{"id":887,"date":"2025-02-05T22:51:20","date_gmt":"2025-02-05T21:51:20","guid":{"rendered":"http:\/\/www2.ual.es\/aquacontrol\/?page_id=887"},"modified":"2026-01-27T16:36:52","modified_gmt":"2026-01-27T15:36:52","slug":"publications","status":"publish","type":"page","link":"http:\/\/www2.ual.es\/aquacontrol\/publications\/","title":{"rendered":"Resume of publications"},"content":{"rendered":"\n

Enhancing pH control in microalgae raceway photobioreactors using 3DoF-KF model-on-demand model predictive control<\/h2>\n\n\n\n
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\nIn this study, we present a data-driven control solution aimed at improving the operational efficiency and environmental sustainability of microalgae raceway photobioreactors. By ensuring robust pH regulation under highly variable outdoor conditions, the proposed approach supports stable microalgal growth, enhanced biomass productivity, and improved process reliability\u2014key factors for applications such as renewable bioenergy production, CO\u2082 capture, and wastewater treatment. The control architecture enables flexible and adaptive operation, allowing the process to respond effectively to both predictable and unforeseen disturbances while minimizing unnecessary control actions. Experimental validation in a pilot-scale raceway reactor demonstrates reliable closed-loop performance and strong disturbance rejection, confirming the potential of this methodology to reduce operational costs, improve resource utilization, and advance the scalability of environmentally friendly microalgae-based production systems.\n<\/p>\n\n\n\n

Authors: Pablo Ot\u00e1lora, Sarasij Banerjee, Mohamed El Mistiri, Owais Khan, Daniel E. Rivera, Jos\u00e9 L. Guzm\u00e1n<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Reinforcement Learning Meets Bioprocess Control Through Behavior Cloning: Real-World Deployment in an Industrial Photobioreactor<\/h2>\n\n\n\n
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In this newly published study, we introduce a novel Reinforcement Learning (RL)\u2013based control framework for pH regulation in open industrial photobioreactors. The proposed approach combines behavior cloning from a nominal PID controller with continual policy adaptation, enabling safe offline training followed by autonomous real-world deployment and daily fine-tuning. Validated both in simulation and during an 8-day industrial experimental campaign, this work represents the first industrial validation of an RL control strategy in a bioprocess setting and highlights the potential of data-driven learning methods to enhance robustness, efficiency, and sustainability in renewable-energy-driven bioprocess systems. <\/p>\n\n\n\n

Authors: Juan D. Gil, Ehecatl Antonio Del Rio Chanona, Jos\u00e9 L. Guzm\u00e1n, Manuel Berenguel<\/p>\n\n\n\n

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Demonstration-scale urban wastewater reclamation using concentrated solar radiation<\/h2>\n\n\n\n
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We\u2019ve demonstrated a novel solar-powered solution for urban wastewater reclamation, applying solar disinfection (SODIS) enhanced with solar concentration to make crop irrigation safer and more sustainable. At the heart of the project is a pioneering demonstration plant in Almer\u00eda, Spain, built around a 36 m\u00b2 Fresnel solar collector and designed for continuous-flow operation. Unlike conventional systems, this setup integrates renewable energy directly into wastewater treatment, significantly cutting operational costs while reducing environmental impacts. To further boost performance, solar\/H\u2082O\u2082 enhancement was explored, leading to even greater microbial inactivation. This breakthrough shows how solar-based technologies can advance wastewater reuse in agriculture, supporting resource efficiency, food security, and climate resilience.<\/p>\n\n\n\n

Authors: Daniel Rodr\u00edguez-Garc\u00eda, Jos\u00e9 Luis Garc\u00eda S\u00e1nchez, Jos\u00e9 Luis Guzm\u00e1n, Zouhayr Arbib, Jos\u00e9 Luis Casas L\u00f3pez, Jos\u00e9 Antonio S\u00e1nchez P\u00e9rez<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Virtual Laboratory for Control Education Using a Solar Collector Field System<\/h2>\n\n\n\n
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In this newly published study, we present an innovative Virtual Lab (VL) built around a Solar Collector Field (SCF), offering students and engineers a hands-on environment to master key control strategies\u2014including PID, predictive, and nonlinear control\u2014under realistic solar energy conditions, which can be vital for microalgea process usng renewable energy. By enhancing digital learning and system modeling skills, this educational tool bridges the gap between theory and application in sustainable process engineering. The SCF Virtual Lab is freely accessible online on any device\u2014bringing advanced control learning to your fingertips.<\/p>\n\n\n\n

Authors: Igor M. L. Pataro; Juan D. Gil; Jos\u00e9 Luis Guzm\u00e1n<\/a>; Manuel Berenguel<\/a><\/p>\n<\/div>\n\n\n\n

