polypropylene reactor

Abstract This work presents a procedure to solve nonlinear dynamic data reconciliation (NDDR) problems with simultaneous parameter estimation based on particle swarm optimization (PSO). The performance of the proposed procedure is compared to the performance of a standard Gauss–Newton (GN) scheme in a real industrial problem, as presented previously by Prata et al. [2006. Simultaneous data reconciliation and parameter estimation in bulk polypropylene polymerizations in real time. Macromolecular Symposia 243, 91–103; 2008. In-line monitoring of bulk polypropylene reactors based on data reconciliation procedures. Macromolecular Symposia 271, 26–37]. Both methods are used to solve the NDDR problem in an industrial bulk propylene polymerization process, using real data in real time for the simultaneous estimation of model parameters and process states. A phenomenological model of the real process, based on the detailed mass and energy balances and constituted by a set of algebraic–differential equations, was implemented and used for interpretation of the actual plant behavior in real time. The resulting nonlinear dynamic optimization problem was solved iteratively on a moving time window, in order to capture the current process behavior and allow for dynamic adaptation of model parameters. Obtained results indicate that the proposed PSO procedure can be implemented in real time, allowing for estimation of more reliable process states and model parameters and leading to much more robust and reproducible numerical performance.

Automatic Control in Mineral Processing Plants: an Overview.

Abstract For controlling a process, one should not forget that for strongly disturbed, poorly modeled and difficult to measure processes, such as those involved in the mineral processing industry, the peripheral tools of the control loop (fault detection and isolation system, data reconciliation procedure, observers, soft sensors, optimizers, model parameter tuners…) are as important as the controller itself. The paper briefly describes each element of this generalized control loop, while putting emphasis on the mineral processing specific cases.

Simultaneous data reconciliation and parameter estimation. Application to a basic oxygen furnace.

In the steel industry, the determination of the control system set-points of batch processes is a common problem. It consists in adjusting the set-points in order to reach the given product specifications thanks to a process model. Small changes in operating conditions may impact final product quality. This is particularly true for the Basic Oxygen Furnace (BOF) where the information collected during a specific batch serves to adjust the set-points of the next batch. For being able to control that type of process, measurements must be made coherent and it may be convenient to use data reconciliation procedure. The proposed paper describes a method allowing simultaneous data reconciliation and model parameter estimation. Parameter estimation results can either be used to update the process model or to detect abnormal parameter variations due, e.g. to fouling, corrosion, degradation of parts of the process.

Robust Data Reconciliation to Determine Basic Oxygen Furnace Set-points

Abstract In the steel industry, the determination of the control system set-points of batch processes is a common problem. It consists in adjusting the set-points in order to reach the given product specifications thanks to a process model. Small changes in operating conditions may impact final product quality. This is particularly true for the Basic Oxygen Furnace (BOF) where the information collected during a specific batch serves to adjust the set-points of the next batch. For being able to control that type of process, measurements must be made coherent and it may be convenient to use data reconciliation procedure. The proposed paper describes a method allowing simultaneous data reconciliation and model parameter estimation. Parameter estimation results can either be used to update the process model or to detect abnormal parameter variations due, e.g. to fouling, corrosion, degradation of parts of the process.

In‐Line Monitoring of Bulk Polypropylene Reactors Based on Data Reconciliation Procedures

AbstractThe main objective of this work is the implementation of a nonlinear dynamic data reconciliation (NDDR) procedure for the in‐line monitoring of the bulk propylene polymerization, as performed in a real industrial site. Special attention is given to monitoring of the melt flow index (MI) of the polymer product obtained in an industrial extruder at different operating conditions. In order to do that, a model is developed to describe the polymerization process and the controlled degradation of polypropylene in a reactive extruder. The model is implemented in real time, allowing for in‐line and real‐time monitoring of the plant operation.

Design of Industrial Reactive Absorption Processes in Sour Gas Treatment Using Rigorous Modelling and Accurate Experimentation

In this paper a rigorous rate-based model for the selective absorption and desorption of sour gases in packed towers within a coke oven gas purification process (VACASULF ®-process) is presented. It considers multi-component mass transfer of CO 2, H 2S, NH 3 and HCN in aqueous potassium hydroxide (KOH) or potash (K 2CO 3) solutions and the influence of chemical reactions on mass transfer using enhancement factors. A modified Pitzer model is used to estimate activity coefficients in the electrolyte solution for the calculation of the chemical and the phase equilibria as well as the reaction kinetics (Brönsted-Bjerrum equation). Using data from the literature this thermodynamically consistent approach was successfully validated for the phase equilibria and the reaction kinetic calculation. Since no experimental data for the above-mentioned chemical system in packed towers is available in the literature, own experiments were carried out in a pilot plant packed tower. A newly developed experimental method using redundant measurements and a Data Reconciliation procedure improved the accuracy of concentration profiles for the gas and the liquid phase. Using eight absorption and eight desorption experiments, as well as industrial on-site measurements of the entire plant, the model was successfully validated. In addition, the model was used for global optimization using evolutionary algorithms, revealing an optimization potential of 30% in operating costs.

Enhancing dynamic data reconciliation performance through time delays identification

In order to improve the performance of data reconciliation methods, an efficient Genetic algorithm (GA) for determining time delays has been developed. Delays are identified by searching the maximum correlation among the process variables. The delay vector (DV) is integrated within a dynamic data reconciliation (DDR) procedure based on Kalman filter through the measurements error model. The proposed approach can be satisfactorily applied not only off-line but also on-line. It was firstly validated in a dynamic process with recycles and feedback control loops. Then, the methodology was successfully applied to a highly non-linear and complex challenging control case study, the Tenessee Eastman benchmark process, demonstrating its robustness in complex industrial problems. This case study required to implement an extended Kalman filter to deal with the existing non-linearities.

Mass balance equilibration: A robust approach using contaminated distribution

The problem of obtaining reliable estimates of the state of a process is a fundamental objective in process supervision, these estimates being used to understand the process behaviour. Measurements are collected in order to know, at each time, the behaviour of the process and give a way to verify if its functioning is those that has been defined and given by the user. For that purpose, a wide variety of techniques has been developed to perform what is currently known as data reconciliation: the underlying idea is to check if the measurements fulfil the model of the process and, if it is not the case, to analyse what are the noises affecting the measurements and finally to correct the measurements. Unfortunately, the measurements may be unknowingly corrupted by gross errors which effects are added to those of the noise. As a result, the data reconciliation procedure can give rise to absurd results and the estimated variables are corrupted by this bias. Several schemes have been suggested to cope with the corruption of normal assumption of the errors by detecting a priori or a posteriori gross errors. Among them, an approach consists to take into account the non ideality of the measurement error distribution using an objective function constructed on contaminated error distribution. This approach has been developed for data reconciliation and has been tested on several applications. In this paper, we restrict our analysis to processes described by linear and bilinear mass balance equations. However, despite this limitation, this kind of models are of current use because they allow to describe total mass and partial mass balances. In the following, we adopt and develop the using of contaminated data distribution for the data reconciliation problem. Section 2 is devoted to shortly recall the background of data reconciliation problem. In Section 3, the proposed robust data reconciliation method is developed. It is thereafter illustrated through an academic example in Section 4.