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12 pages, 9 figures.-- Open access, Scheduling tasks in production facilities are usually hybrid optimization problems of a large combinatorial nature. They involve solving, in near-real time, the integration of the operation of several batch units of continuous dynamics with the discrete manufacture of items in processing lines. Moreover, one has to deal with uncertainty (process delays, unexpected stops) and the management of shared resources (energy, water, etc.) including decisions made by plant operators: still, some tasks in the scheduling layers are done manually. Manufacturing Execution Systems (MESs) are intended to support plant personnel at this level. However, there is still much work to do in terms of performing automatic scheduling, computed in real time, that guides managers to achieve an optimal operation of such complex cyber-physical systems. This work proposes a closed-loop approach to handle the uncertainty arising when facing the online scheduling of supply lines and parallel batch units. These units often share some resources, so effects due to concurrent resource consumption on the system dynamics are explicitly considered in the presented formulation. The proposed decision support system is tested onsite in a tuna cannery, to handle short-term online scheduling of sterilization processes that deal with limited steam, carts, and operators as shared resources, These results are part of project a-CIDiT, grants PID2021-123654OB-C31 and PID2021-123654OB-C32 funded by MCIN/AEI/10.13039/501100011033/FEDER, UE. Funding for open access charge: CRUE-Universitat Politècnica de Valéncia

Poster.-- Close Encounters IIM (3rd Kind), Vigo, 23 June 2022, Proyecto financiado por el Plan de Recuperación, Transformación y Resiliencia y los fondos europeos NextGeneration EU, en el marco del Real Decreto 685/2021, del 3 de agosto

Poster.-- Close Encounters IIM (3rd Kind), Vigo 23 June 2022, The fast increase in bacterial resistance to current antimicrobials Antimicrobial Resistance or AMR) poses a paramount economic and health concern that seems far from being solved. Recent studies [1] estimate 1,27 million the number of deaths worldwide that are directly attributable to AMR only in 2019. Understanding the main mechanisms involved in the emergence, selection, and spread of AMR is critical to determine the optimal therapies to deal with this undesirable phenomenon

1st International Symposium on Catch Identification Technologies, Bergen (Norway), 1st-3rd November 2022, The successful implementation of the Common Fisheries Policy (CFP) depends, at a large extent, on the capacity to quantify total catches on board commercial vessels Because of the large number of fishing vessels and the high number of trips to be monitored, classic monitoring methods mainly based on inspections, are not effective --> The use of electronic devices to quantify fishing catches is gaining relevance The data provided by such devices, in combination with mathematical models, may be used to assess the state of the different fishing stocks and to optimize the fishing activity Increasingly though, technology has quickly developed during the last years to provide vision and artificial intelligence based remote Electronic Monitoring (REM or EM systems) at lower costs, and with more potential to cover large areas than traditional monitoring strategies

9th International Workshop on Marine Technology (MARTECH), virtual, 16-18 June 2021, Monitoring plays a key role in all aspects of fsheries management, including those related to sustainable management of resources, the economic performance of the fshery, and the distribution of benefts from the exploitation of the fshery and environment. In this work, software improvements made on the remote electronic monitoring (REM) device iObserver are described towards the improvement of fsheries monitoring by precisely identifying and quantifying fshing catches on board commercial vessel´s. To this aim, we exploit deep learning and convolutional neural networks (CNNs) capabilities and potential

Monitoring plays a key role in all aspects of fsheries management, including those related to sustainable management of resources, the economic performance of the fshery, and the distribution of benefts from the exploitation of the fshery and environment. In this work, software improvements made on the remote electronic monitoring (REM) device iObserver are described towards the improvement of fsheries monitoring by precisely identifying and quantifying fshing catches on board commercial vessel´s. To this aim, we exploit deep learning and convolutional neural networks (CNNs) capabilities and potential.

