Report on Dynamic Data Reconciliation of Large-Scale Processes

Producción CientíficaAvailability of reliable process information in real time is key in any decision-making procedure. Thus, good industrial decision-support implementations require dealing with gross errors and consideration of process transients in order to get a set of measurements which will be coherent with the basic underlying process dynamics. This report presents dynamic data reconciliation methods and tools adapted to the requirements of industrial environments (large-scale systems and noisy/faulty data). Moreover, basic concepts in literature are extended to artificially increase system redundancy as well as to cope with time-varying parameter estimation. The procedure summarized in this report has been tested in the Lenzing case study.Ingeniería de Sistemas y AutomáticaEuropean Union Horizon 2020 program (grant nº 723575

Slots Startup Synchronization with Shared Resources Dependency

Producción CientíficaIn this work the authors present a new formulation that allows an optimal schedule of batch processes with length dependence on the synchronization of the startup of the processes. It is also keep into account the distribution of shared resources among the devices.European Union, Horizon 2020 research and innovation programme under grant agreement No 723575 (CoPro)MINECO-FEDER (DPI2015-70975-P

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

Economic MPC with Modifier Adaptation using Transient Measurements

Producción CientíficaThis paper presents a method to estimate process dynamic gradients along the transient that combined with the idea of Modifier Adaptation (MA) improves the economic cost fuction of the examples presented. The gradient estimation method, called TMA, aims to reduce the large convergence time required to traditional MA in processes of slow dynamics. TMA is used with an economic predictive control with MA (eMPC+TMA) and was applied in two case studies: a simulation of the Williams-Otto reactor and a hybrid laboratory plant based on the Van de Vusse reactor. The results show that eMPC+TMA could reach the plant real steady-state optimum despite process-model mismatch, due to the inclusion of the effect of process dynamics in the TMA algorithm. Despite the estimation errors, the proposed methodology improved the profit of the experimental case study, with respect to the use of an eMPC with no modifiers, by about 20% for the unconstrained case, and by 130% in the constrained case.Junta de Castilla y León (CLU 2017-09 and UIC 233)FEDER - AEI (PGC2018-099312-B-C31

XXXVIII Jornadas de Automática

Producción CientíficaThis work presents a decision-support tool to address the model-based optimization approach for online load allocation and scheduling of cleaning operations in an evaporation network. The aim is improving the resource efficiency by supplying the optimal solution for a given production goal. The approach includes the semi-automatic update of evaporator models, which is based on historical data for minimal modelling effort. The structure of the problem is formulated via mixed-integer programming and integrated into the plant supervision systems. Production constraints, concerns about the practical implementation and visualization preferences are also taken into account in the design of the prototypical tool.MINECO/FEDER Grant DPI2015-70975 (INOPTCON)EU H2020-SPIRE Grant Agreement nº 723575 (CoPro

Robust integrated production-maintenance scheduling for an evaporation network

Producción CientíficaThis work aims to reduce the global resource consumption in an industrial evaporation network by better tasks management and coordination. The network works in continuous, processing some products in several evaporation plants, so optimal load allocation and product-plant assignment problems appear. The plants have different features (capacity, equipment, etc.) and their performance is affected by fouling inside the heat exchangers and external factors. Hereby, the optimizer has to decide when maintenance operations have to be triggered. Therefore, a mixed production/maintenance scheduling problem arises. The plant behavior is approximated by surrogate linear models obtained experimentally, allowing thus the use of mixed-integer linear optimization routines to obtain solutions in acceptable time. Furthermore, uncertainty in the weather forecast and in the production plan is also considered via a two-stage stochastic programming approach. Finally, a trade-off analysis between other objectives of interest is given to support the decision maker.Spanish Government with project INOPTCON (MINECO/FEDER DPI2015-70975-P)

Optimization of crude oil operations scheduling by applying a two-stage stochastic programming approach with risk management

Producción CientíficaThis paper focuses on the problem of crude oil operations scheduling carried out in a system composed of a refinery and a marine terminal, considering uncertainty in the arrival date of the ships that supply the crudes. To tackle this problem, we develop a two-stage stochastic mixed-integer nonlinear programming (MINLP) model based on continuous-time representation. Furthermore, we extend the proposed model to include risk management by considering the Conditional Value-at-Risk (CVaR) measure as the objective function, and we analyze the solutions obtained for different risk levels. Finally, to evaluate the solution obtained, we calculate the Expected Value of Perfect Information (EVPI) and the Value of the Stochastic Solution (VSS) to assess whether two-stage stochastic programming model offers any advantage over simpler deterministic approaches.Gobierno de España - proyects a-CIDiT (PID2021-123654OB-C31) and InCo4In (PGC 2018-099312-B-C31)Junta de Castilla y León - EU-FEDER (CLU 2017-09, CL-EI-2021-07, UIC 233

Predictive control for hydrogen production by electrolysis in an offshore platform using renewable energies

An Energy Management System (EMS), based on Model Predictive Control (MPC) ideas, is proposed here to balance the consumption of power by a set of electrolysis units in an offshore platform. In order to produce renewable hydrogen, the power is locally generated by wind turbines and wave energy converters and fully used by the electrolyzers. The energy generated at the platform by wind and wave is balanced by regulating the operating point of each electrolysis unit and its connections or disconnections, using an MPC based on a Mixed-Integer-Quadratic-Programming algorithm. This Predictive Control algorithm makes it possible to take into account predictions of available power and power consumption, to improve the balance and reduce the number of connections and disconnections of the devices. Two case studies are carried out on different installations composed of wave and wind energies feeding a set of alkaline electrolyzers. Validation using measured data at the target location of the platforms shows the adequate operation of the proposed EMS

Performance evaluation of a control strategy for photosynthetic biogas upgrading in a semi-industrial scale photobioreactor

Producción CientíficaThe validation of a control strategy for biogas upgrading via light-driven CO2 consumption by microalgae and H2S oxidation by oxidizing bacteria using the oxygen photosynthetically generated was performed in a semi-industrial scale (9.6 m3) photobioreactor. The control system was able to support CO2 concentrations lower than 2% with O2 contents ≤ 1% regardless of the pH in the cultivation broth (ranging from 9.05 to 9.50). Moreover, the control system was efficient to cope with variations in biogas flowrate from 143 to 420 L h−1, resulting in a biomethane composition of CO2 95.5%, O2 < 1% and no H2S. Despite the poor robustness of this technology against failures in biogas and liquid supply (CH4 concentration of 67.5 and 70.9% after 2 h of biogas or liquid stoppage, respectively), the control system was capable of restoring biomethane quality in less than 2 h when biogas or liquid supply was resumed.Junta de Castilla y León y Programa Europeo FEDER (CLU 2017-09) y (UIC 071).European Union’s Horizon 2020 research and innovation programme under grant agreement No. 68924

El Futuro del Control de Procesos

En este artículo se recogen algunas de las tendencias que previsiblemente enmarcarán el futuro del control de procesos. Estas son tanto de carácter socioeconómico como científico-tecnológicas y están basadas en estudios y workshops propiciados por diversos organismos de la UE y de USA. Reflejan además una opinión personal derivada de la experiencia del autor en su vida académica y profesional en estrecho contacto con la industria de procesos