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A new approach to relay-based autotuning PID controllers and their evaluation in pH control of industrial photobioreactors<\/h2>\n\n\n\n
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We\u2019ve developed an adaptive autotuning controller to keep pH levels in microalgae raceway reactors precisely regulated\u2014a key factor for maximizing biomass productivity. Our approach builds on relay-based autotuning, but with a twist: instead of acting on the control signal, the relay is applied directly to the setpoint, allowing seamless integration into existing control loops without seasonal recalibration. The system also incorporates a weather-classification algorithm to adapt to clear or cloudy conditions, ensuring stable performance under fluctuating sunlight. This innovation paves the way for scalable, automated pH regulation in microalgae cultivation, supporting more efficient, resilient, and sustainable bioresource production and enhaced wastewater treatment.<\/p>\n\n\n\n

Authors: Malena Caparroz, Kristian Soltesz, Tore H\u00e4gglund, Jos\u00e9 Luis Guzm\u00e1n, Manuel Berenguel<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Enhancing Solar Furnace Performance by a Robust QFT-Based Control Approach<\/h2>\n\n\n\n
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Discover how advanced control engineering is harnessing the power of the sun at the Plataforma Solar de Almer\u00eda. Using a robust Quantitative Feedback Theory (QFT) approach, researchers have developed a high-performance temperature control system for solar furnace applications\u2014successfully managing uncertainty, solar fluctuations, and system safety. This strategy, combining feedforward control, PI regulation, and anti-windup protection, has proven effective under real conditions. Excitingly, the same control methodology can be adapted to optimize photobioreactor systems, improving the efficiency of wastewater treatment with renewable energy solutions.<\/p>\n\n\n\n

Authors: \u00c1ngeles Hoyo<\/a>; Jos\u00e9 Carlos Moreno<\/a>; Jos\u00e9 Luis Guzm\u00e1n<\/a>; Juan D. Gil; Manuel Berenguel<\/a><\/p>\n<\/div>\n\n\n\n

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Learning-Based Practical Nonlinear Predictive Controller for Solar Thermal Collector Fields<\/h2>\n\n\n\n
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We\u2019ve developed a smart, learning-based controller to boost the performance of solar collector fields (SCFs) used in thermal energy systems. This new approach\u2014called LPNMPC\u2014adapts in real-time to system changes like temperature swings, parameter shifts, and cloudy weather. Tested on a real SCF model from the CIESOL research center (Almer\u00eda, Spain), it reduced errors by up to 27% compared to current predictive methods. By combining learning algorithms with robust control, our system ensures precise temperature regulation and stable operation of solar-powered photobioreactors, even under fluctuating environmental conditions. This approaches can boost renewable energy and wastewater treatment momentum, enabling efficient, resilient, and sustainable bioresource recovery systems.<\/p>\n\n\n\n

Authors: Igor M. L. Pataro, Juan D. Gil, Jos\u00e9 D. \u00c1lvarez, Jos\u00e9 L. Guzm\u00e1n, Jo\u00e3o M. Lemos, Manuel Berenguel<\/p>\n<\/div>\n<\/div>\n\n\n\n

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An Internet of Things platform for heterogeneous data integration: Methodology and application examples<\/h2>\n\n\n\n
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In this work we introduce a next-generation IoT platform tailored for the agro-industrial sector, tackling key challenges in digital transformation such as data heterogeneity, poor interoperability, and limited scalability. Developed using open-source technologies and the FIWARE framework with the OMA NGSI standard, this platform enables seamless integration of diverse devices and systems\u2014paving the way for smarter, more connected industrial operations. With a scalable architecture and embedded industrial models (e.g., climate, production), it supports complex real-world scenarios and has been validated across three different case studies. Extensive testing in a cloud environment confirms its ability to manage high loads while optimizing resource use. A solid step forward in bridging the gap between digital innovation and industrial application!<\/p>\n\n\n\n

Authors: Manuel Mu\u00f1oz Rodriguez, Manuel Torres Gil, Juan Diego Gil, Jos\u00e9 Luis Guzm\u00e1n<\/p>\n<\/div>\n\n\n\n

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Benchmarking of ALBA and ABACO-2 models for algae-bacteria wastewater treatment<\/h2>\n\n\n\n
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Recently, we have dived into two leading digital models\u2014ABACO-2 and ALBA\u2014used to simulate and optimize microalgae-based wastewater treatment. These nature-based systems not only help clean water but also generate valuable biomass. By comparing these models across different environmental conditions and locations (Almer\u00eda, Milan, and Narbonne), we aim to understand their strengths, limitations, and the practical challenges of applying them to new datasets. This work highlights the urgent need for standardized modelling approaches that can adapt to real-world conditions. Key issues like data management, parameter calibration, and monitoring requirements are still hurdles to wider adoption and industrial scale-up. As we move toward a more sustainable and circular bioeconomy, robust and adaptable digital tools like ABACO-2 and ALBA will play a crucial role in transforming wastewater into a resource. This is just the beginning of smarter, greener water solutions!<\/p>\n\n\n\n

