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This article focuses on improving indoor positioning data through data reconciliation. Indoor positioning systems are increasingly used for resource tracking to monitor manufacturing and warehouse processes. However, measurement errors due to noise can negatively impact system performance. Redundant measurement involves the use of multiple sensor tags that provide position data on the same resource, to identify errors in the physical environment. If we have measurement data from the entire physical environment, a map-based average measurement error can be determined by specifying the points in the examined area where measurement data should be compensated and to what extent. This compensation is achieved through data reconciliation, which improves real-time position data by considering the measurement error in the actual position as an element of the variance-covariance matrix. A case study in a warehouse environment is presented to demonstrate how discrepancies in position data from two sensor tags on forklifts can be used to identify layout-based errors. The algorithm is generally capable of handling the multi-sensor problem in the case of indoor positioning systems. The key points are as follows:•The layout-based error detection is determined with the indoor positioning system measurement error.•This article shows how redundant measurements and data reconciliation can improve the accuracy of such systems.•Improving the accuracy of position data with the layout-based error map using a data reconciliation algorithm.

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Looking back at over a decade of research by herself and her group, the author advocates the added value of gas phase measurements and the application of mass balances, as well as the synergetic benefits obtained when combining both. The increased application of off-gas measurements for greenhouse gas emission monitoring offers a great opportunity to look at other components in the gas phase, particularly oxygen. Mass balances should not be strictly reserved for modellers but also prove useful while conducting lab experiments and studying full-scale measurement data. Combining off-gas measurements with mass balances may serve not only to quantify greenhouse gas emission factors and aeration efficiency but also to follow dynamic concentration profiles of dissolved components without dedicated sensors and/or to calculate other unmeasured variables. Mass-balance-based data reconciliation allows for obtaining reliable and accurate data, and even more when combined with off-gas analysis.

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In alumina production, the evaporation as the key process uses recyclable resources and reduces environmental pollution. In fact, the quality of export product with offline and delayed, results in low precision of process control and high energy consumption. To ensure green and efficient production, in this paper, a new prediction method integrating process knowledge and data-driven spatial-temporal adaptive model is put forward. First, to preprocessed production data for ensuring modeling accuracy, data reconciliation technology is adopted. Then, based on material and heat transfer mechanism, for equipment and industrial process, the mechanism models are established. Furthermore, with time difference and moving window model, an error compensation method is utilized in terms of double locally weighted kernel PLS for estimation error in hypothesis-based mechanism modeling. Finally, the data-driven spatial-temporal adaptive model and the process knowledge-based mechanism model are integrated. To illustrate the model feasibility, an industrial sodium aluminate solution evaporation is used. It demonstrates that, for the developed model, the prediction accuracy can reach more than 90% within the ± 2% error range, and effectively estimate the actual product quality and ensure the prediction effect.

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For a stochastic PID feedback control system, the uncertainty of the working environment often leads to the unsatisfied performance of the system, which does not meet the profit requirements. The working environment generally includes external disturbance and measurement noise, etc. Gaussian distributed measurement noise and disturbances are widely considered while non-Gaussian distributed measurement noise and disturbances are rarely considered. In this paper, the performance degradation of Gaussian/non-Gaussian disturbances and measurement noise on a stochastic PID feedback system is considered and analyzed. An efficient method, dynamic data reconciliation (DDR) is developed to filter measurement noise and disturbances and improve the performance of the stochastic PID feedback control system. By utilizing model-based and measured information, DDR avoids time delays in output estimation. With the detailed theoretical analysis and simulation verification, the effectiveness of the proposed DDR technology on the stochastic PID feedback control system is verified. Compared with conventional exponential filters, DDR can achieve better control performance. The proposed DDR is also used for the control system of the DC-AC​ converter. The improved effect of DDR on the output quality is demonstrated by the results.

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This study aimed to link experimental data dealing with complex agroecological systems. For sharing and linking collected data with the generic AEGIS (Agro-Ecological Global Information System) database, our work described in this data paper consists in mapping researcher variables to the AEGIS dictionary variable for different tropical crops (sugarcane, rice, sorghum or cover crops). Additionally, this data paper presents a study case based on sugarcane intercropping systems for evaluating 3 matching measures of variables.