ECCE12: The 12th European Congress of Chemical Engineering, Florence 15-19 September 2019, Current market trends and business globalization have steered industries towards large production volumes, complex alternative recipes and a larger product portfolio, thus leading to processes of increased complexity. Optimal production scheduling has a direct impact on the overall efficiencyof processing facilities and can lead to increased productivity and minimal costs. Although a plethora of works study the optimal production scheduling problem [1], academic developments have been mostly tested on generic but small problem instances. This is mainly due to the computational complexity of real industrial problems, which easily become intractable. This gap between theory and industrial practice has been identified by the scientific community [2] and some recent works have successfully investigated medium to large problem instances [3]. In this work different solution strategies are proposed for a large real-life food industry, The work leading to this publication has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 723575 (Project CoPro) in the ramework of the SPIRE PPP

World Seafood Congress , Reykjavik, 10-13th September 2017

1 póster presentado en International Congress on Engineering and Food (ICEF12, Québec City, Canada, 14-18 June 2015, This work has been funded by the Spanish Ministry of Science and Innovation throughout project ISFORQUALITY (AGL2012-39951-C02-01) and by CSIC throughout project CONTROLA (PIE201270E075)

1 póster.-- 29th EFFoST International Conference, 10-12 November 2015, Athens, Greece, Reduction of fish freshness during the supply chain results into severe losses both from the economic (drastic reductions on market price) and environmental (fish rejection) points of view. In this context, prediction techniques based on mathematical models emerge as useful tools for fish quality assessment. In this work we propose a methodology to forecast fish freshness during storage. In this regard, we found a strong correlation between fish freshness indicators commonly used in the industry, such as the Quality Sensory Method (QSM), and the growth of Specific Spoilage Organisms (SSO). The main idea is to develop a reliable mathematical model which, on the one hand, describes the growth of SSO during storage and, on the other hand, relates the QSM to SSO concentration. Issues such as storage conditions, fishing practices or gutting procedures are taken into account. Such model allows us to predict shelf-life and quality losses. In order to provide the model with reliable predictive capabilities, advanced model calibration techniques are employed. In this regard, optimal experimental design (OED) is used to generate sufficient nonisothermal informative experiments that allow us to reduce the uncertainty in the predictions. Also, the Monte Carlo sampling technique is exploited to detect the parameter confidence intervals and to calculate fish-to-fish variability in the predictions (core predictions). The approach is illustrated using hake (Merluccius merluccius) caught either by bottom-set net or long-line under different storage temperature conditions. Furthermore, the effect of gutting is also studied. This methodology is flexible enough to consider other fish species or stress variables such as atmosphere composition, This work has been funded by the LIFE EU program: project LIFE iSEAS (LIFE13 ENV/ES/000131); by the Spanish Ministry of Science and Innovation: projects ISFORQUALITY (AGL2012-39951-C02-01), RESISTANCE (DPI2014-54085-JIN); and by Xunta de Galicia: contrato programa 2015

1 póster presentado al 12th International Congress on Engineering and Food, Québec 14-18 june 2015, Co-funded under the LIFE+ Environment Program of the European Union

Proceedings of the 11th International Congress on Engineering and Food (ICEF11), Athens, Greece, 2011, The quality of ice-cream is at a large extent determined by the smooth texture and creamy mouth feel which are closely related to the crystal size and product viscosity. Model based computer aided process engineering may enable the design of optimal operation conditions so as to maximize ice-cream quality while minimizing, for example, energy consumption. In this regard, the development of predictive mathematical models describing the ice-cream crystallization process is object of intensive research. However, mathematical modelling is a time consuming task that involves several steps from the definition of the questions to be addressed to the development of a final model with satisfactory predictive capabilities. In this concern, model identification, plays a crucial role in model development since it involves structure characterisation, identifiability analysis, parameter estimation and experimental design. The objective of this work is to perform a complete study of the model identification for the ice-cream crystallization process. A simple yet accurate mathematical model relating the process inputs and outputs was derived, based on the method of moments. The AMIGO toolbox (Advanced Model Identification using global optimization) was used to iteratively identify model unknowns from experimental data obtained in the pilot plant located at CEMAGREF and to design new experiments to improve model predictive capabilities, The authors acknowledge the financial support received from the 7th Framework Programme of the European Union (CAFE Project – Large Collaborative Project KBBE-2007-2-3-01) and the Spanish Ministry of Science and Innovation (MICINN Project AGL2008-05267-C03-01)

