Data Reconciliation under Fuzzy Constraints in Material Flow Analysis

Data reconciliation consists in modifying noisy or unreliable data in order to satisfy a mathematical model (herein a material flow network). The conventional approach relies on least squares minimization. Here we show that the setting of fuzzy sets provides a generalized approach that is more flexible and less dependent on oftentimes debatable probabilistic justifications. Moreover the proposed setting also encompasses constraint-based formulations using intervals

Material flow analysis for identifying rare earth element recycling potentials in the EU-27

International audienceRare earth elements (REEs) are essential for high-techology industrial sectors. This paper presents research on material flow analysis (MFA) applied to REEs in the EU-27. Innovative aspects of this research pertain to (i) considering potential lithospheric stocks of REEs in the EU and (ii) accounting for incomplete and imprecise information in MFA data reconciliation. Results obtained to-date provides a history of EU-27 raw rare earth imports and exports and a methodology for data reconciliation which constitutes an alternative to the classical least-squares method

Nonlinear data reconciliation in material flow analysis with software STAN

AbstractSTAN is a freely available software that supports Material/Substance Flow Analysis (MFA/SFA) under the consideration of data uncertainties. It is capable of performing nonlinear data reconciliation based on the conventional weighted least-squares minimization approach, and error propagation. This paper summarizes the mathematical foundation of the calculation algorithm implemented in STAN and demonstrates its use on a hypothetical example from MFA

Material flow analysis applied to rare earth elements in Europe

International audienceThis paper explores flows and stocks, at the scale of the European Union, of certain rare earth elements (REEs; Pr, Nd, Eu, Tb, Dy and Y) which are associated with products that are important for the decarbonisation of the energy sector and that also have strong recycling potential. Material flow analyses were performed considering the various steps along the value chain (separation of rare earth oxides, manufacture of products, etc.) and including the lithosphere as a potential stock (potential geological resources). Results provide estimates of flows of rare earths into use, in-use stocks and waste streams. Flows into use of, e.g., Tb in fluorescent lamp phosphors, Nd and Dy in permanent magnets and Nd in battery applications were estimated, for selected reference year 2010, as 35, 1230, 230 and 120 tons respectively. The proposed Sankey diagrams illustrate the strong imbalance of flows of permanent magnet REEs along the value chain, with Europe relying largely on the import of finished products (magnets and applications). It is estimated that around 2020, the amounts of Tb in fluorescent lamps and Nd in permanent magnets recycled each year in Europe, could be on the order of 10 tons for Tb and between 170 and 230 tons for Nd

A self-validating control system based approach to plant fault detection and diagnosis

An approach is proposed in which fault detection and diagnosis (FDD) tasks are distributed to separate FDD modules associated with each control system located throughout a plant. Intended specifically for those control systems that inherently eliminate steady state error, it is modular, steady state based, requires very little process specific information and therefore should be attractive to control systems implementers who seek economies of scale. The approach is applicable to virtually all types of process plant, whether they are open loop stable or not, have a type or class number of zero or not and so on. Based on qualitative reasoning, the approach is founded on the application of control systems theory to single and cascade control systems with integral action. This results in the derivation of cause-effect knowledge and fault isolation procedures that take into account factors like interactions between control systems, and the availability of non-control-loop-based sensors

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Incremental Material Flow Analysis with Bayesian Inference

Material Flow Analysis (MFA) is widely used to study the life-cycles of materials from production, through use, to reuse, recycling or disposal, in order to identify environmental impacts and opportunities to address them. However, development of this type of analysis is often constrained by limited data, which may be uncertain, contradictory, missing or over-aggregated. This article proposes a Bayesian approach, in which uncertain knowledge about material flows is described by probability distributions. If little data is initially available, the model predictions will be rather vague. As new data is acquired, it is systematically incorporated to reduce the level of uncertainty. After reviewing previous approaches to uncertainty in MFA, the Bayesian approach is introduced, and a general recipe for its application to Material Flow Analysis is developed. This is applied to map global production of steel, using Markov Chain Monte Carlo simulations. As well as aiding the analyst, who can get started in the face of incomplete data, this incremental approach to MFA also supports efforts to improve communication of results by transparently accounting for uncertainty throughout.ngineering and Physical Sciences Research Council. Grant Numbers: EP/K039326/1, EP/N02351x/

On various ways of tackling incomplete information in statistics

International audienceThis short paper discusses the contributions made to the featured section on Low Quality Data. We further refine the distinction between the ontic and epistemic views of imprecise data in statistics. We also question the extent to which likelihood functions can be viewed as belief functions. Finally we comment on the data disambiguation effect of learning methods, relating it to data reconciliation problems

A systematic grey-box modeling methodology via data reconciliation and SOS constrained regression

Producción CientíficaDeveloping the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows the engineer to transfer the available process knowledge into a model. However, there is still a lack of a flexible but systematic methodology for grey-box modeling which ensures certain coherence of the experimental sub-models with the process physics. This paper proposes such a methodology based in data reconciliation (DR) and polynomial constrained regression. A nonlinear optimization of limited complexity is to be solved in the DR stage, whereas the proposed constrained regression is based in sum-of-squares (SOS) convex programming. It is shown how several desirable features on the polynomial regressors can be naturally enforced in this optimization framework. The goodnesses of the proposed methodology are illustrated through: (1) an academic example and (2) an industrial evaporation plant with real experimental data.Ministerio de Economía, Industria y Competitividad (grant DPI2016-81002-R