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Historically, owing to the increase in processing capacity over the years, validation and adjustment of measurements have become imperative. In particular, concerning discussions related to data and results in analytical chemistry, there is always a need to improve their reliability. The data reconciliation technique has the objective of using measurement redundancies to obtain the best estimate of the true value and, consequently to minimize its uncertainty. Unfortunately, this powerful tool is less known and used by analytical chemists compared to other areas. This approach can be satisfactorily performed in decision-making procedures that focus on chemical analysis, chemometrics, biochemistry analysis, forensics, and environmental sciences, such as in a characterization study, regarding conformance or nonconformance with the specification, doubts related to the malfunctioning of meters and about the compatibility of test methods. This work discusses and sheds light on the importance of data reconciliation, including data reconciliation statistics and application of the technique, traditional data reconciliation in analytical chemistry, principal component analysis based on data reconciliation in analytical chemistry, and fuzzy data reconciliation in analytical chemistry.

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Fractional order PID (FOPID) has received much attention in recent years and has been applied in different fields. However, the parameter tuning of FOPID is a tough problem. In order to design an optimal FOPID controller, a parameter tuning method based on minimum variance control (MVC) rule is proposed in both SISO and MIMO control systems Considering the influence of measurement noise with Gaussian and non-Gaussian distribution, which is generally ignored in the controller design based on MVC rule, this paper applies dynamic data reconciliation (DDR) technology to suppress its influence and improve the control performance of the designed FOPID. In the case of considering Gaussian and non-Gaussian distributed measurement noise, SISO and MIMO control systems are employed to verify the effectiveness of the proposed FOPID parameter tuning and DDR method. The results show that the designed FOPID improves the performance of the control system, and the DDR technology suppresses the negative effect of measurement noise and improves the control performance of the designed FOPID.

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Hybrid renewable energy systems (HRES) are a nexus of various renewable energy sources that have been proposed as a solution to circumvent various issues of renewable energy systems when installed in isolation. During the operation of an HRES, efficient demand-side management of energy and real-time power trading requires accurate estimation of real-time variables for each sub-component. Generally, these variables are corrupted by measurement errors. To address this issue, in this study, we present various frameworks for reconciliation strategies that can be used to rectify the inconsistencies in the sensor measurements of HRES. Specifically, in this study, we evaluate the efficacy of various static and dynamic reconciliation strategies such as Regularized Particle filter (RPF), Ensemble Kalman filter (EnKF), and Extended Kalman filter (EKF) of a candidate HRES system with a solar panel, a fuel cell, and an electrolyzer. Proposed frameworks are evaluated using various simulation-based validation studies. To this end, we have considered different operational scenarios, namely, (i) single-rate sampling, (ii) multi-rate sampling, and (iii) sensor outage, to make the study comprehensive. Simulation results indicate that RPF yields the best estimation accuracy for all three operational scenarios with a performance improvement of 75% from EKF and by 50% from EnKF, with only a fractional increment in computational time.

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Even though sulfur compounds and their transformations may strongly affect wastewater treatment processes, their importance in water resource recovery facilities (WRRF) operation remains quite unexplored, notably when it comes to full-scale and plant-wide characterization. This contribution presents a first-of-a-kind, plant-wide quantification of total sulfur mass flows for all water and sludge streams in a full-scale WRRF. Because of its important impact on (post-treatment) process operation, the gaseous emission of sulfur as hydrogen sulfide (H2S) was also included, thus enabling a comprehensive evaluation of sulfur flows. Data availability and quality were optimized by experimental design and data reconciliation, which were applied for the first time to total sulfur flows. Total sulfur flows were successfully balanced over individual process treatment units as well as the plant-wide system with only minor variation to their original values, confirming that total sulfur is a conservative quantity. The two-stage anaerobic digestion with intermediate thermal hydrolysis led to a decreased sulfur content of dewatered sludge (by 36%). Higher (gaseous) H2S emissions were observed in the second-stage digester (42% of total emission) than in the first one, suggesting an impact of thermal treatment on the production of H2S. While the majority of sulfur mass flow from the influent left the plant through the treated effluent (> 95%), the sulfur discharge through dewatered sludge and gaseous emissions are critical. The latter are indeed responsible for odour nuisance, lower biogas quality, SO2 emissions upon sludge combustion and corrosion effects.