Proceedings of the 11th International Congress on Engineering and Food (ICEF11), Athens, Greece, 2011, Freeze-drying is a dehydration process often employed in high added-value food and biochemical goods.This technique facilitates the later re-hydration of the resulting product while it helps to maintain its organoleptic and nutritional properties. In order to preserve quality, as well as to reduce the costs of a high demanding energy and time consuming process, the proper handling of the product temperature during the operation course becomes critical. Direct – invasive – measurement methods are potentially dangerous to product integrity what calls for new non-invasive strategies as the one proposed in this work. Our approach combines the mathematical modelling of the leading physical phenomena involved in freeze-drying together with experimental data obtained from pressure rise disturbances in the drying chamber. The data collected from pressure essays are employed to complete the partial differential equation system describing heat transfer mechanism. Numerical issues usually related to solving moving boundary problems, as it is the freeze-drying case, are overcome by applying coordinate transformations which lead to a fixed reference frame. On this new framework, order reduction methods can be used as a tool not only to simplify the system analysis and control but to minimize the computational effort as well, especially when 2D or 3D spatial domains with complex geometries are involved, The authors acknowledge the financial support received from the 7th Framework Programme of the European Union (CAFE Project – Large Collaborative Project KBBE-2007-2-3-01) and the Spanish Ministry of Science and Innovation (MICINN Project AGL2008-05267-C03-01)

Proceedings of the 11th International Congress on Engineering and Food (ICEF11), Athens, Greece, 2011, In this contribution, we present a distributed decision-making architecture to optimally command thermal processing operation, despite process uncertainty or sudden process disturbances. The network combines and links in a synchronous way modelling and simulation environments with efficient dynamic optimization and system identification tools and methods. The simulation environment takes the place of a virtual plant providing a complete dynamic representation of the system including the evolution of temperature and pressure in the retort unit as well as temporal and spatial distribution of temperature and quality or safety parameters within the product. Such virtual representation will be regularly confronted with plant measurements to quantify the degree of discrepancy (uncertainty) between real plant and models and react accordingly when such discrepancy becomes unacceptable by re-estimating plant parameters either during the cycle or from batch to batch. The virtual plant will be also accessed by the regulatory system as well as the dynamic optimization module. In the first instance to estimate unmeasured states related with the product status under feed-back control. In the second, to continuously re-compute optimal cycle profiles so to respond to unexpected disturbances or deviations from the prescribed safety constraints while maximizing quality attributes. Experimental evidences of the resulting complete system performance will be given on a pilot plant scenario, This work has been funded by the Spanish Ministry of Science and Innovation (SMART-QC, AGL2008-05267-C03-01), Xunta de Galicia (IDECOP, 08DPI007402PR) and FP7 CAFE project (KBBE-2007-1-212754). A. Arias-Mendez acknowledges financial support from the CSIC JAE-predoc programme

International Conference Engineering and Food, Montpellier (Francia), 7-11 marzo 2004, Food processing plants are good examples of hybrid systems where highly non-linear continuous dynamics are coupled with discrete events. We present a user friendly interface and a library of unit operations for food industry. This simulation environment use new methods in model building and is able to reconstruct plant operation at different levels of detail. The architecture allows the embedment of product transformations into different unit operations, and these into an economic model

International Conference Engineering and Food, Montpellier (Francia), 7-11 marzo 2004, Plant optimization and control usually rely on detailed first principle mathematical models and must be first calibrated and tested by extensive long-term tedious experiments. This contribution presents a number of tools to check system identifiability conditions that will allow us to select the appropriate process variables to do parameter estimation