Authors: Rebecca Nordio, Francesca Casagli, Enrique Rodriguez, Jos\u00e9 Luis Guzm\u00e1n, Olivier Bernard, Gabriel Aci\u00e9n<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Operation, control and assessment of a full-scale membrane distillation unit for treating desalination brine in the context of greenhouse production<\/h2>\n\n\n\n
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Our latest publication presents the first-ever full-scale membrane distillation unit powered by biomass to treat reverse osmosis brine\u2014tailored specifically for greenhouse agriculture. Conducted at IFAPA’s AgroConnect facilities in Almer\u00eda, this innovative system pushes the boundaries of sustainable water management. The achieved results demonstrate that full-scale vacuum-assisted air-gap membrane distillation can take part in a future optimized sustainable water network to supply water for intensive agricultural activity in the south-east of Spain, although a sufficient cooling supply is mandatory to avoid operational issues. A step closer to a circular and climate-resilient food system!<\/p>\n\n\n\n

Authors: Juan Antonio Andr\u00e9s-Ma\u00f1as, Juan Diego Gil, Jorge Antonio Sanchez-Molina, Manuel Berenguel, Guillermo Zaragoza<\/p>\n<\/div>\n\n\n\n

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Data-driven models of a solar field used to power membrane distillation systems: A comparison study<\/h2>\n\n\n\n
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Discover how cutting-edge technology is revolutionizing sustainable water desalination! Our latest research explores the power of data-driven modeling in optimizing solar-powered membrane distillation (MD) systems. By integrating innovative mirrors into solar collector fields, we enhance thermal energy capture, pushing the boundaries of traditional desalination efficiency. With advanced artificial intelligence techniques, our study identifies the most accurate model for predicting system performance, achieving remarkable precision (R\u00b2 = 0.9741). This breakthrough paves the way for smarter, more efficient desalination solutions\u2014tackling water scarcity with renewable energy. Dive into the future of sustainable water production with us!<\/p>\n\n\n\n

Authors: Alejandro Bueso S\u00e1nchez, Juan Diego Gil Vergel, Guillermo Zaragoza<\/p>\n<\/div>\n<\/div>\n\n\n\n

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A hybrid MRAC-PI approach to regulate pH in raceway reactors for microalgae production<\/h2>\n\n\n\n
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Our latest study introduces a hybrid Model Reference Adaptive Control (MRAC) strategy to enhance pH regulation in open raceway reactors for microalgae production, addressing the challenges posed by nonlinear and time-varying system dynamics. Conducted at the IFAPA center-University of Almer\u00eda, the research demonstrates that the MRAC controller significantly outperforms traditional control methods by dynamically adapting to varying conditions, ensuring precise and stable pH regulation. Through real-world experiments spanning twelve days in different months, the proposed approach proved its robustness and industrial viability, contributing to the advancement of adaptive control strategies for sustainable and cost-effective microalgae production.<\/p>\n\n\n\n

Authors: Malena Caparroz, Jos\u00e9 Luis Guzm\u00e1n, Juan Diego Gil, Manuel Berenguel, Francisco Gabriel Aci\u00e9n<\/p>\n<\/div>\n\n\n\n

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A low-cost methodology based on artificial intelligence for contamination detection in microalgae production systems<\/h2>\n\n\n\n
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In our study, we introduce an innovative, low-cost artificial intelligence approach for detecting contamination in microalgae production systems. By developing a neural network that classifies microalgae genera using spectral data (300\u2013750 nm range) and analyzing the softmax layer output, we achieve highly accurate contamination detection. Trained on pure samples of four microalgae genera\u2014Spirulina, Chlorella, Synechococcus, and Scenedesmus\u2014the model demonstrated a macro F1 score of 98.64% during validation. Further testing in different photobioreactors confirmed its reliability for real-world applications, offering a practical solution for continuous monitoring without the need for costly equipment or specialized personnel. This approach enhances reactor maintenance by enabling early contamination detection, supporting more efficient and sustainable microalgae production.<\/p>\n\n\n\n

Authors: Jos\u00e9 Gonzalez-Hern\u00e1ndez, Martina Ciardi, Jos\u00e9 Luis Guzm\u00e1n, Jos\u00e9 Carlos Moreno, Francisco Gabriel Aci\u00e9n<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Enhancing pH control in microalgae raceway photobioreactors using 3DoF-KF model-on-demand model predictive control AVAILABLE IN In this study, we present a data-driven control solution aimed at improving the operational efficiency and environmental sustainability of microalgae raceway photobioreactors. By ensuring robust pH regulation under highly variable outdoor conditions, the proposed approach […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-887","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/www2.ual.es\/aquacontrol\/wp-json\/wp\/v2\/pages\/887","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www2.ual.es\/aquacontrol\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/www2.ual.es\/aquacontrol\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/www2.ual.es\/aquacontrol\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www2.ual.es\/aquacontrol\/wp-json\/wp\/v2\/comments?post=887"}],"version-history":[{"count":38,"href":"http:\/\/www2.ual.es\/aquacontrol\/wp-json\/wp\/v2\/pages\/887\/revisions"}],"predecessor-version":[{"id":1088,"href":"http:\/\/www2.ual.es\/aquacontrol\/wp-json\/wp\/v2\/pages\/887\/revisions\/1088"}],"wp:attachment":[{"href":"http:\/\/www2.ual.es\/aquacontrol\/wp-json\/wp\/v2\/media?parent=887"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}