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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.

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Due to the complexity of the industrial working environment, controllers are susceptible to various disturbance signals, resulting in unsatisfactory control performance. Therefore, it is especially important to assess the controller performance. Considering the harmful effect of measurement noise on controller performance assessment (CPA) based on generalized minimum variance control (GMVC), this paper proposes dynamic data reconciliation (DDR) to improve the accuracy of CPA based on GMVC. The paper first introduces CPA based on GMVC, and then analyzes the influence of measurement noise on GMVC based CPA index. DDR combined with GMVC based CPA is then proposed and analyzed in both SISO and MIMO systems to weaken the impact of measurement noise on CPA index. For both Gaussian distributed noise and non-Gaussian distributed noise, the formulation of DDR is derived from the Bayesian formula and maximum likelihood estimate. The effectiveness of the proposed method is verified in different case studies (involving both SISO and MIMO systems), and further verified by the control process of DC-AC converter. The simulation and experiment results demonstrate that the results of CPA based on GMVC can be obviously improved by using DDR.

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The work deals with the removal by slow pyrolysis of epoxy resin from samples of spent nuclear fuel embedded in this polymer. Beyond the nuclear field, epoxy resin removal by pyrolysis is a typical issue for the recovery of metals in electronic waste. The main objective is to find the optimal conditions to remove hydrogen in the residual solid waste, in order to avoid hydrogen production by radiolysis during storage and so to prevent any risk of overpressure and explosion. The condensable pyrolysis products (tar-water mixture) and the char were characterised and quantified by elemental analyses, while the permanent gases were quantified by gas chromatography. A data reconciliation method was applied to adjust the values of raw measurements in order to complete the mass balances for both C, H, O and N elements and pyrolysis products. After studying the impact of temperature on the pyrolysis balance, experiments on a pilot furnace were conducted at 450 °C, in the frame of a parametric study of the heating rate, argon gas flow rate, resin mass and plateau time. At fixed temperature, we show that the plateau time is the only significant parameter for minimizing the residual hydrogen content in the char.

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Biosynthesis of poly-3-hydroxybutyrate (PHB) as a fermentation product enables the coupling of growth and product generation. Moreover, the reduction of oxygen supply should reduce operative cost and increase product yield. Generation of PHB as a fermentation product depends on the in vivo activity of an NADH-preferring acetoacetyl-CoA reductase. Proof of this concept requires (i) quantification of the cofactor preference, in physiologically relevant conditions, of a putative NADH-preferring acetoacetyl-CoA reductase and (ii) verification of PHB accumulation using an NADH-preferring acetoacetyl-CoA reductase in a species naturally incapable of doing so, for example, Escherichia coli. This dataset contains kinetic data obtained by spectrophotometry and data from a continuous culture of an engineered E. coli strain accumulating PHB under oxygen-limiting conditions. In this dataset it is possible to find (1) enzyme stability assays; (2) initial rates and progress curves from reactions catalyzed by two acetoacetyl-CoA reductases; (3) estimations of the relative use of NADH and NADPH by two acetoacetyl-CoA reductases; (4) estimations of the flux capacity of the reaction catalyzed by an acetoacetyl-CoA reductase; (5) biomass composition of an engineered E. coli strain transformed with a plasmid; (6) calculation of reconciled specific rates of this engineered strain growing on sucrose as the sole carbon source under oxygen limitation and (7) metabolic fluxes distributions during the continuous growth of this engineered strain. Because a relatively small number of acetoacetyl-CoA reductases have been kinetically characterized, data and scripts here provided could be useful for further kinetic characterizations. Moreover, the procedure described to estimate biomass composition could be interesting to estimate plasmid and protein burden in other strains. Application of data reconciliation to fermentations should help to obtain specific rates consistent with the principle of mass and electron conservation. All the required data and scripts to perform these analyses are deposited in a Mendeley Data repository. This article was co-submitted with the manuscript entitled "An NADH preferring acetoacetyl-CoA reductase is engaged in poly-3-hydroxybutyrate accumulation in Escherichiasia. coli".

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With the increasing demand for energy conversation and high efficiency, data quality is of great important to the operation management and monitoring in industrial applications. Data reconciliation, as a data processing technology, provides great potential to improve quality of process data, and is widely used to reduce measurement error and estimate unmeasured parameters. However, there are reactors connected in series in the long-running industrial processes so that liquid material information is difficult to mark and trace, and the liquid material has different residence times in each reactor due to the differences in the internal structure and operation mode. The time-delay in different reactors may be various and time-varying. In this paper, to solve these problems, a multiple time-delay interval estimation based hierarchical data reconciliation method is put forward. First, the multiple time-delay interval estimation is developed according to the process mechanism analysis and modeling. Then, an improved discrete state transition solution approach is presented to solve the data time-matching with multiple time-delay interval estimation for different reactors. Finally, a hierarchical data reconciliation frame is built by data characteristics. The feasible of the proposed data reconciliation method is verified utilizing the industrial application results.

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State estimation is very crucial for process control and optimization in dynamic processes. The particle filter (PF) is a novel and suitable technique for state estimation of nonlinear dynamic process systems. Conventional PFs for nonlinear dynamic process systems rely on the known initial conditions for state variables, such as the known probability density function (PDF) of initial states or the known values of initial states, but the initial conditions of a nonlinear dynamical system are usually unknown in actual industrial processes. In this paper, a novel methodology, PF combined with data reconciliation, is proposed and applied to nonlinear dynamic process systems for state estimation with unknown initial conditions. The measurement test criterion and data reconciliation with sequentially increasing data information are proposed to derive reliable initial values of the state variables under sufficient information of measurements. The interactive information between PF and data reconciliation problems can improve the initial values iteratively. Finally, accurate results of state estimation can be achieved. The effectiveness of the methodology is demonstrated through two nonlinear dynamic systems.

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Erroneous information from sensors affect process monitoring and control. An algorithm with multiple model identification methods will improve the sensitivity and accuracy of sensor fault detection and data reconciliation (SFD&DR). A novel SFD&DR algorithm with four types of models including outlier robust Kalman filter, locally weighted partial least squares, predictor-based subspace identification, and approximate linear dependency-based kernel recursive least squares is proposed. The residuals are further analyzed by artificial neural networks and a voting algorithm. The performance of the SFD&DR algorithm is illustrated by clinical data from artificial pancreas experiments with people with diabetes. The glucose-insulin metabolism has time-varying parameters and nonlinearities, providing a challenging system for fault detection and data reconciliation. Data from 17 clinical experiments collected over 896 hours were analyzed; the results indicate that the proposed SFD&DR algorithm is capable of detecting and diagnosing sensor faults and reconciling the erroneous sensor signals with better model-estimated values.

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Biotechnological industry strives to develop anaerobic bioprocesses fueled by abundant and cheap carbon sources, like sucrose. However, oxygen-limiting conditions often lead to by-product formation and reduced ATP yields. While by-product formation is typically decreased by gene deletion, the breakdown of oligosaccharides with inorganic phosphate instead of water could increment the ATP yield. To observe the effect of oxygen limitation during sucrose consumption, a non-fermentative Escherichia coli K-12 strain was transformed with genes enabling sucrose assimilation. It was observed that the combined deletion of the genes adhE, adhP, mhpF, ldhA, and pta abolished the anaerobic growth using sucrose. Therefore, the biomass-specific conversion rates were obtained using oxygen-limited continuous cultures. Strains performing the breakdown of the sucrose by hydrolysis (SUC-HYD) or phosphorolysis (SUC-PHOSP) were studied in such conditions. An experimentally validated in silico model, modified to account for plasmid and protein burdens, was employed to calculate carbon and electron consistent conversion rates. In both strains, the biomass yields were lower than expected and, strikingly, SUC-PHOSP showed a yield lower than SUC-HYD. Flux balance analyses indicated a significant increase in the non-growth-associated ATP expenses by comparison with the growth on glucose. The observed fructose-1,6-biphosphatase and phosphoglucomutase activities, as well as the concentrations of glycogen, suggest the operation of ATP futile cycles triggered by a combination of the oxygen limitation and the metabolites released during the sucrose breakdown.

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Data provided by in situ sensors is always affected by some level of impreciseness as well as uncertainty in the measurements due to process operation disturbance or material property variance. In-process data precision and reliability should be considered when implementing active product quality control and real-time process decision making in pharmaceutical continuous manufacturing. Data reconciliation is an important strategy to address such imperfections effectively, and to exploit the data redundancy and data correlation based on process understanding. In this study, a correlation between tablet weight and main compression force in a rotary tablet press was characterized by the classical Kawakita equation. A load cell, situated at the exit of the tablet press chute, was also designed to measure the tablet production rate as well as the tablet weight. A novel data reconciliation strategy was proposed to reconcile the tablet weight measurement subject to the correlation between tablet weight and main compression force, in such, the imperfect tablet weight measurement can be reconciled with the much more precise main compression force measurement. Special features of the Welsch robust estimator to reject the measurement gross errors and the Kawakita model parameter estimation to monitor the material property variance were also discussed. The proposed data reconciliation strategy was first evaluated with process control open-loop and closed-loop experimental data and then integrated into the process control system in a continuous tablet manufacturing line. Specifically, the real-time reconciled tablet weight measurements were independently verified with an at-line Sotax Auto Test 4 tablet weight measurements every five minutes. Promising and reliable performance of the reconciled tablet weight measurement was demonstrated in achieving process automation and quality control of tablet weight in pilot production runs.

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Purpose: Reliable process monitoring in real-time remains a challenge for the pharmaceutical industry. Dealing with random and gross errors in the process measurements in a systematic way is a potential solution. In this paper, we present a process model-based framework, which for given sensor network and measurement uncertainties will predict the most likely state of the process. Thus, real-time process decisions, whether for process control or exceptional events management, can be based on the most reliable estimate of the process state. Methods: Reliable process monitoring is achieved by using data reconciliation (DR) and gross error detection (GED) to mitigate the effects of random measurement errors and non-random sensor malfunctions. Steady-state data reconciliation (SSDR) is the simplest forms of DR but offers the benefits of short computational times. We also compare and contrast the model-based DR approach (SSDR-M) to the purely data-driven approach (SSDR-D) based on the use of principal component constructions. Results: We report the results of studies on a pilot plant-scale continuous direct compression-based tableting line at steady-state in two subsystems. If the process is linear or mildly nonlinear, SSDR-M and SSDR-D give comparable results for the variables estimation and GED. SSDR-M also complies with mass balances and estimate unmeasured variables. Conclusions: SSDR successfully estimates the true state of the process in presence of gross errors, as long as steady state is maintained and the redundancy requirement is met. Gross errors are also detected while using SSDR-M or SSDR-D. Process monitoring is more reliable while using the SSDR framework.

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A stepwise experimental design procedure to obtain reliable data from wastewater treatment plants (WWTPs) was developed. The proposed procedure aims at determining sets of additional measurements (besides available ones) that guarantee the identifiability of key process variables, which means that their value can be calculated from other, measured variables, based on available constraints in the form of linear mass balances. Among all solutions, i.e. all possible sets of additional measurements allowing the identifiability of all key process variables, the optimal solutions were found taking into account two objectives, namely the accuracy of the identified key variables and the cost of additional measurements. The results of this multi-objective optimization problem were represented in a Pareto-optimal front. The presented procedure was applied to a full-scale WWTP. Detailed analysis of the relation between measurements allowed the determination of groups of overlapping mass balances. Adding measured variables could only serve in identifying key variables that appear in the same group of mass balances. Besides, the application of the experimental design procedure to these individual groups significantly reduced the computational effort in evaluating available measurements and planning additional monitoring campaigns. The proposed procedure is straightforward and can be applied to other WWTPs with or without prior data collection.

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